[1] Hasan Alkahtani and Theyazn H. H. Aldhyani, “Artificial Intelligence Algorithms for Malware Detection in Android-Operated Mobile Devices”, Sensors 2022, 22, 2268, doi.org/10.3390/s22062268
[2] Daniel Kant, Andreas Johannsen, “Evaluation of AI-based use cases for enhancing the cyber security defense of small and medium-sized companies (SMEs)”, 2022, Society for Imaging Science and Technology, doi.org/10.2352/EI.2022.34.3.MOBMU-38
[3] Zhimin Zhang,  Huansheng Ning, Feifei Shi, Fadi Farha, Yang Xu, Jiabo Xu, Fan Zhang,  Kim?Kwang Raymond Choo, “Artificial intelligence in cyber security: research advances, challenges, and opportunities”, 2021 Springer, doi.org/10.1007/s10462-021-09976-0
[4] Muhammed AbuOdeh, Christian Adkins, Omid Setayeshfar, Prashant Doshi, Kyu H. Lee, “A Novel AI-based Methodology for Identifying Cyber Attacks in Honey Pots”, The Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21), 2021, Association for the Advancement of Artificial Intelligence
[5] Hooman Alavizadeh, Julian Jang-Jaccard, Tansu Alpcan and Seyit A. Camtepe, “A Markov Game Model for AI-based Cyber Security Attack Mitigation”, arXiv:2107.09258v1 [cs.GT] 20 Jul 2021
[6] Thanh Cong Truong, Quoc Bao Diep and Ivan Zelinka, “Artificial Intelligence in the Cyber Domain: Offense and Defense”, Symmetry 2020, 12, 410; doi:10.3390/sym12030410
[7] Jonghoon Lee, Jonghyun Kim, Ikkyun Kim, And Kijun Han, “Cyber Threat Detection based on Artificial Neural Networks using Event Profiles”, VOLUME 7, 2019, DOI 10.1109/ACCESS.2019.2953095, IEEE Access
[8] A.M.S.N. Amarasinghe, W.A.C.H. Wijesinghe, D.L.A. Nirmana, Anuradha Jayakody, A.M.S. Priyankara, “AI Based Cyber Threats and Vulnerability Detection, Prevention and Prediction System”, 2019 International Conference on Advancements in Computing (ICAC), December 5-6, 2019. Malabe, Sri Lanka
[9] Ozan Veranyurt, “Usage of Artificial Intelligence in DOS/DDOS Attack Detection”, International Journal of Basic and Clinical Studies (IJBCS) 2019; 8(1): 23-36, ISSN:2147-1428
[10] Ricardo Calderon, “The Benefits of Artificial Intelligence in Cyber security”, Economic Crime Forensics Capstones, 2019, doi: digitalcommons.lasalle.edu/ecf_capstones
[11] Vishal Dineshkumar Soni, “Role Of Artificial Intelligence in Combating Cyber Threats in Banking”, International Engineering Journal For Research & Development, 4(1), 7, 2019, doi.org/10.17605/OSF.IO/JYPGX
[12] Nitika Khurana, Sudip Mittal, Aritran Piplai, Anupam Joshi, “Preventing Poisoning Attacks on AI Based Threat Intelligence Systems”, 2019 IEEE, 978-1-7281-0824-7/19
[13] Gregory Falco, Arun Viswanathan, Carlos Caldera, And Howard Shrobe, “A Master Attack Methodology for an AI-Based Automated Attack Planner for Smart Cities”, 2018 IEEE ACCESS, doi:1 0.1109/ACCESS.2018.2867556
[14]  Amaan Anwar & Syed Imtiyaz Hassan, “Applying Artificial Intelligence Techniques to Prevent Cyber Assaults”, International Journal of Computational Intelligence Research ISSN 0973-1873 Volume 13, Number 5 (2017), pp. 883-889
[15] Nadine Wirkuttis and Hadas Klein, “Artificial Intelligence in Cyber security”, Volume 1, No. 1, 2017, Academia

[1] Shengli Du, Qiushuo Yan, Lijing Dong, JunfeiQiao, “Secure Consensus of Multiagent Systems With Input Saturation and Distributed Multiple DoS Attacks”, IEEE Transactions on Circuits and Systems II: Express Briefs, Volume: 69, Issue: 4, Year: 2022 
[2] Hamza Chahed, Andreas J. Kassler, “Software-Defined Time Sensitive  Networks Configuration and Management”, 2021 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN), Year: 2021 
[3] Adel Alshamrani, “Reconnaissance Attack in SDN based Environments”, 2020 27th International Conference on Telecommunications (ICT), Year: 2020
[4] Nitin Varyani, Zhi-Li Zhang, David Dai, “QROUTE: An Efficient Quality of  Service (QoS) Routing Scheme for Software-Defined Overlay Networks”, IEEE Access, Volume: 8, Year: 2020 
[5] Jesús Arturo Pérez-Díaz, Ismael AmezcuaValdovinos, Kim-Kwang Raymond Choo, Dakai Zhu, “A Flexible SDN-Based Architecture for Identifying and Mitigating Low-Rate DDoS Attacks Using Machine Learning”, IEEE Access, Volume: 8, Year: 2020  
[6] Abdullah H Almutairi, Nabih T Abdelmajeed, “Innovative signature based intrusion detection system: Parallel processing and minimized database”,2017 International Conference on the Frontiers and Advances in Data Science (FADS), Year: 2017 
[7] MarwaneZekri, Said El Kafhali, Noureddine Aboutabit, Youssef Saadi, “DDoS attack detection using machine learning techniques in cloud computing environments”, 2017 3rd International Conference of Cloud Computing Technologies and Applications (CloudTech), Year: 2017
[8] Alshamrani, A. Chowdhary, A. Pisharody, S. Lu, D. Huang,“A defense system for defeating DDoS attacks in SDN based networks”, In Proceedings of the 15th ACM International Symposium on Mobility Management and Wireless Access, Miami Beach, FL, USA, 21–25 November 2017.
[9] Z. Abaid, M. A. Kaafar, and S. Jha, “Quantifying the impact of adversarial evasion attacks on machine learning based android malware classifiers,” in 2017 IEEE 16th International Symposium on Network Computing and Applications (NCA), IEEE, 2017, pp. 1–10. 
[10] H. Lin and P. Wang, ‘‘Implementation of an SDN-based security defense mechanism against DDoS attacks,’’ in Proc. Joint Int. Conf. Econ. Manage. Eng. (ICEME), Int. Conf. Econ. Bus. Manage. (EBM), Philadelphia, PA, USA, 2016
[11] Barki, Lohit, “Detection of distributed denial of service attacks in software defined networks.” International Conference on Advances in Computing, Communications and Informatics (ICACCI), Sep 2016, pp: 2576-2581
[12] Nanda. S, Zafari. F, DeCusatis. C, Wedaa. E, Yang. B,“Predicting network attack patterns in SDN using machine learning approach”, In Proceedings of the 2016 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN), Palo Alto, CA, USA, 7–10 November 2016.
[13] N. F. Haq, A. R. Onik, M. A. K. Hridoy, M. Rafni, F. M. Shah, and D.M. Farid, “Application of machine learning Approaches in Intrusion Detection system: A survey”, international journal of advanced Research in Artificial Intelligence, vol. 4, no. 3, pp. 9 18, 2015
[14] Gisung Kim, Seungmin Lee, Sehun Kim “A novel Hybrid attackDetection method integrating Anomaly Detection with misuse Detection”, - journal on Expert Systems with Applications, 41(4), March 2014, pp: 1690-1700
[15] B. Biggio, I. Corona, D. Maiorca, B. Nelson, N. Srndi c, P. Laskov, G. Giacinto, and F. Roli, “Evasion attacks against machine learning at test time”, in Joint European conference on machine learning and knowledge discovery in databases. Springer, 2013, pp. 387–402

[1] Korbmacher, R., & Tordeux, A. : Review of Pedestrian Trajectory Prediction Methods: Comparing Deep Learning and Knowledge-based Approaches. ArXiv, abs /(2022). 2111.06740.
[2]  Yi, S., Li, H., Wang, X.: Understanding pedestrian behaviors from stationary crowd groups. In: Proceedings of CVPR (2019)
[3] Yi, S., Li, H., Wang, X.: Pedestrian behavior modeling from stationary crowds with applications to intelligent surveillance. TIP 25(9), 4354–4368 (2016).
[4]  Tang, S., Andriluka, M., Milan, A., Schindler, K., Roth, S., Schiele, B.: Learning people detectors for tracking in crowded scenes. In: Proceedings of ICCV (2016)
[5] Leal-Taix´e, L., Fenzi, M., Kuznetsova, A., Rosenhahn, B., Savarese, S.: Learning an image-based motion context for multiple people tracking. In: Proceedings of 	CVPR (2017)
[6]Zhou, B., Wang, X., Tang, X.: Understanding collective crowd behaviors: learning a mixture model of dynamic pedestrian-agents. In: Proceedings of CVPR (2019)
[7] Chang, M.C., Krahnstoever, N., Ge, W.: Probabilistic group-level motion analysis and scenario recognition. In: Proceedings of ICCV (2018).
[8] Mehran, R., Oyama, A., Shah, M.: Abnormal crowd behavior detection using social force model. In: Proceedings of CVPR (2017)
[9] Mahadevan, V., Li, W., Bhalodia, V., Vasconcelos, N.: Anomaly detection in crowded scenes. In: Proceedings of CVPR (2010)
[10] Yi, S., Wang, X., Lu, C., Jia, J., Li, H.: L0 regularized stationary-time estimation for crowd analysis. TPAMI PP(99), 1 (2016).
[11] Pellegrini, S., Ess, A., Schindler, K., Van Gool, L.: You’ll never walk alone: modeling social behavior for multi-target tracking. In: Proceedings of ICCV (2019)
[12] Xiaoge Zhang, Sankaran Mahadevan, Bayesian neural networks for flight trajectory prediction and safety assessment, Decision Support Systems, Volume 131,2020,113246, ISSN 0167-9236.
[13] S. Lefèvre, C. Laugier and J. Ibañez-Guzmán, "Exploiting map information for driver intention estimation at road intersections," 2011 IEEE Intelligent Vehicles Symposium (IV), Baden-Baden, 2011, pp. 583-588, doi: 10.1109/IVS.2011.5940452.
[14] S. Danielsson, L. Petersson and A. Eidehall, "Monte Carlo based Threat Assessment: Analysis and Improvements," 2007 IEEE Intelligent Vehicles Symposium, Istanbul, 2007, pp. 233-238, doi: 10.1109/IVS.2007.4290120.
[15] Q. Tran and J. Firl, "A probabilistic discriminative approach for situation recognition in traffic scenarios," 2012 IEEE Intelligent Vehicles Symposium, Alcala de Henares, 2012, pp. 147-152, doi: 10.1109/IVS.2012.6232279.
[16] C. E. Rasmussen & C. K. I. Williams, Gaussian Processes for Machine Learning, the MIT Press, 2006, ISBN 026218253X.
[17] Wenjie Luo, Bin Yang, and Raquel Urtasun. Fast and furious: Real time end-to-end 3d detection, tracking and motion forecasting with a single convolutional net. In Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, pages 3569–3577, 2018. 
[18] Amir Sadeghian, Vineet Kosaraju, Ali Sadeghian, Noriaki Hirose, Hamid Rezatofighi, and Silvio Savarese. Sophie: An attentive gan for predicting paths compliant to social and physical constraints. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 1349– 1358, 2019.
[19] Nachiket Deo and Mohan M Trivedi. Convolutional social pooling for vehicle trajectory prediction. arXiv preprint arXiv:1805.06771, 2018.
[20] Rohan Chandra, Uttaran Bhattacharya, Aniket Bera, and Dinesh Manocha. Traphic: Trajectory prediction in dense and heterogeneous traffic using weighted interactions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 8483–8492, 2019
[21] Rohan Chandra, Uttaran Bhattacharya, Christian Roncal, Aniket Bera, and Dinesh Manocha. Robusttp: End-toend trajectory prediction for heterogeneous road-agents in dense traffic with noisy sensor inputs. arXiv preprint arXiv:1907.08752, 2019.
[22] Yuexin Ma, Xinge Zhu, Sibo Zhang, Ruigang Yang, Wenping Wang, and Dinesh Manocha. Trafficpredict: Trajectory prediction for heterogeneous traffic-agents. arXiv preprint arXiv:1811.02146, 2018.
[23] N. Djuric, V. Radosavljevic, H. Cui, T. Nguyen, F.-C. Chou, T.-H. Lin, and J. Schneider. Short-term Motion Prediction of Traffic Actors for Autonomous Driving using Deep Convolutional Networks. ArXiv e-prints, Aug. 2018.
[24] Shengnan Guo, Youfang Lin, Ning Feng, Chao Song, and Huaiyu Wan. Attention based spatial-temporal graph convolutional networks for traffic flow forecasting. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 33, pages 922–929, 2019.
[25] Zhiyong Cui, Kristian Henrickson, Ruimin Ke, and Yinhai Wang. Traffic graph convolutional recurrent neural network: A deep learning framework for network-scale traffic learning and forecasting. arXiv preprint arXiv:1802.07007, 2018.
[26] Kooij, Julian & Schneider, Nicolas & Flohr, Fabian & Gavrila, Dariu. (2014). Context-Based Pedestrian Path Prediction. 10.1007/978-3-319-10599-4_40.
[27] S. K. Jayaraman, C. Creech, L. P. Robert Jr., D. M. Tilbury, X. J. Yang, A. K. Pradhan, and K. M. Tsui, “Trust in AV: An Uncertainty Reduction Model of AV-Pedestrian Interactions,” in Companion 2018 ACM/IEEE Int. Conf. Human-Robot Interaction, 2018, pp. 133–134. 
[28] L. Robert, “The future of pedestrian–automated vehicle interactions,” XRDS: Crossroads, ACM, vol. 25, no. 3, 2019.
[29] K. Saleh, M. Hossny, and S. Nahavandi, “Towards trusted autonomous vehicles from vulnerable road users perspective,” in 2017 11th Annu. IEEE Int. Syst. Conf., SysCon, 2017, pp. 1–7.
 [30] A. Gorrini, G. Vizzari, and S. Bandini, “Towards Modelling Pedestrian-Vehicle Interactions: Empirical Study on Urban Unsignalized Intersection,” arXiv preprint arXiv:1610.07892, 2016. 
[31] A. Alahi, K. Goel, V. Ramanathan, A. Robicquet, L. Fei-Fei, and S. Savarese, “Social LSTM: Human Trajectory Prediction in Crowded Spaces,” in Proc. IEEE Conf. Comput. Vision Pattern Recognition, 2016, pp. 961–971.
[32] Yi, S., Li, H., & Wang, X. (2016). Pedestrian Behavior Understanding and Prediction with Deep Neural Networks. Lecture Notes in Computer Science, 263–279.
[33] Basharat, A., Gritai, A., Shah, M.: Learning object motion patterns for anomaly detection and improved object detection. In: Proceedings of CVPR (2008)
[34] Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of CVPR (2015) 
[35] Simonyan, K., Zisserman, A.: Two-stream convolutional networks for action recognition in videos. In: Proceedings of NIPS (2014) 
[36] Shao, J., Kang, K., Loy, C.C., Wang, X.: Deeply learned attributes for crowded scene understanding. In: Proceedings of CVPR (2015)
[37]. Ji, S., Xu, W., Yang, M., Yu, K.: 3D convolutional neural networks for human action recognition. TPAMI 35(1), 221–231 (2013)

[1] ELkamel, R., & Cherif, A. (2017, August 2). Energy-efficient routing  rotocol to improve energy consumption in wireless sensors networks. International Journal of Communication Systems, 30(17), e3360. https://doi.org/10.1002/dac.3360
[2] Gavrilovska, L. (2009, June 26). Wireless sensor networks: a vision for new networking paradigm. Sensor Review, 29(3). https://doi.org/10.1108/sr.2009.08729caa.002
[3] Rashid, B., & Rehmani, M. H. (2016, January). Applications of wireless sensor networks for urban areas: A survey. Journal of Network and Computer Applications, 60, 192–219. https://doi.org/10.1016/j.jnca.2015.09.008  
[4] Wang, X., Wang, S., Ma, J., & Sun, X. (2010, March 31). Energy-aware Scheduling of Surveillance in Wireless Multimedia Sensor Networks. Sensors, 10(4), 3100–3125. https://doi.org/10.3390/s100403100 
[5] Niedermeier, M., He, X., de Meer, H., Buschmann, C., Hartmann, K., Langmann, B., Koch, M., Fischer, S., & Pfisterer, D. (2015, November 25). Critical Infrastructure Surveillance Using Secure Wireless Sensor Networks. Journal of Sensor and Actuator Networks, 4(4), 336–370. https://doi.org/10.3390/jsan4040336 
[6] Hart, J. K., & Martinez, K. (2006, October). Environmental Sensor Networks: A revolution in the earth system science? Earth-Science Reviews, 78(3–4), 177–191. https://doi.org/10.1016/j.earscirev.2006.05.001 
[7] Stankovic, J. A. (2004, July). Research challenges for wireless sensor networks. ACM SIGBED Review, 1(2), 9–12. https://doi.org/10.1145/1121776.1121780
[8] Rostami, A. S., Badkoobe, M., Mohanna, F., keshavarz, H., Hosseinabadi, A. A. R., & Sangaiah, A. K. (2017, September 21). Survey on clustering in heterogeneous and homogeneous wireless sensor networks. The Journal of Supercomputing, 74(1), 277–323. https://doi.org/10.1007/s11227-017-2128-1
[9] Zhao, L. (2018). Data Aggregation in WSN based on Deep Self-Encoder. International Journal of Performability Engineering. https://doi.org/10.23940/ijpe.18.11.p18.27232730
[10] Liu, M. X., & Wang, X. M. (2014, November). Energy Balance Routing Algorithm Based on Energy Heterogeneous WSN. Applied Mechanics and Materials, 687–691, 3976–3979. https://doi.org/10.4028/www.scientific.net/amm.687-691.3976 
[11] Heinzelman, W., Chandrakasan, A., & Balakrishnan, H. (2002, October). An application-specific protocol architecture for wireless microsensor networks. IEEE Transactions on Wireless Communications, 1(4), 660–670. https://doi.org/10.1109/twc.2002.804190 
[12] Sharma, M. (2012, November 30). Transmission Time and Throughput analysis of EEE LEACH, LEACH and Direct Transmission Protocol: A Simulation Based Approach. Advanced Computing: An International Journal, 3(6), 75–82. https://doi.org/10.5121/acij.2012.3609
[13] Singh, S. K., Kumar, P., & Singh, J. P. (2017). A Survey on Successors of LEACH Protocol. IEEE Access, 5, 4298–4328. https://doi.org/10.1109/access.2017.2666082 
[14] Masdari, M., & Tanabi, M. (2013, December 31). Multipath Routing protocols in Wireless Sensor Networks: A Survey and Analysis. International Journal of Future Generation Communication and Networking, 6(6), 181–192. https://doi.org/10.14257/ijfgcn.2013.6.6.19
[15] Marin-Perianu, R., Scholten, J., Havinga, P., & Hartel, P. (2008, May 9). Cluster-based service discovery for heterogeneous wireless sensor networks. International Journal of Parallel, Emergent and Distributed Systems, 23(4), 325–346. https://doi.org/10.1080/17445760801930948
[16] Hossan, A., & Choudhury, P. K. (2022). DE-SEP: Distance and Energy Aware Stable Election Routing Protocol for Heterogeneous Wireless Sensor Network. IEEE Access, 10, 55726–55738. https://doi.org/10.1109/access.2022.3177190 
[17] Verma, S., & Pathre, A. (2018). Energy Efficient Stable Election Protocol Scheme for Extend the Lifetime of WSN with Isolated Nodes. International Journal of Computer Applications, 180(45), 1–5. https://doi.org/10.5120/ijca2018916963
[18] Muoghalu, C. N., Achebe, P. N., & Aigbodioh, F. A. (2022). Effect Of Increasing Node Density On Performance Of Threshold-Sensitive Stable Election Protocol. International Journal of Advanced Networking and Applications, 13(06), 5183–5187. https://doi.org/10.35444/ijana.2022.13604
[19] Zhao, L., & Tang, Q. (2019). An Improved Threshold-Sensitive Stable Election Routing Energy Protocol for Heterogeneous Wireless Sensor Networks. Information, 10(4), 125. https://doi.org/10.3390/info10040125
[20] Mishra, Y., Singhadia, A., & Pandey, R. (2014). Energy Level Based Stable Election Protocol in Wireless Sensor Network. International Journal of Engineering Trends and Technology, 17(1), 32–38. https://doi.org/10.14445/22315381/ijett-v17p206
[21] Arya, G., & S Chauhan, D. (2013). Modified Stable Election Protocol (M-SEP) for Hierarchical WSN. International Journal of Computer Applications, 79(16), 35–39. https://doi.org/10.5120/13947-1926
[22] Kumar, R., & Kaur, R. (2014). Evaluating the Performance of DEEC Variants. International Journal of Computer Applications, 97(7), 9–16. https://doi.org/10.5120/17017-7299
[23] . Kim, H. S., Abdelzaher, T. F., & Kwon, W. H. (2005). Dynamic delay-constrained minimum-energy dissemination in wireless sensor networks. ACM Transactions on Embedded Computing Systems, 4(3), 679–706. https://doi.org/10.1145/1086519.1086530 
[24] . Jibreel, F. (2019). Improved Enhanced Distributed Energy Efficient Clustering (iE-DEEC) Scheme for heterogeneous Wireless Sensor Network. International Journal of Engineering Research and Advanced Technology, 05(01), 06–11. https://doi.org/10.31695/ijerat.2019.3359
[25] Gupta, S. K., & Singh, S. (2022). Energy Efficient Dynamic Sink Multi Level Heterogeneous Extended Distributed Clustering Routing for Scalable WSN: ML-HEDEEC. Wireless Personal Communications. https://doi.org/10.1007/s11277-022-09967-6 
[26] Jorio, A., El Fkihi, S., Elbhiri, B., & Aboutajdine, D. (2015). An Energy-Efficient Clustering Routing Algorithm Based on Geographic Position and Residual Energy for Wireless Sensor Network. Journal of Computer Networks and Communications, 2015, 1–11. https://doi.org/10.1155/2015/170138
[27] Goel, A. (2020). Energy Efficient Routing in Wireless Sensor Network using a Modified LEACH based Protocol. International Journal for Research in Applied Science and Engineering Technology, 8(1), 14–18. https://doi.org/10.22214/ijraset.2020.1003
[28] Sikandar, A., & Kumar, S. (2015). Energy Efficient clustering in Heterogeneous Wireless Sensor Networks using Degree of Connectivity. International Journal of Computer Networks & Communications, 7(2), 19–31. https://doi.org/10.5121/ijcnc.2015.7202
[29] Mohan, P., Subramani, N., Alotaibi, Y., Alghamdi, S., Khalaf, O. I., & Ulaganathan, S. (2022). Improved Metaheuristics-Based Clustering with Multihop Routing Protocol for Underwater Wireless Sensor Networks. Sensors, 22(4), 1618. https://doi.org/10.3390/s22041618
[30] Yi, J., & Lee, H. (2016). Modeling and performance analysis for a receiver-initiated MAC protocol in wireless sensor networks. International Journal of Distributed Sensor Networks, 12(11), 155014771667655. https://doi.org/10.1177/1550147716676553

[1] IETF RFC 4226, HOTP: An HMAC-Based One-Time
Password Algorithm, Dec. 2005.
[2] Mohammad Mannan, P. C. Van Oorschot, “Security and
Usability: The Gap in Real-World Online Banking”,
NSPW?07, North Conway, NH, USA, Sep. 18-21, 2007.
[3] Sang-Il Cho, hoonjae Lee, Hyo-Taek Lim, Sang-Gon
Lee, “OTP Authentication Protocol Using Stream Cipher
with Clock-Counter”, October, 2009.
[4] Amandeep Choudhary, Shweta Rajan, Akshata Shinde,
Siddeshwar Warkhade, Prof. F.S. Ghodichor, “Online
Banking System using Mobile-OTP with QR-code”,
IJARCCE, vol.6, 4 April, 2017.
[5] ISO/IEC 18004:2000 – Information Technology –
Automatic Identification and Data Capture Techniques –
BarCode Symbology – QR Code, 2000.
[6] Abhas Tandon, Rahul Sharma, Sankalp Sodhiya,
P.M.Durai Raj Vincent, “QR code based secure OTP
distribution scheme for Authentication in Net-Banking”,
International Journal of Engineering and
Technology,5(3):2502-2505, June, 2013.
[7] Qiu-xia Wang; Tie Xu; Pei-zhou Wu, "Application
research of the AES encryption algorithm on the engine
anti-theft system," Vehicular Electronics and Safety
(ICVES), 2011 IEEE International Conference on, vol., no.,
pp.25,29, 10-12 July 2011.
[8] J. S. Tan, “QR code,” Synthesis Journal, Section 3, pp.
59-78, 2008.
[9] M.L.T. Uymatiao, W.E.S. Yu, “Time-based OTP
Authentication via Secure Tunnel (TOAST): A Mobile
TOTP Scheme Using TLS Seed Exchange and Encrypted
Offline Keystore,” IEEE, 2014, pp 225–229.
[10] Jose Rouillard, “Contextual QR Codes”, Proceedings of
the Third International Multi-Conference on Computing in
the Global Information Technology (ICCCGI2008), Athens,
Greece, July 27-August 1, 2008.
[11] Bwalya, M., & Chembe, C. (2020, February 21). A
Security Framework for Mobile Application Systems: Case
of Android Applications. Zambia ICT Journal, 3(2), 31–43.
https://doi.org/10.33260/zictjournal.v3i2.84
[12] Saurabh Shinde, Amar Bhegade, Anita Salve, & Chitra
Bhosale, Prof. Shalaka Deore. (2015, December 17). Mobile
based Anti-Phishing System using Secure QR Code.
International Journal of Engineering Research And, V4(12).
https://doi.org/10.17577/ijertv4is120289
[13] P.S.V, D., P, D., & SK, D. (2020, January 25). An
Enhanced Mutual Authentication Scheme using One-Time
Passwords with Images. International Journal of Computer
Trends and Technology, 68(1), 45–51.
https://doi.org/10.14445/22312803/ijctt-v68i1p111
[14] Choudhary, A., Rajak, S., Shinde, A., Warkhade, S., &
F.S., P. G. (2017, April 30). Online Banking System using
Mobile-OTP with QR-code. IJARCCE, 6(4), 657–661.
https://doi.org/10.17148/ijarcce.2017.64125
[15] Amoah, G. A., & J.B., H. A. (2022, October 20). QR
Code Security: Mitigating the Issue of Quishing (QR Code
Phishing). International Journal of Computer Applications,
184(33), 34–39. https://doi.org/10.5120/ijca2022922425

[1] Adewusi, A.O., Oyedokun, T.B., Bello, M.O.:
Application of artificial neural network to loan recovery
prediction. International Journal of Housing Marke Analysis
(2016)
[2] Chambers, B., Zaharia, M.: Spark: The definitive guide:
Big data processing made simple. ” O?Reilly Media, Inc.”
(2018)
[3] Hamid, A.J., Ahmed, T.M.: Developing prediction
model of loan risk in banks using data mining. Machine
Learning and Applications: An International Journal
(MLAIJ) Vol 3(1) (2016)
[4] Hassan, A.K.I., Abraham, A.: Modeling consumer loan
default prediction using neural netware. In: 2013
INTERNATIONAL CONFERENCE ON COMPUTING,
ELECTRICAL AND ELECTRONIC ENGINEERING
(ICCEEE). pp. 239–243. IEEE (2013)
[5] Klaas, J.: Loan default model trap.
https://www.kaggle.com/jannesklaas/modeltrap, (Accessed
on 13/10/2021)
[6] Lai, L.: Loan default prediction with machine learning
techniques. In: 2020 International Conference on Computer
Communication and Network Security (CCNS). pp. 5–9.
IEEE (2020)
[7] Marqu´es Marzal, A.I., Garc´?a Jim´enez, V., S´anchez
Garreta, J.S.: Exploring the behaviour of base classifiers in
credit scoring ensembles (2012)
[8] Meer, K.: Machine learning models for mortgage default
prediction in pakistan. In: 2021 International Conference on
Artificial Intelligence (ICAI). pp. 164–169. IEEE (2021)
[9] Murray, J.: Default on a loan, united states business law
and taxes guide national credit act (2005). act no. 34 of
2005, republic of south africa (2011)
[10] Odegua, R.: Predicting bank loan default with extreme
gradient boosting. arXiv preprint arXiv:2002.02011 (2020)
[11] Patel, B., Patil, H., Hembram, J., Jaswal, S.: Loan
default forecasting using data mining. In: 2020 International
Conference for Emerging Technology (INCET). pp. 1–4.
IEEE (2020)
[12] Reddy, M.J., Kavitha, B.: Neural networks for
prediction of loan default using attribute relevance analysis.
In: 2010 International Conference on Signal Acquisition and
Processing. pp. 274–277. IEEE (2010)
[13] Rendle, S.: Factorization machines. In: 2010 IEEE
International conference on data mining. pp. 995–1000.
IEEE (2010)
[14] Sheikh, M.A., Goel, A.K., Kumar, T.: An approach for
prediction of loan approval using machine learning
algorithm. In: 2020 International Conference on Electronics
and Sustainable Communication Systems (ICESC). pp.
490–494. IEEE (2020)
[15] Turkson, R.E., Baagyere, E.Y., Wenya, G.E.: A
machine learning approach for predicting bank credit
worthiness. In: 2016 Third International Conference on
Artificial Intelligence and Pattern Recognition (AIPR). pp.
1–7. IEEE (2016)
[16] Wang, B., Liu, Y., Hao, Y., Liu, S.: Defaults
assessment of mortgage loan with rough set and svm. In:
2007 International Conference on Computational
Intelligence and Security (CIS 2007). pp. 981–985. IEEE
[17] Loan Default Prediction Using Spark Machine
Learning Algorithms Aiman Muhammad Uwais and and
Hamidreza Khaleghzadeh

[1] B. Varghese and R. Buyya, „„Next generation cloud
computing: New trends and research directions,?? Future
Gener. Comput. Syst., vol. 79, pp. 849–861, Feb. 2018.
[2] E. Youssef, „„Exploring cloud computing services and
applications,?? J. Emerg. Trends Comput. Inf. Sci., vol. 3,
no. 6, pp. 838–847, Jul. 2012.
[3] Almishal and E. A. Youssef, „„Cloud service providers:
A comparative study,?? Int. J. Comput. Appl. Inf. Technol.,
vol. 5, no. 2, pp. 46–52, May 2014.
[4] R. R. Kumar, S. Mishra and C. Kumar, “A novel
framework for cloud service evaluation and selection using
hybrid MCDM methods,” Arabian Journal for Science and
Engineering, vol. 43, no. 12, pp. 7015–7030, 2018.
[5] S. K. Garg, S. Versteeg and R. Buyya, “A framework
for ranking of cloud computing services,” Future
Generation Computer Systems, vol. 29, no. 4, pp. 1012–
1023, 2013.
[6] A. Tripathi, I. Pathak and D. P. Vidyarthi, “Integration
of analytic network process with service measurement
index framework for cloud service provider
selection,” Concurrency and Computation: Practice and
Experience, vol. 29, no. 12, pp. 1–16, 2017.
[7] J. S. Dyer, “MAUT-multiattribute utility theory,”
in International Series in Operations Research and
Management Science. Vol. 78. New York, NY, USA:
Springer, pp. 265–292, 2005.
[8] K. Govindan and M. B. Jepsen, “ELECTRE: A
comprehensive literature review on methodologies and
applications,” European Journal of Operational Research,
vol. 250, no. 1, pp. 1–29, 2016.
[9] A. Afshari, M. Mojahed and R. Yusuff, “Simple
additive weighting approach to personnel selection
problem,” International Journal of Innovation,
Management and Technology, vol. 1, no. 5, pp. 511–515,
2010.
[10] G. Baranwal and D. P. Vidyarthi, “A cloud service
selection model using improved ranked voting method,
Concurrency and Computation,” Practice and Experience,
vol. 28, no. 13, pp. 3540–3567, 2016.
[11] N. Upadhyay, „„Managing cloud service evaluation
and selection,?? Procedia Comput. Sci., vol. 122, pp. 1061–
1068, 2017.
[12] S. K. Garg, S. Versteeg, and R. Buyya, „„A framework
for ranking of cloud computing services,?? Future Gener.
Comput. Syst., vol. 29, no. 4, pp. 1012–1023,Jun. 2013.
[13] S. K. Garg, S. Versteeg, and R. Buyya, „„SMICloud:
A framework for comparing and ranking cloud services,??
in Proc. 4th IEEE Int. Conf. Utility Cloud Comput., Dec.
2011, pp. 210–218.
[14] F. Nawaz, M. R. Asadabadi, N. K. Janjua, O. K.
Hussain, E. Chang, and M. Saberi, „„An MCDM method
for cloud service selection using a Markov chain and the
best-worst method,?? Knowl.-Based Syst., vol. 159, pp.
120–131, Nov. 2018.
[15] Z. Rehman, F. K. Hussain, and O. K. Hussain,
„„Towards multi-criteria cloud service selection,?? in Proc.
5th Int. Conf. Innov. Mobile Internet Services Ubiquitous
Comput., Jun. 2011, pp. 44–48.
[16] A. M. Mostafa, "An integrated framework for cloud
service selection based on bom and topsis," Computers,
Materials & Continua, vol. 72, no.2, pp. 4125–4142, 2022.
[17] Youssef, Ahmed E.. “An Integrated MCDM
Approach for Cloud Service Selection Based on TOPSIS
and BWM.” IEEE Access 8 (2020): 71851-71865.
[18] Tomar, Abhinav & Kumar, Rakesh & Gupta,
Indrajeet. (2022). Decision making for cloud service
selection: a novel and hybrid MCDM approach. Cluster
Computing. 1-19. 10.1007/s10586-022-03793-y.

[1] Health and economic impact of air pollution in the states of India: the Global Burden of Disease Study 2019.Open AccessPublished:December 21, 2020DOI:https://doi.org/10.1016/S2542-5196(20)30298-9

[2] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4465283

[3] A Study and Analysis of Air Quality Index and Related Health Impact on Public Health

[4] Study of the Effects of Air Pollutants on Human Health Based on Baidu Indices of Disease Symptoms and Air Quality Monitoring Data in Beijing, China.

[5] A. Kumar and P. Goyal, “Forecasting of air quality in delhi using principal component regression technique,” Atmospheric Pollution Research, vol. 2, no. 4, pp. 436–444, 2011.

[6] C. Zhang, J. Yan, Y. Li, F. Sun, J. Yan, D. Zhang, X. Rui, and R. Bie, “Early air pollution forecasting as a service: An ensemble learning approach,” in Proc. IEEE Int. Conf. on Web Services (ICWS), 2017, pp. 636–643.

[7] Z. Wang and Z. Long, “Pm2. 5 prediction based on neural network,” in Proc. IEEE 11th Int. Conf. on Intelligent Computation Technology and Automation (ICICTA), 2018, pp. 44–47. 

[8] K. B. Shaban, A. Kadri, and E. Rezk, “Urban air pollution monitoring system with forecasting models,” IEEE Sensors Journal, vol. 16, no. 8, pp. 2598–2606, 2016.

[9] B. Yeganeh, M. S. P. Motlagh, Y. Rashidi, and H. Kamalan, “Prediction of co concentrations based on a hybrid partial least square and support vector machine model,” Atmospheric Environment, vol. 55, pp. 357–365, 2012

[10] Kurt, A. and A.B. Oktay, 2010. Forecasting air pollutant indicator levels with geographic models 3 days in advance using neural networks. Expert Syst. Appli., 37: 7986-7992. DOI: 10.1016/j.eswa.2010.05.093

[11] Yedukondalu, Gangolu & K., Samunnisa & Bhavsingh, M. & Raghuram, I & Lavanya, Addepalli. (2022). MOCF: A Multi-Objective Clustering Framework using an Improved Particle Swarm Optimization Algorithm. International Journal on Recent and Innovation Trends in Computing and Communication. 10. 143-154. 10.17762/ijritcc.v10i10.5743.
[12] Maloth, Bhav Singh & Anusha, R. & Reddy, R. & Devi, S.Chaya. (2013). Augmentation of Information Security by Cryptography in Cloud Computing. www.ijcst.com. 4.
[13] Samunnisa, K. & Kumar, G. & Madhavi, K.. (2022). Intrusion detection system in distributed cloud computing: Hybrid clustering and classification methods. Measurement: Sensors. 25. 100612. 10.1016/j.measen.2022.100612.

1.	Central Board of Film Certification Home Page, http://www.cbfcindia.tn.nic.in/ (last visited Aug. 21, 2008).
2.	Universal Declaration of Human Rights, 10th December, 1948
3.	Art 19 of Constitution of India 
4.	(1954) S.C.R. 587
5.	1995 AIR 1236, 1995 SCC (2) 161
6.	Confronting the Internet’s Dark Side at 148.
7.	United Nations Secretary-General António Guterres, May 2019
8.	Art 25 (1) of Constitution of India
9.	 "1929 killing of Hindu publisher". TheGuardian.com. 12 March 2015.
10.	1962 CriLJ 564
11.	1958 AIR 1032, 1959 SCR 1211
12.	Pravasi Bhalai Sangathan vs U.O.I. & Ors AIR 2014 SC 1591
13.	 Supreme Court Erred Again: Mistaken on Hate Speech as Free Speech". 5 March 2014. 
14.	 http://www.dailypioneer.com/print.php?printFOR=storydetail&story_url_key=nsa-against-tiwari-abhm-to-challenge-hcs-order&section_url_key=state-editions
15.	http://www.dailypioneer.com/print.php?printFOR=storydetail&story_url_key=nsa-against-tiwari-abhm-to-challenge-hcs-order&section_url_key=state-editions   
16.	  "Blogger arrested in India? Bengal for criticising Islam on social media
17.	 "Two Arrested for Making 'Derogatory' Remarks Against Modi, Adityanath and Hindu Gods"
18.	  A.I.R. 1971 S.C. 481
19.	  (1989) 2 S.C.C. 574, 592.
20.	 IT Minister Kapil Sibal stated specifically in 2012

[1]	Ahalya, M., A. Muktha, M. Veena, G. Vidyashree, 
and V. J. Rehna. "Solar Powered Semi-Automatic Pesticide Sprayer for use in Vineyards."
[2]	Spiewak, Radoslow. "Pesticides as a cause of occupational skin diseases in farmers." Annals of agricultural and environmental medicine 8, no. 1 (2001).
[3]	Joshi D.Bharatbhai,”Automatic Pesticide Sprayer”,2017 IJEDR
[4]	Kassim, A. M., M. F. N. M. Termezai, A. K. R. A. Jaya, A. H. Azahar, S. Sivarao, F. A. Jafar, H. I. Jaafar, and M. S. M. Aras. "Design and development of autonomous pesticide sprayer robot for fertigation farm." International Journal of Advanced Computer Science and Applications 11, no. 2 (2020).
[5]	Mahyuni, Eka Lestari, and Muhammad Makmur Sinaga. "Health impact of pesticide using method at sprayed worker farmer in Sumber Mufakat Village, Karo." In 1st Public Health International Conference (PHICo 2016), pp. 150-154. Atlantis Press, 2016
[6]	I Parmar Milan,2Dafada Jigna, 3Chauhan Kamlesh, 4Mehul Batavia, 5Kapil Raviya and 6Makawana Dhaval, 1,2,3,4,5,6Department of Electrical Engineering, OM Institute of Engineering & Technology, Junagadh, Gujarat, India
[7]	Anuradha, T., K. Ramya, and R. Selvam. "Design and Implementation of Solar Powered Automatic Pesticide Sprayer for Agriculture." In Journal of Physics: Conference Series, vol. 1362, no. 1, p. 012048. IOP Publishing, 2019.
[8]	Design and Development of Multipurpose pesticides sprinkler and fertilizer spreader machine.
[9]	Cozma, Petronela, Laura Carmen Apostol, Raluca Maria Hlihor, Isabela Maria Simion, and Maria Gavrilescu. "Overview of human health hazards posed by pesticides in plant products." In 2017 E-Health and Bioengineering Conference (EHB), pp. 293-296. IEEE, 2017.
[10]	Martini, Ni Putu Devira Ayu, Niam Tamami, and Ali Husein Alasiry. "Design and Development of Automatic Plant Robots with Scheduling System." In 2020 International Electronics Symposium (IES), pp. 302-307. IEEE, 2020.
[11]	Aishwarya, B. V., G. Archana, and C. Umayal. "Agriculture robotic vehicle based pesticide sprayer with efficiency optimization." In 2015 IEEE Technological Innovation in ICT for Agriculture and Rural Development (TIAR), pp. 59-65. IEEE, 2015.
[12]	Prajith, A. S., B. S. Nowfiya, Nadeem Noushad, S. Subi, and Dhinu Lal. "Automatic Agricultural Robot—Agrobot." In 2020 IEEE Bangalore Humanitarian Technology Conference (B-HTC), pp. 1-5. IEEE, 2020.
[13]	Li, Chen, Hui Dong, Xubing Li, Weikang Zhang, Xiaodong Liu, Ligang Yao, and Hao Sun. "Inverse Kinematics Study for Intelligent Agriculture Robot Development via Differential Evolution Algorithm." In 2021 International Conference on Computer, Control and Robotics (ICCCR), pp. 37-41. IEEE, 2021.
[14]	Danton, Adrien, Jean-Christophe Roux, Benoit Dance, Christophe Cariou, and Roland Lenain. "Development of a spraying robot for precision agriculture: An edge following approach." In 2020 IEEE Conference on Control Technology and Applications (CCTA), pp. 267-272. IEEE, 2020.
[15]	Gao, Xinyu, Jinhai Li, Lifeng Fan, Qiao Zhou, Kaimin Yin, Jianxu Wang, Chao Song, Lan Huang, and Zhongyi Wang. "Review of wheeled mobile robots’ navigation problems and application prospects in agriculture." IEEE Access 6 (2018): 49248-49268.
[16]	Zhao, Wei, Xuan Wang, Bozhao Qi, and Troy Runge. "Ground-level mapping and navigating for agriculture based on IoT and computer vision." IEEE Access 8 (2020): 221975-221985.
[17]	C Ghafar, Afif Shazwan Abdul, Sami Salama Hussen Hajjaj, Kisheen Rao Gsangaya, Mohamed Thariq Hameed Sultan, Mohd Fazly Mail, and Lee Seng Hua. "Design and development of a robot for spraying fertilizers and pesticides for agriculture." Materials Today: Proceedings (2021).
[18]	Sharma, Sonal, and Rushikesh Borse. "Automatic agriculture spraying robot with smart decision making." In The International Symposium on Intelligent Systems Technologies and Applications, pp. 743-758. Springer, Cham, 2016.

[1] Gorlatova, Maria & Inaltekin, Hazer & Chiang, Mung. (2018). Characterizing Task Completion Latencies in Fog Computing.
[2] Ling, Tonghua & He, Wenchao & Liu, Xianjun & Zhang, Sheng & Huang, Fu & Hua, Fei. (2022). Fine grid model for the dielectric characteristics of ground?penetrating radar in mixed media. Geophysical Prospecting. 70. 10.1111/1365-2478.13214.
[3] Michael, Dennis & Indrajit, Richardus & Dazki, Erick. (2022). Implementation of Enterprise Architecture in Cloud Computing Companies. Sinkron. 7. 549-559. 10.33395/sinkron.v7i2.11407.
[4] Banger, Nanda & K, Pallavi & Shetty, Mrs. (2022). A Review Paper on Cloud Computing Architecture, Types, Advantages and Disadvantages. International Journal of Advanced Research in Science, Communication and Technology. 14-22. 10.48175/IJARSCT-3144.
[5] Ramesh, Varsha & Nair, Siddharth & Tyagi, Amit. (2022). The Fog/Edge Computing: Challenges, Serious Concerns, and the Road Ahead. 10.1002/9781119792437.ch16.
[6]  Maloth, Bhav Singh & Anusha, R. & Reddy, R. & Devi, S.Chaya. (2013). Augmentation of Information Security by Cryptography in Cloud Computing. www.ijcst.com. 4.
[7] Deokar, Sanjivani & Mangla, Monika & Akhare, Rakhi. (2021). A Secure Fog Computing Architecture for Continuous Health Monitoring. 10.1007/978-3-030-46197-3_11.
[8 Lin, Rongheng & Ye, Zezhou & Wang, Hao & Wu, Budan. (2018). Chronic Diseases and Health Monitoring Big Data: A Survey. IEEE Reviews in Biomedical Engineering. PP. 1-1. 10.1109/RBME.2018.2829704.
[9] Hamid, Hadeal & Rahman, Sk Md Mizanur & Hossain, M. Shamim & Almogren, Ahmad & Alamri, Atif. (2017). A Security Model for Preserving the Privacy of Medical Big Data in a Healthcare Cloud Using a Fog Computing Facility With Pairing-Based Cryptography. IEEE Access. PP. 1-1. 10.1109/ACCESS.2017.2757844.
[10] Kurikala, Geetha & Koppuravuari, Gurnadha & Swapna, A. (2017). Fog Computing : Implementation of Security and Privacy to Comprehensive Approach for Avoiding Knowledge Thieving Attack Exploitation Decoy Technology. International Journal of Scientific Research in Computer Science Engineering and Information Technology. 4. 2456-3307.
[11] Vishwanath, Akhilesh & Peruri, Ramya & He, Jing. (2016). Security in Fog Computing through Encryption. International Journal of Information Technology and Computer Science. 8. 28-36. 10.5815/ijitcs.2016.05.03.
[12] Kraemer, Frank & Bråten, Anders & Tamkittikhun, Nattachart & Palma, David. (2017). Fog Computing in Healthcare – A Review and Discussion. IEEE Access. PP. 9206-9222. 10.1109/ACCESS.2017.2704100.
[13] Mouradian, Carla & Naboulsi, Diala & Yangui, Sami & Glitho, Roch & Morrow, Monique & Polakos, Paul. (2017). A Comprehensive Survey on Fog Computing: State-of-the-Art and Research Challenges. IEEE Communications Surveys & Tutorials. PP. 10.1109/COMST.2017.2771153.
[14] Vishwanath, Akhilesh & Peruri, Ramya & He, Jing. (2016). Security in Fog Computing through Encryption. International Journal of Information Technology and Computer Science. 8. 28-36. 10.5815/ijitcs.2016.05.03.
[15] Alexandru, Adriana & Coardos, Dora. (2016). Big Data în Healthcare and Medical Applications in România. Big Data în Healthcare and Medical Applications in România.
[16] Abouelmehdi, Karim & Beni-Hessane, Abderrahim & Khaloufi, Hayat. (2018). Big healthcare data: preserving security and privacy. Journal of Big Data. 5. 10.1186/s40537-017-0110-7. 
[17] Geetha Kurikala, K Gurnadha Gupta, A. Swapna.(2017). Fog Computing: Implementation of Security and Privacy to Comprehensive Approach for Avoiding Knowledge Thieving Attack Exploitation Decoy Technology.” 2(4).
[18] Malek Ben Salem and Salvatore J. Stolfo(2011). Decoy Document Deployment for Effective Masquerade Attack Detection. 7. 
[19] Nisha Peter. (2015 ). Fog Computing and Its Real Time Applications. International Journal of Emerging Technology and Advanced Engineering, 7.
 [20] Mohamed Firdhous, Osman Ghazali and Suhaidi Hassan. “Fog Computing: Will It Be the Future of Cloud Computing?” ISBN: 978-1-941968-00-0, 2014.
[21]  Komal Thorat, Shweta Dawkhar, Mansi Kulkarni, Samiksha Wale (2020). Preserving the Privacy of Chronical Big Data Using Fog and Cloud Computing.9(6).

[1] S. Bhutada and P. Bhutada, Application of Artificial Intelligence in Cyber Security: in IJERCSE, 2018, 5(4): 214 219
[2] P.V. Alberto, lecture, Topic: Application of Artificial Intelligence (AI) to Network Security?, ITEC 625, University of Maryland, University College, Maryland, Mar. 2018.
[3] Avira, The Application of AI to Cybersecurity An Avira White Paper, Germany, Avira Operation, 2017.
[4] S. A Panimalar, U.G. Pai and K.S. Khan, ?AI Techniques for Cyber Security?, International Research Journal of Engineering and Technology, vol. 5, 3, pp. 122 124, Mar. 2018. Available: https://www.irjet.net [assessed May. 29, 2020]
[5] T.S. Tuang. Diep.Q. B, and Zelinka. I, Artificial Intelligence in the Cyber Domain: Offense and Defense: Symmetry, 2020, 12,410 available: www.mdp.com/journal/symmetry on [assessed Apr. 20, 2020]
[6] E. Kanal, Machine Learning in Cybersecurity: Carnegie Mellon University Software Engineering Institute, available onhttp://insights.sei.cnu.edu./sei_blog/2017/06/machine_learning_in_cybersecurity.html
[7] D. Selma, C. Huseyin and A. Mustafa, Application of Artificial Intelligence Techniques to Combating Cyber Crimes: A Review, International Journal of Artificial Intelligence &Applications, vol. 6, issue 1, pp. 21 39, January 2015.
INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH VOLUME 9, ISSUE 10, OCTOBER 2020 ISSN 2277-8616
[8] T. Enn, ?Artificial Intelligence in Cyber Defense?, in Proceedings of 3rd International Conference on Cyber Conflicts [ICCC], 7--10 June, 2011 Tallin Estonia.
[9] P. Dennis, A. Stuart, ?Global Challenges: Twelve risks that threaten human civilization, Global Challenges Foundation?: 2015,Available: http://globalchallenges.org /wp--content/ uploads/12--Risks--with--infinite--impact.pdf [accessed Jun. 3, 2020]
[10] R. Stuart, D. Daniel, T. Max, ??Research Priorities for Robust and Beneficial Artificial Intelligence‘‘, AI Magazine, vol. 36, issue 4, pp. 105--114, Winter 2015
[11] National Science & Technology Council, ?Artificial Intelligence and Cybersecurity: Opportunities and Challenges? Net. & Info.Tech R&D Sub--comtt and the ML & AI Sub--comtt, 2020.
[12] A. M. Shamiulla, Role of Artificial Intelligence in Cyber Security, International Journal of Innovative Technology and Exploring Engineering, vol. 9 issue 1 pp. 4628--4630, November 2019
[13] P. Pranav, ?Artificial Intelligence in cyber security?, International Journal of Research in Computer Applications & Robotics, vol 4, 1, pp.1--5, May 2016
[14] J. Kivimaa, A. Ojamaa, E. Tyugu. Graded Security Expert System. Lecture Notes in Computer Science, v. 5508. Springer, 2009, 279--286.
[15] B. Christain, D.A. Elizondo and T. Watson, ?Application of artificial neural networks and related techniques to intrusion detection?, World Congress on Computation Intelligence, pp 949--954, 2010
[16] E. Tyugu, ?Artificial Intelligence in Cyber Defense?, International conference on Cyber Conflict, vol. 3, pp. 95--105, Tallinn, Estonia, Jan. 2011
[17] W. Nadine and K. Hadas, ?Artificial Intelligence in Cybersecurity?, Cyber, Intelligence, and Security, vol. 1, 1, pp. 103--119, Jan. 2017
[18] S. Dima, M. Robert, B. Zvi, S. Shahar and E. Yuval, ?Using Artificial Neural Network to Detect Unknown Computer Worms?, Neural Computing and Applications, vol.18, 7, pp. 663--674, Oct. 2009
[19] E. H. Geoffrey, O. Simon and T. Yee--Whye, ?A Fast Learning Algorithm for Deep Belief Nets?, Neural Computation, vol. 18, no. 7, pp. 1527--1554, 2006
[20] V. Thomson, ?Cyber Attacks Could be Predicted with Artificial Intelligence?, iTechPost,www.itechpost.com/articles/1734 7/cyber-attacks--predicted--artificial--intelligen ce--help.htm, Apr. 21, 2016 [Jun. 2, 2020]
[21] S. Franklin and A. Graesser, ?Is It an Agent, or Just a Program? A Taxonomy for Autonomous Agents?, Third International Workshop on Agent Theories, Architectures, and Languages, no. 3, pp. 21--35, 1997
[22] Y. Xia and L. Junshan, ?A Security Architecture Based on Immune Agents for MANET?, International Conference on Wireless Communication and Sensor Computing, no. , pp. 1--5, 2010
[23] M. F. AbRazak, etal, ?Bio--inspired for features optimization and malware detection.Arabian Journal of Science and Engineering, no. 43, pp. 6963––6979. 2018
[24] A. Fatima, etal, ?Android Malware Detection Using Genetic Algorithm based Optimized Feature Selection and Machine Learning?, In Proceedings of the 42nd International Conference on Telecommunications and Signal Processing (TSP), Budapest, Hungary, 1––3 July 2019; pp. 220––223.
[25] R.A.R Ashfaq, etal, ?Fuzziness based semi--supervised learning approach for intrusion detection system?. Inf. Sci. 2017, 378, 484––497.
[26] L. Lazic, ?Benefits from AI in Cyber Security?, The 11th international Conference on Business Information Security, 18th Oct. 2019, Belgrade, Serbia, pp. 1—9

[1] Bhumika Gupta and Monika Negi, Kanika Vishwakarma, Goldi Rawat, Priyanka Badhani ,“Study of Twitter Sentiment Analysis using Machine Learning Algorithms on Python,”.  In International Journal of Computer Applications (0975 – 8887) Volume 165 – No.9, May 2017
[2] Ankita and Nabizath Saleena, ‘‘An Ensemble Classification System for Twitter Sentiment Analysis,’’ in International Conference on Computational Intelligence and Data Science (ICCIDS 2018)
[3] Roza H. Hama Aziz and Nazife Dimililer, ‘‘Twitter Sentiment Analysis using an Ensemble Weighted Majority Vote Classifier, Third International Conference on Advanced Science and Engineering (ICOASE2020) 
[4] Chaudhary Jagrit Varshney , Dr. Ashish Sharma and  Dhirendra Prasad Yadav, ‘‘Sentiment Analysis using Ensemble Classification Technique,’’ IEEE ,2020
[5] Anam Yousaf , Muhammad Umer , Saima Sadiq , Saleem Ullah, Seyefali Mirjalili(Senior Member, IEEE), Vainhav Rupapara , and Michele Nappi, (Senior Member, IEEE) , “Emotion Recognition by Textual Tweets Classification Using Voting Classifier (LR-SGD)”,IEEE,2021
[6] N. F. F. da Silva, E. R. Hruschka, and E. R. Hruschka, ‘‘Tweet sentiment analysis with classifier ensembles,’’ Decis. Support Syst., vol. 66, pp. 170–179, Oct. 2014.
 [7] C. Kariya and P. Khodke, ‘‘Twitter sentiment analysis,’’ in Proc. Int. Conf. Emerg. Technol. (INCET), Jun. 2020, pp. 212–216. 
[8] A. Alsaeedi and M. Zubair, ‘‘A study on sentiment analysis techniques of Twitter data,’’ Int. J. Adv. Comput. Sci. Appl., vol. 10, no. 2, pp. 361–374, 2019. 
[9] A. Bandhakavi, N. Wiratunga, D. Padmanabhan, and S. Massie, ‘‘Lexicon based feature extraction for emotion text classification,’’ Pattern Recognit. Lett., vol. 93, pp. 133–142, Jul. 2017
[10] H. Hakh, I. Aljarah, and B. Al-Shboul, ‘‘Online social media-based sentiment analysis for us airline companies,’’ in New Trends in Information Technology. Amman, Jordan: Univ. of Jordan, Apr. 2017.
[11] R. Xia, C. Zong, and S. Li, ‘‘Ensemble of feature sets and classification algorithms for sentiment classification,’’ Inf. Sci., vol. 181, no. 6, pp. 1138–1152, Mar. 2011. 
[12] M. Umer, S. Sadiq, M. Ahmad, S. Ullah, G. S. Choi, and A. Mehmood, ‘‘A novel stacked CNN for malarial parasite detection in thin blood smear images,’’ IEEE Access, vol. 8, pp. 93782–93792, 2020.

[1]. Pellicer, B., et al. "Ultrasound bioeffects in rats: quantification of cellular damage in the fetal liver after pulsed Doppler imaging." Ultrasound in obstetrics & gynecology 37.6 (2011): 643-648.
[2]. Dhas, Edwin, and M. Suchetha. "Extraction of Fetal ECG From Abdominal and Thorax ECG Using a Non-Causal Adaptive Filter Architecture." IEEE Transactions on Biomedical Circuits and Systems (2022).
[3]. Esteva, A. et al. Dermatologist-level classification of skin cancer with deep neural networks. Nat. 542, 115 (2017).
[4]. Sobhaninia, Zahra, et al. "Fetal ultrasound image segmentation for measuring biometric parameters using multi-task deep learning." 2019 41st annual international conference of the IEEE engineering in medicine and biology society (EMBC). IEEE, 2019.
[5]. Hosny, A., Parmar, C., Quackenbush, J., Schwartz, L. H. &Aerts, H. J. Artificial intelligence in radiology. Nat. Rev. Cancer 18, 500 (2018).
[6]. Litjens, G. et al. A survey on deep learning in medical image analysis. Med. image analysis 42, 60–88 (2017).
[7]. Das, Sraddha, et al. "Deep learning architecture based on segmented fundus image features for classification of diabetic retinopathy." Biomedical Signal Processing and Control 68 (2021): 102600.
[8]. Guo, Z., Zhang, L. and Zhang, D., 2010. A completed modeling of local binary pattern operator for texture classification. IEEE transactions on image processing, 19(6), pp.1657-1663.
[9]. Jabid, Taskeed, Md Hasanul Kabir, and Oksam Chae. "Local directional pattern (LDP)–A robust image descriptor for object recognition." 2010 7th IEEE international conference on advanced video and signal based surveillance. IEEE, 2010.
[10]. Dalal, Navneet, and Bill Triggs. "Histograms of oriented gradients for human detection." 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR'05). Vol. 1. Ieee, 2005.
[11]. Ryou, H. et al. Automated 3d ultrasound biometry planes extraction for first trimester fetal assessment. In Machine Learning in Medical Imaging, 196–204 (2016).
[12] Li, Y. et al. Standard plane detection in 3d fetal ultrasound using an iterative transformation network. Medical Image Computing and Computer Assisted Intervention – MICCAI 2018, 392–400 (2018).
[13]. Maraci, M., Bridge, C., Napolitano, R., Papageorghiou, A. & Noble, J. A framework for analysis of linear ultrasound videos to detect fetal presentation and heartbeat. Med. Image Analysis 37, 22–36 (2017).
[14] Sushma, T. V., et al. "Classification of Fetal Heart Ultrasound Images for the Detection of CHD." Innovative Data Communication Technologies and Application. Springer, Singapore, 2021. 489-505.
[15] Qiao, Sibo, et al. "RLDS: An explainable residual learning diagnosis system for fetal congenital heart disease." Future Generation Computer Systems 128 (2022): 205-218.
[16] Xie, H. N., et al. "Using deep?learning algorithms to classify fetal brain ultrasound images as normal or abnormal." Ultrasound in Obstetrics &Gynecology 56.4 (2020): 579-587.
[17]. Lee, Lok Hin, Yuan Gao, and J. Alison Noble. "Principled Ultrasound Data Augmentation for Classification of Standard Planes." International Conference on Information Processing in Medical Imaging. Springer, Cham, 2021.
[18]. Attallah, Omneya, Maha A. Sharkas, and Heba Gadelkarim. "Fetal brain abnormality classification from MRI images of different gestational age." Brain sciences 9.9 (2019): 231.
[19]. Kumari, S. Sandhya, and K. Sandhya Rani. "Empirical mode Decomposition and Dual Sigmoid Activation Function-Based Faster RCNN for Big Data Doppler Scan Image Classification." measurement 8.9 (2021): 151-165.
[20]. Suchetha, M., et al. "Region of interest-based predictive algorithm for subretinal hemorrhage detection using faster R-CNN." Soft Computing 25.24 (2021): 15255-15268.
[21]. Donoser, Michael, and Horst Bischof. "Efficient maximally stable extremal region (MSER) tracking." 2006 IEEE computer society conference on computer vision and pattern recognition (CVPR'06). Vol. 1. Ieee, 2006.
[22]. Bay, Herbert, et al. "Speeded-up robust features (SURF)." Computer vision and image understanding 110.3 (2008): 346-359.
[23]. Prabha, K., and I. Shatheesh Sam. "A novel blind color image watermarking based on Walsh Hadamard Transform." Multimedia Tools and Applications 79.9 (2020): 6845-6869.
[24]. Jiang, Huaizu, and Erik Learned-Miller. "Face detection with the faster R-CNN." 2017 12th IEEE international conference on automatic face & gesture recognition (FG 2017). IEEE, 2017.
[25]. Burgos-Artizzu, Xavier P., et al. "Evaluation of deep convolutional neural networks for automatic classification of common maternal fetal ultrasound planes." Scientific Reports 10.1 (2020): 1-12.
[26]. Sandler, M., Howard, A., Zhu, M., Zhmoginov, A. & Chen, L.-C. Mobilenetv2: Inverted residuals and linear bottlenecks. CVPR(2018).
[27]. Simonyan, K. & Zisserman, A. Very deep convolutional networks for large-scale image recognition. CoRR abs/1409.1556 (2014).
[28]. Hu, J., Shen, L. & Sun, G. Squeeze-and-excitation networks. CoRR abs/1709.01507 (2017).
[29]. He, K., Zhang, X., Ren, S. & Sun, J. Deep residual learning for image recognition. CoRR abs/1512.03385 (2015).
[30]. Szegedy, C. et al. Going deeper with convolutions. CoRR abs/1409.4842 (2014).
 [31]. Burgos-Artizzu, Xavier P., et al. "Evaluation of deep convolutional neural networks for automatic classification of common maternal fetal ultrasound planes." Scientific Reports 10.1 (2020): 1-12.
 [32]. Xie, S., Girshick, R., Dollar, P., Tu, Z. & He, K. Aggregated residual transformations for deep neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1492–1500 (2017).

[1] T. K. Bahiru, D. K. Singh, and E. A. Tessfaw, “Comparative study on data mining classification algorithms for predicting road traffic accident severity,” in 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT). IEEE, 2018, pp. 1655–1660.

[2] W. H. Organization, Global status report on alcohol and health 2018. World Health Organization, 2019.

[3] A. Jamal, M. Zahid, M. Tauhidur Rahman, H. M. Al-Ahmadi, M. Almoshaogeh, D. Farooq, and M. Ahmad, “Injury severity prediction of traffic crashes with ensemble machine learning techniques: a comparative study,” International journal of injury control and safety promotion, pp. 1–20, 2021.

[4] H. Al Najada and I. Mahgoub, “Big vehicular traffic data mining: Towards accident and congestion prevention,” in 2016 International Wireless Communications and Mobile Computing Conference (IWCMC). IEEE, 2017, pp. 256–261.

[5] M. Chong, A. Abraham, and M. Paprzycki, “Traffic accident data mining using machine learning paradigms,” in Fourth International Conference on Intelligent Systems Design and Applications (ISDA’04), Hungary, 2018, pp. 415–420.

[6] H. Al Najada and I. Mahgoub, “Anticipation and alert system of congestion and accidents in vanet using big data analysis for intelligent transportation systems,” in 2016 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2016, pp. 1–8.

[7] A. Elfar, A. Talebpour, and H. S. Mahmassani, “Machine learning approach to short-term traffic congestion prediction in a connected environment,” Transportation Research Record, vol. 2672, no. 45, pp. 185–195, 2018.

[8] A. Iranitalab and A. Khattak, “Comparison of four statistical and machine learning methods for crash severity prediction,” Accident Analysis & Prevention, vol. 108, pp. 27–36, 2017.

[9] R. E. AlMamlook, K. M. Kwayu, M. R. Alkasisbeh, and A. A. Frefer, “Comparison of machine learning algorithms for predicting traffic accident severity,” in 2019 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT). IEEE, 2019, pp. 272–276.

[10] H. Ibrahim and B. H. Far, “Data-oriented intelligent transportation systems,” in Proceedings of the 2014 IEEE 15th International Conference on Information Reuse and Integration (IEEE IRI 2014). IEEE, 2019, pp. 322–329.

[11] J. Paul, Z. Jahan, K. F. Lateef, M. R. Islam, and S. C. Bakchy, “Prediction of road accident and severity of bangladesh applying machine learning techniques,” in 2020 IEEE 8th R10 Humanitarian Technology Conference (R10-HTC). IEEE, 2020, pp. 1–6.

[12] “Road safety data - data.gov.uk,” https://data.gov.uk/dataset/ cb7ae6f0-4be6-4935-9277-47e5ce24a11f/road-safety-data, (Accessed on 12/07/2022).

[13] “Attribute-relation file format (arff),” https://www.cs.waikato.ac.nz/ml/weka/arff.html, (Accessed on 09/07/2022).

[14] T. Jayalakshmi and A. Santhakumaran, “Statistical normalization and back propagation for classification,” International Journal of Computer Theory and Engineering, vol. 3, no. 1, pp. 1793–8201, 2011.

[15] S. A. Arhin and A. Gatiba, “Predicting crash injury severity at unsignalized intersections using support vector machines and naive bayes classifiers,” Transportation Safety and Environment, vol. 2, no. 2, pp. 120–132,
2020.

[16] M.-M. Chen and M.-C. Chen, “Modeling road accident severity with comparisons of logistic regression, decision tree and random forest,” Information, vol. 11, no. 5, p. 270, 2020.

[17] M. S. Satu, S. Ahamed, F. Hossain, T. Akter, and D. M. Farid, “Mining traffic accident data of n5 national highway in bangladesh employing decision trees,” in 2017 IEEE Region 10 Humanitarian Technology Conference (R10-HTC). IEEE, 2017, pp. 722–725.

[18] M. Taamneh, S. Alkheder, and S. Taamneh, “Data-mining techniques for traffic accident modeling and prediction in the united arab emirates,” Journal of Transportation Safety & Security, vol. 9, no. 2, pp. 146–166, 2017.

[19] N. Kamboozia, M. Ameri, and S. M. Hosseinian, “Statistical analysis and accident prediction models leading to pedestrian injuries and deaths on rural roads in iran,” International journal of injury control and safety promotion, vol. 27, no. 4, pp. 493–509, 2020.

[20] S. Haynes, P. C. Estin, S. Lazarevski, M. Soosay, and A.-L. Kor, “Data analytics: Factors of traffic accidents in the uk,” in 2019 10th International Conference on Dependable Systems, Services and Technologies (DESSERT). IEEE, 2019, pp. 120–126.

[21] J. Gan, L. Li, D. Zhang, Z. Yi, and Q. Xiang, “An alternative method for traffic accident severity prediction: using deep forests algorithm,” Journal of advanced transportation, vol. 2020, 2020.

[1] S. Parnerkar, A. V. Jain, and C. Birchha, ‘‘An approach to efficient software bug prediction using regression analysis and neural networks,’’ Int. J. Innov. Res. Computer. Commun. Eng., vol. 3, no. 10, Oct. 2015. 
[2] A. V. Phan, M. L. Nguyen, and L. T. Bui, ‘‘Convolutional neural networks over control flow graphs for software defect prediction,’’ in Proc. IEEE 29th Int. Conf. Tools Artif. Intell. (ICTAI), Nov. 2017, pp. 45–52. 
[3] E. Erturk and E. A. Sezer, ‘‘Iterative software fault prediction with a hybrid approach,’’ Appl. Soft Comput., vol. 49, pp. 1020–1033, Dec. 2016.
 [4] R. Kumar and D. Gupta, ‘‘Software Bug Prediction System Using Neural Network,’’ Eur. J. Adv. Eng. Technol., vol. 3, no. 7, pp. 78–84, 2016. 
[5] I. B. Y. Goodfellow and A. Courville, Deep Learning, 1st ed. Cambridge, U.K.: MIT Press, 2016. 
[6] S. Haykin, Networks and Learning Machines. London, U.K.: Pearson, 2009. 
[7] Y.-S. Su and C.-Y. Huang, ‘‘Neural-network-based approaches for software reliability estimation using dynamic weighted combinational models,’’ J. Syst. Softw., vol. 80, no. 4, pp. 606–615, Apr. 2007. 
[8] A. Pahal and R. S. Chillar, ‘‘A hybrid approach for software fault prediction using artificial neural network and simplified swarm optimization,’’ IJARCCE, vol. 6, no. 3, pp. 601–605, Mar. 2017. 
[9] Y. LeCun and Y. H. Bengio And Hinton, ‘‘Deep learning,’’ Nature, vol. 521, no. 7553, pp. 436-444, 2015. 
[10] S. Yang, L. Chen, T. Yan, Y. Zhao, and Y. Fan, ‘‘An ensemble classification algorithm for convolutional neural network based on AdaBoost,’’ in Proc. IEEE/ACIS 16th Int. Conf. Comput. Inf. Sci., May 2017, pp. 401–406.
[11] C. Farabet, B. Martini, P. Akselrod, S. Talay, Y. LeCun, and E. Culurciello, ‘‘Hardware accelerated convolutional neural networks for synthetic vision systems,’’ in Proc. IEEE Int. Symp. Circuits Syst., May 2010, pp. pp. 257–260. 
[12] C. W. S. Jin Jin and M. J. Ye, ‘‘Artificial neural network-based metric selection for software fault-prone prediction model,’’ IET Software, vol. 6, no. 6, pp. 479–487, Dec. 2012. 
[13] C. Zhang, P. Patras, and H. Haddadi, ‘‘Deep learning in mobile and wireless networking: A survey,’’ IEEE Commun. Surveys Tuts., vol. 21, no. 3, pp. 2224–2287, 3rd Quart., 2019. 
[14] D. Kaur, A. Kaur, S. Gulati, and M. Aggarwal, ‘‘A clustering algorithm for software fault prediction,’’ in Proc. Int. Conf. Comput. Commun. Technol. (ICCCT), Sep. 2010, pp. 603–607. 
[15] M. Park and H. Hong, ‘‘Software fault prediction model using clustering algorithms determining the number of clusters automatically,’’ Int. J. Softw. Eng. Appl., vol. 8, no. 7, pp. 199–204, 2014. 
[16] R. S. Wahono and N. S. Herman, ‘‘Genetic feature selection for software defect prediction,’’ Adv. Sci. Lett., vol. 20, no. 1, pp. 239–244, Jan. 2014. 
[17] H. Wang, T. M. Khoshgoftaar, J. Van Hulse, and K. Gao, ‘‘Metric selection for software defect prediction,’’ Int. J. Softw. Eng. Knowl. Eng., vol. 21, no. 02, pp. 237–257, Mar. 2011. 
[18] J. Li, P. He, J. Zhu, and M. R. Lyu, ‘‘Software defect prediction via convolutional neural network,’’ in Proc. IEEE Int. Conf. Softw. Qual., Rel. Secur. (QRS), Jul. 2017, pp. 318–328. 
[19] H. Khanh Dam, T. Pham, S. Wee Ng, T. Tran, J. Grundy, A. Ghose, T. Kim, and C.-J. Kim, ‘‘A deep tree-based model for software defect prediction,’’ 2018, arXiv:1802.00921. [Online]. Available: http://arxiv.org/abs/1802.00921 
[20] S. D. Chandra, ‘‘Software defect prediction based on classification rule mining,’’ Dept. Comput. Sci. Eng., Nat. Inst. Technol. Rourkela, Rourkela, India, Tech. Rep., 2013.

[1] Reid, K. (2010). Management of School Attendance in the UK. Educational Management Administration & Leadership, 38(1), 88–106.
https://doi.org/10.1177/1741143209352387
[2] R.S., D. (2020). Attendance Authentication System Using Face Recognition. Journal of Advanced Research in Dynamical and Control Systems, 12(SP4), 1235–1248. https://doi.org/10.5373/jardcs/v12sp4/20201599
[3] Panditpautra, V., Goswami, A., Khavare, A., & Ambadekar, S. (2019). Biometric Attendance Management System Using Raspberry Pi. SSRN Electronic Journal. Published. https://doi.org/10.2139/ssrn.3368163
[4] Sharma, D., & Ashok, D. (2012). An Empirical Analysis Over the Four Different Feature-Based Face and Iris Biometric Recognition Techniques. International Journal of Advanced Computer Science and Applications,
3(10). https://doi.org/10.14569/ijacsa.2012.031013
[5] Kulkarni, P., Patil, Y., Dagade, A., & Kapse, A. (2020). Finger Print Based Attendance System Using Arduino. SSRN Electronic Journal. Published.
https://doi.org/10.2139/ssrn.3645323
[6] Qureshi, R. (2020). The Proposed Implementation of RFID based Attendance System. International Journal of Software Engineering & Applications, 11(3), 59–69.
https://doi.org/10.5121/ijsea.2020.11304
[7] Shah, D. (2020). Quick Response (QR) Code based Attendance Marking System. International Journal of Computer Applications, 177(33), 43–47.
https://doi.org/10.5120/ijca2020919816
[8] Gaikwad, P., Narule, S., Thakre, N., & Chandekar, P.
(2017). RFID Technology Based Attendance Management System. International Journal Of Engineering And Computer Science. Published. https://doi.org/10.18535/ijecs/v6i3.10
[9] History Of Time And Attendance Systems | Blog. (2011, August 26). Redcort. https://www.redcort.com/itsabout-time/history-of-time-and-attendance-systems
[10] Tu, Y. J., Zhou, W., & Piramuthu, S. (2021). Critical risk considerations in auto-ID security: Barcode vs. RFID. Decision Support Systems, 142, 113471.
https://doi.org/10.1016/j.dss.2020.113471
[11] Shukla, S. (2013). RFID based Attendance Management System. International Journal of Electrical and Computer Engineering (IJECE), 3(6).
https://doi.org/10.11591/ijece.v3i6.3961

[1]	United Nations Children’s Fund (UNICEF), ”Education: Reimagine Education”, (2021)
[2]	World Bank, “COVID-19 (Coronavirus) Response”, (2021) 
[3]	United Nations Educational, Scientific and Cultural Organization. “COVID-19 Impact on Education”, (2020) 
[4]	Department of Education Bayanihan sa Paaralan, “National Brigada Eskwela Kick-Off”, (2021)
[5]	W.D. Changiwa, “Taking social media to a university classroom: teaching and classrooms”, SAGE Publications, California, (2017)
[6]	S. McCombes, “Descriptive Research”, Published on May 15, 2020
[7]	M. Kuhfeld, J. Soland, B. Tarasawa, A. Johnson, E. Ruzek & J. Liu, “Projecting the Potential Impact of COVID-19 School Closures on Academic Achievement”, Educational Researcher, 49(8), 549-565, (2020).
[8]	D.D. Lazo & M.F.D. de Guzman, “Strategic Intervention Material: A Learning Approach in Teaching Economics during the Distance Education", International Journal of Computer Engineering in Research Trends, 8(5): pp: 76-84, (2021)
[9]	N, Dizon, M.F.D de Guzman, L. Uy, & A. Ganaden, “Education Concerns in Public Secondary Schools of Division of Zambales, Philippines: An Education Response to COVID 19 Pandemic of 2020”, EAS Journal of Humanities and Cultural Studies (EAS J Humanit Cult Stud) - ISSN: 2663-0958 (Print) & ISSN: 2663-6743. (Online) Volume-3 | Issue-1| (2021)
[10]	R. Catacutan & M.F.D. de Guzman, “The Project- Based Learning (PBL) Approach in Secondary Social Studies Instruction at Zone 2, Division of Zambales”, Philippines. International Journal of Scientific & Engineering Research (IJSER), Volume 8, Issue 11 ISSN 2229-5518, (2017)
[11]	M.F.D. de Guzman & R. Ecle, “The Social Studies Curriculum Standards in Junior Secondary Schools; Input to Quality Instruction and Students’ Civic Competence”, International Journal of Computer Engineering in Research Trends. Volume-6, Issue-2, 2019 Regular Edition. E-ISSN: 2349-7084, (2019)
[12]	H. T. Hou, T. F. Yu, F.D. Chiang, Y.H. Lin, K.E. Chang, C.C. Kuo, “Development and evaluation of mind tool-based blogs to promote learners’ higher order cognitive thinking in online discussions”, J Educ Comput Res 58(2):343–363, (2020)
[13]	C.O. Okoro & C.U.Okoro, “Teachers’ Understanding and Use of Thematic Approach in Teaching and Learning of Social Studies in Rivers State”, European Centre for Research Training and Development UK. International Journal of Education, Learning and Development Vol.4, No.3, pp.64-69, (2016)
[14]	B. Rahal, N. Mansour, G. Zaatari, “Towards developing a sustainable faculty development program: an initiative of an American Medical School in Lebanon”, J. Med. Liban., 63, pp. 213-217, (2015)
[15]	A. Chaudhuri, “Survey Sampling”, Florida: CRC Press Taylor & Francis Group, (2019)
[16]	H. Park, B. Byun, J. Sim, H. Han, Y.S. Baek, “Teachers’ Perceptions and Practices of STEAM Education in South Korea”, Eurasia Journal of Mathematics, Science & Technology Education, 12(7), 1739-1753, (2016)
[17]	E.O. Adu, G. Galloway, O. Olaoye, “Teachers’ characteristics and students attitude towards economics in secondary schools: Students’ perspectives”, Mediterranean Journal of Social Sciences, 5(16), 455-462, (2014)
[18]	N.V. Smirnova, “Economics across the curriculum: integration of economic concepts into various disciplines. Perspectives on Economic Education Research”, American Institute for Economic Research, 10(1), 21-40, (2016)
[19]	B. Vasiliki, K. Panagiota, S.K. Maria, “A new teaching method for teaching economics in secondary education”, Journal of Research & Method in Education, 6(2), 86-93, (2016)
[20]	N. Baharuddin & R. Mohamad, “Factors Affecting the Usage of Library E-Service in the Aftermath of COVID- 19 Pandemic”, (2020)
[21]	C. Saxena, H. Baber & P. Kumar, “Examining the Moderating Effect of Perceived Benefits of Maintaining Social Distance on E-learning Quality during COVID-19 Pandemic”, Journal of Educational Technology Systems, 4723952097779, (2020)
[22]	K. Turvey, & N. Pachler, “Design principles for fostering pedagogical provenance through research in technology supported learning”, Computers and Education, 146, 103736, (2020). 
[23]	A. Kumar, K. Teotia, “A Constructivism: a dynamic approach of teaching learning social science at upper primary level”, Research Scholar Principal, DIET, Dilshad Garden, Delhi, (2017).
[24]	N. Ali, F. Eassa & E. Hamed, “Personalized Learning Style for Adaptive E-Learning System”, International Journal of Advanced Trends in Computer Science and Engineering. 223-230. (2019).
[25]	J. Lee, H.D. Song, & A. Hong, “Exploring factors, and indicators for measuring students’ sustainable engagement in e-learning”, Sustainability, 11, 985, (2019).
[26]	M. Ruzmetova, “Applying Gilly Salmon’s Five Stage Model for Designing Blended Courses”, Dil ve Edebiyat Ara?t?rmalar?, 17, 271-290, (2018)
[27]	A. P. Sarmiento, “MSCrim Learning Modules: A Self-Learning Kit in Criminology”, (2020)
[28]	A.M. Vergara, “Development, Effectiveness and Acceptability of Module for the Problem Solving and Critical Thinking Skills of Alternative Learning System in District of Tanay II”, Tomas Claudio Memorial College, Morong, Rizal, (2017)
[29]	E. Ramos, M.F.D. de Guzman & F. Rico, “Utilization of Self-Learning Module in the New Normal and Academic Achievement in Economics of Students in Public Secondary Schools," International Journal of Computer Engineering In Research Trends, 8(5): pp: 85-94, May -2021
[30]	I. Amin, A. Yousaf, S. Walia, & M. Bashir, “What Shapes E-Learning Effectiveness among Tourism Education Students? An Empirical Assessment during COVID19”, Journal of Hospitality, Leisure, Sport & Tourism Education, Volume 30, 100337, ISSN 1473-8376, (2022).
[31]	H. Erol, “Views of Social Studies Teachers on E-Learning. International Education Studies”, Vol. 14, No. 6; 2021 ISSN 1913-9020 E-ISSN 1913-9039 Published by Canadian Center of Science and Education, (2021)
[32]	S., Mubaroh, L., Fujiyanti, & M. S. Pratama, “Increasing Students' Digital Literacy Utilizing Edublogs as a Learning Media,” Advances in Social Science, Education and Humanities Research, Volume 547. (2020).  
[33]	E. Barkley & C. Major, “Student Engagement Techniques: A handbook for college faculty”, Jossey-Bass, 10:047028191X, (2020).
[34]	C.M. Cheng & M. B. Abu Bakar, “The Impact of Using Modules in the Teaching and Learning of English in Malaysian Polytechnics: An Analysis of the Views and Perceptions of English Language Teaching, Pendidikan, Malaysia”, (2017)
[35]	Mestiola, A. Renso, P. Glizelle, Naquita & S. Nerissa, Tantengco, “Enhancing Social Studies Teaching for 21st Century Learners in Secondary Education” in HONAI: International Journal for Educational, Social, Political & Cultural Studies, Volume 1(2), November, pp.83-100, (2018).
[36]	M. Singh, P.Gupta & Goswami, V., “Usefulness of edublogs as a tool to enhance teaching learning process among student teachers” International Journal of Research - Granthaalayah, Vol. 4, No. 11 (2016): 44-51, (2016)

[1]	Hrastinski, S. (2008). Asynchronous and synchronous e-learning. Educause quarterly, 31(4), 51-55.
[2]	Watson, J., Gemin, B., Ryan, J., Wicks, M. (2009). Keeping Pace with K-12 Online Learning: An Annual Review of State-Level Policy and Practice. ERIC
[3]	Viewsonic (2020). The pros and cons of asynchronous distance learning. 
[4]	Shank, P. (2020). (The Right) Learning Modalities to deliver digital learning. eLearning Industry.
[5]	Cook, T. D., Zhu, N., Klein, A., Starkey, P., & Thomas, J. (2020). How much bias results if a quasi-experimental design combines local comparison groups, a pretest outcome measure and other covariates?: A within study comparison of preschool effects. Psychological Methods, 25(6), 726–746. 
[6]	Bolliger, D. U. (2004). Key factors for determining student satisfaction in online courses. International Journal on E-learning, 3(1), 61-67.
[7]	Moore, M. (1997). Theory of transactional distance. In D. Keegan (Ed.), Theoretical principles of distance education (pp. 22–38). New York: Routledge. Moore, M. & Kearsley, G. (1996). Distance education: A systems review. Belmont: Wadsworth Publishing Company
[8]	McIsaac, M., & Gunawardena, C. (1996). Distance Education. In D. H. Jonassen (Ed.), Handbook of research for educational communications and technology: A project of the Association for Educational Communications and Technology (pp. 403–437). NY: Simon and Schuster
[9]	Moreno, R., & Mayer, R. E. (1999). Cognitive principles of multimedia learning: The role of modality and contiguity. Journal of Educational Psychology, 91(2), 358–368. https://doi.org/10.1037/0022-0663.91.2.358
[10]	Richard E. Mayer & Roxana Moreno (2003) Nine Ways to Reduce Cognitive Load in Multimedia Learning, Educational Psychologist, 38:1, 43-52, DOI: 10.1207/S15326985EP3801_6
[11]	Baer, J., & Vargas, D. (2021). Effects of using video lessons in the Mathematics achievement of senior high school learners. Central Luzon State university.
[12]	Peteros, E., Gamboa, A., Etcuban, J., Dinauanao, A., Sitoy, R., & Arcadio, R. (2020). Factors affecting Mathematics performance of junior High School Students. International Electronic Journal of Mathematics Education.
[13]	Seaton, M., Parker, P., Marsh, H. W., Craven, R. G., & Yeung, A. S. (2014). The reciprocal relations between self-concept, motivation, and achievement: juxtaposing academic self-concept and achievement goal orientations for mathematics success. Educational Psychology, 34(1), 49-72. https://doi.org/10.1080/01443410.2013.825232
[14]	O’Connor, B.R. & Norton, S. (2016). Investigating Students’ Mathematical Difficulties with Quadratic Equations. ERIC
[15]	Guolin, L., & Douglas, W. (2016). The effects of video tutorials as supplement in evaluating students’statistics performance. Researchgate.net.
[16]	McDaniel, S. & Green, L. (2012). Independent Interactive Inquiry-Based Learning Modules Using Audio-Visual Instruction In Statistics. eScholarship
[17]	Ibrahim, A., Greenwood, C., & Wheeler, D. (2012). Effects of segmenting, signaling, and weeding on learning from educational video. Learn Media Technol
[18]	Sa?lam, R., & ALACACI, C. (2012). A comparative analysis of quadratics unit in Singaporean, Turkish and IBDP mathematics textbooks. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 3(3).
[19]	Szpunar KK, Jing HG, Schacter DL. (2013). Overcoming overconfidence in learning from video-recorded lectures: implications of interpolated testing for online education. J Appl Res Mem Cogn. 2014;3:161–164.
[20]	Guo P., Kim, J, Robin, L (2014). Proceedings of the First ACM Conference on Learning at Scale. New York. How video production affects student engagement: an emprirical study of MOOC videos
[21]	Baer, J., & Vargas, D. (2021). Effects of using video lessons in the Mathematics achievement of senior high school learners. Central Luzon State university.
[22]	Brame, J. & Kathryn (2016). Effective Educational Videos: Principles and Guidelines for Maximizing Student Learning from Video Content, 1-6
[23]	Skinner, P. S., Wheatley, D. M., Knopfmeier, K. H., Reinhart, A. E., Choate, J. J., Jones, T. A. & Palikonda, R. (2018). Object-based verification of a prototype Warn-on-Forecast system. Weather and forecasting, 33(5), 1225-1250.
[24]	Jain, S., Kumar, A., Mandal, S., Ong, J., Poutievski, L., Singh, A., ... & Vahdat, A. (2013). B4: Experience with a globally-deployed software defined WAN. ACM SIGCOMM Computer Communication Review, 43(4), 3-14.
[25]	Didis, M. G. & Erbas, A. K. (2015). Performance and difficulties of students in formulating and solving quadratic equations with one unknown. Educational Sciences: Theory & Practice, 15(4), 1137-1150.

[26]	Mashudi, M., Komariah, K., & Irvan, M. F. (2021). The use of audio-visual media in improving Culinary students learning outcomes in Chicken Carcass material. Jurnal Pendidikan Vokasi, 11(1), 14-23.
[27]	Hwang,  G.  J.,  Lai,  C.  L.,  &  Wang,  S.  Y.  (2015).  Seamless  flipped  learning:  a  mobile technology-enhanced flipped classroom with effective learning strategies. Journal of Computers in Education, 2(4), 449-473
[28]	Johnson, L., Becker, S.  A.,  Estrada, V., &  Freeman,  A. (2014). NMC horizon  report:  K (pp. 1-52). The New Media Consortium.
[29]	Brame, J. & Kathryn (2016). Effective Educational Videos: Principles and Guidelines for Maximizing Student Learning from Video Content, 1-6
[30]	Ali, Shoukot (2019). Impacts of Watching Videos on Academic Performance at University Level. Researchgate.net
[31]	Lapada, A. A., & Lapada, A. A. (2017). Audio-visual aided instruction in science among high school students in the Philippines. International Journal of Education and Research, 5(7), 139-156.
[32]	Baer, J., & Vargas, D. (2021). Effects of using video lessons in the Mathematics achievement of senior high school learners. Central Luzon State university.
[33]	Banna, J., Lin, M.-F. G., Stewart, M., & Fialkowski, M. K. (2015). Interaction matters: Strategies to promote engaged learning in an online introductory nutrition course. Journal of Online Learning and Teaching, 11(2), 249–261
[34]	Xie, H., Liu, W., & Bhairma, J. (2018). Analysis of synchronous and asynchronous E-learning environments. In 2018 3rd Joint International Information Technology, Mechanical and Electronic Engineering Conference (JIMEC 2018) (pp. 270-274). Atlantis Press.
[35]	Rapanta, C., Botturi, L., Goodyear, P., Guardia, L., & Koole, M. (2020). Online university teaching during and after the COVID -19 crisis: refocusing teacher presence and learning activity. Postdigital Sci. educ. 
[36]	Hsu, H., Wang, C., & Levesque-Bristol, C. (2019). Reexamining the impact of self-determination theory on learning outcomes in the online learning environment. Educ. Inf. Technol. 
[37]	Nieuwoudt, J. E. (2020). Investigating synchronous and asynchronous class attendance as predictors of academic success in online education. Australasian J. Educ. Technol. 
[38]	Fabriz, S., Mendzheritskaya, J., & Stehle, S. (2021). Impact of synchronous and asynchronous setting of online teachning and learning in higher education on students’ learning experience during COVID 19. Frontiersin.org

[1]		Abernathy, D. J.(2020). What is E- learning? 
[2]	Vanve, A., Gaikwad, R. & Shelar, K.(2016)A new trend e-learning in education system. International Research Journal of Engineering and Technology (IRJET). Volume: 03 Issue.04.
[3]	Al-Azawei, A., Parslow, P., & Lundqvist, K. (2016). Barriers and opportunities of e-learning implementation in Iraq: A case of public universities. The International Review of Research in Open and Distributed Learning, 17(5). 
[4] 	McNeill, C., What is Descriptive Research? 
[5]	Jovancic, N. (2020). The What and The How of Descriptive Research. 
[6]	Mercado, C. (2018). Readiness Assessment tool for e-learning. 
[7]	Geary, D 2012, ‘Evolutionary educational psychology’, in K Harris, S Graham & T Urdan (eds), APA Educational Psychology Handbook, vol. 1, American Psychological Association, Washington D.C., pp. 597-621
[8]	Sife, A., Lwoga, E. & Sanga, C. (2017). New technologies for teaching and learning: 
Challenges for higher learning institutions in developing countries. 
[9]	Adiyarta, K., Napitupulu, D., Rahim, R., Abdullah, D., & Setiawan, M. (2017). Analysis of e-learning implementation readiness based on integrated elr model
[10]	Issa, R. & Jaaron, A. (2017). Measuring e-learning readiness: the case of Palestinian public secondary schools
[11]	Blasiman, R. N., Larabee, D., & Fabry, D. (2018). Distracted students: A comparison of multiple types of distractions on learning in online lectures. Scholarship of Teaching and Learning in Psychology, 4(4), 222–230
[12]	Mercado, C. (2018). Readiness Assessment tool for e-learning 
[13]	Azimi, H. (2013). Readiness for Implementation of E-Learning in Colleges of Education
[14] 	Gil, L., & Dargano (2018). Influences on pre-service teachers’ preparedness to use ICTs in the classroom

[1] Prajwal Rao, Nishal Ancelette Pereira, And Raghuram Srinivasan, “Convolutional Neural Networks For Lung Cancer Screening In Computed Tomography (CT) Scans”, 978-1-5090-5256-1/16/$31.00_C 2016 IEEE
[2] Sheila Ramaswamy, Karen Truong, "Pulmonary Nodule Classification With Convolutional Neural Networks", Stanford University@2016.
[3] Ayman El-Baz, Garth M. Beach, Georgy Gimel’farb, Kenji Suzuki, Kazunori Okada,Ahmed Elnakib, Ahmed Soliman, And Behnoush Abdollahi, “Computer-Aided Diagnosis Systems For Lung Cancer: Challenges And Methodologies” International Journal Of Biomedical Imaging, Article ID 942353, 46 Pages, 2013
[4] Rotem Golan, Christian Jacob, J¨Org Denzinger, "Lung Nodule Detection In CT Images Using Deep Convolutional Neural Networks", 978- 1-5090-0620-5/16/$31.00_C 2016 IEEE
[5] Julie Yang, Si Yong Yeo, Jia Mei Hong, Sum Thai Wong, Wai Teng Tang, Zhen Zhou Wu, Gary Lee, Sulin Chen, Vanessa Ding, Brendan Pang, Andre Choo, Yi Su, “A Deep Learning Approach For Tumor Tissue Image Classification”, Researchgate, DOI: 10.2316/P.2016.832-025, February 2016.
[6] Allison M Rossetto And Wenjin Zhou, "Deep Learning For Categorization Of Lung Cancer CT Images”, 2017 IEEE/ACM International Conference On Connected Health: Applications, Systems And Engineering Technologies (CHASE), 978-1-5090-4722-2/17 $31.00, DOI 10.1109/CHASE.2017.59, © 2017 IEEE.
[7] Jiuxiang Gu, Zhenhua Wang, Jason Kuen, Lianyang Ma, Amir Shahroudy, Bing Shuai, Ting Liu, Xingxing Wang, Li Wang, Gang Wang, Jianfei Cai, Tsuhan Chen, " Recent Advances In Convolutional Neural Networks", Sciencedirect, Pattern Recognition, Vol 77, Pages 354- 377, 2015.
[8] Md. Badrul Alam Miah, Mohammad Abu Yousuf, “Detection Of Lung Cancer From CT Image Using Image Processing And Neural Network", 2nd Int'l Conf On Electrical Engineering And Information & Communication Technology (ICEEICT) 2015 Jahangirnagar University, Dhaka-1342, Bangladesh, 21-23 May 2015, 978-1-4673-6676-2/15/$31.00 ©2015 IEEE.
[9] Xin-Yu Jin, Yu-Chen Zhang, Qi-Liang Jin, “Pulmonary Nodule Detection Based On CT Images Using Convolution Neural Network2016 9th International Symposium On Computational Intelligence And Design, 2473-3547/16 $31.00, DOI 10.1109/ISCID.2016.52, © 2016 IEEE.
[10] Takada, Ruchita. “Lung Nodule Detection And Classification Using Machine Learning Techniques.” (2018). 
[11] Charity, Faridoddin Et Al. “Automatic Lung Segmentation In Computed Tomography Images Using Active Shape Model.” 2020 IEEE International Conference On Electrical Engineering And Photonics (Eexpolytech) (2020): 156-159.
[12]  Samundeeswari, P. And Ramalingam Gunasundari. “A Novel Multilevel Hybrid Segmentation And Refinement Method For Automatic Heterogeneous True NSCLC Nodules Extraction.” 2020 5th International Conference On Devices, Circuits And Systems (ICDCS) (2020): 226-235.
[13] Karthikeyan, M. V. Et Al. “Lung Cancer Detection Using CT SCAN Images.” International Journal Of Advanced Research In Science, Communication And Technology (2021): N. Pag.
[14] Fenimore, C. , Armato, S. , Aberle, D. , Brown, M. , Henschke, C. , Mcnitt-Gray, M. , Macmahon, H. , Mclennan, G. , Meyer, C. , Reeves, A. And Yankelevitz, D. (2011), The Lung Image Database Consortium (LIDC) And Image Database Resource Initiative (IDRI): A Completed Reference Database Of Lung Nodules On CT Scans, Medical Physics
[15]  Miah, M.B.A., & Yousuf, M.A. (2015) “Detection Of Lung Cancer From CT Image Using Image Processing And Neural Network.” 2015 International Conference On Electrical Engineering And Information Communication Technology (ICEEICT): 1-6.

[1] A Timeline of COVID-19 Developments in 2020. (2021, Jan 02). (ajmc.com) Retrieved Feb 24, 2021, from https://www.ajmc.com/view/a-timeline-of-covid19-developments-in-2020
[2] Apricio, M., & Costa, C. J. (2015). Data visualization. Communication design quarterly review, 3(1), 7-11.
[3] Barrett, P., Hunter, J., & Miller, J. T. (2005). Matplotlib--A Portable Python Plotting Package. Astronomical data analysis software and systems XIV, 347, 91.
[4] Chppell, B. (2020, Maarch 11). Coronavirus: COVID-19 Is Now Officially A Pandemic, WHO Says. (npr.org) Retrieved Feb 24, 2021, from https://www.npr.org/sections/goatsandsoda/2020/03/11/814474930/coronavirus-covid-19-is-now-officially-a-pandemic-who-says
[5] Day, T., Park, A., Madras, N., Gumel, A., & Wu, J. (2006). When is quarantine a useful control strategy for emerging infectious diseases? American journal of epidemiology, 163(5), 479-485.
[6] Elbagir, S., & Yang, J. (2019). Twitter sentiment analysis using natural language toolkit and VADER sentiment. Proceedings of the International MultiConference of Engineers and Computer Scientists, 122, 16.
[7] Elflein, J. (2021, feb 24). Covid-119 cases worldwide as of feb 24, 2021. (statista.com) Retrieved feb 24, 2021, from https://www.statista.com/statistics/1043366/novel-coronavirus-2019ncov-cases-worldwide-by-country/
[8] Fernando, D. (2018). Visualisasi Data Menggunakan Google Data Studio. Prosiding Seminar Nasional Rekayasa Teknologi Informasi| SNARTISI.
[9] Friendly, M. (2008). A brief history of data visualization. Handbook of data visualization, 15-56.
[10] Ghebreysus, T. A. (2020). Addressing mental health needs: an integral part of COVID?19 response. World Psychiatry, 19(2), 129.
[11] Hutto, C., & Gibert, E. (2014). Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media, 8(1).
[12] Hyland, P., Shevlin, M., McBride, O., Murphy, J., & Karatzias, T. (2020). Anxiety and depression in the Republic of Ireland during the COVID?19 pandemic. Acta Psychiatrica Scandinavica, 142(3), 249-256.
[13] Islam, M. A., Barna, S. D., Raihan, H., Khan, M. N., & Hossain, M. T. (2020). Depression and anxiety among university students during the COVID-19 pandemic. Bangladesh: A web-based cross-sectional survey PloS one, 15(8), e0238162.
[14] Kim, A. E., Hansen, H. M., Murphy, J., Richard, A. K., Duke, J., & Allen, J. A. (2013). Methodological considerations in analyzing Twitter data. Journal of the National Cancer Institute Monographs, 2013(47), 140-146.
[15] Lavanya, A., Panwar, D., Jaime, L., & et. al. (2022). Event-Based Multi-Model Classification to Assess the User Participation Levels on Twitter. In N. Thakur, & B. D. Parameshachari (Eds.), Human-Computer Interaction and Beyond: Advances Towards Smart and Interconnected Environments-II (pp. 76-120 (45)). Bentham Books.
[16]  Mane, S. B., Sawant, Y., Kazi, S., & Shinde, V. (2014). Real time sentiment analysis of twitter data using hadoop. International Journal of Computer Science and Information Technologies, 5(3), 3098-3100.
[17] McGinty, E. E., Presskreischer, R., Han, H., & Barry, C. L. (2020). Psychological distress and loneliness reported by US adults in 2018 and April 2020. Jama, 324(1), 93-94.
[18] Mucchetti, M. (2020). Google Data Studio. BigQuery for Data Warehousing, 401-416.
[19] Murphy, S. A. (2013). Data visualization and rapid analytics: Applying tableau desktop to support library decision-making. Journal of Web Librarianship, 7(4), 465-476.
[20] Ozdin, S., & Bayrak, O. S. (2020). Levels and predictors of anxiety, depression and health anxiety during COVID-19 pandemic. Turkish society: The importance of gender International Journal of Social Psychiatry, 66(5), 504-511.
[21] Reece, A. G., Reagan, A. J., Lix, K. L., & Dodds, P. S. (2017). Forecasting the onset and course of mental illness with Twitter data. Scientific reports, 7(1), 1-11.
[22] Roser, M. (2020, March 04). The Spanish flu (1918-20): The global impact of the largest influenza pandemic in history. Retrieved from ourworldindata.org: https://ourworldindata.org/spanish-flu-largest-influenza-pandemic-in-history
[23] Sedoc, J., Buechel, S., Nachmany, Y., Buffone, A., & Ungar, L. (2019). Learning word ratings for empathy and distress from document-level user responses. arXiv preprint arXiv:1912.01079.
[24] Talbot, J., Charron, V., & Konkle, A. (2021). Feeling the Void: Lack of Support for Isolation and Sleep Difficulties in Pregnant Women during the COVID-19 Pandemic Revealed by Twitter Data Analysis. International Journal of Environmental Research and Public Health, 18(2), 393.
[25] Tausczik, Y. R., & Pennebaker, J. W. (2010). The psychological meaning of words: LIWC and computerized text analysis methods. J. Lang. Soc. Psychol, 29(1), 24-54.
[26] Telea, A. C. (2014). Data visualization: principles and practice. CRC Press.
[27] VanderPlas. (2016). Python data science handbook: Essential tools for working with data. O'Reilly Media, Inc.
[28] Wagh, B., Shinde, J. V., & Kale, P. A. (2018). A Twitter sentiment analysis using NLTK and machine learning techniques. International Journal of Emerging Research in Management and Technology, 6(12), 37-44.
[29] Wang, C., Pan, R., Wan, X., & Tan, Y. (2020). A longitudinal study on the mental health of general population during the COVID-19 epidemic in China. Brain, behavior, and immunity, 87, 40-48.
[30] Wesley, R., Eldridge, M., & Terlecki, P. T. (2001). An analytic data engine for visualization in tableau. Proceedings of the 2011 ACM SIGMOD International Conference on Management of data, 1185-1194.
[31] Zastrow, M. (2015). Data visualization: Science on the map. Nature News, 519(7541), 119.

REFERENCES

[1] 	M. S. Thordal, J. C. Bennetsen and H. H. Koss, "Review for practical application of CFD for the determination of wind load on high-rise buildings," Journal of Wind Engineering and Industrial Aerodynamics, pp. 155-168, 2019. 
[2] 	V. R. Shinde and V. S. Shingade, "Review on Computational Evaluation of Wind Pressure on Tall Buildings using CFD and Sd," INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH & TECHNOLOGY (IJERT), pp. 1-6, 2018. 
[3] 	K. Hu, S. Cheng and Y. Qian, "CFD Simulation Analysis of Building Density on Residential Wind Environment," Journal of Engineering Science & Technology Review, pp. 1-8, 2018. 
[4] 	X. F. Yu, Z. N. Xie, J. B. Zhu and M. Gu, "Interference effects on wind pressure distribution between two high-rise buildings," Journal of Wind Engineering and Industrial Aerodynamics,, pp. 188-197, 2015. 
[5] 	P. K. Sharma and S. R. Parekar, "Drag Coefficient of Tall Building by CFD Method using ANSYS," IRJET Journal, pp. 1-6, 2019. 
[6] 	J. C. Bennetsen, M. S. Thordal and S. Capra, "Towards a standard CFD set-up for wind load assessment of high-rise buildings: Part 2–Blind test of chamfered and rounded corner high-rise buildings," Journal of Wind Engineering and Industrial Aerodynamics, p. 104282, 2020. 
[7] 	W. S. Karrar., A. M. Shyama and M. Jassim, "High-rise building wind analysis using computational fluid dynamics and dynamic analysis using etabs program," International Journal, pp. 1-8, 2020. 
[8] 	S. K. Verma, A. K. Roy, S. Lather and M. Sood, "CFD simulation for wind load on octagonal tall buildings," Int J Eng Trends Technol, pp. 211-216, 2015. 
[9] 	N. S. Fouad, G. H. Mahmoud and N. E. Nasr, "Comparative study of international codes wind loads and CFD results for low rise buildings," Alexandria engineering journal, pp. 3623-3639, 2018. 
[10] 	Q. Xing and J. Qian, "CFD analysis of wind interference effects of three high-rise buildings," Journal of Asian Architecture and Building Engineering, pp. 487-494, 2018.

[1] M. H. Miraz, M. Ali, P. S. Excell, and R. Picking, “A Review on Internet of Things (IoT), Internet of Everything (IoT) and Internet of Nano Things (IoNT)”, in 2015 Internet Technologies and Applications (ITA), pp. 219– 224, Sep. 2015, DOI: 10.1109/ITechA.2015.7317398. 
[2] P. J. Ryan and R. B. Watson, “Research Challenges for the Internet of Things: What Role Can OR Play?,” Systems, vol. 5, no. 1, pp. 1–34, 2017. 
[3] M. Miraz, M. Ali, P. Excell, and R. Picking, “Internet of Nano-Things, Things and Everything: Future Growth Trends”, Future Internet, vol. 10, no. 8, p. 68, 2018, DOI: 10.3390/fi10080068. 
[4] E. Borgia, D. G. Gomes, B. Lagesse, R. Lea, and D. Puccinelli, “Special issue on" Internet of Things: Research challenges and Solutions".,” Computer Communications, vol. 89, no. 90, pp. 1–4, 2016. 
[5] K. K. Patel, S. M. Patel, et al., “Internet of things IOT: definition, characteristics, architecture, enabling technologies, application future challenges,” International journal of engineering science and computing, vol. 6, no. 5, pp. 6122–6131, 2016. 
[6] S. V. Zanjal and G. R. Talmale, “Medicine reminder and monitoring system for secure health using IOT,” Procedia Computer Science, vol. 78, pp. 471–476, 2016. 
[7] R. Jain, “A Congestion Control System Based on VANET for Small Length Roads”, Annals of Emerging Technologies in Computing (AETiC), vol. 2, no. 1, pp. 17–21, 2018, DOI: 10.33166/AETiC.2018.01.003. 
[8] S. Soomro, M. H. Miraz, A. Prasanth, M. Abdullah, “Artificial Intelligence Enabled IoT: Traffic Congestion Reduction in Smart Cities,” IET 2018 Smart Cities Symposium, pp. 81–86, 2018, DOI: 10.1049/cp.2018.1381. 
[9] Mahmud, S. H., Assan, L. and Islam, R. 2018. “Potentials of Internet of Things (IoT) in Malaysian Construction Industry”, Annals of Emerging Technologies in Computing (AETiC), Print ISSN: 2516-0281, Online ISSN: 2516-029X, pp. 44-52, Vol. 2, No. 1, International Association of Educators and Researchers (IAER), DOI: 10.33166/AETiC.2018.04.004. 
[10] Mano, Y., Faical B. S., Nakamura L., Gomes, P. G. Libralon, R. Meneguete, G. Filho, G. Giancristofaro, G. Pessin, B. Krishnamachari, and Jo Ueyama. 2015. Exploiting IoT technologies for enhancing Health Smart Homes through patient identification and emotion recognition. Computer Communications, 89.90, (178-190). DOI: 10.1016/j.comcom.2016.03.010.

[1] C. Guillemot and O. Le Meur, “Image inpainting: Overview and recent advances,” IEEE Signal Process. Mag., vol. 31, no. 1, pp. 127144, Jan.2014
[2] Bertalmio, M., Sapiro, G., Caselles, V. and Ballester, C. (2000) Image Inpainting. Proceedings of ACM SIGGRAPH, New Orleans, July 2000, 417-424.
[3] A. Criminisi, P. Prez, and K. Toyama, “Region filling and object removal by exemplar-based image inpainting,” IEEE Trans. Image Process., vol.13, no. 9, pp. 12001212, Sep. 2004.
[4] A. Efros and T. K. Leung, “Texture synthesis by non-parametric sampling,” in Proc. 7th IEEE ICCV, vol. 2. Sep. 1999, pp. 10331038.
[5] Criminisi A, Perez P, Toyama K. Region filling and object removal by exemplar-based image inpainting. IEEE Trans Image Processing. 2004; 13:1200–1212.
[6] Criminisi A, Perez P, Toyama K. Object removal by exemplar-based image inpainting. Proc IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2003; p. 721–728.
[7] Aujol J.F, Ladjal S, Masnou S. Exemplar-Based Inpainting from a Variational Point of View. SIAM Journal on Mathematical Analysis. 2010; 42:1246–1285.
[8] M. Bertalmio, G. Sapiro, V. Caselles, and C. Ballester, “Image inpainting,” in Proceedings of the 27th annual conference on Computer graphics and interactive techniques (SIGGRAPH '00), pp. 417–424, July 2000.
[9] A. A. Efros and T. K. Leung, “Texture synthesis by non-parametric sampling,” in Proceedings of the 7th IEEE International Conference on Computer Vision (ICCV'99), pp. 1033–1038, Corfu, Greece, September 1999.
[10] [24] M. Bertalmio, L. Vese, G. Sapiro, and S. Osher, “Simultaneous structure and texture image inpainting,” IEEE Transactions on Image Processing, vol. 12, no. 8, pp. 882–889, 2003.
[11] A. Rare?, M. J. T. Reinders, and J. Biemond, “Edge-based image restoration,” IEEE Transactions on Image Processing, vol. 14, no. 10, pp. 1454–1468, 2005.
[12] O. Elharrouss, N. Almaadeed, S. Al-Maadeed, et al., Image inpainting: a review, Neural Process. Lett. 51 (2020) 2007–2028, https://doi.org/10.1007/s11063-019- 10163-0.
[13] Z.P. Qiang, L.B. He, X. Chen, D. Xu, Survey on deep learning image inpainting methods, J. Image Graph. 24 (03) (2019) 0447–0463.
[14] C. Barnes, E. Shechtman, A. Finkelstein, and B. G. Dan, ‘‘PatchMatch: A randomized correspondence algorithm for structural image editing,’’ ACM Trans. Graph., vol. 28, no. 3, pp. 1–11, 2009. 
[15] M. Bertalmio, G. Sapiro, V. Caselles, and C. Ballester, ‘‘Image inpainting,’’ Siggraph, vol. 4, no. 9, pp. 417–424, 2005.
[16] A. Criminisi, P. Perez, and K. Toyama, ‘‘Object removal by exemplarbased inpainting,’’ in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., Nov. 2003, pp. 1–8
[17] A. Efros and T. Leung, ‘‘Texture synthesis by non-parametric sampling,’’ in Proc. 7th IEEE Int. Conf. Comput. Vis., vol. 2, Sep. 1999, pp. 1033–1038. 
[18] F. Farahnakian, P. Liljeberg, and J. Plosila, ‘‘LiRCUP: Linear regression based CPU usage prediction algorithm for live migration of virtual machines in data centers,’’ in Proc. 39th Eur. Conf. Softw. Eng. Adv. Appl., Sep. 2013, pp. 357–364. 
[19] I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, X. Bing, and Y. Bengio, ‘‘Generative adversarial networks,’’ in Proc. Adv. Neural Inf. Process. Syst., vol. 3, 2014, pp. 2672–2680.
[20] A. Krizhevsky, I. Sutskever, and G. E. Hinton, ‘‘ImageNet classification with deep convolutional neural networks,’’ in Proc. Int. Conf. Neural Inf. Process. Syst., 2012, pp. 1097–1105.
[21] Z. Qiang, L. He, and X. Dan, ‘‘Exemplar-based pixel by pixel inpainting based on patch shift,’’ in Proc. CCF Chin. Conf. Comput. Vis., 2017, pp. 370–382
[22] Deepak Pathak, Philipp Krahenbuhl, Jeff Donahue, Trevor Darrell, and Alexei A. Efros, “Context Encoders: Feature Learning by Inpainting,” Proc. International Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
 [23] Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio, “Generative Adversarial Nets,” in Advances in Neural Information Processing Systems (NeurIPS), 2014.
[24] Chao Yang, Xin Lu, Zhe Lin, Eli Shechtman, Oliver Wang, and Hao Li, “High-Resolution Image Inpainting using Multi-Scale Neural Patch Synthesis,” Proc. International Conference on Computer Vision and Pattern Recognition (CVPR), 2017.
[25] Satoshi Iizuka, Edgar Simo-Serra, and Hiroshi Ishikawa, “Globally and Locally Consistent Image Completion,” ACM Trans. on Graphics, Vol. 36, ?4, Article 107, Publication date: July 2017.
[26] Ugur Demir, and Gozde Unal, “Patch-Based Image Inpainting with Generative Adversarial Networks,” https://arxiv.org/pdf/1803.07422.pdf.
[27] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, “Deep Residual Learning for Image Recognition,” Proc. Computer Vision and Pattern Recognition (CVPR), 27–30 Jun. 2016.
[28] Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A. Efros, “Image-to-Image Translation with Conditional Adversarial Networks,” Proc. Computer Vision and Pattern Recognition (CVPR), 21–26 Jul. 2017.
[29] Zhaoyi Yan, Xiaoming Li, Mu Li, Wangmeng Zuo, and Shiguang Shan, “Shift-Net: Image Inpainting via Deep Feature Rearrangement,” Proc. European Conference on Computer Vision (ECCV), 2018.
[30] Jiahui Yu, Zhe Lin, Jimei Yang, Xiaohui Shen, Xin Lu, and Thomas S. Huang, “Generative Image Inpainting with Contextual Attention,” Proc. Computer Vision and Pattern Recognition (CVPR), 2018.
[31] Yi Wang, Xin Tao, Xiaojuan Qi, Xiaoyong Shen, and Jiaya Jia, “Image Inpainting via Generative Multi-column Convolutional Neural Networks,” Proc. Neural Information Processing Systems, 2018.
[32] Guilin Liu, Fitsum A. Reda, Kevin J. Shih, Ting-Chun Wang, Andrew Tao, and Bryan Catanzaro, “Image Inpainting for Irregular Holes Using Partial Convolution,” Proc. European Conference on Computer Vision (ECCV), 2018.
[33] Kamyar Nazeri, Eric Ng, Tony Joseph, Faisal Z. Qureshi, Mehran Ebrahimi, “EdgeConnect: Generative Image Inpainting with Adversarial Edge Learning,” Proc. International Conference on Computer Vision (ICCV), 2019.
[34] Jiahui Yu, Zhe Lin, Jimei Yang, Xiaohui Shen, Xin Lu, and Thomas Huang, “Free-Form Image Inpainting with Gated Convolution,” Proc. International Conference on Computer Vision (ICCV), 2019.

1] Xuan Luo, Wei Cai, Zehua Wang, Xiuhua Li, and Victor C. M. Leung, “A Payment Channel Based Hybrid Decentralized Ethereum Token Exchange,”presented at the IEEE International Conference on Blockchain and Cryptocurrency (ICBC), Seoul,  Korea (South), 14-17 May 2019, pp. 49-50.

[2] Sina Rafati Niya, Eryk Schiller, Ile Cepilov, Fabio Maddaloni, K¨ursat Aydinli, Timo Surbeck, Thomas Bocek, Burkhard Stiller , “Adaptation of Proof-of-Stake-based Blockchains for IoT Data Streams ,”presented at the IEEE International Conference on Blockchain and Cryptocurrency (ICBC), Seoul,  Korea (South), 14-17 May 2019, pp. 15-16.

[3] Robert Norvill, Mathis Steichen, Wazen M. Shbair, Radu State, “Demo: Blockchain for the Simplification and Automation of KYC Result Sharing,” presented at the IEEE International Conference on Blockchain and Cryptocurrency (ICBC), Seoul,  Korea (South), 14-17 May 2019, pp. 9-10.

[4] Chaehyeon Lee, Heegon Kim, Sajan Maharjan, Kyungchan Ko and James Won-Ki Hong, “Blockchain Explorer based on RPC-based Monitoring System '',  presented at the IEEE International Conference on Blockchain and Cryptocurrency (ICBC), Seoul,  Korea (South), 14-17 May 2019, pp. 117-119.

[5] Sina Rafati Niya, Danijel Dordevic, Atif Ghulam Nabi, Tanbir Mann, Burkhard Stiller, “A Platform-independent, Generic-purpose, and Blockchain-based Supply Chain Tracking”, presented at the IEEE International Conference on Blockchain and Cryptocurrency (ICBC), Seoul,  Korea (South), 14-17 May 2019, pp. 11-12.

[6] Santosh Pandey, Gopal Ojha, Bikesh Shrestha, Rohit Kumar, “BlockSIM: A practical simulation tool for optimal network design, stability and planning”, presented at the IEEE International Conference on Blockchain and Cryptocurrency (ICBC), Seoul,  Korea (South), 14-17 May 2019, pp. 11-12.

[7] Imran Makhdoom, Farzad Tofigh, Ian Zhou, Mehran Abolhasan, Justin Lipman., “A Proof-of-Honesty based Consensus Protocol for Blockchain-based IoT Systems”, presented at the IEEE International Conference on Blockchain and Cryptocurrency (ICBC), Toronto, ON, Canada,  2-6 May 2020.

[8] Ryo Kawahara, (2017), “Verification of customizable blockchain consensus rule using a formal method”, presented at the IEEE International Conference on Blockchain and Cryptocurrency (ICBC), Toronto, ON, Canada,  2-6 May 2020.

[9] Felix Franz, Tobias Fertig, Andreas E. Schutz, “Democratization of Smart Contracts: A Prototype for Automated Contract Generation”, presented at the IEEE International Conference on Blockchain and Cryptocurrency (ICBC), Toronto, ON, Canada,  2-6 May 2020.

[10] Qinghua Lu, Mark Staples, Hugo O’Connor, Shiping Chen, Adnene Guabtni, “Software Architecture for Blockchain-based Trade Certificate Systems”, presented at the IEEE International Conference on Blockchain and Cryptocurrency (ICBC), Toronto, ON, Canada,  2-6 May 2020.

[11] Chunyu Mao, Anh-Duong Nguyen, Wojciech Golab, “Performance and Fault Tolerance Trade-offs in Sharded Permissioned Blockchains”, presented at the IEEE International Conference on Blockchain and Cryptocurrency (ICBC), Toronto, ON, Canada,  2-6 May 2020.

[1]	Hu Peng, “Application Research on Face Detection Technology based on Open CV in Mobile Augmented Reality”, International Journal of Signal Processing, Image Processing and Pattern Recognition (2015).

[2]	San Gunes, Okan Sanli, Ovgu, Ozturk Ergun, "Augmented Reality Tool for Markerless Virtual Try-on around Human Arm", IEEE (2015).

[3]	Jayashree Yadav J, “Try on Application for Virtual Ornamental Room ", International Journal of Innovative Research in Science, Engineering, Technology (2017).

[4]	Zhen Xiong, Lin Zhi, Jie Jiang, “The Research of Developing Virtual Jewelry Worn System Based on ARToolkit", IEEE (2018).

[5]	Gaurav Salunke, Himanshu More, Rigved Shete, Prakash Kawade, “Virtual Jewellery Shopping using Augmented Reality”, International Journal of Engineering Research & Technology (2020).

[6]	G.Rajaram and B.Anandavenkatesan, "Virtual Ornaments and Fabric Try-on Reality Application", International Journal of Engineering Research & Technology (2014).

[7]	Lingyan Jiang, Jian Yao, Baopu Li, Fei Fanf, Qi Zhang, Max Q.H Meng, "Automatic Body Feature Extraction from Front and Side Images", Journel of Software Engineering and Applications (2012). 

[8]	Ioannis Pachoulakis, Kostas Kapetanakis, "Augmented Reality Platforms for the Virtual Fitting Rooms", International Journel of the Multimedia and Its Application (2014).

[9]	Teddy Mantoro, Suhendi, "Multi-Faces Recognition Process Using Haar Cascades and Eigenface Methods", IEEE (2018). 

[10]	Young Jae Lee, Dae Ho Lee, "Research on Detecting Face and Hands for Motion-based Game Using Web Camera", International Conference on Security Technology (2008). 

[11]	Aras Dargazany, Mircea Nicolescu, "Human Body Parts Tracking using Torso Tracking", Ninth International Conference on the Information Technology(2012).

[1] Susovan Jana and Matangini Chattopadhyay, An event-driven university campus navigation system on android platform, 2015 Applications and Innovations in Mobile Computing (AIMoC), 10.1109/AIMOC.2015.7083850, February-2015.
[2] Ler S.N., Zainon W.M.N.W. (2014) Campus Mobile Navigation System Based on Shortest-Path Algorithm and Users Collaborations. In: Jeong H., S. Obaidat M., Yen N., Park J. (eds) Advances in Computer Science and its Applications. Lecture Notes in Electrical Engineering, vol 279. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41674-3_113

[3] Sunil Bendre, Narendra Patil, Dhananjay Kanawade, Sagar Kandekar, Rutuja Kirpal, Event Based Campus Navigation System, International Journal of Computer Science and Information Technologies, Vol. 7 (1) , 462-464, 2016.

[4] Batt, Steven and Harmon, Oskar R. and Tomolonis, Paul, Learning Tableau: A Data Visualization Tool (August 18, 2019). Available at SSRN: https://ssrn.com/abstract=3438993 or http://dx.doi.org/10.2139/ssrn.3438993.

[5] Akhtar, Nikhat & Tabassum, Nazia & Perwej, Dr.Asif & Perwej, Dr. Yusuf. (2020). Data analytics and visualization using Tableau utilitarian for COVID-19 (Coronavirus). Global Journal of Engineering and Technology Advances. Volume 3. Page 28-50. 10.30574/gjeta.2020.3.2.0029.

[6] Sungsoo (Ray) Hong, Rafal Kocielnik, Min-Joon Yoo, Sarah Battersby, Juho Kim, Cecilia Aragon, Designing Interactive Distance Cartograms to Support Urban Travelers, 10th IEEE Pacific Visualization Symposium, April18, 2017.

[7] Ko, Inseok & Chang, Hyejung. (2017). Interactive Visualization of Healthcare Data Using Tableau. Healthcare Informatics Research. 23. 349. 10.4258/hir.2017.23.4.349.

[8] Hasan, Ahmed & Samsudin, Khairulmizam & Ramli, Abd Rahman & raja abdullah, raja syamsul azmir & Ismaeel, Salam. (2009). A Review of Navigation Systems (Integration and Algorithms). Australian Journal of Basic and Applied Sciences. 3. 943-959.
[9] Kunhoth, J., Karkar, A., Al-Maadeed, S. et al. Indoor positioning and wayfinding systems: a survey. Hum. Cent. Comput. Inf. Sci. 10, 18 (2020). https://doi.org/10.1186/s13673-020-00222-0

[10] Dae Hyun Kim, Vidya Setlur, and Maneesh Agrawala. 2021. Towards Understanding How Readers Integrate Charts and Captions: A Case Study with Line Charts. In CHI Conference on Human Factors in Computing Systems (CHI ’21), May 8–13, 2021, Yokohama, Japan. ACM, New York, NY, USA, 11 pages. https://doi.org/10.1145/3411764.3445443

1.	Hojjat M., Mohsen K., Javad A. S. & Ghasem P., “The Effect of Computer Games on Speed, Attention and Consistency of Learning Mathematics among Students” Procedia - Social and Behavioral Sciences,Volume 176, 2015 ,Pages 419-424.
2.	Ahmad T., Rohani & Bayat, Sahar (2012). Collaborative problem-based learning in mathematics: A cognitive load perspective. Procedia - Social and Behavioral Sciences. 32. 344–350. 10.1016/j.sbspro.2012.01.051.
3.	Mihaela V., Monica P., “Boosting Romanian Students’ Interest in Learning Mathematics through the Constructivist Approach”, Procedia - Social and Behavioral Sciences, Volume 127,2014, Pages 108-113, ISSN 1877-0428.
4.	Wei, M. H., & Dzeng, H. (2014). A comparison study of math education and math performance between Asian countries and the United States. Journal of Socialomics, 3(02), 2167-0358. 
5.	Sangcap, P. G. (2010). Mathematics-related Beliefs of Filipino College Students: Factors Affecting Mathematics and Problem-Solving Performance. Procedia - Social and Behavioral Sciences. 8. 465-475. 10.1016/j.sbspro.2010.12.064.
6.	SEI-DOST & MATHTED, (2011). Mathematics framework for Philippine basic education. Manila: SEI-DOST & MATTED. ISBN 978-971-8600-48-1
7.	Bahman M., On the Effect of Cooperative Learning on General English Achievement of Kermanshah Islamic Azad University Students, Procedia - Social and Behavioral Sciences, Volume 98, 2014, Pages 1249-1254, ISSN 1877-0428.
8.	Tisha L.N. Emerson, Linda E., KimMarie M.G., Cooperative learning and personality types, International Review of Economics Education, Volume 21, 2016, Pages 21-29, ISSN 1477-3880.
9.	Valdez, A., Lomoljo, A., Dumrang, S., & Didatar, M. (2015). Developing critical thinking through activity –based and Cooperative Learning Approach in teaching high school chemistry. International Journal of Social Science and Humanity, 5(1), 139-141.
10.	Tebabal, A. & Kahssay, G. (2011), Effects of Student-Centred approach in Improving Students’ Graphical Interpretation Skills and Conceptual Understanding of Kinematical Motion. Latin-American Journal of Physics Education, 5(2), 374-381.
11.	Khan, G. N., & Inamullah, H. M. (2011). Effect of student’s Team Achievement Division(STAD) on Academic Achievement of Students. Asian Social Science,7(12),211.
12.	Razak, F. (2016). The Effect of Cooperative Learning on Mathematics Learning Outcomes Viewed from Students’ Learning Motivation. JRAMathEdu (Journal of Research and Advances in Mathematics Education), 1(1),49-55.
13.	Gul, F. & Shehzad, S. (2015). Effects of Cooperative Learning on Students’ Academic Achievement. Journal of Education and Learning. 9.246-255.10.11591/ edulearn.y9i3.2071.
14.	Dendup, T., & Onthanee, A. (2020). Effectiveness of Cooperative Learning on English Communicative Ability of 4th Grade Students in Bhutan. International Journal of Instruction, 13(1), 255-266.
15.	Hagan, J.E., Amoaddai, S., Lawer, V.T., Atteh, E, “Students’ perception towards mathematics and its effects on academic performance” Asian Journal of Education and Social Studies 8(1),8-14,2020.
16.	Rincon, G. A., Fernández Cézar, R. & Hernandez, C.F., “Beliefs about mathematics and academic performance: A descriptive-correlational analysis” J. Phys.: Conf. Ser. 1514 012021.
17.	Gamit, A. & Antolin J. & Gabriel, A.(2017). The Effects of Cooperative Learning in Enhancing the Performance Level of Grade-10 Mathematics Students in Talavera National High School in the Philippines. Journal of Applied Mathematics and Physics. 05.2386-2401.10.4236/jamp.2017.512195.
18.	Abdullah, S., “The Effectiveness of Cooperative Learning in the Class of Inferential Statistics” International Journal of Recent Educational Research 2(6), 614-622, 2021.
19.	Hossain, A., & Tarmizi, R. A. (2013). Effects of Cooperative Learning on Students’ Achievement and Attitudes in Secondary Mathematics. Procedia-Social and Behavioral Sciences, 93, 473-477. 
20.	Chan, L. L. & Idris, N., 2017. “Cooperative Learning in Mathematics Education,” International Journal of Academic Research in Business and Social Sciences, Human Resource Management Academic Research Society, International Journal of Academic Research in Business and Social Sciences, vol. 7(3), pages 539-553, March.
21.	Li, Qingxia; Yang, Xinyao; Payne, Gloria, Applied Cooperative Learning in Teaching Developmental Mathematics Courses. European Journal of Educational Sciences, EJES, June 2015 edition Vol.2, 2015, No. 2 ISSN 1857-6036
22.	Linn, R. & Miller, M. (2005). Measurement and Assessment in Teaching (9th Ed.). Upper Saddle River NJ: Merrill-Prentice Hall.
23.	Hwang, N. C. R., Lui, G., & Tong, Y. J. W., Marian. (2008). Cooperative learning in a passive learning environment: A replication and extension. Issues in Accounting Education, 23(1), 67-75. DOI: 10.2308/ iace.2008.23.1.67
24.	Zakaria, E., Solfitri, T., Daud, Y., & Abidin, Z. (2013). Effect of Cooperative Learning on Secondary School Students’ Mathematics Achievement. Creative Education, 4, 98-100. 
25.	Tsay, M and M. Brady. (2010). A Case Study of Cooperative Learning and Communication Pedagogy: Does working in teams make a difference? Journal of the Scholarship of Teaching and Learning, Vol. 10, No. 2, June 2010, pp. 78-89.
26.	Alshammari, N.M. (2015). Effects of cooperative learning on academic Performance of college students in Saudi Arabia.

1.	Abun, Damianus & Magallanes, Theogenia & Encarnacion, Mary & Lalaine, Sylvia. (2019). “The Attitude of Graduate students toward Research and their Intention to Conduct Research in the Future”. SSRN Electronic Journal. 10.2139/ssrn.3807893.
2.	Stangor, C. (2011). Research methods for the behavioral sciences. (Laureate Education, Inc., custom ed.). Boston: Houghton Mifflin Company.
3.	Papanastasiou, E. (2005). “Factor Structure of the "Attitudes toward Research Scale”. Statistics Education Research Journal, 4, 16-26.
4.	PCW. (2018). Philippine Commission on Women. May 15. http://pcw.gov.ph/statistics/201405/statistics-filipino-women-and-mens-education.
5.	Lally, M., & Valentine-French, S. (2017). Lifespan Development: A psychological Perspective. San Francisco California, USA.
6.	Wolff, Charlotte & Bogert, Niek & Jarodzka, Halszka & Boshuizen, Henny. (2015). “Keeping an Eye on Learning: Differences Between Expert and Novice Teachers’ Representations of Classroom Management Events”. Journal of Teacher Education. 66. 68-85. 10.1177/0022487114549810.
7.	Borg, S., & Alshumaimeri, Y. (2012). “University teacher educators’ research engagement: Perspectives from Saudi Arabia. Teaching and Teacher Education”, 28(3), 347–356. doi:10.1016/j.tate.2011.10.011
8.	Munir, Nosheen & Bolderston, Amanda. (2009). “Perceptions and Attitudes toward Conducting Research: A Nuclear Medicine Student Perspective”. Journal of Medical Imaging and Radiation Sciences. 40. 183-189. 10.1016/j.jmir.2009.09.005.
9.	Oguan, F., Bernal, M.M., Christine, M., & Pinca, D. (2014). “Attitude and Anxiety towards Research, Its Influence on the Students' Achievement in the Course”. Asian journal of management sciences & education, 3, 165-172.
10.	Laliene, R., & Sakalas, A. (2012). “Interaction Between R&D and Economic Indicators in Lithuania”. Economics & Management, 17(1), 194-201.
11.	Lee, C. (2015). The Myth of the Off-Limits. Retrieved from https://blog.apastyle.org/apastyle/research/
12.	Sison, C. B. (2019). “Research Attitude and Capabilities of Selected Academic Librarians Towards Preparation in Conducting Research. Library Philosophy and Practice” (e-Journal).
13.	Seher, U., Remziye, S., Kizilcik, O.Z. & ?lker, A. (2018). “Attitude of Nursing Students toward Scientific Research A Cross-Sectional Study in Turkey”. Journal of Nursing Research, Vol. 26, issue, 5, pp. 356-361
14.	Siamian, Hasan & Mahmoudi, Roghayeh & Habibi, Fatemeh & Latifi, Masoomeh & ZareGavgani, Vahideh. (2016). “Students' Attitudes Towards Research at Mazandaran University of Medical Sciences in 2015”. Materia Socio Medica. 28. 468. 10.5455/msm.2016.28.468-472

[1] Parrocha, A., HEIs May Hold Limited Face-to-face Classes in MGCQ Areas. Quezon City, PH: Philippine News Agency, 2020
[2] Azzahra, N. F., Addressing Distance Learning Barriers in Indonesia amid the COVID-19 Pandemic. South Jakarta: Center for Indonesian Policy Studies, 2020
[3] Ghavifekr, S., Afshari, M., and Amla Salleh, Management strategies for E-Learning system as the core component of systemic change: A qualitative analysis. Life Science Journal, 9(3), 2190-2196, 2012
[4] Arnseth, H.C., & Hatlevik, O.E., Challenges in aligning pedagogical practices and pupils’ competencies with the Information Society’s demands: The case of Norway. In S. Mukerji & P. Triphati (Eds.), Cases on technological adaptability and transnational learning: Issues and challenges. Hershey: IGI global, 2010

[5] Chapelle, C., Computer applications in second language acquisition: Foundations for teaching, testing and research. Cambridge: Cambridge University Press, 2011
[6] McNeill, C., What is Descriptive Research? Retrieved    
      from coursehero.com/file/45886380/February-15docx/, 
     2018
[7] Musiyandaka, D., Ranga, G. and Kiwa, J. F., An Analysis of Factors Influencing Success of ICT4D Projects: A Case Study of the Schools Computerisation Programme in Mashonaland West Province, Zimbabwe. Journal of Community Informatics, 2013
[8] Bindu, C. N., Impact of ICT on teaching and learning: A literature review. International Journal of Management and Commerce Innovations, 4(1), 24-31, 2016
[9] Sahlin, J. S., Tsertsidis, A., and Islam, M. S., Usages and impacts of the integration of information and communication technologies (ICTs) in elementary classrooms: case study of Swedish municipality schools. Interactive Learning Environments, 25(5), 561-579, 2017
[10] Farrell, Glen and Shafika Isaacs, Survey of ICT and Education in Africa: A Summary Report, Based on 53 Country Surveys. Washington, DC: info Dev. / World Bank. Available at http://www.infodev.org/en/Publication.353.html Jamieson-Proctor, R., Albion, P., Finger, G., Cavanagh, R., Fitzgerald, R., Bond, T., & Grimbeek, P. (2013). Development of the TTF TPACK Survey Instrument. Australian Educational Computing, 27(3),26-35, 2007
[11] Assia, M. B., ICT and Reading: In the Technology-Enhanced Extensive Reading Classroom. The Case of 1st Year EFL Students at Hassiba Ben-Bouali University of Chlef (Algeria). Revue académiques des sciences sociales et humaines, 8, 3, 2012
[12] Apple Computer, The impact of technology on students’ achievement, 2002
[13] CEO Forum on Education Technology, School technology and readiness report: Key building blocks for student achievement in the 21st century: integrating digital content. Washington, DC: Author, 2001
[14] Earle, S. D., The integration of instructional technology in public education. Promises and Challenges. Educational Technology, 2003
[15] Williams, M. D., Technology integration in education. In S. C. Tan & F. L. Wong (Eds.), Teaching and learning with technology: An Asia-pacific perspective, Singapore: Prentice Hall, 17-31, 2003
[16] Skyrabin, M., Zhang, J., How the ICT development level and usage influence student achievement in reading, mathematics, and science. Computers & Education,    Volume 85 2015, Pages 49-58,  ISSN 0360-1315, 2015
[17] Bhaurao, P. B., Role of ICT in Indian digital education system. Indian Streams Research Journal, 5(2), 1-5, 2015
[18] Lu, Z., Hou, L and Huang, X., A research on a student-centered teaching model in an ICTbased English audio-video speaking class. International Journal of Education and Development using Information and Communication Technology, vol. 6, pp.101-123, 2010
[19] Faghih, B., Azadehfar, D., Reza, M., & Katebi, P., User interface design for elearning software. arXiv preprint arXiv:1401.6365, 2014
[20] Montoya, S., The Importance of Monitoring and Improving ICT Use in Education Post-Confinement, 2020
[21] Albirini, A., Teachers’ attitudes toward information and communication technologies: The case of Syrian EFL teachers. Computers & Education, 47(4), 373-398, 2006
[22] Mbaeze, I. C., Ukwandu, E., and Anudu, C., The influence of information and communication technologies on students’ academic performance. Journal of Information Technology Impact, 10(3), 129-136. Available from https://www.researchgate. net/publication/272179181_The_Influence_of_Information_and_Communication_Technologies_on_Students’_Academic_ Performance, 2010
[23] Wael Sh. Basri, Jehan A. Alandejani, Feras M. Almadani, "ICT Adoption Impact on Students’ Academic Performance: Evidence from Saudi Universities", Education Research International, vol. 2018, Article ID 1240197, 9 pages, 2018. https://doi.org/10.1155/2018/1240197, 2018

1.	Gafoor, K. A., & Kurukkan, A. (2015). “Why High School Students Feel Mathematics Difficult? An Exploration of Affective Beliefs”. Pedagogy of Teacher Education - Trends and Challenges. Kozhikode, Kerala.
2.	Guinocor, M., Almerino, J., Mamites, I., Lumayag, C., Villagas, M., & Capuyan, M. (2020). “Mathematics Performance of Students in a Philippine State University”. International Electronic Journal of Mathematics Education, 15. doi:10.29333/iejme/7859
3.	Rameli, M. M., & Kosnin, A. M. (2017, December 18). “Challenges in Mathematics Learning: A Study from School Students' Perspective”. Retrieved from Research Gate:https://www.researchgate.net/publication/321873178
4.	Paris, J. (2019, December 4). “Philippines ranks among lowest in reading, math, and science in 2018 study”. Rappler. Retrieved from https://www.rappler.com/nation/philippines-ranking-reading-math-science-pisa-study-2018
5.	Magsambol, B. (2020, December 9). “PH lowest among 58 countries in math, science – a global assessment”. Rappler. Retrieved from https://www.rappler.com/nation/filipino-students-lagging-behind-math-science-timms-international-results-2019
6.	Mc Combes, S. (2019). Descriptive Research. Scribbr.
7.	Rameli, M. M. (2016). “Malaysian School Students' Math Anxiety: Application of Rasch Measurement Model”. Jurnal Pendidikan Nusantara, pp. 56-61. Retrieved from https://www.researchgate.net/publication/321873451
8.	Usher, E. L., & Pajares, F. (2009). “Sources of self-efficacy in mathematics: A validation study”. Contemporary Educational Psychology, 34, pp. 89-101.
9.	Kandemir, M. A., & Perkman, R. A. (2017). “Examining Validity of Sources of Mathematics Self-Efficacy Scale in Turkey”. European Journal of Education Studies, vol. 3(11), pp. 69-88. doi:10.5281/zenodo.1041845
10.	Prajapati, J., Patel, A., & Raninga, P. (2014). “Facial age group classification”. IOSR Journal of Electronics and Communications Engineering, vol. 9(2), pp. 33-39. doi:10.9790/2834-09123339
11.	Lopez, J., & Malay, C. A. (2019, May). “Awareness and Attitude Towards Climate Change of Selected Senior High Students in Cavite, Philippines”. Asia Pacific Journal of Multidisciplinary Research, vol. 7(2), pp. 56-62.
12.	Bonnie, R. (2015). “National Center for Biotechnology Information”. Retrieved from https://www.ncbi.nlm.nih.gov/books/NBK284782/
13.	Jabor, M. K., Machtmes, K., Kungu, K., Buntat, Y., & Nordin, M. S. (2011). The Influence of Age and Gender on the Students' Achievement in Mathematics. Presented at 2011 International Conference on Social Science and Humanity. Vol. 5, pp. 304-308. Singapore: IACSIT.
14.	Buctot, D. B., Kim, N., & Kim, S. (2020). “The role of nomophobia and smartphone addiction in the lifestyle profiles of junior and senior high school students in the Philippines”. Social Science and Humanities Open. vol. 2, pp. 2590-2911. doi:https://doi.org/10.1016/j.ssaho.2020.100035
15.	Ibañez, C., & Mangunay, K. (2016, May 29). “DepEd: Ready for senior high. Philippine Daily Inquirer”. Retrieved from https://newsinfo.inquirer.net/788008/deped-ready-for-senior-high
16.	Maglipot, M. H. (2020). “DepEd reports over 24 M enrollees this school year. Manila Bulletin”. Retrieved from https://mb.com.ph/2020/10/01/deped-reports-over-24-m-enrollees-this-school-year/
17.	Tus, J. (2019). “Self-Efficacy and Its Influence on the Academic Performance of the Senior High School Students”. Journal of Global Research in Education and Social Science, vol. 13(6), pp. 213-218.
18.	Paechter, M., Macher, D., Martskvishvili, K., Winner, S., & Papousek, I. (2017, July). “Mathematics Anxiety and Statistics Anxiety. Shared but Also Unshared Components and Antagonistic Contributions to Performance in Statistics”. Frontiers in Psychology, vol. 8. doi:https://doi.org/10.3389/fpsyg.2017.01196
19.	Prodromou, T., & Frederiksen, N. (2018). “The Effects of Mathematics Anxiety on Primary Students”. Retrieved from https://eric.ed.gov/?id=ED592472
20.	Iglesia, A. B., Dundain, H. H., Miñoza, M. V., & Casimiro, A. B. (2021). “Teaching Mathematics in 'Different Tongues': An Analysis of Mathematics Anxiety and Proficiency among Elementary-Grade Learners”. Linguistics and Culture Review, vol. 5(2), pp. 860-891. doi: https://doi.org/10.37028/lingcure.v5nS2.1523
21.	Estonanto, A. (2017, December). “Math Anxiety and Academic Performance in Pre-Calculus of Selected Senior High School in Sorsogon State College”. Journal of Higher Education Research Disciplines, vol. 2(2).
22.	Reyes, J. D. (2019). “Mathematics Anxiety and Self-Efficacy: A Phenomenological Dimension”. Journal of Humanities and Education Development (JHED), vol. 1(1), pp. 22-34.
23.	Lu, Y., Li, Q., Patrick, H., & Mantzicopoulos, P. (2021, December 6). “"Math Gives Me a Tummy Ache!" Mathematics Anxiety in Kindergarten”. The Journal of Experimental Education, vol. 89(2), pp. 362-378. doi:https://doi.org/10.1080/00220973.2019.1680518
24.	Nyakudya, M. N., & Nyakudya, U. N. (2020). “A Study of Math Anxiety in Male and Female Students at BA ISAGO University: Empirical Evidence”. Open Journal of Social Sciences, vol. 8, pp. 345-358. doi:10.4236/jss.2020.810023
25.	Norbu, P., & Dukpa, P. (2021). “Investigating the Level of the Tenth-grade Students' Self-Efficacy in Mathematics and its Impact on their Performance: A Study in Pemagatshel District”. Asian Research Journal of Mathematics, pp. 34-46. doi:10.9734/ARJOM/2021/v17i230272
26.	Tossavainen, T., Rensaa, R., & Johansson, M. (2021). “Swedish first-year engineering students' views of mathematics, self-efficacy and motivation and their effect on task performance”. International Journal of Mathematics Education in Science and Technology, vol. 52(1), pp.  23-38. doi:https://doi.org/10.1080/0020739X.2019.1656827
27.	Estonanto, A. J., & Dio, R. V. (2019). “Factors causing Mathematics Anxiety of Senior High School Students in Calculus”. Asian Journal of Education and E-learning, vol. 7(1). doi:https://doi.org/10.24203/ajeel.v7i1.5701
28.	Mukuka, A., Mutarutinya, V., & Balimuttajjo, S. (2021). “Mediating Effect of Self-efficacy on the Relationship between Instruction and Students' Mathematical Reasoning”. Journal on Mathematics Education, vol. 12(1), pp. 73-92.
29.	Nipaz, J. G., Belecina, R. R., & Garvida, M. D. (2016). “Language of Encouragement: Effects on Mathematics Anxiety, Self-efficacy and Mathematics Performance of College Students in the Philippines”. World Journal of Research and Review, vol.  2(5), pp. 09-14.
30.	In'am, A., & Sutrismo, E. (2021). “Strengthening Students' Self-efficacy and Motivation in Learning Mathematics through the Cooperative Learning Model”. International Journal of Instruction, vol. 14(1), pp. 395-410. doi:https://doi.org/10.29333/iji.2021.14123a
31.	Espino, M., Pereda, J., Recon, J., Perculeza, E., & Umali, C. (2017). “Mathematics Anxiety and Its Impact on the Course and Career Choice of Grade 11 Students”. International Journal of Education, Psychology and Counseling, vol. 2(5), pp. 99-119.
32.	Odumodu, C. N., & Unachukwa, G. C. (2021). “Effect of Jigsaw Learning Strategy on the Test Anxiety of Students with Mathematics Anxiety in Anambra State Secondary Schools”. International Journal of Education and Evaluation, vol. 7(2), pp. 52-59.
33.	. Delgado, K. D., & Kassim, S. (2019). “Mathematics Anxiety among Young Filipino Learners: Investigating the Influence of Gender and Socio-Economic Status”. Science International (Lahore), vol. 31(3), pp.  575-579. Retrieved from https://eric.ed.gov/?id=ED596724
34.	Asikhia, O. A. (2021). “Gender Effect on Mathematics Anxiety of Secondary School Students in Ogun West Senatorial District, Nigeria”. Asian Journal of Education and Social Studies,  vol. 15(4), pp. 17-23. doi:10.9734/AJESS/2021/v15i430386
35.	Lacia, M. R., Ben, F., & Elizar, E. (2020). “Examining the Gender and Grade Level Differences in Mathematics Anxiety”. Presented at 10th Annual International Conference on Social Science. Banda Aceh, Indonesia.
36.	Alico, J. C., Maraorao, U. D., & Maraorao, R. D. (2017). “Personal Variables and Anxiety in English and Mathematics: Correlational and Comparative Investigation among Pre-University Students”. International Journal for Innovation Education and Research, vol. 5(11), pp. 48-61. Retrieved from https://ijier.net/ijier/article/view/852
37.	Erdem, E. (2017). “A Current Study on Grade/Age and Gender-Related Chance in Mah Anxiety”. European Journal of Education Studies, vol. 3(6), pp.  396-413.
38.	Winarso, W., & Haqq, A. A. (2019). “Psychological disposition of student; Mathematics anxiety versus happiness learning on the level education”. International Journal of Trends in Mathematics Education Research, vol. 2(1), pp.  19-25. doi:https://doi.org/10.33122/ijtmer.v2i1.32
39.	Pellizzari, M., & Billari, F. (2012). “The younger, the better? Age-related differences in academic performance at university”. Journal of Population Economics, vol. 25, pp. 697-739.
40.	Ducay, J. T., & Alave, A. D. (2021). “Self-Efficacy, Anxiety and Academic Performance in Mathematics of Junior High School Students”. Globus Journal of Progressive Education, vol. 11(1), pp. 41-46.
41.	Cuevas, M. M., & Berou, M. L. (2016). “Students' Mathematics Self-Efficacy and Anxiety as Correlates to Academic Performance”. University of Bohol Multidisciplinary Research Journal, vol. 4, pp. 1-18.
42.	Probst, J. R. (2019). “A Causal-Comparative Analysis of Mathematics Self-Efficacy Based on Gender and Math Acceleration”. Unpublished Thesis. Retrieved from https://digitalcommons.liberty.edu/cgi/viewcontent.cgi?article=3104&context=doctoral
43.	Ozdemir, A., Karasan, S., & Sahal, M. (2020). “An examination of the relationship between secondary school students' abstract thinking skills, self-efficacy perceptions, and attitudes towards mathematics”. Participatory Educational Research Journal. doi:http://dx.doi.org/10.17275/per.21.45.8.2
44.	Hiller, S. E., Kitsantas, A., Cheema, J. E., & Poulou, M. (2020). “Mathematics anxiety and self-efficacy as predictors of mathematics literacy”. International Journal of Mathematical Education in Science and Technology. doi:https://doi.org/10.1080/0020739X.2020.1868589
45.	Mariera, N. M., Murugami, M., & Muthee, J. (2021). “Mathematics Efficacy Among Students with Dyscalcula in Public High Schools in Kandra Sub-Country, Murunga County, Kenya”. European Journal of Education Studies, vol. 8(7), pp. 267-275. doi:10.46827/ejes.v8i7.3828
46.	Negara, H. P., Nurlaelah, E., Wahyudin, Hernan, T., & Tamur, M. (2021). “Mathematics self-efficacy and mathematics performance in online learning”. Journal of Physics. doi:10.1088/1742-6596/1882/1/012050
47.	Rozgonjuk, D., Kraav, T., Mikkor, K., Puurand, K. O., & Taht, K. (2020). “Mathematics anxiety among STEM and social sciences students: the roles of mathematics self-efficacy, and deep and surface approach to learning”. International Journal of STEM Education, 1-11. doi:10.1186/s40594-020-00246-z
48.	Jiang, R., Liu, R.-d., Star, J., Zhen, R., Wang, J., Hong, W., . . . Fu, X. (2021). “How mathematics anxiety affects students ‘in?exible perseverance in mathematics problem-solving: Examining the mediating role of cognitive re?ection”. British Journal of Educational Psychology, pp. 237-260. doi:10.1111/bjep.12364
49.	Salazar, R. E., & Basierto, R. C. (2021). “Classroom Learning Environment and Self-Efficacy in Mathematics of Freshmen Engineering Students of the University of Eastern Philippines”. Asian Journal of Advanced Research and Reports, vol. 15(2), pp. 94-101. doi:10.9734/AJARR/2021/v15i230371
50.	Van der Beek, J., Van der Ven, S., Kroesbergen, E. H., & Leseman, P. (2017, April 25). “Self-concept mediates the relation between achievement and emotions in mathematics”. British Journal of Educational Psychology, vol. 87(3), pp. 478-495. doi:https://doi.org/10.1111/bjep.12160
51.	Delima, N., Rahmah, M. A., & Akbar, A. (2018). “The Analysis of Students' Mathematical Thinking based on Their Mathematics Self-Concept”. Journal of Physics: Conference Series. IOP Publishing. doi:10.1088/1742-6596/1108/1/012104
52.	Jameel, H. T., & Ali, H. H. (2016). “Causes of Poor Performance in Mathematics from the Perspective of Students, Teachers, and Parents”. American Scientific Research Journal for Engineering, Technology, and Sciences, vol. 15(1), pp. 122-136.

[1] Al-Turjman, F. (2021, June). Guest editorial: Next generation drone-IoT integrated networks. Internet of Things, 14, 100270. https://doi.org/10.1016/j.iot.2020.100270
[2]  Chehri, A., Chaibi, H., Saadane, R., Hakem, N., & Wahbi, M. (2020). A Framework of Optimizing the Deployment of IoT for Precision Agriculture Industry. Procedia Computer Science, 176, 2414–2422. https://doi.org/10.1016/j.procs.2020.09.312
[3] Shimada, E. (2021, February). Sustaining the Commons and the Use of Neonicotinoid Pesticides Considered in Terms of Agricultural Marketing: Economic Policy implications of farmers’ and agricultural communities’ pesticides use. International Journal of Economic Policy Studies, 16(1), 331–353. https://doi.org/10.1007/s42495-022-00080-x
[4] Nagaraja, G. S., Soppimath, A. B., Soumya, T., & Abhinith, A. (2019, December). IoT based smart agriculture management system. In 2019 4th International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS) (pp. 1-5). IEEE.
[5] Khelifa, B., Amel, D., Amel, B., Mohamed, C., & Tarek, B. (2015, July). Smart irrigation using internet of things. In 2015 Fourth International Conference on future generation communication technology (FGCT) (pp. 1-6). IEEE.
[6] Sinwar, D., Dhaka, V. S., Sharma, M. K., & Rani, G. (2020). AI-based yield prediction and smart irrigation. Internet of Things and Analytics for Agriculture, Volume 2, 155-180
[7] Stafford, J. V. (2000). Implementing precision agriculture in the 21st century. Journal of agricultural engineering research, 76(3), 267-275.
[8]  Sreekantha, D. (2016). Automation in agriculture: a study. International Journal of Engineering Science Invention Research & Development, 2(10), 823-833.
[9] Bong, C. P., Lim, L. Y., Lee, C. T., Van Fan, Y., & Klemes, J. J. (2018). The role of smart waste management in smart agriculture. Chemical Engineering Transactions, 70, 937-942.
[10] Barrett, C. B., Lynam, J., Place, F., Reardon, T., & Aboud, A. A. (2002). Towards improved natural resource management in African agriculture. In Natural resources management in African agriculture: understanding and improving current practices (pp. 287-296). Wallingford UK: CABI Publishing.
[11] Steensland, A., & Zeigler, M. (2021). Productivity in agriculture for a sustainable future. In The Innovation Revolution in Agriculture. Springer.
[12] Cassidy, E. S., West, P. C., Gerber, J. S., & Foley, J. A. (2013). Redefining agricultural yields: from tonnes to people nourished per hectare. Environmental Research Letters, 8(3), 034015.
[13] Kamilaris, A., Kartakoullis, A., & Prenafeta-Boldú, F. X. (2017). A review on the practice of big data analysis in agriculture. Computers and Electronics in Agriculture, 143, 23-37.
[14] Puranik, V., Ranjan, A., & Kumari, A. (2019, April). Automation in agriculture and IoT. In 2019 4th international conference on internet of things: smart innovation and usages (IoT-SIU) (pp. 1-6). IEEE.
[15] Saha, A. K., Saha, J., Ray, R., Sircar, S., Dutta, S., Chattopadhyay, S. P., & Saha, H. N. (2018, January). IOT-based drone for improvement of crop quality in agricultural field. In 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC) (pp. 612-615). IEEE.
[16] Piramuthu, S. (2021). IoT, Environmental Sustainability, Agricultural Supply Chains. Procedia Computer Science, 204, 811-816.
[17] Ramachandran, V., Ramalakshmi, R., Kavin, B. P., Hussain, I., Almaliki, A. H., Almaliki, A. A., ... & Hussein, E. E. (2022). Exploiting IoT and its enabled technologies for irrigation needs in agriculture. Water, 14(5), 719.
[18] Baranwal, T., & Pateriya, P. K. (2016, January). Development of IoT based smart security and monitoring devices for agriculture. In 2016 6th International Conference-Cloud System and Big Data Engineering (Confluence) (pp. 597-602). IEEE.
[19] Muangprathub, J., Boonnam, N., Kajornkasirat, S., Lekbangpong, N., Wanichsombat, A., & Nillaor, P. (2019). IoT and agriculture data analysis for smart farm. Computers and electronics in agriculture, 156, 467-474.
[20] Debauche, O., Trani, J. P., Mahmoudi, S., Manneback, P., Bindelle, J., Mahmoudi, S. A., ... & Lebeau, F. (2021). Data management and internet of things: A methodological review in smart farming. Internet of Things, 14, 100378.
[21] Dagar, R., Som, S., & Khatri, S. K. (2018, July). Smart farming–IoT in agriculture. In 2018 International Conference on Inventive Research in Computing Applications (ICIRCA) (pp. 1052-1056). IEEE.
[22] Kaushik, I., & Prakash, N. (2021, May). Applicability of IoT for Smart Agriculture: Challenges & Future Research Direction. In 2021 IEEE World AI IoT Congress (AIIoT) (pp. 0462-0467). IEEE.
[23] Verdouw, C., Wolfert, S., & Tekinerdogan, B. (2016). Internet of Things in agriculture. CABI Reviews, (2016), 1-12.
[24] Fastellini, G., & Schillaci, C. (2020). Precision farming and IoT case studies across the world. In Agricultural internet of things and decision support for precision smart farming (pp. 331-415). Academic Press.
[25] Rawal, S. (2017). IOT based smart irrigation system. International Journal of Computer Applications, 159(8), 7-11.
[26] Kassim, M. R. M. (2020, November). Iot applications in smart agriculture: Issues and challenges. In 2020 IEEE conference on open systems (ICOS) (pp. 19-24). IEEE.
[27] Yadav, S., Garg, D., & Luthra, S. (2020). Analysing challenges for internet of things adoption in agriculture supply chain management. International Journal of Industrial and Systems Engineering, 36(1), 73-97.

[1].AzzamSleit, Nada Misk, FatimaBadwan, Tawfiq Khalil(2013),Cloud Computing Challenges With Emphasis On Amazon Ec2 And Windows Azure, IJCNC, Vol.5, No.5, DOI : 10.5121/ijcnc.2013.5503.
[2] ProsantaGope et al. 2016. BSN-Care: A Secure IoTbased Modern Healthcare System Using Body Sensor Network. IEEE Sensors Journal. 16(5): 1368-1376. 
[3] Tzonelih Hwang et al. 2016. Untraceable Sensor Movement in Distributed IoT Infrastructure. IEEE Sensors Journal. 15(9): 5340-5348. 
[4] Tae-Yoon Kim et al. 2015. Multi-Hop WBAN Construction for Healthcare IoT Systems. IEEE Platform Technology and Service (PlatCon), International Conference. pp. 27-28. 
[5] CharalamposDoukas et al. 2015. Bringing IoT and Cloud Computing towards Pervasive Healthcare. IEEE Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS), International Conference. pp. 922-926. 
[6] Tianhe Gong et al. 2015. A medical Health care system for privacy protection based on IoT. IEEE Parallel Architecture, Algorithms and Programming (PAAP). pp. 217-222. 
[7] Lin Yang et al. 2014. A Home Mobile Healthcare System for Wheelchair Users. Proceedings of the 2014 IEEE 18th International Conference on Computer Supported Cooperative Work in Design. pp. 609-614.
[8] J. Wu, S. Yuan, S. Ji, G. Zhou, Y. Wang, and Z. Wang, “Multi-agent system design and evaluation for collaborative wireless sensor network in large structure health monitoring,” Expert Systems with Applications, vol. 37, no. 3, pp. 2028–2036, 2010. 
[9] K. Liu, C. Wang, and S. Liu, “A novel mobile data collection algorithm for wireless sensor networks,” Adhoc& Sensor Wireless Networks, vol. 36, no. 1-4, pp. 285–311, 2017. 
[10] S. Vaudenay, "On the Weak Keys in Blowsh," Fast Software Encryption, Third International Workshop Proceedings, SpringerVerlag, 1996, pp. 27-32.
[11]. P. Karthigai Kumar and K. Baskaran. 2010. An ASIC implementation of low power and high throughput blowfish crypto algorithm. Microelectron. J. 41, 6 (June 2010), 347-355.
[12] Public Key Cryptography - Applications Algorithms and Mathematical Explanations

[1]	Marques, O. (2011). Practical image and video processing using MATLAB. Hoboken: Wiley-IEEE Press.
[2]	Sandhya, Tatekalva., Prof.Usha Rani,  M., & Dr.Maruthi Krishna, M. (2017). Image Processing Techniques for Measuring the Acetabular Cup Orientation from Anteversion Angle of Revised Cemented Total Hip Arthroplasty (THA). International journal of Emerging Technology in Computer Science & Electronics (IJETCSE), ISSN: 0976-1353, volume 24, issue 1.
[3]	David G. Lowe. (2004). Distinctive Image Features from Scale-Invariant Key points. Computer Science Department University of British Columbia, Vancouver, B.C., Canada.
[4]	Marius, Muja., Radu. BogdanRusuy., Gary Bradskiy., & David Lowe. (2011). REIN - A Fast, Robust, Scalable Recognition Infrastructure. University of British Columbia, Canada. IEEE Xplore, published in IEEE International conference on Robotics and Automation (ICRA). DOI: 10.1109/ICRA.2011.5980153. e-ISBN: 978-1-61284-385-8.
[5]	T. Sandhya., & Prof. M. Usha Rani. (2016). CONTEMPORARY REVISION ANALYSIS OF THE HIP REPLACEMENT REGISTRIES IN INDIA. International Journal of Computational Science, Mathematics and Engineering. Volume-3-Issue-11-November-2016 ISSN-2349-8439
[6]	T. Sandhya., & Prof. M. Usha Rani. (2019). Need for Computerized Automated Machine (CAM) for finding THR issues in Patients. International Journal of Innovative Technology and Exploring Engineering (IJITEE). ISSN: 2278-3075,Volume-8, Issue-7C2.
[7]	S.J. Ferguson.,  J.T. Bryant., R. Ganz., & K. Ito. (2003). An in vitro investigation of the acetabularlabral seal in hip joint mechanics. Journal of Biomechanics. volume 36, issue 2, pages 171-178.
[8]	Joachim Pfeil., & W.E.Siebert. (2010). Minimally Invasive Surgery in Total Hip Arthroplasty. Heidelberg: Springer. Doi: https://doi.org/10/1007/978-3-642-00897-9.
[9]	Chen-Kun Liawa., Rong-SenYangb., Sheng-MouHoub., Tai-Yin Wuc., & Chiou-Shann. (2006). A Simple Mathematical Standardized Measurement of Acetabulum Anteversion after Total Hip Arthroplasty. ISSN 1748-670X print/ISSN 1748-6718 online.
[10]	G. Wu, S. Siegler., P. Allard., C. Kirtley., A. Leardini., D. Rosenbaum., M. Whittle., D. D. D’Lima., L. Cristofolini., H. Witte., O. Schmid., I. Stokes. (2002). ISB Recommendation on Definitions of Joint Coordinate System of Various Joints for the Reporting of Human Joint Motion-part I: Ankle, Sip, and Spine. Journal of Biomechanics, 2002, 35(4): 543-48.
[11]	C. Nikou., B. Jaramaz., A. M. DiGioia., & T. J. Levison. (2000). Description of Anatomic Coordinate System and Rationale for use in an Image-guided Total Hip Replacement System. Medical Image Computing and Computer-Assisted Intervention-MICCAI. Lecture Notes of Computer Science. Vol. 1935, pp. 1188-1194. 
[12]	Rafael C, Gonzalez., & Richard E.Woods. (2008). Digital Image Processing. published by Pearson Edition, Inc. Publishing as Prentice Hall, copyright © 2008. ISBN: 978-81-317-2695-2.
[13]	Don, Fussell. (2010). Image Processing. University of Texas at Austin CS384G - Computer Graphics Fall.
[14]	Elaine Rich., & Kelvin Knight. (2009). Artificial Intelligence. TMH.
[15]	Fayyad U., Piatetsky-Shapiro G., & Smyth, P. (1996). From Data Mining to Knowledge Discovery in Databases.  AI Magazine, American Association for Artificial Intelligence.

[1]	Armbrust, M., Fox, A., Griffith, R., Joseph, A.D., Katz, R., Konwinski, A., Lee, G., Patterson, D., Rabkin, A., Stoica, I., et al., A view of cloud computing. Commun. ACM 53 (4), 50–58, 2010.

[2]	Hung, S.-H., Shih, C.-S., Shieh, J.-P., Lee, C.-P., Huang, Y.-H.,. Executing mobileapplications on the cloud: framework and issues. Comput. Math. Appl. 63 (2),573–587, 2012.

[3]	Giurgiu, I., Riva, O., Juric, D., Krivulev, I., Alonso, G.,. Calling the cloud: enablingmobile phones as interfaces to cloud applications. In: Proceedings of the 10thACM/IFIP/USENIX International Conference on Middleware. Springer-Verlag NewYork, Inc., p. 5, 2009.

[4]	Goudarzi, M., Zamani,Cardellini, V., Person, V.D.N., Di Valerio, V., Facchinei, F., Grassi, V., Presti, F.L.,Piccialli, V.. A game-theoretic approach to computation offloading in mobilecloud computing. Math. Program. 157 (2), 421–449, 2016.

[5]	Zhou, B., Dastjerdi, A.V., Calheiros, R., Srirama, S., Buyya, R., b. mcloud: acontext-aware offloading framework for heterogeneous mobile cloud. IEEE Trans.Serv. Comput. 10 (5), 797–810, 2015.

[6]	Enzai, N.I.M., Tang, M.,. A heuristic algorithm for multi-site computationoffloading in mobile cloud computing. ProcediaComput. Sci. 80, 1232–1241, 2016.

[7]	Choudhari T, Moh M, Moh T-S. Prioritized task scheduling in fog computing. In: Proceedings of the ACMSE 2018 Conference; 2018; Richmond, KY.

[8]	Bittencourt LF, Diaz-Montes J, Buyya R, Rana OF, Parashar M. Mobility-aware application scheduling in fog computing. IEEE Cloud Comput.;4(2):26-35, 2017.

[9]	Mishra S, Jain S. Ontologies as a semantic model in IoT. Int J Comput Appl. 2018. https://doi.org/10.1080/1206212X.2018.1504461

[10]	Nguyen BM, ThiThanhBinh H, Do Son B. Evolutionary algorithms to optimize task scheduling problem for the IoT based bag-of-tasks application incloud–fog computing environment. Applied Sciences.;9(9):1730, 2019.

[11]	Mai L, Dao N-N, Park M. Real-time task assignment approach leveraging reinforcement learning with evolution strategies for long-term latencyminimization in fog computing. Sensors.;18(9):2830, 2018.

[12]	M. Shelar, S. Sane, V. Kharat, and R. Jadhav, “Autonomic and energy-aware resource allocation for efficient management of cloud data centre,” in 2017 Innovations in Power and Advanced Computing Technologies (i-PACT), pp. 1-8, IEEE, 2017.

[13]	Viswanath, G., and P. Venkata Krishna. "Hybrid encryption framework for securing big data storage in multi-cloud environment." Evolutionary Intelligence (2020): 1-8.

[14]	Kavitha, Modepalli, and P. Venkata Krishna. "IoT-Cloud-Based Health Care System Framework to Detect Breast Abnormality." In Emerging Research in Data Engineering Systems and Computer Communications, pp. 615-625. Springer, Singapore, 2020.

[15]	Kavitha, S., and P. Venkata Krishna. "Realistic Sensor-Cloud Architecture-Based Traffic Data Dissemination in Novel Road Traffic Information System." In Emerging Research in Data Engineering Systems and Computer Communications, pp. 639-653. Springer, Singapore, 2020.