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International Journal of Computer Engineering in Research Trends. Scholarly, Peer-Reviewed,Open Access and Multidisciplinary
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Paper Title | : | Review on Data Aggregation Techniques in Internet of Things |
Authors | : | Mr. Guguloth Ravi, Dr.M. Swamy Das, , , |
Affiliations | : | Research Scholar,Department of CSE,University College of Engineerng(UCE),Osmania University(OU),Hyderabad,Telangana,India. |
Abstract | : | The "Internet of Things" is a new paradigm that consists of several connected and related instruments with embedded sensing components that communicate with one other and with central nodes across a cordless network and the internet. IoT-enabled health care systems have recently attracted a lot of attention due to the importance of human health. However, because IoT networks are large-scale and battery-powered, it is necessary to set up appropriate energy and resource management systems for them. How to effectively provide information to the right users is a challenging problem for data management. To aid in machine-to-machine communication with linked data, cheap solutions for semantic IoT include dependable circulation distribution systems. In response to specific system queries provided by users, the system compiles integrated information streams produced by multiple collectors and delivers pertinent data to relevant users. To meet the demands of high efficiency data flow propagation in two conditions, such as point-to-point systems and flow breeding in wireless transmission systems, two novel information structures must be developed. Analysis of techniques using real-world datasets reveals that they are much more effective at sending linked information streams than the current technology. In order to maximize the utilization of the network lifetime, this study proposes SUNFLOWER-ALGORITHM based information gathering systems for IoT-enabled in various applications. |
: | 10.22362/ijcert/2021/v8/i12/v8i1206 | |
DOI Link | : | https://doi.org/10.22362/ijcert/2021/v8/i12/v8i1206 |
[1] Generalized Attack Protection in the Kirchhoff-Law-Johnson-Noise Secure Key Exchanger by Authors GERGELY VADAI, ZOLTAN GINGL, AND ROBERT MINGESZ, 2016. [2] Hitch Hiker 2.0: a binding model with flexible data aggregation for the Internet-of-Things, Gowri Sankar Ramachandran 2016. [3] An architecture for aggregating information from distributed data nodes for industrial internet of things Tao Zhua , Sahraoui Dhelim, 2016. [4] Grey Wolf based compressive sensing scheme for data gathering in IoT based heterogeneous WSNs, Ahmed A. El-Sawy year of 2017. [5] Privacy-preserving protocols for secure and reliable data aggregation in IoT-enabled Smart Metering systems by Samet Tonyali a, Kemal Akkaya a , Nico Saputroa , A. Selcuk Uluagac a , Mehrdad Nojoumian, 2018. [6] A lightweight privacy-preserving data aggregation scheme with provable security for internet-of-things, Sunday Oyinlola Ogundoyin 2019. [7] A Novel Low-complexity Compressed Data Aggregation Method for Energy-constrained IoT Networks, Amarlingam M, K. V. V. Durga Prasad, P Rajalakshmi, Sumohana S. Channappayya, and C. S. Sastry 2020. [8] F LEACH: a fuzzy based data aggregation scheme for healthcare IoT systems Seyedeh Nafseh Sajedi, Mohsen Maadani, 2021. [9] ?DSC2 DAM: beta dominating set centered Cluster Based Data Aggregation mechanism for the Internet of Things, Ab Rouf Khan, Mohammad Ahsan Chishti 2022. [10] S. Madden, M. Franklin, J. Hellerstein, W. Hong, TAG: A tiny aggregation service for ad-hoc sensor networks, SIGOPS Operating System Review 36 (2002) 131–146. [11] C. Alcaraz, P. Najera, J. Lopez, R. Roman, Wireless sensor networks and the internet of things: Do we need a complete integration?, in: 1st International Workshop on the Security of the Internet of Things (SecIoT10), 2010. [12] S. Tonyali, O. Cakmak, K. Akkaya, M.M. Mahmoud, I. Guvenc, Secure data obfuscation scheme to enable privacy-preserving state estimation in smart grid ami networks, IEEE Internet Things J. 3 (5) (2016) 709–719. [13] Stop smart meters. URL http://stopsmartmeters.org. [14] N. Saputro, K. Akkaya, Performance evaluation of smart grid data aggregation via homomorphic encryption, in: Wireless Communications and Networking Conference (WCNC), 2012 IEEE, IEEE, 2012, pp. 2945–2950. [15] S. Tonyali, N. Saputro, K. Akkaya, Assessing the feasibility of fully homomorphic encryption for smart grid ami networks, in: 2015 Seventh International Conference on Ubiquitous and Future Networks, (ICUFN), IEEE, 2015, pp. 591–596. [16] S. Tonyali, K. Akkaya, N. Saputro, A.S. Uluagac, A reliable data aggregation mechanism with homomorphic encryption in smart grid ami networks, in: Consumer Communications and Networking Conference (CCNC), 2016 IEEE, IEEE, 2016, pp. 557–562. [17] C. Rottondi, M. Savi, D. Polenghi, G. Verticale, C. Kraus, Implementation of a protocol for secure distributed aggregation of smart metering data, in: 2012 International Conference on Smart Grid Technology, Economics and Policies, (SG-TEP), IEEE, 2012, pp. 1–4. [18] C. Rottondi, G. Verticale, C. Krauss, Distributed privacy-preserving aggregation of metering data in smart grids, IEEE J. Sel. Areas Commun. 31 (7) (2013) 1342–1354. [19] C. Rottondi, G. Verticale, C. Kraus, Secure distributed data aggregation in the automatic metering infrastructure of smart grids, in: 2013 IEEE International Conference on Communications, (ICC), IEEE, 2013, pp. 4466–4471. [20] P. Paillier, Public-key cryptosystems based on composite degree residuosity classes, in: International Conference on the Theory and Applications of Cryptographic Techniques, Springer, 1999, pp. 223–238.
Paper Title | : | Prediction of Knee Osteoarthritis Using Deep Learning |
Authors | : | Mr.P. SIVA, , , , |
Affiliations | : | Department of Computer Science and Engineering, Matrusri Engineering College, Hyderabad, Telangana, India. |
Abstract | : | Knee osteoarthritis (OA) is a disease that increases in incidence and prevalence with advancing age, resulting in symptomatic knee OA in those over the age of 60, around 10 per cent of men and 13 per cent of women. Knee osteoarthritis (OA) is a chronic degenerative joint disease characterized by cartilage loss and changes in bones underneath it, causing pain and functional disability. The main clinical symptoms of knee. OA are pain and stiffness, particularly after activity, leading to reduced mobility and quality of life, and eventually resulting in knee replacement surgery. OA is one of the leading causes of global disability in people aged 65 and older, and its burden is likely to increase in the future with the ageing of the population and rise in obesity worldwide. OA is mainly diagnosed in clinical studies by means of medical images. X-ray imaging creates pictures of the inside of your body. The images show the parts of your body in different shades of black and white. This is because different tissues absorb different amounts of radiation. Calcium in bones absorbs x-rays the most, so bones look white. The typical symptoms of KOA include pain, stiffness, decreased joint range of motion, and gait dysfunctions, which worsen in accordance with an increase in the disease progression. OA is mainly diagnosed through medical images. It can be predicted using x-ray or mri images. The primary goal of this project was to develop an automated classification model fr Knee Osteoarthritis, based on the Kellgren-Lawrence(KL) grading system, using radiographic imaging and obtain satisfactory results for further diagnosis. |
: | 10.22362/ijcert/2022/v8/i12/v8i1205 | |
DOI Link | : | https://doi.org/10.22362/ijcert/2022/v8/i12/v8i1205 |
[1] Y. Zhang and J. M. Jordan, "Epidemiology of osteoarthritis", Clinics Geriatric Med., vol. 26, no. 3, pp. 355, 2010. [2] K. D. Brandt, M. Doherty and L. S. Lohmander, Osteoarthritis, Oxford, U.K.:Oxford Univ. Press, pp. 598, 2003. [3] The burden of musculoskeletal diseases in the United States, Rosemont, IL, USA, 2008. [4] C. R. Chu, A. A. Williams, C. H. Coyle and M. E. Bowers, "Early diagnosis to enable early treatment of pre-osteoarthritis", Arthritis Res. Therapy, vol. 14, no. 3, pp. 212, 2012. [5] D. Bhatia, T. Bejarano and M. Novo, "Current interventions in the management of knee osteoarthritis", J. Pharmacy Bioallied Sci., vol. 5, no. 1, pp. 30-38, 2013. [6] H. Shim, S. Chang, C. Tao, J. H. Wang, C. K. Kwoh and K. T. Bae, "Knee cartilage: Efficient and reproducible segmentation on high-spatial-resolution MR images with the semiautomated graph-cut algorithm method", Radiology, vol. 251, no. 2, pp. 548-565, 2009. [7] J. L. Jaremko, R. W. T. Cheng, R. G. W. Lambert, A. F. Habib and J. L. Ronsky,"Reliability of an efficient MRI-based method for estimation of knee cartilage volume using surface registration", Osteoarthritis Cartilage, vol. 14, no. 9, pp. 914-922, 2006. [8] Y. Yin, X. Zhang, R. Williams, X. Wu, D. Anderson and M. Sonka, "LOGISMOS—Layered optimal graph image segmentation of multiple objects and surfaces: Cartilage segmentation in the knee Joint", IEEE Trans. Med. Imag., vol. 29, no. 12, pp. 2023-2037, Dec. 2010. [9] Abedin, J. et al. (2019) “Predicting knee osteoarthritis severity: comparative modeling based on patient’s data and plain X-ray images,” Scientific reports, 9(1), p. 5761. [10] Alexos, A. et al. (2020) “Prediction of pain in knee osteoarthritis patients using machine learning: Data from Osteoarthritis Initiative,” in 2020 11th International Conference on Information, Intelligence, Systems and Applications (IISA. IEEE, pp. 1–7). [11] Bandyopadhyay, S. and Sharma, P. (2016) “Detection of Osteoarthritis using Knee X-Ray Image Analyses: A Machine Vision based Approach.” Available at: https://www.semanticscholar.org/paper/b5c55c6c40c389cb203bb654c78b2a3a306ffe4e (Accessed: August 4, 2021). [12] Bany Muhammad, M. et al. (2019) “Deep ensemble network for quantification and severity assessment of knee osteoarthritis,” in 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA). IEEE, pp. 951–957. [13] Chen, P. et al. (2019) “Fully automatic knee osteoarthritis severity grading using deep neural networks with a novel ordinal loss,” Computerized medical imaging and graphics: the official journal of the Computerized Medical Imaging Society, 75, pp. 84–92. [14] Chen, T. and Guestrin, C. (2016) “XGBoost: A Scalable Tree Boosting System,” arXiv [cs.LG]. Available at: http://arxiv.org/abs/1603.02754 (Accessed: August 10, 2021). [15] IEEE. Kwon, S. B. et al. (2020) “Machine learning-based automatic classification of knee osteoarthritis severity using gait data and radiographic images”, IEEE access: practical innovations, open solutions, 8, pp. 120597–120603. [16] " Knee Osteoarthritis pain prediction from X-ray imaging: Data from Osteoarthritis Initiative" Jorge I. Galván-Tejada, Víctor Treviño, José M. Celaya-Padilla, José G. Tamez-Peña.
Paper Title | : | Detecting Phishing Websites Using Natural Language Processing |
Authors | : | Dr. Sherif Kamel Hussein, Dr. Aboubaker Wahballah, Mrs. Amal Alosaimi, , |
Affiliations | : | Arab East College for graduate studies - Riyadh - KSA ( and) October University for Modern Sciences and Arts - Giza - Egypt |
Abstract | : | Phishing is one of the most cyber attacking tools. It targets both users and organizations. Several solutions have been proposed for detecting and preventing phishing websites, emails and SMSs. However, more research works are required to improve the phishing detection techniques such as improving the detection scalability and reducing false positive and negative alerts. This paper proposes a website phishing detection system based on natural language processing (NLP) features such as statements, words, and characters frequency. The proposed system first enables any user to find out if a specific website is phishing or not and, second, provides a search engine that 24/7 searches for the phishing websites and informs the system administrator (or publishes alerts online) about that. The system is evaluated in terms of its scalability and accuracy. The system accuracy here relies on the number of false-positive, false negative, true positive, and true negative alerts. |
: | 10.22362/ijcert/2021/v8/i12/v8i1204 | |
DOI Link | : | https://doi.org/10.22362/ijcert/2021/v8/i12/v8i1204 |
[1]. Verma, R., Shashidhar, N., & Hossain, N. , “Detecting Phishing Emails the Natural Language Way”,Computer Security–ESORICS 2012, 824-841. [2]. Patil, P.; Devale, P. “A literature survey of phishing attack technique”, Int. J. Adv. Res. Comput. Commun. Eng. 2016, 5, 198–200. 17. [3]. Rakesh M. Verma and Nabil Hossain. “Semantic feature selection for text with application to phishing email detection”, InProc. 16th International Conference on Information Security and Cryptology ICISC, Revised Selected Papers, pages 455–468. Springer, 2013. [4]. R. M. Mohammad, F. Thabtah, L. McCluskey, “Tutorial and critical analysis of phishing websites methods,”, Computer Science Review, vol. 17, pp. 1-24, 2015. [5]. Kang-Leng Chiew , Kelvin S. C. Yong , Choon Lin Tan:” A survey of phishing attacks: Their types, vectors and technical approaches”, Expert Syst. Appl,106: 1-20 [6]. Rakesh Verma, Narasimha Shashidhar, and Nabil Hossain, “Detecting phishing emails the natural language way”. European Symposium on Research in Computer Security, pages 824–841. Springer, 2012. [7]. J. Kang and D. Lee, “Advanced white list approach for preventing access to phishing sites,”, Proc. International Conference on Convergence Information Technology (ICCIT 2007), pp.491-496, 2007. [8]. Y. Cao, W. Han, and Y. Le, ?”Anti-phishing based on automated individual white-list”, Proceedings of the 4th ACM workshop on Digital identity management. New York, NY, USA: ACM, 2008, pp. 51–60. [9]. M. Sharifi and S. H. Siadati, “A phishing sites blacklist generator,” , IEEE/ACS International Conference on Computer Systems and Applications, pp. 840-843, 2008. [10]. P. Prakash, M. Kumar, R. R. Kompella, and M. Gupta, “Phishnet: predictive blacklisting to detect phishing attacks,”, Proc. IEEE INFOCOM, 2010, pp.1-5, 2010. [11]. Ardi C, Heidemann J , Auntietuna: “personalized content-based phishing detection”, NDSS usable security workshop (USEC). https://doi.org/10.14722/usec.2016.23012 [12]. Hongming Che, Qinyun Liu, Lin Zou, Hongji Yang, Dongdai Zhou, Feng Yu, “A Content-Based Phishing Email Detection Method”, QRS Companion 2017: 415-422 [13]. Peng, T., Harris, I. and Sawa, Y.,” Detecting phishing attacks using natural language processing and machine learning”, IEEE 12th International Conference on Semantic Computing (ICSC) (pp. 300-301), 2018. [14]. Egozi, G. and Verma, R., “Phishing Email Detection Using Robust NLP Techniques”, IEEE International Conference on Data Mining Workshops (ICDMW) (pp. 7-12), November 2018. [15]. L. Wenyin, G. Huang, L. Xiao Yue, Z. Min, X. Deng, “Detection of phishing webpages based on visual similarity,”, Special interest tracks and posters of the 14th International Conference on World Wide Web, pp. 1060-1061, 2005. [16]. Y. Fu, L. Wenyin and X. Deng, "Detecting phishing web pages with visual similarity assessment based on earth mover's distance (EMD)," , IEEE Transactions on Dependable and Secure Computing, vol. 3, no. 4, pp. 301-311, 2006.
Paper Title | : | Virtual Controller: managing a remote computer using network communication |
Authors | : | Ms.Lekha Tummala, Mr.Hruthik Gavva, Ms.Maanvitha Gona , Ms.Lakshmi Tulasi.P, |
Affiliations | : | 1,2,3,4: B.Tech Student, Department of CSE, CVR College Of Engineering, Rangareddy Dist, Telangana,India. |
Abstract | : | Conventionally, the computer system has a monitor, CPU, keyboard, and mouse. To perform several activities on the computer such as typing a word document, opening some file or doing any operation on a computer, we need to sit in front of a computer with hardware devices such as keyboard and mouse. Moreover, sitting in front of a computer for hours, one suffers from eye problems and other health issues. So, to overcome such cases, a virtual controller program is developed. It allows any computer to control other PCs remotely. This paper aims at administering a remote computer using network communication. The remote computer acts as a client, and the controlling computer acts as a server. Any number of clients can be connected to the server. The core function of the client is sending a screenshot of the client's desktop at a predefined amount of time. A new frame is generated for each client, and the screenshot is displayed on the server-side. The client's control is communicated in mouse movement and keystrokes. It allows the IT administrators to access the database of their computer remotely. It is utilized by doing the operations on PC like sending video |
: | 10.22362/ijcert/2021/v8/i12/v8i1203 | |
DOI Link | : | https://doi.org/10.22362/ijcert/2021/v8/i12/v8i1203 |
[1] Greg Travis, the JDK1.4 tutorial” [2]. Herbert Schlitz, Complete Reference Java, 2nd Edition”. [3]. Kim Topley, Core Java Foundation Class [4]. O’Reilly, Java Network Programming [5]. DEITEL & DEITel JAVA HOW TO program [6[ GRADY BOOCH, JAMES RUMBAUGH, IVAR JACOBSON The Unified Modeling Language User Guide: [7]. Accessibility and the Swing Set, Mark Andrews, The Swing Connection, Sun Microsystems, 1999. http://java.sun.com/products/jfc/tsc/articles/accessibility/inde x.html [8] Coming Swing API Changes for Java 2 SDK, Standard Edition, v. 1.4, The Swing Connection, Sun Microsystems, 2001. http://java.sun.com/products/jfc/tsc/articles/merlin/index.htm l [ 9] Component Orientation in Swing, Ralph Karr, The Swing Connection, Sun Microsystems, 1999. http://java.sun.com/products/jfc/tsc/articles/bidi/index.html [10] Core Java Foundation Classes (Core Series), Kim Topley, Prentice Hall, 1998. 11. The Element Interface, Scott Violet, the Swing Connection, Sun Microsystems, 1999. http://java.sun.com/products/jfc/tsc/articles/text/element_inte rface/
Paper Title | : | Serverless Web Application on the Cloud framework |
Authors | : | Ms.Mereddy Samhitha, Mr.Preetham Paul Bapuram, Mr.Mittapally Vineeth Kumar, , |
Affiliations | : | 1: B.Tech Student, Department of CSE,Vidya Jyothi Institute of Technology, Hyderabad,Telangana, India. 2,3: B.Tech Student, Department of CSE, CVR College Of Engineering, Rangareddy Dist, Telangana,India. |
Abstract | : | During the COVID-19 pandemic, convalescent plasma donors are dearly needed. Most of the recovered patients are not eligible to donate plasma, and from the minimal number of suitable donors, many are not coming forward to donate the plasma. The donors' count is already meager, and when someone needs plasma urgently, it has become challenging to find a donor. People are using social media to circulate their requests for plasma. Then they might find a donor, and it would be already late by that time. This process is inefficient and very much time taking. There is a chance that the plasma request could not reach the donors who are willing to donate plasma and help save a life. Instead, suppose there are platforms wherein the donors who are willing to donate plasma can register, and the needy can register their request when a request for plasma comes in. In that case, the eligible donors from the already existing pool of donors shall be presented to the requester. In this way, the donor search process can be improvised. Such a platform would end the pain in finding a donor and reduce the search time in finding a potential donor. And such a platform, built using serverless technology, will have more outstanding technical capabilities. |
: | 10.22362/ijcert/2021/v8/i12/v8i1202 | |
DOI Link | : | https://doi.org/10.22362/ijcert/2021/v8/i12/v8i1202 |
[1] https://www.researchgate.net/publication/312040779_Buildi ng_Web_Application_Using_Cloud Computing [2] https://en.wikipedia.org/wiki/Amazon_Web_Services. [3] https://aws.amazon.com/lambda/web-apps [4] https://docs.aws.amazon.com/lambda [5] https://docs.aws.amazon.com/apigateway/latest/developergui de [6] https://docs.aws.amazon.com/amazondynamodb/latest/APIR eference. [7] https://ec.europa.eu/cefdigital/wiki/display/CEFDIGITAL/bi g+data+test+infrastructure
Paper Title | : | Minimal Rule Based Classifier on Diabetic Dataset Using Machine Learning Techniques |
Authors | : | Mrs.Madhavaram Swapna, Ms.Dunna Nikitha Rao, Dr.D.William Albert, , |
Affiliations | : | 1:M.Tech Student, Dr.K.V. Subba Reddy College of engineering for Women,Kurnool, Andhra Pradesh, India. 2: Assistant Professor, Department of CSE, KMM Institute of technology and Science, Tirupati., 3: Professor and HOD, Department of CSE, Dr.K.V. Subba Reddy College of engineering for Women,Kurnool, Andhra Pradesh, India. |
Abstract | : | Diabetes mellitus is a chronic, lifelong disorder that affects a large number of people. As a result, finding the most relevant clinical registries and performing fast computer-aided pre-diagnoses and diagnoses will become increasingly important in clinical practise. This paper investigates the use of basic rule-based classifiers over a diabetes dataset utilising PCA (Principal Component Analysis) in order to predict diabetic risk and enhance the classification performance of the classifiers. Specifically, PCA will compress the smallest feature correlation among the features and predict the disease in order to enhance classification performance. As a consequence, PCA increases the classification performance while simultaneously decreasing the computation time required by the system. The classification performance of the Pima Indians Diabetes Dataset is examined with and without PCA, and the performance assessment metrics of precision, recall, accuracy, and F1 Score are used to evaluate the classification performance. |
: | 10.22362/ijcert/2021/v8/i12/v8i1201 | |
DOI Link | : | https://doi.org/10.22362/ijcert/2021/v8/i12/v8i1201 |
[1] D. Soumya and B Srilatha, Late stage complications of diabetes and insulin resistance, J Diabetes Metab. 2(167) (2011) 2- 7. [2] K. Papatheodorou, M. Banach, M. Edmonds, N. Papanas, D. Papazoglou, Complications of Diabetes, J. of Diabetes Res. 2015 (2015), 1-5. [3] L. Mamykinaa, et al., Personal discovery in diabetes self-management: Discovering cause and effect using self-monitoring data, J. Biomd. Informat. 76 (2017) 1–8. [4] A. Nather, C. S. Bee, C. Y. Huak, J. L.L. Chew, C. B. Lin, S. Neo, E. Y. Sim, Epidemiology of diabetic foot problems and predictive factors for limb loss, J. Diab. and its Complic. 22 (2) (2008) 77-82. [5] Shiliang Sun, A survey of multi-view machine learning, Neural Comput. & Applic. 23 (7–8) (2013) 2031–2038. [6] M. I. Jordan, M. Mitchell, Machine learning: Trends, perspectives, and prospects, Science. 349 (6245) (2015) 255-260. [7] P. Sattigeri, J. J. Thiagarajan, M. Shah, K.N. Ramamurthy, A. Spanias, A scalable feature learning and tag prediction framework for natural environment sounds , Signals Syst. and Computers 48th Asilomar Conference on Signals, Systems and Computers.( 2014) 1779-1783. [8] Alic, Berina & Gurbeta Pokvic, Lejla & Badnjevic, Almir. (2017). Machine Learning Techniques for Classification of Diabetes and Cardiovascular Diseases. 10.1109/MECO.2017.7977152. [9] K. Kourou, T. P.Exarchos, K. P.Exarchos, M. V.Karamouzis, D. I.Fotiadis, Machine learning applications in cancer prognosis and prediction, Computation. and Struct. Biotech. J. 13 ( 2015) 8-17. [10] Song Y, Cook NR, Albert CM, Van Denburgh M, Manson JE: Effect of homocysteine-lowering treatment with folic acid and B vitamins on risk of type 2 diabetes in women: a randomized, controlled trial. Diabetes 2009; 58: 1921– 1928. [11] Sathar, G., Naveen, S., Varma, D.V., Reshma, M., & Nayak, J. (2020). COMPARATIVE ANALYSIS OF CLASSIFICATION ALGORITHMS FOR DIABETIC PREDICTION. [12] Ibrahim, N.H., Mustapha, A., Rosli, R., & Helmee, N.H. (2013). A Hybrid Model of Hierarchical Clustering and Decision Tree for Rule-based Classification of Diabetic Patients. [13] Chandrakala, & Madhuri, S. (2020). Analysis of Eye Retina for Diabetic Detection using PCA & SVM Methods. [14] Li, T., Jia, Y., Wang, S., Wang, A., Gao, L., Yang, C., & Zou, H. (2019). Retinal Microvascular Abnormalities in Children with Type 1 Diabetes Mellitus Without Visual Impairment or Diabetic Retinopathy. Investigative ophthalmology & visual science, 60 4, 990-998 .
Paper Title | : | Transportation System Using Integrated GPS |
Authors | : | Sahana Reddy Kongara, Depa Sai Sree , Rasamalla Sushmitha, Vanapally Rishitha, |
Affiliations | : | 1,2,3 and 4: IV Year,CSE Dept,CVR College of Engineering, Vastunagar, Mangalpalli (V), Ibrahimpatnam (M), Rangareddy (D), Telangana, India |
Abstract | : | Bus tracking is an application that tracks a bus and gathers the distance to each station along its route. Tracking System involves the installation of an electronic device in a vehicle, with an installed Android App on any SMART phone to enable the Administrator/User to track the vehicle's location. There are two applications one for server and the other for the client. Buses carry GPS devices• to track their positions. By these positions to server are periodically updated. Client application displays map showing the position of bus. It shows where buses are on a map and provide users the updated information at different time interval. The server will monitor location and will store its data in the database. It is a real-time system as this method automatically sends the information on the GPS system to a central computer or system/SMART phone. At the Bus Arrival the User gets an alert/notification. Since this is an android application we use SQLite, SQL server database for the backend. The users can get flexibility of planning travel using the app, to decide on when to catch the bus. The waiting time of the user can be reduced. Simple mode of communication is the key feature of the Bus Tracking system. |
: | 10.22362/ijcert/2021/v8/i11/v8i1104 | |
DOI Link | : | https://doi.org/10.22362/ijcert/2021/v8/i11/v8i1104 |
1] S.Kiruthivasan, C.Madan Deepakumar, Decision Support System For Call Taxi Navigation Using GIS-GPS Integratio, Charles Vlcek, Patricia Mclain, and Michael Murphy, “GPS/Dead Reckoning for Vehicle Tracking in the Urban Canyon Environment”, Trimble Navigation, Ltd., 1993. [2] D. J. Maguire, “An Overview and Definition of GIS”, Geographical Information Systems, Vol. 1, pp. 9-20, 1992 [3] Michael Goodchild, “Accuracy of Spatial Databases”, Tayler Francis, 1989. [4] O. Guenther and A. Buchmann, “Research Issues In Spatial Databases”, SIGMOD RECORD, Vol. 19, No. 4, pp. 61-68, 1990. [5] R. H. Guting, “An Introduction to Spatial Database Systems”, VLDB Journal, Vol. 3, No. 4, pp. 357-399, 1994. [6] Robert L. French, “Land Vehicle Navigation and Tracking”, Global Positioning System : Theory and Applications, Vol. 164, pp. 275-301, 1996 [7] Ronald Braff, “Applications of the GPS to Air Traffic Control”, Global Positioning System : Theory and Applications, Vol. 164, pp. 327-374, 1996 [8] Steven E. Shladover, “Research and Development Needs for Advanced Vehicle Control Systems”, IEEE Computer Society, 1993. [9] W. Richard Stevens, UNIX Network Programming, Prentice-Hall International, Inc., pp. 258-277, 1994 [10] ASP.NET WEB API- https://dotnet.microsoft.com/apps/aspnet/apis [11]MS SQL SERVER- https://www.microsoft.com/en-in/sql-server/sql-server-downloads [12] XAMARIN.FORMS- https://dotnet.microsoft.com/apps/xamarin/xamarin-form
Paper Title | : | A Web-Mart Design Using Angularjs |
Authors | : | B.Shreyank, T. Varun , Peddi.P.M. Laasya Rao, , |
Affiliations | : | 1, 2, 3 : IV Year,CSE Dept,CVR College of Engineering, Vastunagar, Mangalpalli (V), Ibrahimpatnam (M), Rangareddy (D), Telangana, India |
Abstract | : | Most consumers are looking online for information that will help them make smarter purchasing decisions. In fact, according to the eCommerce Foundation, 88% of consumers will research product information before they make a purchase online or in the store. This buying behaviour trend emphasizes the importance of a website for today’s businesses. This paper describes the implementation of a web-mart design using AngularJS. The cart uses PayPal and Google Wallet payment services. Adding other providers is fairly easy. This designed product supports online payment infrastructure like integrating all the credit or debit card payments and also supports the e-wallet and other UPI because offering more payment options should increase sales. |
: | 10.22362/ijcert/2021/v8/i11/v8i1103 | |
DOI Link | : | https://doi.org/10.22362/ijcert/2021/v8/i11/v8i1103 |
Website References: - https://angularjs.org/ https://www.tutorialspoint.com/angularjs/index.htm https://www.guru99.com/angularjs-tutorial.html https://www.youtube.com/playlist?list=PL7BTNHMTJY6jAH4-5X6vyD-qPqcMu2Q0u https://www.w3schools.com/angular/ Textbooks References: - “Learn AngularJS in 1 Day” written by Krishna Rungta. “Angular: Up and Running” was written by Shyam Seshadri. “AngularJS, JavaScript, and jQuery All in One” written by Brad Dayley and Brendan Dayley
Paper Title | : | Distributor Management System |
Authors | : | B.Sainath, M. Krishna Teja, K.Dinesh, , |
Affiliations | : | 1, 2, 3 :IV Year,CSE Dept,CVR College of Engineering, Vastunagar, Mangalpalli (V), Ibrahimpatnam (M), Rangareddy (D), Telangana, India |
Abstract | : | Distributor Management System Is a web based application, which is developed for a particular company for maintaining and analyzing the sales of the product. This project is a module under website development for a particular computer hardware company. Here all communications held through internet and its database has all the updated information of the sales details. The main objective of the project is to analyze the sales of the products by a manager through the details supplied by the distributors, sales managers and representatives. It is very useful for the distributors, sales managers to know about the sales of the products done by them and by others in particular area/zone. This system gives complete analysis about the moving of the product in the market and the person responsible for selling the product in that area. It is computerized to improve the efficiency of the organization by reducing the cost of marinating data and minimizing the time involved in handling the data. Depending on the access rights given the users can process different modules. |
: | 10.22362/ijcert/2021/v8/i11/v8i11 | |
DOI Link | : | https://doi.org/10.22362/ijcert/2021/v8/i11/v8i11 |
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Paper Title | : | Twitter Sentiment Analysis Using Machine Learning |
Authors | : | Pasunooru Santosh Reddy, Bheempaka Sai Kumar, Reddy Reddy Jahnavi Reddy, , |
Affiliations | : | IV Year,CSE Dept,CVR College of Engineering, Vastunagar, Mangalpalli (V), Ibrahimpatnam (M), Rangareddy (D), Telangana , India |
Abstract | : | Sentiment analysis is the process of identifying and categorising the emotions expressed in text. When tweets are analysed, they typically generate a large amount of sentiment data. This information allows us to better understand people's perspectives on a variety of issues. This study tries to classify tweets based on their sentiment. They can express either positive or negative emotions. Twitter is a social networking and micro blogging platform that allows users to post 140-character status updates or opinions. It has about 200 million registered users, 100 million active users, and half of them log in every day, resulting in nearly 250 million tweets per day. Because of the widespread use, we want to reflect the prevalent attitude by analysing tweets. Predicting political elections and macroeconomic phenomena like stock exchanges necessitates a look at public sentiment. We attempt to categorise the tweets as positive or negative. To represent the "tweet," it must also extract valuable elements from the text, such as unigrams and bigrams. We use machine learning methods to analyse sentiment using the collected features. Individual models did not provide high accuracy on their own. So we created an Ensemble Model that predicts based on a majority vote using Naive Bayes, Logistic Regression, and Support Vector Machines. |
: | 10.22362/ijcert/2021/v8/i11/v8i1101 | |
DOI Link | : | https://doi.org/10.22362/ijcert/2021/v8/i11/v8i1101 |
[1] Ankita, Nabizath Saleena “An ensemble classification system for twitter sentimental analysis”,International Conference On Computational System for Twitter Sentiment Analysis (ICCIDS) Procedia Computer Science 132(2018)937-946 [2] Zhao Jianqiang , Gui Xlaolin, and Zhang Xuejun “Deep convolution neutral network for twitter sentimental analysis” Jan 1,2018 ACCESS.2017.2776 [3] Brendan o’ Connor , Ramnath Balasubramaniyan , Bryan R. Routledge , Noah A. Smith “ Linking Text Sentimental to public opinion time series” International Conference On Web and Social Media(ICWSM),2010,volume 11,nos.122-129,pp.1-2 [4] Bala Durga Dharmavarapu,Jayang Bayana “Sarcasm dection in twitter using sentimental analysis” International Journal of Recent Technology and Engineering(IJRTE) volume 8,issue -1 may 2019 [5] Akshi Kumar and Teeja Mary Sebastian “sentiment analysis on twitter” International Journal of computer [6] P.Nakov,A.Ritter,S.Rosenthal, F.Sebastiani, and V.Stoyanov, “SemEval-2016 task 4: Sentiment analysis in Twitter,” in proc.10th Int.Work.Semant.Eval.,Jun.2016,pp1-18. [7] I.H.Witten, E.Frank,M.A.Hall, and C.J.pal, Data Mining: PracticalMachine Learnimg Tools and Techniques.San Mateo,CA.USA:Morgn Kaufmann,2016 [8] Avinash Surnar, Sunil Sonawane “Review for twitter sentiment analysis using various method” International Journal Of Advanced Research In Computer Engineering And Technology(IJARCET) Volume 6,Issue 5, May 2017 [9] P. A. Gutierrz, M. Perez-Ortiz, J. Sanchez-Monedero, F. Fernandez-Navarro, and C. Hervas-Martinez, “Ordinal regression methods: Survey and experimental study,” IEEE Trans. Knowl. Data Eng., vol.28, no. 1,pp. 127-146, jan. 2016. [10] N. M. Dhanya and U.C Harish “Sentimental analysis on twitter data on demonetization using machine learning techniques” Springer International Publishing AG 2018 DOI:10.1007/978-3-319-71767-8_19 [11] B. J. Jansen, M. Zhang, K. Sobel, and A. Chowdury. Micro-blogging as online word of mouth branding. In CHI EA '09: Proceedings of the 27th international conference extended abstracts on Human factors in computing systems, New York, NY, USA, 2009. ACM [12] G. Mishne. Experiments with mood classification in blog posts. In 1st Workshop on Stylistic Analysis Of Text For Information Access, 2005. [13] K. Nigam, J. Larerty, and A. Mccallum. Using maximum entropy for text classification. In IJCAI-99 Workshop on Machine Learning for Information Filtering, 1999. [14] B. Pang and L. Lee. Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2008. [15] B. Pang, L. Lee, and S. Vaithyanathan. Thumbs up? Sentiment classification using machine learning techniques. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2002. [16] Go, Alec, Richa Bhayani, and Lei Huang. "Twitter sentiment classification using distant supervision." CS224N Project Report, Stanford 1 (2009)
Paper Title | : | Risk Projection for Readmission of Congestive Heart Failure Patients on Big Data Solutions |
Authors | : | Sreeja.D, N.Ananda Reddy, , , |
Affiliations | : | 1: PG Student, Dept of Computer Science And Engineering, Siddartha Educational Academy Group of Institutions,C. Gollapalli, Tirupati,AP,India; 2: Assistant Professor, Dept of Computer Science And Engineering, Siddartha Educational Academy Group of Institutions,C. Gollapalli,Tirupati,AP,India |
Abstract | : | Big Data is a collection of data that is large or complex to process using on-hand database management tools or data processing applications. It is becoming very difficult for companies to store, retrieve and process the ever-increasing data. In other words we can say, Big Data is term given to humungous amount of data which is difficult to store and process. The issue lies in using the traditional system is, how to store and analyze Big Data. Risk prediction involves integration of clinical factors with socio-demographic factors like health conditions, disease parameters, hospital care quality parameters, and a variety of variables specific to each health care provider making the task increasingly complex. Unsurprisingly, many of such factors need to be extracted independently from different sources, and integrated back to improve the quality of predictive modeling. Such sources are typically voluminous, diverse, and vary significantly over the time. This project takes Apache Hadoop, an intrinsic part for storing, retrieving, evaluating and processing huge volumes of data for processing effectively. In this work, we study big data driven solutions to predict the 30-day risk of readmission for congestive heart failure (CHF) incidents. We will predict this process by using Logistic Regression and Naive Bayes classification on the basis of data collected from patients. The results are remarkable after the comparison between the two techniques and presented through confusion matrix. |
: | 10.22362/ijcert/2021/v8/i10/v8i1002 | |
DOI Link | : | https://doi.org/10.22362/ijcert/2021/v8/i10/v8i1002 |
[1] Krumholz H. M., Normand S. L. T., Keenan P. S., Lin Z. Q., Drye E.E., Bhat K. R., Wang Y. F., Ross J. S., Schuur J. D., and Stauer B. D..Hospital 30-day heart failure readmission measure methodology.Report prepared for the Centers for Medicare & Medicaid Services. [2] Amarasingham R, Moore BJ, Tabak YP, Drazner MH, Clark CA, Zhang S, Reed WG, Swanson TS, Ma Y, Halm EA. An automated model to identify heart failure patients at risk for 30-day readmission or death using electronic medical record data. Journal of Medical Care, 10:981-988, Feb. 2010. [3] An automated model to identify heart failure patients at risk for 30-day readmission or death using electronic medical record data. [4] Koelling T. M., Johnson M. L., Cody R. J., and Aaronson. K. D.Discharge education improves clinical outcomes in patients with chronic heart failure. Circulation, 111(2):179- 185, Jan. 2005. [5] Impact of prior admissions on 30-day readmissions in medicare heart failure inpatients. [6] Meadam N., Verbiest N., Zolfaghar K., Agarwal J., Chin S., Basu Roy S., Teredesai A., Hazel D., Reed L., Amoroso P. Exploring Preprocessing Techniques for Prediction of Risk of Readmission for Congestive Heart Failure Patients. In Data Mining and Healthcare Workshop, in conjunction with the 19th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2013.
Paper Title | : | Technological Singularity – An Overview of What the Future Holds |
Authors | : | Anand Pagnis, Vedansh Tembhre , Pranjal Vyas, , |
Affiliations | : | 1: Prodigy Public School, Pune, India , 2: Ekaynaa School, Indore ,India; 3: St. Mary’s Convent School, Ujjain,India |
Abstract | : | Background/Objectives: In this research we aim to present a collective analysis of the past ideas and researches on the topic of Technological Singularity and how human kind has developed from it since then. Methods/Statistical analysis: We used data and evidences from several references to put forth a clear picture of the current developments in the field of singularity. We also used a survey method involving PhD. Researchers in the field of A.I. and asked their opinion on several questions, challenges and achievements in the area of Technological Singularity. Findings: We found out that the major sectors of development are yet to be discovered which will helps us achieve Technological Singularity. The closest being Nano-bots and a merger of several inter-related fields such as AI, quantum computing, machine learning and many more to help develop a completely independent robot. A majority of Researchers also believe that we will achieve technological singularity within the next 80 years or so. Improvements/Applications: This research only consists of existing data and the survey conducted is done on a small scale at one single place. The resources and background of research might impact the opinion of researchers elsewhere. |
: | 10.22362/ijcert/2021/v8/i10/v8i1001 | |
DOI Link | : | https://doi.org/10.22362/ijcert/2021/v8/i10/v8i1001 |
1) Ben Goertzel, “ Human-level artificial general intelligence and the possibility of a technological singularity: A reaction to Ray Kurzweil's The Singularity Is Near, and McDermott's critique of Kurzweil “Volume 171, Issue 18, December 2007, Pages 1161-1173 , 2007 2) Robots and AI at work: the prospects for singularity. New Technology, Work, and Employment, 33 (3) . pp. 205-218 , 2018 3) Technological Singularity: What Do We Really Know?, MDPI, issue 6 April,2018 pp 1-9 4) Pei Wang , Kai Liu , Quinn Dougherty Conceptions of Artificial Intelligence and Singularity, MDPI, issue 8 April 2018 pp 1-15 5) M. Farnsworth ,C. Bell,S. Khan,T. Tomiyama “Autonomous Maintainence for Through-Life Engineering” , Springer, pp 395-419|, 2014 6) Robin Hanson ,“Economics of the singularity”, IEEE Spectrum, issue July 2008 pp 45-50 7) Mayur Umesh Ushir, Prof. Pooja Kadam , “Impact of Technological Singularity in Human Life ”, IOSR Journal of Computer Engineering , Volume-2 (4th - Somaiya International Conference on Technology and Information Management (SICTIM'18)), pp 06-09.
Paper Title | : | Empirical mode Decomposition and Dual Sigmoid Activation Function-Based Faster RCNN for Big Data Doppler Scan Image Classification |
Authors | : | Ms.S. Sandhya Kumari, Dr.K.Sandhya Rani, , , |
Affiliations | : | 1:Research Scholar, Dept. of Computer Science, SPMVV, Tirupati, India ; 2: Professor, Dept. of Computer Science, SPMVV, Tirupati, India |
Abstract | : | This paper proposes a big data Doppler scan image classification system that uses empirical mode decomposition (EMD) and dual sigmoid activation function-based faster region convolutional neural network (R-CNN). This approach initially arranges the pixels of the Doppler scan images in a zig-zag order sequence. This one-dimensional sequence is decomposed to L number of intrinsic mode function (IMF) using the EMD algorithm. The spectrums of the decomposed IMF are estimated using the one-dimensional Fourier transforms. Required IMFs are then selected based on the frequency spacing estimated on the Fourier spectrum. The resultant image is then reconstructed using the selected IMF’s. The resultant Doppler scan image has less redundant information and is trained using a faster RCNN algorithm. Instead of using the traditional activation functions, the proposed faster RCNN uses a dual sigmoid activation function that classifies the Doppler images into five classes. The classes are namely Maternal cervix, Thorax, Femur, Brain, Abdomen, and other regions. The experimental evaluation uses the parametersnamelyF1 score, specificity, accuracy, sensitivity, precision, and time complexity with the big data Doppler ultrasound scan image dataset that contains 12400 images collected from 1792 patients. |
: | 10.22362/ijcert/2021/v8/i09/v8i0901 | |
DOI Link | : | https://doi.org/10.22362/ijcert/2021/v8/i09/v8i0901 |
[1]LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nat. 521, 436–444 (2015). [2]Hosny, A., Parmar, C., Quackenbush, J., Schwartz, L. H. &Aerts, H. J. Artificial intelligencein radiology. Nat. Rev. Cancer 18, 500 (2018). [3] 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. [4] Suchetha, M., Rajiv Raman, and Edwin Dhas. "Region of Interest based Predictive Algorithm for SubretinalHemorrhage Detection using Faster R-CNN." (2021). [5] Litjens, G. et al. A survey on deep learning in medical image analysis. Med. image analysis 42, 60–88 (2017). [6] Esteva, A. et al. Dermatologist-level classification of skin cancer with deep neural networks. Nat. 542, 115 (2017). [7] 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). [8] Ryou, H. et al. Automated 3d ultrasound biometry planes extraction for first trimester fetal assessment. In Machine Learning inMedical Imaging, 196–204 (2016). [9] Li, Y. et al. Standard plane detection in 3d fetal ultrasound using an iterative transformation network. Medical Image Computing andComputer Assisted Intervention – MICCAI 2018, 392–400 (2018). [10] Lee, LokHin, 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. [11]Attallah, Omneya, Maha A. Sharkas, and HebaGadelkarim. "Fetal brain abnormality classification from MRI images of different gestational age." Brain sciences 9.9 (2019): 231. [12]Sridar, Pradeeba, et al. "Decision fusion-based fetal ultrasound image plane classification using convolutional neural networks." Ultrasound in medicine & biology 45.5 (2019): 1259-1273. [13] 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. [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]Subba, Basant, and Prakriti Gupta. "A tfidfvectorizer and singular value decomposition based host intrusion detection system framework for detecting anomalous system processes." Computers & Security 100 (2021): 102084. [17]Dubey, Rahul, et al. "Automated diagnosis of muscle diseases from EMG signals using empirical mode decomposition based method." Biomedical Signal Processing and Control 71 (2022): 103098. [18]Gonzales-Martínez, Rosa, et al. "Hyperparameters Tuning of Faster R-CNN Deep Learning Transfer for Persistent Object Detection in Radar Images." IEEE Latin America Transactions 20.4 (2022): 677-685. [19] Simonyan, K. & Zisserman, A. Very deep convolutional networks for large-scale image recognition. CoRR abs/1409.1556 (2014). [20] Sandler, M., Howard, A., Zhu, M., Zhmoginov, A. & Chen, L.-C. Mobilenetv2: Inverted residuals and linear bottlenecks. CVPR(2018). [21] Szegedy, C. et al. Going deeper with convolutions. CoRR abs/1409.4842 (2014). [22] He, K., Zhang, X., Ren, S. & Sun, J. Deep residual learning for image recognition. CoRR abs/1512.03385 (2015). [23] 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). [24] Hu, J., Shen, L. & Sun, G. Squeeze-and-excitation networks. CoRR abs/1709.01507 (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.
Paper Title | : | Urbanization and the Slum Dwellers’ “Right to the City”: The Case of Pag-Asa, Olongapo City, Philippines |
Authors | : | Cecilia C. Garson, Roy N. Villalobos, Marie Fe D. de Guzman, , |
Affiliations | : | 1,2,3 : Graduate School, President Ramon Magsaysay State University (PRMSU), Iba, Zambales, Philippines |
Abstract | : | Background/Objectives: Olongapo city is a unique case because it is the most urbanized city in Central Luzon. Additionally, the former United States Naval Base is located in Olongapo City. This study will argue that more than just areas of ‘unsafe housing, cramped space, disease, vice and poverty’, these communities, like Pag-asa are areas of resistance of the underprivileged, the disadvantaged and the marginalized groups against the government’s denial of their right to the city. Methods/Statistical analysis: This study is a qualitative research which utilized a descriptive case study design to have an in depth understanding on the everyday struggle of the slum dwellers in Pag-Asa Olongapo City. Findings: This study sought to answer how the struggle for the right to the city is manifested in slums by using Pag-asa, Olongapo City as the case study. The resistance may not always be in a form of an overt social movement that is why aside from their organization, relocatees who returned to Pag-Asa as well as renters that continue to settle in Pag-Asa despite knowing that the area is in threat of demolition, are also crucial actors of resistance. The proximity to work is the main reason why most of the dwellers refuse to leave the place, more so, it shows that slum dwellers are willing to face everyday risks just to remain in the area. While hopelessness, poverty and blight are uncontentious images involved when talking about slums, it is interesting to note that the slum dwellers in Pag-asa are living harmoniously. The people treat each other as family, hence, it is one of the reasons why despite the relocation site offered by the government, the dwellers prefer to stay in the community. Improvements/Applications: Based on the fieldworks conducted for this study, other studies aiming to challenge the dominant and marginalizing discourses on the urban poor can also focus on the role of women in resistance movements through using the feminist approach of ‘the personal is the political’. The result of this study may help the government design more inclusive urban policies and programs that can reflect the interests of both the LGU and the dwellers. Lastly, it is also interesting to look at the implications of the slow violence that the urban poor is experiencing in relation to how this affects their mentality towards themselves. |
: | 10.22362/ijcert/2021/v8/i8/v8i803 | |
DOI Link | : | https://doi.org/10.22362/ijcert/2021/v8/i8/v8i803 |
1. Carmody, P. & Owuso F. “Neoliberalism, Urbanization and Change in Africa: The Political Economy of Heterotopias, Journal of African Development, Vol.18,No.18,pp.61-73, 2016. 2. Garrido, M. Z. “The Patchwork City, Class, Space, Politics in Metro Manila”, Chicago, Illinois.University of Chicago Press. Doi:cdn.fbsx.com, 2019 3. UN-HABITAT, “State of the World’s Cities 2012/2013 Prosperity of Cities UN HABITAT”, 2013 4. Ron Mahabir, R., Crooks, I.A., Croitoru, I.A. & Agouris, P., “The study of slums as social and physical constructs: challenges and emerging research opportunities”, Regional Studies, Regional Science, Volume 3, 2016 - Issue 1, 2016 5. West J., “Urbanization and Slums in Asia”. Asian Century Institute, 2014 6. UN HABITAT, “Global report on human settlementS 2011, Cities and Climate Change”, United Nations Human Settlements Programme, 2011 7. Lagman, M., “Informal settlements as spatial outcomes of everyday forms of resistance: the case of three depressed communities in Quezon City”, Political Social Science Review, 2012 8. Lefebvre, H., “The production of space”, Malden, MA: Blackwell. Original work published in 1974. 1991 9. Harvey, D. “The right to the city”, New Left Review, Vol. 53, pp. 23-40, 2008 10. Marcuse, P. “Reading the Right to the City: Analysis of Urban trends, culture, Theory, policy, action”, Retrieved July 10,2019, from http://www.tandfonline.com, 2014 11. Kumar, K. “Sociology and the Englishness of English Social Theory”, First Published March 1, 2001 Research Article, 2001 12. Davis, M. “Planet of Slums. New York, USA: The Guardian, 2006 13. Landa J. F., “Experience and Perspectives in a Slum Neighborhood: An Anthropological View”, PSR 21, pp. 223-28, 197, 14. Niva, V, Taka, M & Varis, O. “Rural-Urban Migration and the Growth of Informal Settlements: A Socio-Ecological System Conceptualization with Insights through a Water Lens”. www.mdpi.com/journal/sustainability.DOI:10.3390/su11123487, 2019 15. Ballesteros, M. M., “Linking Poverty and the Environment: Evidence from Slums in the Philippine Cities” Phil. Institute for Development Studies, 2010 16. World Economic Forum, “From Slum to Success Story: This is Ciudad Neza”, www.weforum. January 18, 2021 17. World Bank, “The World Bank Annual Report 2017”, Washington, DC: World Bank. © World Bank. https://openknowledge.worldbank.org/handle/10986/27986 License: CC BY-NC-ND 3.0 IGO. 2017 18. Bayat, A., “From ‘dangerous classes’ to ‘quiet rebels’: Politics of the urban subaltern in the global south”, In Sage Journals: International Sociology, Vol. 15(3), pp. 533–557, 2000 19. Mahmud, T., “Slums, Slumdogs, and Resistance”, In American University Journal of Gender, Social Policy & the Law (Vol. 18(3), pp. 685-710, 2010 20. Scott, J., “Beyond the war of Words: Cautious Resistance and Calculated Conformity" in Amoore, L. The Global Resistance, London and New York: Routledge, 2005 21. Harvey D. “A Brief History of Neoliberalism”, Oxford University Press Inc., New York. 2012 22. UN Habitat III, “Urbanization and Development Emerging Futures”, UN HabiTat III for a better Urban Future, 2016
Paper Title | : | Distance Learning in Secondary School of Zone 3, Division of Zambales, Philippines: Parents’ Experiences |
Authors | : | Junerey F. Bactad, Marilyn M. Gutierrez, Leila L. Ravana, Marie Fe D. de Guzman, |
Affiliations | : | 1 : Philippine Merchant Marine Academy, San Narciso, Zambales Philippines ; 2, 3, 4* : Graduate School, President Ramon Magsaysay State University (PRMSU), Iba, Zambales, Philippines |
Abstract | : | Background/Objectives: The research was an investigation of the experiences of parents on distance/remote learning during the COVID19 Pandemic time. The respondents were 309 parents of grade 10 students enrolled in Secondary School of Zone 3, Division of Zambales, Philippines. It was conducted on the 4th Quarter for the school year 2020-2021. Methods/Statistical analysis: This study employed a descriptive research method with the survey questionnaire as the research instrument. The statistical treatment of this study utilized a descriptive statistical tools such as percentage and mean and ANOVA was the inferential statistics used. All the data obtained from the instrument were tallied, tabulated, analyzed and interpreted accordingly. Findings: Results revealed that the respondents are female parents, finished secondary level of education, self-employed and are low income earners. The parents are home teachers during the distance/remote learning in COVID19 pandemic time. The ANOVA computation result showed a significant difference on the perceived level of agreement of parent-respondents on their experiences as to care-giving responsibility when grouped according to highest educational attainment. There is significant difference on the perception of experiences as to accomplishing modules when grouped according to highest educational attainment and occupation. There is significant difference on the perception of experiences as to parental distress when grouped according to sex, highest educational attainment and occupation. There is significant difference on the perception of experiences as to children’s wellbeing when grouped according to highest educational attainment, occupation and monthly income. A plan was proposed in order for the parents to cope with the challenges faced in distance/remote learning during the COVID19 Pandemic. Improvements/Applications: Proposed to heads of public secondary schools the prepared Plan in order for the parents to cope with the challenges faced in distance/remote learning during the COVID19 Pandemic. Future researchers may conduct a follow up study on parents’ experiences as to how they cope, counter and provide solutions on the negative effects of on children’s education during pandemic. |
: | 10.22362/ijcert/2021/v8/i8/v8i802 | |
DOI Link | : | https://doi.org/10.22362/ijcert/2021/v8/i8/v8i802 |
1. World Health Organization (WHO), “WHO Director-General’s opening remarks at the Mission briefing on COVID-19”, 2020 2. U. S. Department of Health & Human Services, “The impact of COVID-19 on education. Administration for Children and Families”, May 2020. 3. United Nations Educational, Scientific and Cultural Organization (UNESCO), “Covid-19 Impact on Education Data, COVID-19 Education Disruption and Response”, The United Nations Educational, Scientific and Cultural Organization, UNESCO. Paris, France, 2020 4. J. Z. Tria, “The COVID-19 Pandemic through the Lens of Education in the Philippines: The New Normal”, ResearchGate, 2020 5. Department of Education Sulong Edukalidad (2020). Learning Opportunities shall be Available. The Basic Education Learning Continuity Plan in the Time of COVID-19. 6. T. Koskela, K., Pihlainen, S., Piispa-Hakala, R., Vornanen, J. Hämäläinen, “Parents’ Views on Family Resiliency in Sustainable Remote Schooling during the COVID-19 Outbreak in Finland’, Published: 24 October 2020 7. J., Cohen, & K. Kupferschmidt, “Countries test tactics in 'war' against COVID-19”, Science, 367(6484), 1287-1288, (2020). 8. G. Wang, Y. Zhang, J. Zhao, J. Zhang & F. Jiang, “Mitigate the effects of home confinement on children during the COVID-19 outbreak”, Lancet 395, 945–947, 2020 9. A. Garbe, G. Ogurlu, N. Logan, & P. Cook, “COVID-19 and Remote Learning: Experiences of Parents with Children during the Pandemic”, American Journal of Qualitative Research December 2020, Vol. 4 No. 3, pp. 45-65, 2020 10. Psychosocial Centre “Remote Psychological First Aid during the COVID-19 outbreak Interim guidance”, Psychosocial Centre, 2020 11. S. Woofter, “Book Review: Building Equity: Policies and Practices to Empower All Learners”, American Journal of Qualitative Research, 3(1), 136- 139, 2019 12. Y. Cabot, “What is descriptive research method according to?”, 2020 13. S. Aryal, “Questionnaire- Types, Format, Questions”, January 4, 2020 14. Spinelli M, Lionetti F, Pastore M & Fasolo M (2020) Parents’ Stress and Children’s Psychological Problems in Families Facing the COVID-19 Outbreak in Italy”, Front. Psychol. 11:1713, 2020 15. L.H.C. Janssen, M.L.J. Kullberg, B. Verkuil, N. van Zwieten, MCM Wever, LAEM van Houtum et al., “Does the COVID-19 pandemic impact parents’ and adolescents’ well-being? An EMAstudy on daily affect and parenting”, PLoS ONE 15 (10): e0240962. https://doi.org/10.1371/journal. pone.0240962, 2020 16. Y.R.P. Dangle & Y.D. Sumaoang, “The Implementation of Modular Distance Learning in the Philippine Secondary Public Schools”, Advanced Research in Teaching and Education, 2020 17. F. Barrera-Osorio, M.L. Bertrand, L. Linden, & F. Perez-Calle, “Conditional Cash Transfers in Education: Design Features, Peer and Sibling Effects Evidence from a Randomized Experiment in Colombia”, Policy Research Working Paper, 2016 18. T. Alon, (2020), “The Impact of COVID-19 on Gender Equality, Northwestern University”, 27 March 2020 19. E. Reyes-Chua, B.G. Sibbaluca, R.D. Miranda, GB. Palmario, RP. Moreno, & JPT. Solon, “The status of the implementation of the e-learning classroom in selected higher education institutions in region IV-a amidst the COVID-19 crisis”, Journal of Critical Reviews ISSN- 2394-5125 Vol 7, Issue 11, 2020. 20. Philippine Institute for Development Studies (PIDS), “Social Class”, PIDS 2019 Annual Report, 2019 21. National Economic and Development Authority, “Philippines Development Plan 20112016 Midterm Update. Pasig. 22. H. Jinshan, (2020). Parents grapple with e-learning as Chinese schools stay shut. 2020 23. L. Dalton, E. Rapa, & A. Stein, “Protecting the psychological health of children through effective communication about COVID-19. Lancet Child Adolesc. Health 4:346–347, 2020. 24. Z.H. Duraku, & L. Hoxha, “The impact of COVID-19 on education and on the well-be ing of teachers, parents, and students: Challenges related to remote (online) learning and opportunities for advancing the quality of education. Research Gate, 2020 25. J.J. Miller, M.E. Cooley, B.P. Mihalec & Adkins, “Examining the Impact of COVID 19 on Parental Stress: A Study of Foster Parents”, Child and Adolescent Social Work Journal, 2020 26. United Nations Development Programme (UNDP), “A Pulse of Poverty: Application of Citizen-Centered Innovation”, UNDP Philippines. 2020
Paper Title | : | Monitoring and Controlling Student’s behavior in Online Education |
Authors | : | Sherif Kamel Hussein, Khalid Jumaan Aldamashg, Ahmed Saad AlSaadan, , |
Affiliations | : | 1: Associate Professor- Department of Communications and Computer Engineering, October University for Modern Sciences and Arts, Giza, Egypt Head Of Computer Department – Arab East Colleges – Riyadh- KSA ; 2: Master of computer science, Arab east college, Riyadh, kingdom of Saudi Arabia ; 3: Master of computer science, Arab east college, Riyadh, kingdom of Saudi Arabia |
Abstract | : | Distance Learning may lose monitoring the learning process such as lack of direct follow-up to the study on the website and the lack of direct communication with the trainer. In this research the Authors will discuss multiple solutions that may help the governance of Distance Learning ( DL) by using some of the available software solutions and modern technologies, such as AI and FURIA that help in achieving the goals in the research as an enhancement method of E-learning. The results of the research reached by the authors were the possibility of monitoring the educational process of distance education using some modern technologies such as the FURIA algorithm and DLIB library, which enables face reading and analysis while maintaining privacy and security for the student, which helps to overcome weaknesses and increase confidence between the educational facility and the trainee, in addition to some Improvements that develop distance education on different educational platforms. |
: | https://doi.org/10.22362/ijcert/2021/v8/i8/v8i801 | |
DOI Link | : | https://doi.org/10.22362/ijcert/2021/v8/i8/v8i801 |
[1]https://www.taylorfrancis.com/books/e/9781410609519/chapters/10.4324/9781410609519-22. [2]https://thesai.org/Downloads/Volume7No12/Paper_46-Credibility_Evaluation_of_Online_Distance_Education_Websites.pdf [3] Chen, C., Yang, J., Tang, X., Song, N., & Lu, J. (2019, June). The Application of Distance Education based on QR Code. In 3rd International Conference on Economics and Management, Education, Humanities and Social Sciences (EMEHSS 2019). Atlantis Press.? [4] He, W., Xu, G., & Kruck, S. E. (2019). Online IS education for the 21st century. Journal of Information Systems Education, 25(2), 1.? [5] Estacio, R. R., & Raga Jr, R. C. (2017). Analyzing students online learning behavior in blended courses using Moodle. Asian Association of Open Universities Journal.? [6] Cavus, N. (2015). Distance learning and learning management systems. Procedia-Social and Behavioral Sciences, 191(2), 872-877.? [7] Pardo, A., Han, F., & Ellis, R. A. (2016). Combining university student self-regulated learning indicators and engagement with online learning events to predict academic performance. IEEE Transactions on Learning Technologies, 10(1), 82-92.? [8] Leontyeva, Irina A. "Modern distance learning technologies in higher education: Introduction problems." Eurasia journal of mathematics, science and technology education 14.10 (2018): em. [9] W. v. d. V. W. W. Kiavash Bahreini, "A fuzzy logic approach to reliable real-time recognition of facial emotions," 6 February 2019. [10] P. I. R. a. MUNEESWARAN, "Emotion recognition based on facial components," 28 march 2018.
Paper Title | : | Sentiment Analysis on Movie Review Data Using Ensemble Vote Classifier Technique |
Authors | : | Mr. A. Naresh, Dr. P. Venkata Krishna , Mr. M Bhavsingh , , |
Affiliations | : | 1:Department of Computer Science ,Bharathiar University,Tamilnadu, India; 2: Department of Computer Science,Sri Padmavati Mahila Visvavidyalayam,Tirupati, India;3:Department of Computer Science and Engineering, G.Pullaiah College of Engineering and Technology,Kurnool,AP,India |
Abstract | : | Sentiment analysis is a procedure of investigating sentiments or feelings expressed in the text. It classifies whether the given text is positive or negative or sometimes neutral also, based on the classification level on a given document or sentence. To encourage the customers in better decision making and to find perspectives on others and furthermore helps in choosing the buy with the opinion of different customers. In this article a novel ensemble vote classifier technique with logistic regression, random forest and XGB classifiers is proposed and sentiment analysis on real-time movie review data is analyzed using proposed technique. Experimental results shows improved accuracy with balanced class weight compared to without balanced class weight. |
: | 10.22362/ijcert/2021/v8/i7/v8i701 | |
DOI Link | : | https://doi.org/10.22362/ijcert/2021/v8/i7/v8i701 |
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Paper Title | : | Parents’ Difficulties and Coping Mechanism towards Successful Learning in Public Secondary Schools during Remote Learning |
Authors | : | Ianne Joy Y. Maniquiz, Marie Fe D. de Guzman, Leila L. Ravana, , |
Affiliations | : | 1 : Polytechnic College of Botolan, Botolan, Zambales, Philippines; 2*3: President Ramon Magsaysay State University (PRMSU), Iba, Zambales, Philippines |
Abstract | : | Background/Objectives: This study investigated the difficulties of parents in their children's education and their coping mechanisms towards successful learning during the remote learning amidst pandemic. The respondents are parents of students of senior high school of Botolan, Iba and Palauig Districts of Zone II, Department of Education, Division of Zambales, Philippines during the first quarter of the school year 2020-2021. Methods/Statistical analysis: The present study was descriptive research. ANOVA was used to test the hypothesis. Findings: Results revealed that the parents are female, belong to middle adulthood, have four children, high school graduates, and belong to the poverty threshold. The parents agreed that they had encountered difficulties in their children's successful learning during the pandemic time, mainly limited educational resources. Therefore, the parents agreed upon the approach strategy as their coping mechanism towards successfully learning their children during the pandemic. The Analysis of Variance result found a significant difference in parents' perception of the Approach Strategy and Avoidant Strategy as Coping Mechanisms in teaching their children toward successful learning in terms of age. Moreover, there is a significant difference in parents' perception of the neither Neither Approach nor Avoidant Strategy as Coping Mechanism in teaching their children toward successful learning in several children. A Model/Plan to address parents' difficulties in children's successful learning was prepared and proposed by the researchers. Improvements/Applications: The researchers will propose the prepared Model Plan to address the difficulties and challenges encountered by parents in children's successful learning during the pandemic to the Administrators of Public Secondary Schools Division of Zambales for further review and future implementation. |
: | 10.22362/ijcert/2021/v8/i06/v8i0601 | |
DOI Link | : | https://doi.org/10.22362/ijcert/2021/v8/i06/v8i0601 |
1. United Nations Coordinated Appeal, “Global Humanitarian Response Plan COVID-19”, April – December 2020 2. J. Wadley, This is the impact home-schooling is having on parents. World Economic, 2020 3. Y. Belfali, “What can parents do to help their children learn and grow during the coronavirus crisis?”, OECD Education and Skills Today, April 23, 2020 4. M, Morelli, E. Cattelino R. Baiocco C. Trumello A. Babore, C. Candelori & A. Chirumbolo,’ Parents and Children during the COVID-19 Lockdown: The Influence of Parenting Distress and Parenting Self- Efficacy on Children’s Emotional Well-Being. Front. Psychol. 11:584645, 2020 5. L., Craig, & B. Churchill, Dual-earner parent couples’ work and care during COVID-19”, Gender, Work, & Organization, 2020 6. C.S. Carver, “Brief-COPE Scale”, Miami University, College of Arts and Science, Psychology, 1997 7. A. Orbeta, & V. Paque, “Pantawid Pamilya Pilipino Program: Boon or Bane?”, Philippine Institute for Development Studies Surian sa mga Pag-aaral Pangkaunlaran ng Pilipinas, 2016 8. U. Almazan, “Influence of Conditional Cash Transfer Program to the Living Condition of the Households”, International Journal of Psychology and Behavioral Sciences 2014, 4(5): 173-178, 2014 9. J.A. Sembrano, M.F.D. de Guzman, & N.B.A. Orge, “Impact of Conditional Cash Transfer (CCT) Program to the Socio-Economic Condition of Household Recipients of Iba, Zambales Philippines”, Asia Pacific Journal of Education, Arts and Sciences, Volume 7, No. 2, April 2020 10. R.C., Dizon Jr, M.F.D. de Guzman, & N.V.A. Orge, “Training Needs on Learning Delivery Modalities of Senior High School Teachers of Zambales, Philippines: Response to the Changes in the Basic Education during the Pandemic”, EAS Journal of Humanities and Cultural Studies. Volume-3 | Issue-1| Jan-Feb 2021 11. F., M. L. Barrera-Osorio, L. Bertrand, Linden, & F. Perez-Calle. “Conditional Cash Transfers in Education: Design Features, Peer and Sibling Effects Evidence from a Randomized Experiment in Colombia”, Policy Research Working Paper, 2016. 12. Y.R.P. Dangle & Y.D. Sumaoang, “The Implementation of Modular Distance Learning in the Philippine Secondary Public Schools”, Advanced Research in Teaching and Education, 2020 13. E. Reyes-Chua, B.G. Sibbaluca, R.D. Miranda, GB. Palmario, RP. Moreno & JPT. Solon, “The status of the implementation of the e-learning classroom in selected higher education institutions in region IV-a amidst the COVID-19 crisis”, Journal of Critical Reviews ISSN- 2394-5125 Vol 7, Issue 11, 2020. 14. National Economic and Development Authority, “Philippines Development Plan 20112016 Midterm Update”, Pasig 2014 15. Philippine Institute for Development Studies (PIDS), Social Class. PIDS Annual Report, 2019 16. T., Koskela, K., Pihlainen, S., Piispa-Hakala, R., Vornanen, J.Hämäläinen, “Parents’ Views on Family Resiliency in Sustainable Remote Schooling during the COVID-19 Outbreak in Finland”, Published: 24 October 2020 17. H. Prime, M. Wade, & D.T. Browne, “Risk and Resilience in Family Well-Being during the COVID-19 Pandemic”, © 2020 American Psychological Association 2020, Vol. 75, No. 5, 631– 643, 2020 18. W. Tso, R.S. Wong, K., Tung, N. Rao, K.W. Fu, J.S. Yam, & G.T. Chua, “Vulnerability and resilience in children during the COVID 19 pandemic”, European Child & Adolescent Psychiatry, 2020 19. A.-M. Kuusimäki, L. Uusitalo-Malmivaara, & K. Tirri, “Parents’ and Teachers’ Views on Digital Communication in Finland”, Educ. Res. Int. 1–7, 2019 20. UNESCO, “Covid-19 Impact on Education Data. COVID-19 Education Disruption and Response”,The United Nations Educational, Scientific and Cultural Organization, UNESCO. Paris, France, 2020 21. G. Wang, Y. Zhang, J., Zhao, J. Zhang, & F. Jiang, “Mitigate the effects of home confinement on children during the COVID-19 outbreak”, Lancet 395, 945–947, 2020 22. M, Spinelli, F, Lionetti, M. Pastore & M. Fasolo, “Parents’ Stress and Children’s Psychological Problems in Families Facing the COVID-19 Outbreak in Italy”, Front. Psychol. 11:1713, 2020 23. G. Moss, R. Allen, A. Bradbury, S. Duncan, S. Harmey & R. Levy, “A duty of care and a duty to teach: Educational priorities in response to the COVID-19 crisis”, UCL Institute of Education, 2020 24. A.M., Mazzella-Ebstein, K.S. Tan, K.S. Panageas, J.E. Arnetz, & M. Barton-Burke, “The Emotional Intelligence, Occupational Stress, and Coping Characteristics by Years of Nursing Experiences of Newly Hired Oncology Nurses”, Volume : 8 | Issue : 4 | Page : 352-359, 2021 25. J. Povey, A. K. Campbell, L.-D. Willis, M. Haynes, M. Western, S. Bennett, E. Antrobus, & C. Pedde, “Engaging parents in schools and building parent-school partnerships: The role of school and parent organization leadership”, International Journal of Educational Research, 79, 128–141, 2016 26. M.-A. Okkolin, T. Koskela, P. Engelbrecht, & H. Savolainen, “Capability to be Educated—Inspiring and Inclusive Pedagogical Arrangements from Finnish Schools”, J. Human Dev. Capab., 19, 421–47, 2018 27. M. L. Piccirillo, M., Taylor Dryman, & R. G., Heimberg, “Safety behaviors in adults with social anxiety: Review and future directions”, Behavior Therapy, 47(5), 675–687. 2015.11.005, 2016 28. J. Vetoniemi, & E. Kärnä, “Being included—Experiences of social participation of pupils with special education needs in mainstream schools”, Int. J. Incl. Educ. 2019, 1–15, 2019 29. G. Myers, The effect of social coping resources and growth-fostering relationships on infertility stress in women, Psychology. Journal of Mental Health Counselling, 2002 30. V. Fritz, M. Verhoevenstephanie & S. Essick, “What recent trends suggest for PFM performance in a COVID-19 impacted world”, October 05, 2020. 31. Public Health England Guidance on social distancing for everyone in the UK, 26 March 2020 32. The Academy of Medical Science Impacts of the COVID-19 pandemic. 1 Sept 2020
Paper Title | : | Comparative Study of Algorithms to Recognise Handwritten Digits |
Authors | : | Ms. Anushka Sharma, Mr. Prateek Dhawan, Ms. Swarnalatha P, , |
Affiliations | : | 1*: Computer Science Department, VIT University, Vellore, Tamil Nadu, India; 2*: Computer Science: Bioinformatics Department, VIT University, Vellore, Tamil Nadu, India |
Abstract | : | The paper shows how different estimations can be applied to test the exactness of the neural associations. We separate the display of the Back spread estimation with changing getting ready plans and the ensuing power term in feed-forward neural associations. In a relationship, we analyze the essential backslide estimation, which makes a choice based on the value of an immediate blend of the features. In this paper, Neural Associations are used with an MNIST dataset of 70000 digits and 250 assorted creating styles. Logistic Regression is a measurable model that, in its fundamental structure, utilizes a strategic capacity to demonstrate a twofold reliant variable, albeit a lot more intricate expansions exist. In relapse examination, calculated relapse (or logit relapse) assesses the boundaries of a strategic model (a type of paired relapse). This equivalent examination shows the exactness of these computations in distinguishing physically composed digits, with Backpropagation unequivocally expecting close 95.06% of the test dataset when it was run multiple times, and the essential logistic regression correctly anticipating close 99% with a hidden layer and 92% without a hidden layer. |
: | 10.22362/ijcert/2021/v8/i05/v8i0503 | |
DOI Link | : | https://doi.org/10.22362/ijcert/2021/v8/i05/v8i0503 |
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Paper Title | : | Utilization of Self-Learning Module in the New Normal and Academic Achievement in Economics of Students in Public Secondary Schools |
Authors | : | Edgar S. Ramos, Marie Fe D. de Guzman, Felipa M. Rico, , |
Affiliations | : | 1: Naugsol Integrated School, Subic, Zambales, Philippines ; 2&3: President Ramon Magsaysay State University (PRMSU), Iba, Zambales, Philippines |
Abstract | : | The present study focused on determining the usefulness of the utilized Self-Learning Modules (SLM) for teaching Economics in public secondary schools of Zone 4, Department of Education, Division of Zambales, Philippines during the remote/distance education. The grades in the 1st Quarter and 2nd Quarter (SY2020-2021) of Grade 9 students in Economics were secured. The Social Studies teachers were identified to assess the evidence of the usefulness of the components of an SLM. The study utilized a descriptive experimental research design. Summative Tests and Evaluation checklist for Self-Learning Modules was utilized as a research instrument. Descriptive and inferential statistics were used for analysis of data. The level of academic achievement before using the SLM in Economics was very ‘Satisfactory’ and ‘Outstanding’ after the utilization of SLM, hence the SLM was very useful and effective. It was found that the features/components for SLM for Economics in terms of Paper Design and Layout, Illustration and Printing, Contents, Presentation and Organization were Very Evident. The study revealed further that there is no significant difference between the first quarter achievement of students and the utilized SLM in Economics as evaluated by the teachers. There is no significant difference between the second quarter achievement of students and the utilized SLM in Economics as evaluated. There is no significant difference between the students’ first quarter and second quarter’s achievement in Economics. This study prepared an Enhanced Self-Learning Module in Economics based from the results of teachers’ evaluation. The researchers recommend that the Social Studies teachers and Department Heads may prioritize the Content feature of SLMs in terms of creativity and innovation of presentation of topics/lessons |
: | 10.22362/ijcert/2021/v8/i05/v8i0502 | |
DOI Link | : | https://doi.org/10.22362/ijcert/2021/v8/i05/v8i0502 |
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Dizon Jr, M.F.D. de Guzman & N.B.A. Orge, “Training Needs on Learning Delivery Modalities of Senior High School Teachers of Zambales, Philippines: Response to the Changes in the Basic Education during the Pandemic,” EAS Journal of Humanities and Cultural Studies, ISSN: 2663-0958 (Print) & ISSN: 2663-6743 (Online) Volume-3 | Issue-1| Jan-Feb 2021 [8] P. V. Padmapriya, “Effectiveness of self-learning modules on achievement in Biology among secondary school students”, International Journal of Education and Psychological Research (IJEPR), 2015 [9] M. Sinco, “Strategic intervention materials: A tool in improving students’ academic performance”, International Journal for Research in Applied and Natural Science, 2020 [10] K. Grover, “Online social networks and the self-directed learning experience during a health crisis”, Int. J. Self-Direct. Learn. 12, 1–15, 2015 [11] N. R., Boyer, & P. Usinger, “Tracking pathways to success: triangulating learning success factors”, Int. J. 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Deliquiña, & M.F.D. de Guzman, “Differentiated Instructions in the Kto12 Social Studies Program and Students’ Academic Performance”, American Journal of Humanities and Social Sciences Research, Volume-5, Issue-4, pp-474-481, 2021 [18] P. Stark, & R. Freishtat, “An evaluation of course evaluations. ScienceOpen Research”, 2014 [19] M. C. R. Selga, “Instructional materials development: A Work text in Science, Technology and Society”, LCCB Development Education Journal of Multidisciplinary Research, 2(1), 1-1., 2013 [20] J. Richards, “Advantages and disadvantages of using instructional materials in teaching ESL”, 2013 [21] C.M. Ambayon, “Modular-Based Approach and Students’ Achievement in Literature”, International Journal of Education & Literacy Studies ISSN: 2202-9478, 2020 [22] J. L. Hughes, “Programmed Instruction for Schools and Industry”, Chicago: Research Association, Inc. 2012. [23] Y. D. Reyes, & R. G. De Guia, “Development of English Work Text in English 101”, International Journal of Science and Research (IJSR), 2017 [24] S. Sadiq, & S. Zamir, “Effectiveness of Modular Approach in Teaching at University Level”, Journal of Education and Practice, 5(17), 104, 2014 [25] M.H. Rabaeh, “The effectiveness of the modules strategy based on perfection and research in gaining the chemical concepts for the students of the 10th grade in Jordan”, Ph.D Thesis Amman Arab University for Higher Studies, 2012 [26] W. Dejene, “The practice of modularized curriculum in higher education institution: Active learning and continuous assessment in focus”, Cogent Education 6: 1611052, 2019 [27] M. J. F. Tan-Espinar & R. S. Ballado, “Content Validity and Acceptability of a Developed Worktext in Basic Mathematics 2. Asia Pacific Journal of Multidisciplinary,” 2017 [28] L.C. 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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 [34] J.R.E.E. Salcedo, “Acceptability of a Developed Teaching Module on Selected Writings of Jose Rizal”, SAJST, Volume 1, Issue 1, 2016 P-ISSN: 2672-2984 E-ISSN: 2672-2992, 2017 [35] Southampton UCU, “Module Evaluations and Simply Better”, 2019
Paper Title | : | Strategic Intervention Material: A Learning Approach in Teaching Economics during the Distance Education |
Authors | : | Daisy D. Lazo, Marie Fe D. de Guzman, , , |
Affiliations | : | 1: Zambales National High School, Iba, Zambales, Philippines ; 2*: President Ramon Magsaysay State University (PRMSU), Iba, Zambales, Philippines |
Abstract | : | Background/Objectives: This study assessed the Strategic Intervention Material (SIM) effectiveness in Social Studies - Economics among Grade 9 Junior High School of Zambales National High School, Iba Zambales, Philippines during remote/distance learning (COVID19 pandemic) school year 2020-2021. The Strategic Intervention Material (SIM) is a learning material that helps the students to master competency-based skills which were not able to develop during a regular class. The present study focused on the students' performance before and after implementing SIM-based instruction on Economics. Methods/Statistical analysis: This study utilized a descriptive – experimental research design. A total of 259 students and 39 educators were the respondents. Findings: Findings revealed that academic performance of the students before the utilization of Strategic Intervention Material (SIM) in Economics resulted in a descriptive equivalent of Did Not Meet Expectation or Fair. At the same time, the performance of the same group of students after the utilization SIM in Economics was Satisfactory. The t-Test results found a highly significant difference in the pre-test and post-test versions of the students utilizing the SIM in Economics. When the SIM was subjected to evaluation by experts (educator-evaluators), it was found that the Content, Structure, and Usability compositions/requirements of acceptability and usefulness were very evident. Improvements/Applications: This study suggested the consideration of preparation of SIM on enhancing the development of desirable values and traits and free from bias contents; more active learning activities aimed to increase motivation, understanding and for the development of critical and higher-order thinking; and better efficiency of its assessment tools and techniques. |
: | 10.22362/ijcert/2021/v8/i05/v8i0501 | |
DOI Link | : | https://doi.org/10.22362/ijcert/2021/v8/i05/v8i0501 |
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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, April 2016. [19] 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). [20] 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). [21] A. Chumdari, S. Budiyono, & N. Suryani, “Implementation of thematic instructional model in elementary school”, International Journal of Educational Research Review,3(4),23-31, 2018 [22] H. Park, S.Y. 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 [23] 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 [24] 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 [25] C.U. Idoko & A. Emmanuel, “Teachers’ effectiveness in teaching economics: Implication for secondary education”, International Journal of Innovative Research & Development, 4(2), 69-72, 2015 [26] M.J.F Tan-Espinar & R.S. Ballado, “Content Validity and Acceptability of a Developed Worktext in Basic Mathematics 2”, Asia Pacific Journal of Multidisciplinary Research, 5(1), 2017 [27] AP Sarmiento, “MSCrim Learning Modules: A Self-learning Kit in Criminology”, 2020 [28] AM 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] K McGoldrick, K., “Using the theory of service-learning as a tool for teaching economic theory”, In: McGoldrick, K., Ziegert, A. (Eds.), Putting the Invisible Hand to Work: Concepts and Models for Service-learning in Economics. Edward Zlotkowski, 2016 [30] R.M. Seco-Macarandan, “Assessment of the Araling Panlipunan Modules in the K-12 Curriculum: Enhanced Instructional Materials Development”, International Conference on Economics, Social Sciences and Languages, 2014 [31] R Rajapaksha & R.D. Chathurika, “Problems Faced by Preschool Teachers When Using Teaching Aids in the Teaching Learning Process”, International Journal of Multidisciplinary Studies (IJMS). Volume 2, Issue I, 2015. [32] K Sejpal, “The Development of Expertise in Pedagogy”, The Charles W. Hunt Memorial Lecture for the American Association of Colleges for Teacher Education, New Orleans, LA, February 2016.
Paper Title | : | Effective E-Governance Paradigm through Cloud Environment |
Authors | : | Neha Paliwal, Dr Bright keswani, , , |
Affiliations | : | 1*: Computer Science, Mahaveer College of Commerce, rajasthan University, Jaipur, India; 2: Computer Science, Suresh Gyan Vihar University, Jaipur, India |
Abstract | : | Cloud technologies give a fundamental move in the provisioning of figuring asset inside the administration. This research article characterizes the difficulties that leaders face while surveying the achievability of the adequacy of cloud technologies in their associations, and characterizes our Cloud Effectiveness Outline, which has been made to continue this procedure. The system gives a structure to continue chiefs in choosing their interests, and coordinating these worries to appropriate strategies that might be taken to determine them. Expenditure Prototyping is the principle experienced strategy in the structure and this research article currents its adequacy by exhibiting how professionals may utilize it to analyze the operating cost of conveying their IT outlines on the cloud. The Expenditure Prototyping procedure is assessed through a contextual analysis of an administration that is taking into tallies the movement of not some of its IT outlines to the cloud. The contextual investigation currents that running outlines on the cloud through a conventional „always on? approach might be less practical, and the flexible idea of the cloud must be taken to decrease Expenditures. Hence, chiefs must have the option to paradigm the varieties in asset utilization and their systems? arrangement decisions to take precise quotes. |
: | 10.22362/ijcert/2021/v8/i04/v8i0401 | |
DOI Link | : | https://doi.org/10.22362/ijcert/2021/v8/i04/v8i0401 |
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Paper Title | : | A Novel Approach for Payment Systems in Accordance with Fingerprint Authentication |
Authors | : | Mr. Piyush Suthar, Ms. Neha Pandya, , , |
Affiliations | : | 1: Information Technology Department, SPCE, Sankalchand Patel University, Visnagar, India ; 2: Computer Engineering Department, GPERI, Gujarat Technological University, Mehsana, India |
Abstract | : | Currently in the developing countries like India, the scenario of monetary transactions is rapidly moving towards cashless payments. Cashless payment modes include the vanishing debit card system, mobile wallets, UPI, and contactless cards. To use above stated payment modes, either there is a need of mobile phone or a contactless card or both. So, to eliminate these limiting approaches it requires primarily two aspects one is fund security and the other is customer comfort. So, to deal with both the aspects a system can be implemented by incorporating proposed fingerprint payment system. If we consider Finger print payment system, then it is the safest and secure one among all other biometric authentication systems [1]. The proposed system will greatly enhance customer comfort by providing easiness in any kind of payment by just a tap of a finger, further security aspects can be dealt with inclusion of several proposed protection measures. |
: | 10.22362/ijcert/2021/v8/i03/v8i0302 | |
DOI Link | : | https://doi.org/10.22362/ijcert/2021/v8/i03/v8i0302 |
[1] T.Sabhanayagam, Dr. V. Prasanna Venkatesan and Dr. K. Senthamaraikannan, “A Comprehensive Survey on Various Biometric Systems”, International Journal of Applied Engineering Research Volume 13, Number 5 (2018) pp. 2276-2297. [2] https://www.statista.com/outlook/296/119/digital-payments/india#market-users [3] Domingo, D. Chan, A.Watson (2000) "Smart card research and advanced applications" Series: IFIP International Federation for Information Processing, Springer, 2000,Vol. 52 [4] N.Galy, B.Charlot, and B.Courtois (2007) "A Full Fingerprint Verification System for a Single-Line Sweep Sensor" IEEE Sensors Journal, 2007,Vol. 7, pp. 1054-1065 [5] www.htgadvancesystem.com. [6] Jain K. Anil, Ross Arun and Prabhakar Salil, “An introduction to biometric recognition”, IEEE Transactions on Circuits and Systems for Video Technology, 2004, vol.14, no.1, pp. 4-20. [7] A. K. Jain, A. Ross and S. Pankanti, “Biometrics, A Tool for Information Security”, IEEE Transactions on Information Forensics And Security, 2006, vol.1, no.2, pp. 125 – 144. [8] S.Prabhakar, S.Pankanti and A.K.Jain, “Biometric Recognition, Security and Privacy Concerns”, IEEE Security & Privacy, 2003, pp. 33-42. [9] A. Al-Ajlan, “Survey on fingerprint liveness detection,” in Proc. Int. Workshop Biometrics Forensics (IWBF), 2013, pp. 1–5. [10] Zhihua Xia, Chengsheng Yuan, Rui Lv, Xingming Sun, Neal N. Xiong, and Yun-Qing Shi (2018) "A Novel Weber Local Binary Descriptor for Fingerprint Liveness Detection" IEEE Transactions on systems, man, and cybernetics, IEEE 2018 [11] Nenad Badovinac, Dejan Simic, Beograd, Serbia (2019) "A Multimodal Biometric Authentication (MBA) in Card Payment Systems" International Conference on Artificial Intelligence: Applications and Innovations (IC-AIAI), 2019. [12] Abdullah Saud, Nazar Elfadil(2020) "Biometric Authentication by Using Fingerprint Recognition System" 2020 International Journal of Scientific Engineering and Science, 2020. [13] https://uidai.gov.in/
Paper Title | : | Evaluation of State-Wise Epidemiological Outbreak of COVID-19 Cases In India by Data Analysis Approach to Forecast the Coronavirus Disease Pandemic |
Authors | : | Sumana Sikdar, Pradyut Sarkar, Sibnath Kayal, , |
Affiliations | : | 1 : Research Scholar, Department of Computer Science & Engineering, MAKAUT. Kalyani, West Bengal, India - 741249. 2 : Department of Computer Science and Engineering, Maulana Abul Kalam Azad University of Technology (MAKAUT), Kalyani, West Bengal, India - 741249. 3 : Department of Metallurgical Engineering, O. P. Jindal University (OPJU), Raigarh, Chhattisgarh, India - 496109 |
Abstract | : | Data analysis is very sophisticated tool in recent coronavirus pandemic to find the trend of spreading pattern for controlling the infection. In this perspective, predictive analytics can be useful for data analysis to forecast the coronavirus pandemic. This paper presents the infection pattern of coronavirus disease, termed as COVID-19, in top seven states in India. Prophet Algorithm forecasting model was used to analyze state-wise spreading pattern of coronavirus disease with respect to confirmed, deaths and cured cases. This predictive model can be very helpful to government and healthcare communities to combat this deadly virus by initiating suitable actions to control its spread. |
: | 10.22362/ijcert/2021/v8/i3/v8i301 | |
DOI Link | : | https://doi.org/10.22362/ijcert/2021/v8/i3/v8i301 |
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Paper Title | : | An Optimized KNN Model for Signature-Based Malware Detection |
Authors | : | Tsehay Admassu Assegie, , , , |
Affiliations | : | Department of Computer Science, Aksum Institute of Technology, Aksum University, Axum, Ethiopia |
Abstract | : | Malware is a computer program developed with the intent of disrupting, stealing, and compromising a computer system. In recent advances in technology and internet use, malware has become the major problem in computer society. In this research, an optimal K-nearest Neighbor (KNN) based malware detection and classification model is proposed. The proposed malware detection model is based on application programming interface (API) call sequence analysis and classification. The dataset is collected from an online Kaggle data repository which consists of 42,797 malicious application programming interface (API) call sequences and 1,079 non-malicious application programming interface (API) call sequences. The Nearest Neighbor (KNN) algorithm is applied to the dataset to create a model that detects malware. Finally, the accuracy of the proposed KNN based malware detection model is evaluated, and the result shows that the accuracy of 98.17% is achieved in the detection of malware using the model. The proposed model is significantly essential for detecting real-time intrusion on computer systems. |
: | 10.22362/ijcert/2021/v8/i02/v8i022 | |
DOI Link | : | https://doi.org/10.22362/ijcert/2021/v8/i02/v8i022 |
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Paper Title | : | Opportunities and threats of E-commerce in Social Media: Perspective of Bangladeshi female seller |
Authors | : | Md. Obaidullah, Abdullah Al Zubayer, , , |
Affiliations | : | Department of Public Administration |
Abstract | : | Background/Objectives: E-commerce is a new-fangled means of business in the present world. Social media based business derived from it. Both men and women are doing business in Facebook based e-commerce method. This study explores the opportunities and threats of e-commerce for Bangladeshi women on Facebook. Methods: The study is qualitative-explorative. Data was qualitatively thematic analysed through manual coding. Purposive sampling was used in this study for collecting data. We have collected data from 20 women in Bangladesh from 20th August to 30th August. Among those women, 65% are from urban areas, and the rest are from rural areas. Findings and Conclusion: The results of the study demonstrate that there are many opportunities and threats of Facebook based e-commerce for Bangladeshi women businessmen. Women are making maximum profit through minimum investment, as there is no advertisement cost and taxes. In reverse, they face problems like cyberbullying, lack of skill, internet access, and harassment. Lastly, in conclusion, some corrective measures have been mentioned. |
: | 10.22362/ijcert/2021/v8/i02/v8i0205 | |
DOI Link | : | https://doi.org/10.22362/ijcert/2021/v8/i02/v8i0205 |
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Paper Title | : | An Experimental Study on the Accuracy and Efficiency of Some Similarity Measures for Collaborative Filtering Recommender Systems |
Authors | : | Abba Almu, Ziya’u Bello, , , |
Affiliations | : | Dept. of Mathematics, Computer Science Unit, Usmanu Danfodiyo University, P.M.B 2346, Sokoto – Nigeria. |
Abstract | : | Similarity measures are the core component used by the neighborhood based collaborative filtering recommendations to predict the user's ratings in item-based or user-based recommender algorithms. The collaborative filtering has been implemented with different similarity measures but ignores to consider the time taken by the similarity measures to make accurate predictions in different application domains. This paper intended to help recommender systems developers to identify suitable similarity measure depending upon the application domain to be used with less execution time and error rate. It also takes the effect of neighbrhood sizes (k) on the prediction accuracy and efficiency into consideration. The experimental evaluations were conducted on the four similarity measures with the same dataset using Python programming language implementation. The evaluation metrics considered during the experiments are Execution Time, Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The results of the evaluation demonstrated that, Manhattan Distance similarity measure had the best accuracy as well as the efficiency of predictions in this study. |
: | 10.22362/ijcert/2021/v8/i03/v8i0204 | |
DOI Link | : | https://doi.org/10.22362/ijcert/2021/v8/i03/v8i0204 |
[1] Ajay A. & Minakshi C. Similarity Measures used in Recommender Systems: A Study: International Journal of Engineering Technology Science and Research (IJETSR), 2017, 4 (6): 619-626. [2] Ali S. S., Saeed A., & Teh Y. W. A Comparison Study on Similarity and Dissimilarity Measures in Clustering Continuous Data: PLoS ONE, 2015, 10 (12): 1-20. [3] Elmutafa, S. A. A. Principle of Computer System: LAMBERT Academic Publishing (LAP), 2015, pp. 1-2. [4] Jayvardhan , S., Thomas, V., and Yadav, M. L. Prediction Accuracy Comparison Of Similarity Measures In Memory Based Collaborative Filtering Recommender Systems. International Journal of Engineering Research & Technology (IJERT), 2013, 2 (6): 1105- 1108. [5] Lamis, A. H., Chadi A. J., Jacques B. A., & Jacques D. Similarity Measures for Collaborative Filtering Recommender Systems IEEE Middle East and North Africa Communications Conference (MENACOMM), 2018, pp. 1-5. [6] Madhuri A. B. Comparative study of similarity measures for item based top n recommendation. Unpublished project for Bachelor of Technology Degree in Computer Science and Engineering: National Institute of Technology, Rourkela (Deemed University), 2014. [7] Harper, F. M., & Konstan, J. A. The MovieLens Datasets: History and Context. ACM Transactions on Interactive Intelligent Systems (TiiS), 2015, 5(4): 1–19. [8] Minh-Phung T. D., Dung V. N, & Loc N. Model-based Approach for Collaborative Filtering: The 6th International Conference on Information Technology for Education (IT@EDU2010) Ho Chi Minh city,2010, Vietnam. 218-219. [9] Odunayo, E. O., Ibrahim A.A., Adeleye, S. F., & Olumide, O. O. A Comparative Analysis of Euclidean Distance and Cosine Similarity Measure for Automated Essay-Type Grading: Journal of Engineering and Applied Sciences, 2018, 13 (11): 4198¬-4204. [10] Perlibakas, V. Face Recognition Using Principal Component Analysis and Wavelet Packe Decomposition. Informatica, Lith. Acad. Sci., 2004, 15 (2) pp. 243-250. [11] Ping, H. Q., and Ming X. Research on Several Recommendation Algorithms. Procedia Engineering, 2012, 29: 2427-2431. [12} Shalini C. S., Hong X.., & Shri R. Measures of Similarity in Memory-Based Collaborative Filtering Recommender System – A Comparison: MISNC '17 Bangkok, 2017, Thailand, pp.1-8. [13] Suganeshwari, G., & Syed I. S. P. A Comparison Study on Similarity Measures in Collaborative Filtering Algorithms for Movie Recommendation: International Journal of Pure and Applied Mathematics, 2018, 119 (15):1495-1505. [14] Taner A. Efecan K. & Zeki B. Comparison of Collaborative Filtering Algorithms with various Similarity Measures for Movie Recommendation: International Journal of Computer Science, Engineering and Applications (IJCSEA), 2016, 6 (3):12-13.
Paper Title | : | Challenges and Opportunities for Higher Education amid COVID-19 Pandemic |
Authors | : | Dr. Rakesh C Ramola, , , , |
Affiliations | : | HNB Garhwal University |
Abstract | : | The COVID-19 pandemic has significantly disrupted the higher education sector all across the world. It has also posed a serious challenge to the existing system of higher education in India. The spread of COVID-19 pandemic has forced higher education sector of the country to shift its bases online and almost all universities and colleges have started teaching their students through online platforms. Despite of all odds, the country made significant progress in online education to the students of higher studies. It was felt that more technically sound professionals are required to improve the quality of online teaching in future. The institutions should also come forward to improve the quality of education through modern technology. They must also develop the facility for providing online education to all enrolled students in the country. The current situation should be seized as an opportunity to develop new techniques to impart education that would be user friendly and accessible to all. The country should try and use the current opportunity to improve its educational base. It is about time that regulatory authorities and educationists of the country should work out strategies and spell out rules and regulations to be followed for quality education in the future. |
: | 10.22362/ijcert/2021/v8/i2/v8i203 | |
DOI Link | : | https://doi.org/10.22362/ijcert/2021/v8/i2/v8i203 |
[1] Liu, Y.C., Kuo, R.L. and Shih, S.R. (2020) COVID-19: The first documented corona virus pandemic in history. Biomedical Journal 43, 328-333. [2] OWD (2020) Our World in Data https://ourworldindata.org/coronavirus-source-data as on 31 July 2020 [3] Worldometers (2020) Coronavirus Cases: Reported Cases and Deaths by Country, Territory, or Conveyance. https://www.worldometers.info/coronavirus as on 31 July 2020 [4] MoHFW (2020) Ministry of Health and Family Welfare, Govt of India https://www.mohfw.gov.in, 20 May 2020 [5] Reddy, K.S., Xie, E. and Tang Q. (2016) Higher education, high-impact research, and world university ranking: A case study of India and comparison with China. Pacific Science Review B: Humanities and Social Sciences 2, 1-21 [6] UGC (2019) University Grants Commission Annual Report (2018-2019). UGC, New Delhi, India, pp. 1-300. [7] BBC (2020) Cambridge University: All lectures to be online-only until summer of 2021 https://www.bbc.com/news/education-52732814, 19 May 2020 [8] Hodges, C. B., Moore, S., Lockee, B. B., Trust, T. and Bond, M. A. (2020) The difference between emergency remote teaching and online learning, EDUCAUSE Review. 27 March 2020 [9] Loannidis, J.P.A., Cripps, S. and Tanner, M.A. (2020) Forecasting for COVID-19 has failed. Int. J. Forecasting. Available online. https://doi.org/10.1016/j.ijforecast.2020.08.004. [10] Anderson, N. (2020) College students want answers about fall, but schools may not have them for months, The Washington Post. 23 April 2020 [11] Ries, B. and Wagner, M. (2020) Universities begin considering the possibility of cancelling in-person classes until 2021, www.cnn.com, 15 April 2020
Paper Title | : | Analysis of Gender Equality & Social Representation: A Bangladesh Perspective |
Authors | : | Avijeet Paul, Sohana Sultana, , , |
Affiliations | : | 1 & 2: Department of Accounting, Premier University, Chittagong, Bangladesh |
Abstract | : | Gender equality is considering the empowerment of a person while it should not be confined only to empowering someone. The main purposes of this study are to identify how gender equality is socially represented by both male & female perspective and do male and female support each other to build gender equality in Bangladesh. To formulate this basic research design we followed the survey through questionnaire and secondary analysis. Structured, Close-ended, and 3 points Likert scale has been used in the questionnaire. Data were collected from a total of 60 respondents equally both male and female. Goggle form was used to collect data and MS Excel was used to analyze data. The findings of this study say, maximum people do not show gender inequality socially. That means the social representation of gender equality is exists in maximum respondents. Both the male and female also support each other to build gender equality socially. Most of the study considers on gender perspective either gender equality or gender inequality but we consider how people represent gender equality socially so this study extends the previous study and which will help the academicians, rechargers, policymakers for similar socio-cultural nature or other countries. |
: | 10.22362/ijcert/2021/v8/i2/v8i202 | |
DOI Link | : | https://doi.org/10.22362/ijcert/2021/v8/i2/v8i202 |
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Paper Title | : | An Improved Method for Achieving Optimum Efficacy of CPU Scheduling |
Authors | : | Priyanka Sharma, Sultan Singh Saini, , , |
Affiliations | : | 1,2: Dept. of Computer Science & Engg., Jaipur Engineering College, Jaipur, Rajasthan, India. |
Abstract | : | Over the past decades, with huge published efforts a good amount of research community related to CPU scheduling field has demonstrated that an act of utilized system can significantly improve with an adoption of effectual algorithm. However, each and every offered algorithm of CPU scheduling process made an endeavour to augment an act of system but most of techniques fails to maintain their efficiency with rapid growth of process or other amendments take place in system. Additionally, dissimilar method executes differently and offered several unique associated restrictions that generate the need of implementation of a new optimized method for enhancing an act of adopted system. This paper discussed an optimized version of Round Robin (RR) CPU scheduling algorithm. The vast experimental fallouts pointedly denoted the remarks of the proposed method in contrast to other offered practices, proposed approach effectively improves the scheduling act of CPU with attaining low length of task switching, average waiting and turnaround time span. |
: | 10.22362/ijcert/2021/v8/i2/v8i201 | |
DOI Link | : | https://doi.org/10.22362/ijcert/2021/v8/i2/v8i201 |
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Paper Title | : | Fighting Crime and Insecurity in Nigeria: An Intelligent Approach |
Authors | : | Mubarak Albarka Umar, Abubakar Aliyu Machina, Muazzamu Ibrahim, Ja’afar A. Nasir,Abdullahi Saheed Salahudeen, Musa Mustapha, Isiaka Shuaibu |
Affiliations | : | 1: Katsina State Institute of Technology and Management, Katsina, Nigeria. 2,4,5,6: Changchun University of Science and Technology, Jilin, China. 3: Binyaminu Usman Polytechnic, Hadejia, Jigawa, Nigeria. 7: Federal University of Technology, Minna, Niger, Nigeria. |
Abstract | : | Over the years, insecurity and crime have been significant issue in Nigeria. While the country successfully dealt with the past insecurity challenges conventionally, the government has failed to contain the new insecurity and crime challenges, especially that of the well-known Boko Haram lingering for over a decade now. This is due to various reasons, mainly the use of the same outdated, futile strategy. Several researchers have proposed numerous ways of tackling such insecurity challenges, mostly via a conventional approach; however, very few researchers proposed a more technological approach towards combating the insecurity challenges. In this paper, we discussed some modern technologies and how they can be applied to fight the new insecurities and crimes in Nigeria. We proposed the use of a Central Database as a backbone model serving as a central point of reference for all law enforcement agencies. Various modern technologies such as Facial recognition surveillance, Automatic plate number recognition, GIS and Crime Mapping, and Voice recognition are proposed to be integrated and used to identify and predict criminal activities, thus, mitigating the nation’s prolonged insecurity and terrorism vulnerabilities. |
: | 10.22362/ijcert/2021/v8/i1/v8i102 | |
DOI Link | : | https://doi.org/10.22362/ijcert/2021/v8/i1/v8i102 |
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Paper Title | : | Review of green e-commerce security |
Authors | : | Mr. Sritha Zith Dey Babu, Mr. Rajat Goyal, Mr. Vishesh Chaudhary, Mr. Ayush Pal, Saurabh Verma |
Affiliations | : | Chandigarh University |
Abstract | : | Data Science is the backbone of future technology. It is already developing at an exponential rate but there still a lot of future scope for the same. Data Science uses many scientific calculations and algorithms to extract meaningful and useful data from structured or unstructured data.¬ Data Science is completely based upon statistics and data analysis. But also there is a bad for every good in the same way with the emerging technology, the ratio of cybercrime is also increasing at an exponential rate. Every day new technology emerges and on the next day, bad people find a way to get a profitable way out of it. At present time one of the most vulnerable technology is E-Currency. On regular basis, hackers hack the bank accounts of general people and steal money just because the person doesn’t have much knowledge about it. Sometimes it also happens from the server-side. E-Currency is simply virtual money. And anything related to computer science can’t be full-proof. There must be a loophole and the hackers make use of it until it is discovered and sorted. The research aims to integrate data science with E-Currency to make it more secure. The LIFO encryption techniques come under data science user neural networking for encryption and the decryption of the data. Neural networking is the neuron of data science. We can understand its complexity by knowing the fact that it mimics the human brain, which is considered the most complex thing in this universe. Neural networking used extremely complicated mathematical algorithm to predict the future as well as the past and this is what makes it one of the most complex studying fields at present time. It is one of the most secured techniques present till now and it is being used in many sectors like banking, army data transmission, aeronautical science, etc. The private hash key generated between the sender and receiver will be most secured and will help in money transfer without a single chance of data breach. All the data will be stored in the hidden layer which is next to impossible to pass through. Because the neural network will give only output and there will be infinite inputs for the same output.¬ Thus, it will be safer to use E-Currency and make it a part of our day-to-day life. |
: | 10.22362/ijcert/2021/v8/i1/v8i101 | |
DOI Link | : | https://doi.org/10.22362/ijcert/2021/v8/i1/v8i101 |
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Citations Indices | All |
Citations | 1026 |
h-index | 14 |
i10-index | 20 |
Source: Google Scholar |
Acceptance Rate (By Year) | |
Year | Rate |
2021 | 10.8% |
2020 | 13.6% |
2019 | 15.9% |
2018 | 14.5% |
2017 | 16.6% |
2016 | 15.8% |
2015 | 18.2% |
2014 | 20.6% |