i-manager Publications

i-manager's Journal on Information Technology (JIT)

Current Issue Vol. 12 Issue 4

Most Cited

Volume 11 Issue 3 June - August 2022

Research Paper

Machine Learning and Deep Learning Techniques for Detecting Fraud Involving Credit Cards

Aastha Joshi* , Nirmal Gaud**

* Samrat Ashok Technological Institute, Vidisha, Madhya Pradesh, India.

** Department of Computer Science and Engineering, Samrat Ashok Technological Institute, Vidisha, Madhya Pradesh, India.

Joshi, A., and Gaud, N. (2022). Machine Learning and Deep Learning Techniques for Detecting Fraud Involving Credit Cards. i-manager’s Journal on Information Technology, 11(3), 1-12. https://doi.org/10.26634/jit.11.3.18987

Abstract

A credit card is still a highly common form of installment that allows for cashless purchases and is acceptable both offline and online to conduct payments and other trades. The prevalence of cloned credit cards is rising along with innovation. Budgetary fraud always seems to get worse when international communication gets better. These infringements have generated billions of dollars. These actions are executed with such skill that they vaguely align actual commercial transactions. Simple plan requirements and other less composite techniques won't function as a consequence. In order to lessen confusion and re-establish order, all banks currently need a well-organized approach for identifying credit card fraud. It has taken into consideration a number of common machine learning and deep learning methods for the classification of default accounts, which basically entails analyzing credit card fraud. It consist of synthetic neural networks, decision trees, random forests, and logistic regression (ANN). For every approach, the model has been trained and its accuracy has been assessed. The classification computations are merged to create a sample, and then it is compared to different deep learning and machine learning algorithms.

References

[1]. Akila, S., & Reddy, U. S. (2017, November). Risk based bagged ensemble (RBE) for credit card fraud detection. In 2017, International Conference on Inventive Computing and Informatics (ICICI), (pp. 670-674). IEEE. https://doi.org/10.1109/ICICI.2017.8365220

[2]. Bahnsen, A. C., Aouada, D., Stojanovic, A., Ottersten, B. (2016). Feature engineering strategies for credit card fraud detection. Expert Systems with Applications, 134–142. https://doi.org/10.1016/j.eswa.2015.12.030

[3]. Bolton, R. J., & Hand, D. J. (2002). Statistical fraud detection: A review. Statistical Science, 17(3), 235-255. https://doi.org/10.1214/ss/1042727940

[4]. Cody, T., Adams, S., & Beling, P. A. (2018, April). A utilitarian approach to adversarial learning in credit card fraud detection. In 2018, Systems and Information Engineering Design Symposium (SIEDS), (pp. 237-242). IEEE. https://doi.org/10.1109/SIEDS.2018.8374743

[5]. Krivko, M. (2010). A hybrid model for plastic card fraud detection systems. Expert Systems with Applications, 37(8), 6070-6076. https://doi.org/10.1016/j.eswa.2010.02.119

[6]. Melo-Acosta, G. E., Duitama-Munoz, F., & Arias- Londono, J. D. (2017, August). Fraud detection in big data using supervised and semi-supervised learning techniques. In 2017, IEEE Colombian Conference on Communications and Computing (COLCOM), (pp. 1-6). IEEE. https://doi.org/10.1109/ColComCon.2017.8088206

[7]. Pozzolo, A. D., Boracchi, G., Caelen, O., Alippi, C., & Bontempi, G. (2017). Credit card fraud detection: A realistic modeling and a novel learning strategy. IEEE Transactions on Neural Networks and Learning Systems, 29(8), 3784-3797. https://doi.org/10.1109/TNNLS.2017.2736643

[8]. Rafalo, M. (2017). Real-Time Fraud Detection in Credit Card Transactions. Retrieved from https://www.slideshare.net/mrafalo/realtime-fraud-detection-incredit-card-transactions

[9]. Randhawa, K., Loo, C. K., Seera, M., Lim, C. P., & Nandi, A. K. (2018). Credit card fraud detection using adaboost and majority voting. IEEE Access, 6, 14277-14284. https://doi.org/10.1109/ACCESS.2018.2806420

[10]. Roy, A., Sun, J., Mahoney, R., Alonzi, L., Adams, S., & Beling, P. (2018, April). Deep learning detecting fraud in credit card transactions. In 2018, Systems and Information Engineering Design Symposium (SIEDS), (pp. 129-134). IEEE. https://doi.org/10.1109/SIEDS.2018.8374722

[11]. Saha, S. (2018). A Comprehensive Guide to Convolutional Neural Networks-the ELI5 Way. Retrieved from https://towardsdatascience.com/a-comprehensive-guide-to-convolutional-neural-networks-the-eli5-way-3bd2b1164a53

[12]. Sorournejad, S., Zojaji, Z., Atani, R. E., & Monadjemi, A. H. (2016). A survey of credit card fraud detection techniques: data and technique oriented perspective. ArXiv, abs/1611.06439, (pp. 1-26). https://doi.org/10.48550/arXiv.1611.06439

[13]. West, J. & Bhattacharya, M. (2016). Some experimental issues in financial fraud mining. Procedia Computer Science, 80, 1734-1744. https://doi.org/10.1016/j.procs.2016.05.515

[14]. Zareapoor, M., Seeja, K. R., & Alam, M. A. (2012). Analysis on credit card fraud detection techniques: Based on certain design criteria. International Journal of Computer Applications, 52(3), 35-42. https://doi.org/10.5120/8184-1538

[15]. Zeager, M. F., Sridhar, A., Fogal, N., Adams, S., Brown, D. E., & Beling, P. A. (2017, April). Adversarial learning in credit card fraud detection. In 2017, Systems and Information Engineering Design Symposium (SIEDS), (pp. 112-116). IEEE. https://doi.org/10.1109/SIEDS.2017.7937699

Full Article (HTML)

Pdf

Research Paper

Comparative Analysis of Machine Learning Approaches for Sentiment Analysis of Students' Online Learning Feedback during Covid-19

Deepti Singh Kshatriya* , Snehlata Barde**

*-** School of Information Technology, MATS University, Raipur (CG), India.

Kshatriya, D. S., and Barde, S. (2022). Comparative Analysis of Machine Learning Approaches for Sentiment Analysis of Students' Online Learning Feedback during Covid-19. i-manager’s Journal on Information Technology, 11(3), 13-19. https://doi.org/10.26634/jit.11.3.19093

Abstract

Sentiment analysis aids in determining if a person's feelings are neutral, negative, or positive. Many machine learning and deep learning algorithms exist for assessing people's attitudes on various social media networks. Many researchers focused on students' emotional identification. The purpose of this paper is to analyze the sentiments of academic students regarding the online class experience conducted during the COVID-19 pandemic situation. For this work, the Term Frequency-Inverse Document Frequency (TF-IDF) model is used for the feature extraction and comparison of eight machine learning models were tested for the classification, such as Support Vector Classifier, Multinomial Naïve Bayes, Decision Tree, K-Nearest-Neighbors (KNN), Random Forest, AdaBoost Classifier, Bagging Classifier, Extreme Gradient Boosting Classifier (XGB) and F-Score, accuracy, precision, and Recall are the performance criteria examined. With a test accuracy of 0.97 and precision of 1.0, Multinomial Naive Bayes achieves the highest accurate model.

References

[1]. Bahrawi, B. (2019). Sentiment analysis using random forest algorithm-online social media based. Journal of Information Technology and Its Utilization, 2(2), 29-33. https://doi.org/10.30818/jitu.2.2.2695

[2]. Chauhan, G. S., Agrawal, P., & Meena, Y. K. (2019). Aspect-based sentiment analysis of students' feedback to improve teaching–learning process. Information and Communication Technology for Intelligent Systems, 107, 259-266. https://doi.org/10.1007/978-981-13-1747-7_25

[3]. Elbagir, S., & Yang, J. (2018, December). Sentiment analysis of twitter data using machine learning techniques and scikit-learn. In Proceedings of the 2018 International Conference on Algorithms, Computing and Artificial Intelligence, 1-5. https://doi.org/10.1145/3302425.3302492

[4]. Elnadree, R. S., El-Sisi, A. B., & Atwa, W. S. (2022). Performance investigation of features extraction and classification approaches for sentiment analysis systems. International Journal of Computers and Information, 9(1), 1-14. https://doi.org/10.21608/IJCI.2021.65578.1044

[5]. Geetha, R., Padmavathy, T., & Anitha, R. (2021). Prediction of the academic performance of slow learners using efficient machine learning algorithm. Advances in Computational Intelligence, 1(4), 1-12. https://doi.org/10.1007/s43674-021-00005-9

[6]. Hew, K. F., Hu, X., Qiao, C., & Tang, Y. (2020). What predicts student satisfaction with MOOCs: A gradient boosting trees supervised machine learning and sentiment analysis approach. Computers & Education, 145, 103724. https://doi.org/10.1016/j.compedu.2019.103724

[7]. Kandhro, I. A., Chhajro, M. A., Kumar, K., Lashari, H. N., & Khan, U. (2019). Student feedback sentiment analysis model using various machine learning schemes: a review. Indian Journal of Science and Technology, 12(14), 1-9. https://doi.org/10.17485/ijst/2019/v12i14/143243

[8]. Katragadda, S., Ravi, V., Kumar, P., & Lakshmi, G. J. (2020, March). Performance analysis on student feedback using machine learning algorithms. In 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS), IEEE, 1161-1163. https://doi.org/10.1109/ICACCS48705.2020.9074334

[9]. Kavitha, R. K. (2019). Sentiment research on student feedback to improve experiences in blended learning environments. International Journal of Innovative Technology and Exploring Engineering (IJITEE), 8(11S), 159-163. https://doi.org/10.35940/ijitee.K1034.09811S19

[10]. Novendri, R., Callista, A. S., Pratama, D. N., & Puspita, C. E. (2020). Sentiment analysis of YouTube movie trailer comments using naïve bayes. Bulletin of Computer Science and Electrical Engineering, 1(1), 26-32. https://doi.org/10.25008/bcsee.v1i1.5

[11]. Okoye, K., Arrona-Palacios, A., Camacho-Zuñiga, C., Achem, J. A. G., Escamilla, J., & Hosseini, S. (2022). Towards teaching analytics: a contextual model for analysis of students' evaluation of teaching through text mining and machine learning classification. Education and Information Technologies, 27(3), 3891-3933. https://doi.org/10.1007/s10639-021-10751-5

[12]. Rustam, F., Khalid, M., Aslam, W., Rupapara, V., Mehmood, A., & Choi, G. S. (2021). A performance comparison of supervised machine learning models for Covid-19 tweets sentiment analysis. Plos One, 16(2), e0245909. https://doi.org/10.1371/journal.pone.0245909

[13]. Tamrakar, L., Shrivastava, P., & Ghosh, S. M. (2021). An analytical study of feature extraction techniques for student sentiment analysis. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(11), 2900-2908.

[14]. Umair, M., Hakim, A., Hussain, A., & Naseem, S. (2021). Sentiment analysis of students' feedback before and after COVID-19 pandemic. International Journal on Emerging Technologies, 12(2), 177-182.

Full Article (HTML)

Pdf

Review Paper

ML-Based Stock Analysis

Devish* , Deependra Trivedi, Ankit Khare*

*-*** Department of Information Technology, Shri Ramswaroop Memorial College of Engineering and Management, Lucknow, Uttar Pradesh, India.

Devish, Trivedi, D., and Khare, A. (2022). ML-Based Stock Analysis. i-manager’s Journal on Information Technology, 11(3), 20-27. https://doi.org/10.26634/jit.11.3.18795

Abstract

Researchers are exploring various methods for effectively predicting prices in the stock market. Useful forecasting systems allow traders to better understand data such as future trends. In addition, investors have a great advantage as the analysis provides future market conditions. One such method is the machine learning algorithms for prediction. The aim of this work is to improve the quality of stock market output as predicted using the value of shares.

References

[1]. Ashfaq, R. A. R., Wang, X. Z., Huang, J. Z., Abbas, H., & He, Y. L. (2017). Fuzziness based semi-supervised learning approach for intrusion detection system. Information Sciences, 378, 484-497. https://doi.org/10.1016/j.ins.2016.04.019

[2]. Atsalakis, G. S., & Valavanis, K. P. (2009). Surveying stock market forecasting techniques–Part II: Soft computing methods. Expert Systems with Applications, 36(3), 5932-5941. https://doi.org/10.1016/j.asoc.2015.07.008

[3]. Atsalakis, G. S., & Valavanis, K. P. (2010). Surveying stock market forecasting techniques-Part I: Conventional methods. Journal of Computational Optimization in Economics and Finance, 2(1), 45-92.

[4]. Beck, T., & Levine, R. (2004). Stock markets, banks, and growth: Panel evidence. Journal of Banking & Finance, 28(3), 423-442. https://doi.org/10.1016/S0378-4266(02)00408-9

[5]. Biswal, A., (2022). Stock Price Prediction Using Machine Learning: An Easy Guide. Retrieved from https://www.simplilearn.com/tutorials/machine-learning-tutorial/stock-price-prediction-using-machine-learning.

[6]. Bohn, T. A. (2017). Improving Long Term Stock Market Prediction with Text Analysis. M.S. Thesis, Western University, London, Canada.

[7]. Bouktif, S., Fiaz, A., & Awad, M. (2020). Augmented textual features-based stock market prediction. IEEE Access, 8, 40269-40282. https://doi.org/10.1109/ACCESS.2020.2976725

[8]. Chavan, P. S., & Patil, S. T. (2013). Parameters for stock market prediction. International Journal of Computer Technology and Applications, 4(2), 337.

[9]. Chong, E., Han, C., & Park, F. C. (2017). Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies. Expert Systems with Applications, 83, 187-205. https://doi.org/10.1016/j.eswa.2017.04.030

[10]. Gupta, A., & Dhingra, B. (2012, March). Stock market prediction using hidden markov models. In 2012, Students Conference on Engineering and Systems (pp. 1-4). IEEE. https://doi.org/10.1109/SCES.2012.6199099

[11]. Holland, J. H. (1992). Adaptation in Natural and Artificial Systems: An Introductory Analysis With Applications to Biology, Control, and Artificial Intelligence. MIT press.

[12]. Hu, Y., Liu, K., Zhang, X., Su, L., Ngai, E. W. T., & Liu, M. (2015). Application of evolutionary computation for rule discovery in stock algorithmic trading: A literature review. Applied Soft Computing, 36, 534-551.

[13]. Jasic, T., & Wood, D. (2004). The profitability of daily stock market indices trades based on neural network predictions: Case study for the S&P 500, the DAX, the TOPIX and the FTSE in the period 1965–1999. Applied Financial Economics, 14(4), 285-297. https://doi.org/10.1080/0960310042000201228

[14]. Kim, K. J., & Han, I. (2000). Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index. Expert Systems with Applications, 19(2), 125-132. https://doi.org/10.1016/S0957-4174(00)00027-0

[15]. Kim, M. J., Min, S. H., & Han, I. (2006). An evolutionary approach to the combination of multiple classifiers to predict a stock price index. Expert Systems with Applications, 31(2), 241-247. https://doi.org/10.1016/j.eswa.2005.09.020

[16]. Lee, M. C. (2009). Using support vector machine with a hybrid feature selection method to the stock trend prediction. Expert Systems with Applications, 36(8), 10896-10904.

[17]. Liao, Z., & Wang, J. (2010). Forecasting model of global stock index by stochastic time effective neural network. Expert Systems with Applications, 37(1), 834-841. https://doi.org/10.1016/j.eswa.2009.05.086

[18]. Milosevic, N. (2016). Equity forecast: Predicting long term stock price movement using machine learning. arXiv preprint arXiv:1603.00751.

[19]. Schumaker, R. P., & Chen, H. (2009). Textual analysis of stock market prediction using breaking financial news: The AZFin text system. ACM Transactions on Information Systems (TOIS), 27(2), 1-19.

[20]. Wei, C. C., Chen, T. T., & Lee, S. J. (2013, July). K-NN based neuro-fuzzy system for time series prediction. In 2013, 14th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (pp. 569-574). IEEE.

[21]. Weng, B., Lu, L., Wang, X., Megahed, F. M., & Martinez, W. (2018). Predicting short-term stock prices using ensemble methods and online data sources. Expert Systems with Applications, 112, 258-273. https://doi.org/10.1016/j.eswa.2018.06.016

[22]. Yeh, C. Y., Huang, C. W., & Lee, S. J. (2011). A multiple-kernel support vector regression approach for stock market price forecasting. Expert Systems with Applications, 38(3), 2177-2186. https://doi.org/10.1016/j.eswa.2010.08.004

[23]. Zhang, G., Xu, L., & Xue, Y. (2017). Model and forecast stock market behavior integrating investor sentiment analysis and transaction data. Cluster Computing, 20(1), 789-803.

Full Article (HTML)

Pdf

Review Paper

A Comprehensive Review on IoT-Based Healthcare Security through Blockchain Technology

Kedar Nath Singh* , Arvind Kumar Upadhyay**

*-** Amity University, Gawalior, Madhaya Pradesh, India.

Singh, K. N., and Upadhyay, A. K. (2022). A Comprehensive Review on IoT-Based Healthcare Security through Blockchain Technology. i-manager’s Journal on Information Technology, 11(3), 28-33. https://doi.org/10.26634/jit.11.3.19062

Abstract

Blockchain is a rapidly developing modern technology being used in various fields, including IoT-based healthcare, which is widely used nowadays because of its ability to increase the security, robustness, and reliability of distributed systems. Key features such as data immutability, decentralization, transparency, privacy, and distributed ledger are highly necessary to make blockchain an attractive technology. This paper gives a comprehensive survey of the application of IoT based blockchain technology. IoT and blockchain are required to enable real-time information processing and transaction implementation in an orderly manner. Digital data has become important for health services due to the hectic routine of daily life for patients and doctors. This study sheds light on the use of blockchain for IoT-based healthcare.

References

[1]. Abdelmaboud, A., Ahmed, A. I. A., Abaker, M., Eisa, T. A. E., Albasheer, H., Ghorashi, S. A., & Karim, F. K. (2022). Blockchain for IoT Applications: Taxonomy, Platforms, Recent Advances, Challenges and Future Research Directions. Electronics, 11(4), 1–35. https://doi.org/10.3390/electronics11040630

[2]. Adere, E. M. (2022). Blockchain in healthcare and IoT: A systematic literature review. Array, 14, 100139. https://doi.org/10.1016/j.array.2022.100139

[3]. Agrawal, R., Verma, P., Sonanis, R., Goel, U., De, A., Kondaveeti, S. A., & Shekhar, S. (2018, April). Continuous security in IoT using blockchain. In 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 6423-6427). IEEE.

[4]. Al-madani, A. M., & Gaikwad, A. T. (2020, February). IoT data security via blockchain technology and servicecentric networking. In 2020 International Conference on Inventive Computation Technologies (ICICT) (pp. 17-21). IEEE. https://doi.org/10.1109/ICICT48043.2020.9112521

[5]. Alabdali, A. M. (2022). A Novel Framework of an IOT Blockchain-Based Intelligent System. Wireless Communications and Mobile Computing, 2022. https://doi.org/10.1155/2022/4741923

[6]. Aljemy, K., Alanazi, M., Alsofiry, M., & Baig, A. (2019). Improving IoT security using blockchain. In 2019 IEEE 10th GCC Conference and Exhibition, GCC 2019. https://doi.org/10.1109/GCC45510.2019.1570521015

[7]. Alqassem, I., & Svetinovic, D. (2014, December). A taxonomy of security and privacy requirements for the Internet of Things (IoT). In 2014 IEEE International Conference on Industrial Engineering and Engineering Management (pp. 1244-1248). IEEE. https://doi.org/10.1109/IEEM.2014.7058837

[8]. Andrea, I., Chrysostomou, C., & Hadjichristofi, G. (2015, July). Internet of Things: Security vulnerabilities and challenges. In 2015, IEEE symposium on computers and communication (ISCC) (pp. 180-187). IEEE. https://doi.org/10.1109/ISCC.2015.7405513

[9]. Ataşen, K., & Üstünel, H. (2019, October). Designing a Secure IoT Network by Using Blockchain. In 2019, 3rd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) (pp. 1-4). IEEE. https://doi.org/10.1109/ISMSIT.2019.8932728

[10]. Biswas, K., & Muthukkumarasamy, V. (2017). Securing smart cities using blockchain technology. Proceedings – 14th IEEE International Conference on Smart City, 1392–1393. https://doi.org/10.1109/HPCCSmartCity-DSS.2016.0198

[11]. Chondrogiannis, E., Andronikou, V., Karanastasis, E., Litke, A., & Varvarigou, T. (2022). Using blockchain and semantic web technologies for the implementation of smart contracts between individuals and health insurance organizations. Blockchain: Research and Applications, 3(2), 100049. https://doi.org/10.1016/j.bcra.2021.100049

[12]. Dorri, A., Kanhere, S. S., Jurdak, R., & Gauravaram, P. (2017). Blockchain for IoT security and privacy: The case study of a smart home. In 2017 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2017, 618–623. https://doi.org/10.1109/PERCOMW.2017.7917634

[13]. Fakhri, D., & Mutijarsa, K. (2018, October). Secure IoT communication using blockchain technology. In 2018 International Symposium on Electronics and Smart Devices (ISESD) (pp. 1-6). IEEE. https://doi.org/10.1109/ISESD.2018.8605485

[14]. Huh, S., Cho, S., & Kim, S. (2017, February). Managing IoT devices using blockchain platform. In 2017, 19th International Conference on Advanced Communication Technology (ICACT) (pp. 464-467). IEEE. https://doi.org/10.23919/ICACT.2017.7890132

[15]. Kai, K. A. N. G., Pang, Z. B., & Cong, W. (2013). Security and privacy mechanism for health internet of things. The Journal of China Universities of Posts and Telecommunications, 20, 64-68. https://doi.org/10.1016/S1005-8885(13)60219-8

[16]. Kumar, R. L., Khan, F., Kadry, S., & Rho, S. (2022). A Survey on blockchain for industrial Internet of Things: Blockchain for Internet of Things. Alexandria Engineering Journal, 61(8), 6001–6022. https://doi.org/10.1016/j.aej.2021.11.023

[17]. Li, D., Deng, L., Cai, Z., & Souri, A. (2022). Blockchain as a service models in the Internet of Things management: Systematic review. Transactions on Emerging Telecommunications Technologies, 33(4), 1–17. https://doi.org/10.1002/ett.4139

[18]. Mohanta, B. K., Jena, D., Ramasubbareddy, S., Daneshmand, M., & Gandomi, A. H. (2020). Addressing security and privacy issues of IoT using blockchain technology. IEEE Internet of Things Journal, 8(2), 881-888. https://doi.org/10.1109/JIOT.2020.3008906

[19]. Salih Mohammed, M. H. (2021). A hybrid framework for securing data transmission in internet of things (IoTs) environment using blockchain approach. 2021 IEEE International IoT, Electronics and Mechatronics Conference, IEMTRONICS 2021 - Proceedings. https://doi.org/10.1109/IEMTRONICS52119.2021.9422587

[20]. Sivaraman, V., Gharakheili, H. H., Vishwanath, A., Boreli, R., & Mehani, O. (2015, October). Network-level security and privacy control for smart-home IoT devices. In 2015 IEEE 11th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob) (pp. 163-167). IEEE. https://doi.org/10.1109/WiMOB.2015.7347956

[21]. Xu, L. D., Lu, Y., & Li, L. (2021). Embedding blockchain technology into IoT for security: A survey. IEEE Internet of Things Journal, 8(13), 10452-10473. https://doi.org/10.1109/JIOT.2021.3060508

Full Article (HTML)

Pdf

Survey Paper

Electronic Resources and its Utilities in Libraries – Future Aspects and Impact

Senthur Velmurugan V.*

Kongu Arts and Science College (Autonomous), Nanjanapuram, Erode, Tamilnadu, India.

Velmurugan, V. S. (2022). Electronic Resources and its Utilities in Libraries – Future Aspects and Impact. i-manager’s Journal on Information Technology, 11(3), 34-38. https://doi.org/10.26634/jit.11.3.19052

Abstract

This paper focuses on the various types of e-Assets. Today, the openness of e-resources in an academic library is wellknown. It furthermore analyzes the inspiration driving the use of e-Resources, the benefits, subject consideration status and overall client achievements that are looked at by clients while getting to e-resources, and the impact of e-resources on clients.

References

[1]. Berzins, K. & Hudson, A. (2011). The Use of EResources: A Snapshot of E-Resource Use among Linking London LLN Partner Institutions. Linking London Lifelong Learning Network, 1-40.

[2]. FlexiPrep. (n.d.). Electronic Resources: Introduction, E-Resources: Advantages and Disadvantages (For CBSE, ICSE, IAS, NET, NRA 2023). Retrieved from https://www.flexiprep.com/NIOS-Notes/Senior-Secondar y/Library-Science/NIOS-Library-Science-Unit-8-Electronic-Resources-Part-1.html

[3]. Graham, P. L., Notz, W. I., Pearl, D. K., & Stasny, E. A. (2003). Electronic resources: the Ohio state university EESEE project. Journal of Applied Mathematics and Decision Sciences, 7(2), 85-92. https://doi.org/10.1207/S15327612JAMD0702_4

[4]. Kenchakkanavar, A. Y. (2014). Types of e-resources and its utilities in library. International Journal of Information Sources and Services, 1(2), 97-104.

[5]. Lisbdnetwork. (2022). Advantages & Disadvantages of Electronic Resource (E-resource). Retrieved from https://www.lisedunetwork.com/advantages-disadvantages-electronic-resource-e-resource/

[6]. Nwabueze, A. U., & Urhiewhu, L. O. (2015). Availability and use of digital information resources by undergraduates of universities in Delta and Edo States, Nigeria. International Journal of Digital Library Services, 5(2), 1-12.

[7]. Swain, D. K., & Panda, K. C. (2009). Use of electronic resources in business school libraries of an Indian state: A study of librarians' opinion. The Electronic Library, 27(1), 74-85. https://doi.org/10.1108/02640470910934605

[8]. Velumani, K. (2013). An Investigation in to the Impact of E-Resources in Modern Library and Information Centers. Retrieved from https://www.semanticscholar.org/paper/An-investigation-in-to-the-impact-of-eresources-in-Velumani/940795bfb6a8b99aaab3acb5d4aa1932284d252c

i-manager's Journal on Information Technology (JIT)

Current Issue Vol. 12 Issue 4

Most Read

Most Cited

Volume 11 Issue 3 June - August 2022

Machine Learning and Deep Learning Techniques for Detecting Fraud Involving Credit Cards

Aastha Joshi* , Nirmal Gaud**

* Samrat Ashok Technological Institute, Vidisha, Madhya Pradesh, India. ** Department of Computer Science and Engineering, Samrat Ashok Technological Institute, Vidisha, Madhya Pradesh, India.

Joshi, A., and Gaud, N. (2022). Machine Learning and Deep Learning Techniques for Detecting Fraud Involving Credit Cards. i-manager’s Journal on Information Technology, 11(3), 1-12. https://doi.org/10.26634/jit.11.3.18987

Abstract

References

Full Article (HTML)

Pdf

Comparative Analysis of Machine Learning Approaches for Sentiment Analysis of Students' Online Learning Feedback during Covid-19

Deepti Singh Kshatriya* , Snehlata Barde**

*-** School of Information Technology, MATS University, Raipur (CG), India.

Kshatriya, D. S., and Barde, S. (2022). Comparative Analysis of Machine Learning Approaches for Sentiment Analysis of Students' Online Learning Feedback during Covid-19. i-manager’s Journal on Information Technology, 11(3), 13-19. https://doi.org/10.26634/jit.11.3.19093

Abstract

References

Full Article (HTML)

Pdf

ML-Based Stock Analysis

Devish* , Deependra Trivedi**, Ankit Khare***

*-*** Department of Information Technology, Shri Ramswaroop Memorial College of Engineering and Management, Lucknow, Uttar Pradesh, India.

Devish, Trivedi, D., and Khare, A. (2022). ML-Based Stock Analysis. i-manager’s Journal on Information Technology, 11(3), 20-27. https://doi.org/10.26634/jit.11.3.18795

Abstract

References

Full Article (HTML)

Pdf

A Comprehensive Review on IoT-Based Healthcare Security through Blockchain Technology

Kedar Nath Singh* , Arvind Kumar Upadhyay**

*-** Amity University, Gawalior, Madhaya Pradesh, India.

Singh, K. N., and Upadhyay, A. K. (2022). A Comprehensive Review on IoT-Based Healthcare Security through Blockchain Technology. i-manager’s Journal on Information Technology, 11(3), 28-33. https://doi.org/10.26634/jit.11.3.19062

Abstract

References

Full Article (HTML)

Pdf

Electronic Resources and its Utilities in Libraries – Future Aspects and Impact

Senthur Velmurugan V.*

Kongu Arts and Science College (Autonomous), Nanjanapuram, Erode, Tamilnadu, India.

Velmurugan, V. S. (2022). Electronic Resources and its Utilities in Libraries – Future Aspects and Impact. i-manager’s Journal on Information Technology, 11(3), 34-38. https://doi.org/10.26634/jit.11.3.19052

Abstract

References

Full Article (HTML)

Pdf

* Samrat Ashok Technological Institute, Vidisha, Madhya Pradesh, India.

** Department of Computer Science and Engineering, Samrat Ashok Technological Institute, Vidisha, Madhya Pradesh, India.

Devish* , Deependra Trivedi, Ankit Khare*