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Detection of Fraudulent data in IOT devices using Machine Learning framework
Ms. P. Ramya Sri
Pages: 1-9 | First Published: 04 Jul 2022
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Abstract
The Internet of Things (IoT) refers to a network of physical objects, or "things," that are based in software and other technologies in order to communicate and exchange data with other devices and systems over the internet. These gadgets range from common domestic items to high-tech industrial instruments. Currently more than 7 millions users are connected today. Experts predict that by 2023, there will be more than 10 billion connected IoT devices, and by 2026, there will be 23 billion. IoT devices generate a vast volume of data in a variety of modalities, with differing data quality characterised by their speed in terms of time and location reliance. In such a scenario, machine learning algorithms can play a critical role in providing biotechnology-based security and permission, as well as anomaly detection to improve the usability and security of internet-of-things (IoT) platforms The detection of IoT devices using the Machine Learning framework is offered to achieve this goal. Five machine learning models are evaluated using various metrics with a vast collection of input feature sets in this ML framework. Each model calculates a spam score based on the input attributes that have been adjusted. This score represents the IoT device's dependability under various conditions. The proposed technique is validated using certain common appliances. The acquired findings support the suggested scheme's effectiveness in comparison to other current schemes.
Keywords—IoT, detection, Security, machine learning

References 
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Human Activity Recognition using CNN and Pretrained Machine Learning Models.
Dr. N Vinaya Kumari
Pages: 10-16 | First Published: 05 Jul 2022
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Abstract
People's lives are enriched by human activity recognition (HAR), which extracts action-level details about human behaviour from raw input data. There are a variety of uses for Human Activity Recognition, including elderly surveillance systems, abnormal behaviour, and so forth. However, deep learning models such as Convolutional Neural Networks have outperformed traditional machine learning methods. As a result of CNN, it is possible to extract features and reduce computational costs. Transfer Learning, on the other hand, refers to the use of pre-trained machine learning models that can be used to detect human activity using a special type of Artificial Neural Network known as Leveraging CNN. Resnet-34 Use of a CNN model can provide detection accuracy up to 96.95 percent for human activity recognition Numerous studies and research have been conducted on HAR. There are only a few models in the majority of the paper, however What we know is that the more data we have, the better the model and the more accurate the model will be.
Keywords—HAR, CNN, KNN, CCTV, ROC, SGD, RELU.

References
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Minimizing the Misinformation in Social Networksusing Heuristic Greedy Algorithm
Dr. M. Jaganathan
Pages: 17-23 | First Published: 05 Jul 2022
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Abstract
In recent years, online social media has grown in popularity, and a vast volume of information has circulated over social media platforms, altering people's access to information. The credibility of information material is being questioned, and various types of misinformation are using social media to propagate quickly. The importance of network space administration and maintaining a trusted network environment cannot be overstated. In this paper, we look at a new challenge termed the activity minimization of misinformation influence (AMMI) problem, which involves removing a group of nodes from the network in order to reduce the total amount of misinformation interaction between nodes (TAMIN). To put it another way, the AMMI challenge is to choose K nodes from a given social network G to block in order to minimize the TAMIN.We demonstrate that the objective function is neither submodular nor supermodular, and we suggest a heuristic greedy algorithm (HGA) for removing the top K nodes.
Keywords: Comment Filtering, Common Interests,misinformation blocking,social network.

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https://doi.org/10.1007/s10586-018-1757-3
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35. Dr. V. Senthil kumar, Mr. P. Jeevanantham, Dr. A. Viswanathan, Dr. Vignesh Janarthanan, Dr. M. Umamaheswari, Dr. S. Sivaprakash Emperor Journal of Applied Scientific Research “Improve Design and Analysis of Friend-to-Friend Content Dissemination System ”Volume - 3 Issue - 3 2021
36. Vignesh Janarthanan, A.Viswanathan, M. Umamaheswari, “Neural Network and Cuckoo Optimization Algorithm for Remote Sensing Image Classification ", International Journal of Recent Technology and Engineering., vol. 8, no. 4, pp. 1630-1634, Jun. 2019.

Stock Price Prediction Using Machine Learning
Dr. N. Vinaya Kumari
Pages: 24-29 | First Published: 05 Jul 2022
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Abstract
Shares in publicly traded companies, or equity shares, can be purchased and sold on the stock market. There are three basic types of stock market investors. All three types of investors: FII (foreign institutional investors), DII (domestic investors), and retail investors. Foreign Institutional Investors, such as mutual funds and banks, who have the experience and knowledge necessary to make their investment. Non-professional investors are known as retail investors. Machine learning is used to make an accurate prediction with less risk management in the stock market because there is a lot of uncertainty there. Through forecasting and LSTM (Long/Short Term Memory), we can theoretically predict stock prices through the use of machine learning. Effective stock market prediction gives us some suggestions on trading strategies, which is why stock market prediction is so important when it comes to investments. There is, however, no way to guarantee that the data will be 100% accurate because of future uncertainty in the field of study. For stock price prediction, this paper reviews studies on machine learning techniques and algorithms.

References 
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6. Viswanathan, A., Arunachalam, V. P., & Karthik, S. (2012). Geographical division traceback for distributed denial of service. Journal of Computer Science, 8(2), 216.
7. Anurekha, R., K. Duraiswamy, A. Viswanathan, V.P. Arunachalam and K.G. Kumar et al., 2012. Dynamic approach to defend against distributed denial of service attacks using an adaptive spin lock rate control mechanism. J. Comput. Sci., 8: 632-636.
8. Umamaheswari, M., & Rengarajan, N. (2020). Intelligent exhaustion rate and stability control on underwater wsn with fuzzy based clustering for efficient cost management strategies. Information Systems and e-Business Management, 18(3), 283-294.
9. Babu, G., & Maheswari, M. U. (2014). Bandwidth Scheduling for Content Delivery in VANET. International Journal of Innovative Research in Computer and Communication Engineering IJIRCCE, 2(1), 1000-1007.
10. Viswanathan, A., Kannan, A. R., & Kumar, K. G. (2010). A Dynamic Approach to defend against anonymous DDoS flooding Attacks. International Journal of Computer Science & Information Security.
11. Kalaivani, R., & Viswanathan, A. HYBRID CLOUD SERVICE COMPOSITION MECHANISM WITH SECURITY AND PRIVACY FOR BIG DATA PROCESS., International Journal of Advanced Research in Biology Engineering Science and Technology, Vol. 2,Special Issue 10, ISSN 2395-695X.
12. Ardra, S., & Viswanathan, A. (2012). A Survey On Detection And Mitigation Of Misbehavior In Disruption Tolerant Networks. IRACST-International Journal of Computer Networks and Wireless Communications (IJCNWC), 2(6).
13. Dr. V. Senthil kumar, Mr. P. Jeevanantham, Dr. A. Viswanathan, Dr. Vignesh Janarthanan, Dr. M. Umamaheswari, Dr. S. Sivaprakash Emperor Journal of Applied Scientific Research “Improve Design and Analysis of Friend-to-Friend Content Dissemination System ”Volume - 3 Issue - 3 2021