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2024 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2024

作者： Chauhan, Tania Renjith, P.N. Vellore Institute of Technology Chennai India Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350348460

This study offers a novel approach to tackling the increasing problem of workplace stress by utilizing machine learning techniques. Professionals in modern work contexts experience heightened levels of stress due to demanding job requirements, technical challenges, and the common expectation of perpetual availability. This work presents a comprehensive stress detection and mitigation method that utilizes advanced machine learning techniques, including image processing and real-time monitoring, to identify indicators of stress and offer personalized treatments. The system comprises several components, encompassing data collection and user interfaces, with the objective of promoting a more salubrious work environment and enhancing employee welfare and efficiency. © 2024 IEEE.

关键词： image processing machine learning Mental stress real-time monitoring stress recognition

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Advanced Strategies for Detecting Ransomware Threats 9

Advanced Strategies for Detecting Ransomware Threats

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Santhi, H. Sathish, Nalagatla Akash, Daddala Reddy Sai, Vidivada Chowdary Adithya Ganesh, Peddineni Sai Venkata Madhira Reddy, Yashwanth School of Computer Science and Engineering Vellore Institute of Technology TamilNadu Vellore632014 India

ISBN: (纸本)9798350377972

Ransomware is one of the key threats that have been created to lock the important services and data. New techniques like machine learning and blockchain are being used for enhancing the detection and mitigation process. However, these continue to face major challenges in terms of time lags arising out of delayed technology adoptions, gaps in security protocols, and inaccessibility of certain detection mechanisms within the systems to identify real-time advanced ransomware. In addition, fast restoration and ensuring minimum downtime for operations is still difficult. tackling these challenges by the integration of Machine Learning with traditional security techniques to enable early threat detection, accurate mitigation strategies, and quick restoration. Besides, it employs machine learning toward the correct identification of ransomware behavior and continuous adaptation to developing threats. The goal is to develop a hard, anticipatory defensive system that incurs the least possible blow from ransomware, thereby focusing on future research directions to better perfect such technologies. © 2024 IEEE.

关键词： Restoration

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Strategies for classifying water quality in the Cauvery River using a federated learning technique

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International Journal of Cognitive Computing in engineering 2023年第1期4卷 187-193页

作者： J, Vellingiri K, Kalaivanan P, Gopinath M C, Gobinath Subramaniam, Prabhakar Rontala Rangarajan, Sarathkumar School of Information Technology and Engineering Vellore Institute of Technology Vellore632014 India School of Computer Science and Engineering Vellore Institute of Technology Vellore632014 India Information System and Technology University of KwaZulu-Natal Durban South Africa Victorian Institute of Technology Melbourne Australia

Artificial intelligence methods are emerging techniques used in the field of environmental protection, especially in the analysis of air, water, and soil quality. AI analyzes vast amounts of environmental data to predict pollution and provide decision-makers with the information they need to develop efficient policies. One of the most important problems in environmental analysis is data security, and many organizations are actively working to ensure the secure collection, storage, and utilization of sensitive environmental data. In addition, organizations are focusing on developing strategies to protect their data from malicious attacks, such as cyber-attacks, as well as from accidental misuses, like unauthorized access. For this purpose, we have introduced a novel water quality prediction using the Federated Learning Technique. Federated learning enables multiple parties to collaborate and train a model on their local data without sharing it with others, thereby preserving data privacy. The proposed method is applied to a Cauvery River dataset of water quality parameters, and the results demonstrate that the PSO-optimized federated learning process achieves better prediction accuracy of 87%, a precision of 85%, a recall of 93%, and an 89% F1 score. © 2023

关键词： Machine learning

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Multifunctional Pre-processing Approach for Diabetes Mellitus Prediction Using Ensemble Learning 3

Multifunctional Pre-processing Approach for Diabetes Mellitu...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Divya, S. Sudha, M. Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India

ISBN: (纸本)9798350372120

Diabetes mellitus when untreated can result in a number of health issues. It is a metabolic disease marked by abnormal blood glucose levels. Early detection of diabetes improves a person's long-term health by halting its progression. Making better medical decisions can be greatly aided through the implementation into practice automated techniques that can precisely identify pre- and diabetic situations. In this paper Synthetic Minority Oversampling Technique and Adaptive Oversampling Technique is used to address class imbalance problem and Chi-square test, Recursive feature elimination methods are involved in detecting and extracting relevant components from unprocessed data. These techniques are applied to the framework and compared using different ensemble learning classifiers such as XGBoost, AdaBoost, LightGBM and CATBoost. The result shows us CATBoost with ADASYN and RFE provides us with better accuracy of 0.96%. © 2024 IEEE.

关键词： Adaptive boosting

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Enhanced Traffic Management in IoT-Integrated Internet of Vehicles (IoV) with Optimized Routing and Deep Learning Algorithm 5th

Enhanced Traffic Management in IoT-Integrated Internet of Ve...

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5th International Conference on Advances in Distributed Computing and Machine Learning, ICADCML 2024

作者： Sahoo, Arundhati Tripathy, Asis Kumar School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9789819735228

The integration of the Internet of Things (IoT) into vehicular networks has paved the way for the exciting development of the Internet of Vehicles (IoV) within Intelligent Transportation Systems (ITS). These breakthroughs bring about enhancements in urban transportation services, including improved quality and interaction, reduced costs, more efficient resource utilization, and enhanced traffic management capabilities. The incorporation of IoV into transportation has the potential to address various traffic-related challenges in smart cities. Nevertheless, optimizing routing and monitoring network traffic presents significant challenges, primarily due to limited node-to-node communication capabilities. To tackle these problems, this study proposes the BiLSTM-GreedyNet, which combines the Improved Greedy Perimeter Stateless Routing Algorithm with the Bi-LSTM neural network technology in a 5G network environment. The system delivers impressive results, as evidenced by simulations in MATLAB, boasting a remarkable 98.22% accuracy rate, a low 34.2 MJ energy consumption, and a high Packet Delivery Ratio (PDR) of 91%, all tested with a network of 100 nodes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Internet of things

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AI Powered Indicatorless Algorithmic Trading Bot for Cryptocurrency and Financial Market

AI Powered Indicatorless Algorithmic Trading Bot for Cryptoc...

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2024 IEEE International Conference on Emerging Technologies in computer science for Interdisciplinary Applications, ICETCS 2024

作者： Olorunsheye, Akinlemi Olushola Meenakshi, S.P. Vellore Institute of Technology Department of Computer Science and Engineering Tamil Nadu Vellore India

ISBN: (纸本)9798350372502

Accurate prediction of the future movement of the financial instruments is a major factor that assures profitability and minimize risk in a volatile, unpredictable and complex financial and cryptocurrency market. Many researchers have proposed various ways to correctly forecast the future movements of these instruments with various technical indicators but losses are still in the *** this research, we have designed a price action based AI powered indicatorless algorithmic trading bot that can correctly predict the future direction of any financial instrument in the cryptocurrency and financial market with the capacity to ensure profitability while minimizing risk. The system is indicatorless, and uses price action and artificial intelligence algorithm for forecasting market movement and eventual execution of a buy or a sell trade with little or no human *** result shows an improved performance compared to others methods with a very low maximum risk drawdown of 1.06% with a profitability ratio of more than 297% over a 22 months period of back testing using EURO vs. USD (EURUSD) currency pair. © 2024 IEEE.

关键词： Financial markets

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Enhanced Road Safety Through Intelligent Lane Detection and Driver Drowsiness Monitoring 15

Enhanced Road Safety Through Intelligent Lane Detection and ...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Arunpandian, J. Gunasekaran, V. Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India

ISBN: (纸本)9798350370249

The innovative system outlined in the provided research demonstrates a significant stride towards addressing the growing concern of road safety. Lane departure and driver drowsiness are two major causes of road accidents. In this paper, we present an observation of road lane and drowsiness detection system that uses a camera to capture real-time video footage of the road ahead and applies image processing techniques to detect and track road lanes. The system's road lane detection module employs the canny edge detection algorithm and Hough transform to detect lane lines accurately. The drowsiness detection module of the system analyzes the driver's facial features and tracks the driver's eye movements, blink frequency to identify signs of drowsiness. If the driver found to be sleepy, it issues an audio alert to the driver. The system's capability to detect and track road lanes with precision, along with its ability to identify signs of driver drowsiness, underscores its potential as a promising solution for enhancing road safety. © 2024 IEEE.

关键词： Canny edge algorithm drowsiness lane detection road safety vehicles

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Design of a Hybrid AI- based Phishing Website Detection using LSTM, CNN, and Random Forest based Ensemble Learning Analysis 8

Design of a Hybrid AI- based Phishing Website Detection usin...

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8th International Conference on Electronics, Communication and Aerospace technology, ICECA 2024

作者： Kavya, S. Sumathi, D. Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India

ISBN: (纸本)9798350367904

Phishing attacks are always surfacing as key threats against internet users, necessitating advanced detection methods. Blacklist-based systems and rule-based models of phishing detection generally have had critical limitations in dealing with evolving tactics and new phishing schemes. Some of these approaches fail to cope with the temporal and visual patterns of phishing sites, which are crucial for timely and accurate detection. To overcome these difficulties, this work introduces a hybrid AI-based phishing website detection model that utilizes several machine learning and deep learning techniques to improve the accuracy of the detection and remove false positives. The proposed model uses LSTM networks, Genetic Algorithms, Random Forest, and CNN through the stacking ensemble framework. Since LSTM is adopted to capture the temporal dependencies in the website traffic and user interaction patterns, this model can effectively model their phishing behavior over time. GA is used for bioinspired feature selection to reduce the dimensionality of features while optimizing model performance. Random Forest is used as a base layer addressing structured features like URL characteristics and WHOIS information. CNNs are incorporated to extract feature content from a webpage and images that carry various visual indicators often used in phishing attacks including counterfeit logos or banners. A meta-classifier is then used to combine the outputs of LSTMs, CNN, and RF and generate the final classification to boost the detection rate. The proposed hybrid model surpasses the existing techniques and facilitates the analysis of temporal, visual, and structured data, making the detection considerably more accurate. Achieving accuracy of as much as 96-97% and having an AUC of 0.97 with a false positive rate below 3%, the model then impacts the more powerful and more flexible phishing detection system, which is then capable of being more protective against higher sophisticated phishing te

关键词： Phishing

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A Comprehensive Study on Performance of Serial and Parallel Web Crawlers 3

A Comprehensive Study on Performance of Serial and Parallel ...

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3rd IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Parajuli, Elina Devi, K. Vimala Prashanth, Narayanan Bashyal, Ashutosh School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9798350361186

Web crawlers, also known as web spiders or web robots, are automated programs that systematically browse the World Wide Web, indexing web pages and gathering data for various purposes. This paper addresses the inefficiency of default Python crawlers, caused by the Global Interpreter Lock (GIL), in the context of web page collection for search engines. It aims to create a concurrent Python crawler and compare its performance with a single-threaded counterpart. The crawler will undergo three iterations: a sequential version, a concurrent version using thread pool executor, and an aiohttp version for concurrent requests. Performance metrics such as speed of retrieval, internal processing, and memory requirements will be analyzed. The goal is to develop an efficient Python crawler for continuous web crawling, providing insights for developers and users, and comparing Python's performance with another programming language. © 2024 IEEE.

关键词： Python

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A preprocessing method combined with an ensemble framework for the multiclass imbalanced data classification

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International Journal of computers and Applications 2022年第12期44卷 1178-1185页

作者： Pavan Kumar, M.R. Jayagopal, Prabhu School of Computer Science and Engineering Vellore Institute of Technology Vellore India School of Information Technology & Engineering Vellore Institute of Technology Vellore India

Skewed distributions appear in many real-world classification problems. Skewed distributions, underrepresented classes, and multiple overlapping regions in multiclass imbalanced datasets deteriorate the performance of existing classification algorithms and approaches. In this context, we combine a novel preprocessing procedure to tackle minority classes in multiclass imbalanced problems with an ensemble framework. The preprocessing method oversamples the minority classes based on normalized probability, and then an ensemble called a stacked generalization framework is used to train the model. The motive behind combining the ensemble framework and the preprocessing procedure is to enhance the overall classification performance of the classifier for multiclass imbalanced problems. Experimental results on 20 multiclass imbalanced datasets show that the proposed preprocessing method with the ensemble framework outperforms the representative approaches in 13 datasets for macro average arithmetic (MAvA) and mean F-measure (MFM) metrics. In the case of state-of-the-art techniques, the proposed approach steered 14 datasets for the MAvA metric and 15 datasets for the MFM metric to success. © 2019 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Classification (of information)

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