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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Chell, Subhadeep Chakare, Sainath Sohan, Pinnarwar Sandosh, S. Vellore Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Vellore Institute of Technology School of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350378092

In the intricate field of web API development, the imperative for robust security measures is more pronounced than ever. This research delves into the realm of real-time threat detection and mitigation in web API development, drawing parallels with real-time threat detection methodologies employed in JavaScript. The study underscores the pressing need for advanced security protocols in an era marked by escalating reliance on online applications. A distinguishing feature of this research is its emphasis on harnessing the power of web APIs for real-time threat detection. It provides an in-depth exploration of monitoring techniques and logging mechanisms in web APIs, and presents a comprehensive overview of real-time threat mitigation strategies. These insights are not merely theoretical but are grounded in practical application, offering a pragmatic guide for developers navigating the complex landscape of web API security. The research methodology is rigorous and multifaceted, encompassing an extensive literature review on real-time threat detection and online application security. This is supplemented with illustrative code examples and case studies, providing a tangible context for the theoretical concepts discussed. This research makes a significant contribution to the existing body of knowledge on real-time threat detection using web APIs. It offers invaluable insights for developers and security practitioners, equipping them with the knowledge and tools to safeguard web applications against emerging threats in the digital landscape. © 2024 IEEE.

关键词： cybersecurity mitigation real-time threat detection web-API

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Anomaly Detection in Surveillance Videos Using Deep Learning and SVM Based Data Reduction Method 5

Anomaly Detection in Surveillance Videos Using Deep Learning...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Hareesh Narayan, B.K. Chowdhury, Abishi Shukla, Pankaj School of Computer Science and Engineering Vellore Institute of Technology Chennai India School of Advanced Science Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350383522

Anomaly detection in video data is a crucial subject that has gained a significant research implication in computer vision with several real-time applications. The researchers worldwide developed innovative solutions in this context in the diverse areas of research specific practical applications. This paper emphasizes over the importance of anomaly detection in a public environment along with its applications for the same. A brief survey of prominent existing techniques with a comparative analysis has been presented in this paper in order to highlight the relevant techniques, available datasets, considered metrics for analysis, and future scope of improvements. In this context, deep learning (DL) approaches like Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks are prominently used for anomaly detection. In this paper, Support Vector Machine (SVM) along with convolution 2D LSTM based data reduction approach is presented which is used to keep only the important data over incremental addition of the data. The experimental analysis, performed on benchmark dataset DCSASS, shows the efficacy of the proposed approach when compared with state-of-the-art approaches as it maintains significant accuracy while ensuring effective data reduction. Furthermore, the proposed approach demonstrates the training and validation loss analysis. It reduces the errors as time progresses which is directly proportional to the incremental addition of data. © 2024 IEEE.

关键词： Accuracy Anomaly Detection Dataset Deep Learning Long Short-Term Memory Support Vector Machine

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A novel optimised GWOAN algorithm for scheduling task and power consumption in MEC network

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International Journal of Intelligent Systems Technologies and Applications 2023年第4期21卷 386-401页

作者： Sivakumar, P. Murugan, Murali Somasundaram, S.K. Prabhu, S. Nagendra School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai600127 India Engineering Retail Business Professional Georgia United States School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Computational Intelligence SRM Institute of Science and Technology Tamil Nadu Chennai632014 India

In recent decades, the 5G and internet of things (IoT) are occupied with several applications like face recognition, traffic control, video surveillance and telecommunication, etc. Mobile-edge computing (MEC) is a promising paradigm in wireless communication which is carried by the computational scheduling of mobile devices. To enhance the computation design, the AlexNet of the DL model is applied which is based on the convolutional neural network (CNN) used to train a large number of attributes. To provide an optimal solution, the metaheuristics algorithm of grey wolf optimisation (GWO) is combined with an AlexNet which is named a novel grey wolf optimisation-based AlexNet (GWOAN) algorithm. In the proposed GWOAN algorithm, the AlexNet hyperparameters (weights, biases and other parameters) are fine-tuned by GWO and then performed a classification. As a result, the GWOAN has achieved a higher scheduling task and low latency than the standard AlexNet, ResNet-18 and VGGNet-16 respectively. © 2023 Inderscience Enterprises Ltd.

关键词： Optimization

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Uncovering the Hidden Patterns of the COVID-19 Global Pandemic: An in-Depth Data Analytics Approach

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SN computer science 2024年第8期5卷 981页

作者： Sarobin, M. Vergin Raja Rathore, Jinen Mishra, Rajat Moolya, Vinay Vittal Seth, Yash School of Computer Science and Engineering Vellore Institute of Technology TN Chennai 600127 India

COVID-19 is a highly infectious respiratory illness caused by the novel coronavirus. It was first identified in Wuhan, China in December 2019 and has since spread globally, infecting and killing a vast number of people, leading to a worldwide pandemic. The pandemic has left the world in disarray. We wished to apply data analytics and regression models to understand and study the data – OwiD (Our World in Data) real time covid dataset - to analyse and draw trends and factors that led to the widespread of the virus. Doing so, allows us to identify key factors and trends that played a vital role in the rapid spread of the virus. We can thus determine the underlying hidden patterns of key factors. This will help provide a better understanding and determine the potential reasons COVID-19 took the world by storm with its fast-paced spread. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： COVID-19 Data analytics OWiD (Our World in Data) Pandemic Regression Virus

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Quantum Kernel based Support Vector Machine with Quantum Approximate Optimization Algorithm Embedding for improved Lung Cancer Prediction 15

Quantum Kernel based Support Vector Machine with Quantum App...

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

作者： Dutta, Shivanya Shomir Sandeep, Sahil Sridevi, S. Sridhar, Mohan Ram Singh, Chaitanya Vellore Institute of Technology School of Computer Science and Engineering Chennai India Vellore Institute of Technology Scope Chennai India

ISBN: (纸本)9798350370249

The integration of classical machine learning with quantum computing has sparked a remarkable revolution, opening avenues to tackle previously insurmountable challenges in diverse fields, particularly in clinical applications. One such challenging task is to predict lung cancer with minimal clinical samples and features. The prominent objective of this proposed research is to introduce a novel Quantum Kernel Support Vector Machine (QKSVM) constructed with the Quantum Kernel Circuit (QKC) enriched with Quantum Approximation Optimization Algorithm (QAOA) embedding circuit to classify highly complex lung cancer using a minimal number of samples and minimal feature dimensions. In this context, to compare the classification performance of the transformative potential of the proposed QAOA embedding QKSVM classifier model, we implemented two other variants of the QKC leveraging Angle embedding (Angle Embedding QKSVM) and Instantaneous Quantum Polynomial embedding (IQP Embedding QKSVM). We also compared their performance with that of a classical SVM classifier. In this study, we delve into the transformative potential classification performance of the proposed QAOA Embedding-based QKSVM classifier, which is contrasted particularly in scenarios with limited sample size and feature dimensions. Our investigation reveals the quantum kernel's superiority, even when operating with smaller datasets. This remarkable performance is attributed to the quantum models' robustness in extracting intricate diagnosing features, enabling it to extract more pertinent insights from fewer data points. Notably, the QKSVM model trained with a significantly selected feature vector of 4-Dimensions (4D) with Principal Component Analysis (PCA) emerges as the frontrunner in lung cancer predictions, achieving an outstanding test accuracy of 94.64% and the highest area under curve (AUC) metric of 82.33%. © 2024 IEEE.

关键词： Lung Cancer Prediction Quantum Approximate Optimization Algorithm(QAOA) embedding Quantum Kernel Support Vector Machine(QKSVM)

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Enhancing Phishing Detection Through Advanced Machine Learning Techniques 5

Enhancing Phishing Detection Through Advanced Machine Learni...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Sreenivasan, S. Rout, Amrit Pal, Tarun Kumar Ramachandran, Prakash Vellore University of Technology School of Electronics and Engineering Vellore India Vellore University of Technology School of Computer Science and Engineering Vellore India Vellore Institute of Technology School of Electronics Engineering Vellore India

ISBN: (纸本)9798331504403

With the rapid growth of internet usage, phishing attacks have become increasingly prevalent, presenting a major challenge to cybersecurity. This paper introduces a sophisticated method for detecting phishing websites by leveraging advanced machine learning algorithms. Specifically, Evaluated the performance of Logistic Regression, Linear Regression, and Multinomial Naive Bayes models in distinguishing phishing websites based on their URL features. The study utilizes a robust dataset comprising over 100,000 URLs, each meticulously classified as either legitimate or phishing. Through comprehensive data preprocessing and an innovative optimization pipeline, Refined the Logistic Regression model to achieve a detection accuracy of 98.0369%. Additionally, incorporated the web parameter analysis to enhance detection precision and minimize false positives. This work significantly contributes to the development of more resilient cybersecurity strategies, addressing key issues such as feature selection, handling large datasets, and adapting to the constantly evolving nature of phishing tactics. © 2024 IEEE.

关键词： Phishing

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Leveraging Location Data for Enhanced Contact Tracing in Educational Settings 2

Leveraging Location Data for Enhanced Contact Tracing in Edu...

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Kedia, Sakshi Ashish Jesseman Devamirtham, N. Harishankar, S. Erramchetty, Sudheer Kumar Vellore Institute of Technology Department of Computer Science and Engineering Chennai India SCOPE Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350365269

Contact tracing stands as a crucial pillar in global infectious disease control, where innovative methodologies can significantly bolster its effectiveness. This study introduces a framework tailored for analyzing contact data within educational settings, focusing on interactions among 500 students and an infected individual. The resulting algorithm adeptly identifies potential encounters, empowering public health officials to assess transmission risks and optimize intervention strategies. Addressing privacy concerns, anonymized student data is utilized, emphasizing the delicate balance between public health priorities and individual privacy rights. Furthermore, this research illustrates the framework's adaptability to real-world scenarios through the integration of authentic location data and disease-specific parameters. Offering scalability and cost-effectiveness, this framework presents a blueprint for enhancing contact tracing strategies amidst evolving health crises, highlighting technology's transformative potential in infectious disease control. © 2024 IEEE.

关键词： Disease control

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Development and Implementation of a Smart Aquarium Controller for Optimized Environmental Monitoring and Management 2

Development and Implementation of a Smart Aquarium Controlle...

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2nd International Conference on Cyber Physical Systems, Power Electronics and Electric Vehicles, ICPEEV 2024

作者： Mathav, Roopesh Madheshwaran Rithesh, Naga Ra, Nawin Kumba, Krishna School of Computer Science & Engineering Vellore Institute of Technology Chennai India School of Electrical Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350388664

The proposed research aims to simplify the process of maintaining and monitoring aquariums. Automated operations like temperature, light, and water level monitoring can be performed using the Arduino NANO. The three primary functions of this work includes light control, water level monitoring, and temperature monitoring. A feature of Wi-Fi is remote control;the Wi-Fi module serves as a server and connects to the Arduino serial port and a computer via a local Wi-Fi network. Through the personalized website, you can monitor and control the Arduino. You may adjust the light and see the current water temperature and level through the interface. The LCD panel shows the percentage of water level and the temperature reading. The system can be extended to monitor carbon dioxide (CO2) levels in planted aquariums, which is crucial for plant health. Furthermore, ammonia and pH levels will be monitored to prevent harmful conditions for fish and other aquatic creatures. © 2024 IEEE.

关键词： Wi-Fi

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Deep Neural Networks for Diagnosis of Parkinson's Disease Using Replicated Acoustic Features 1

Deep Neural Networks for Diagnosis of Parkinson's Disease Us...

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1st International Conference on Computing, sciences and Communications, ICCSC 2024

作者： Likhitha, Janga Sai Shahid, Afzal Hussain Reddy, Anamareddy Sravann School of Computer Science and Engineering Vellore Institute of Technology Amaravati India

ISBN: (纸本)9798350353648

Parkinson's disease is a neurodegenerative disease that affects a person's non-motor and motor functions causing tremors, stiffness, difficulty in moving, fatigue, anxiety, sleep disorders, etc. This condition happens because of the absence of dopamine-producing cells in the brain, and its symptoms worsen over time. This paper proposes a method for diagnosing Parkinson's disease using replicated acoustic features. This paper uses feature selection algorithms namely Fisher's, mRMR, Boruta, and ANOVA (Analysis of Variance) to find out the most discriminative features, and these features were fed into Random Forest (RF) and Deep neural networks (DNNs) to diagnose Parkinson's disease. 99.17% is the highest accuracy is achieved with the DNNs with Boruta and DNNs with ANOVA models. This is a significant improvement compared to previous studies. This result suggests that the replicated acoustic features can be used to diagnose Parkinson's disease. © 2024 IEEE.

关键词： Neurodegenerative diseases

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AI-Optimized Irrigation for Sustainable Agriculture

AI-Optimized Irrigation for Sustainable Agriculture

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2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Hussain, Mansoor Karthikeyan, N. Maurya, Ipsit Sinha, Subhratha School Of Computer Science And Engineering Vellore Institute Of Technology Chennai India School Of Electornics And Engineering Vellore Institute Of Technology Chennai India

ISBN: (纸本)9798350364828

This paper presents an innovative AI-enhanced irrigation system designed to optimize water management in agriculture. The system integrates advanced technologies such as IoT, sensor networks, and artificial intelligence algorithms to achieve precise and efficient irrigation scheduling. Leveraging real-time data from sensors including soil moisture, temperature, and humidity, combined with historical weather forecasts, the system employs a dynamic algorithm to make informed irrigation decisions. Experimental evaluation conducted over a week-long period using a garden rose as a test subject demonstrated the system's ability to maintain optimal soil moisture levels within the range of 60-75%, while significantly reducing water consumption compared to conventional methods. Simulation results further validated the system's effectiveness in predicting soil moisture levels and optimizing irrigation scheduling. Key metrics including enhanced crop output, reduced water usage, and adherence to sustainable farming practices were used to assess the superiority of the proposed model. Overall, the AI-enhanced irrigation system presents a promising solution for sustainable agriculture, offering improved water conservation, enhanced crop productivity, and efficient resource utilization. © 2024 IEEE.

关键词： Water conservation

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