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

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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Real-Time Network Optimization in 6G minimizing End-to-End Delay Using SARSA Algorithm

Real-Time Network Optimization in 6G minimizing End-to-End D...

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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

As 6G networks take off, there is an increasing need for effective system optimization strategies that can decrease end-to-end (e2e) delay and improve the general efficiency of the network. The SARSA (State-Action-Reward-State-Action) algorithm, which can learn from the network ecosystem and optimize network parameters accordingly, is the foundation of the unique real-time network optimization approach that is suggested in this research. With the aid of a simulation environment that replicates the actual 6G network environment, we assess the suggested approach. Our findings show that, the SARSA algorithm may substantially decrease e2e delay and improve network performance. In general, this research offers an intriguing plan for network optimization in 6G networks and emphasizes the SARSA algorithm's potential for improving real-time network reliability. © 2024 IEEE.

关键词： 6G e2e delay Network Optimization SARSA algorithm

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Timestamp Based OTP and Enhanced RSA Key Exchange Scheme with SIT Encryption to Secure IoT Devices

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Journal of Cyber Security and Mobility 2023年第1期12卷 77-102页

作者： Kollipara, V.N. Hemanth Kalakota, Sai Koushik Chamarthi, Sujith Ramani, S. Malik, Preeti Karuppiah, Marimuthu School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632 014 India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Department of Computer Science and Engineering & Information Science Presidency University Karnataka Bengaluru560064 India

The Internet of Things (IoT) has become an emerging technology and is expected to connect billions of more devices to the internet in the near future. With time, more and more devices like wearables, intelligent home systems, and industrial automation devices are getting connected to the internet. IoT devices primarily transfer data using wireless communication networks, introducing more vulnerabilities like man-in-the-middle-attacks and eavesdropping. These security concerns are customary for any device communicating over the internet because of its intrinsic open nature. These problems are usually subdued by conventional cryptographic algorithms used in typical systems that are power-hungry and computationally intensive, making them infeasible to be used in IoT devices since they run on lowpowered chips, limiting performance, memory, and bandwidth. Hence, there is a requirement to adopt lightweight cryptographic algorithms that can abate the security issues while using low computational resources, which is the constraint in the given scenario. Hence, we propose an end-to-end secured IoT system that ensures the system's integrity is never compromised using lightweight cryptographic algorithms. We propose a three-module system, where the first module handles user authentication using a time-based one-time password, the second secures communication using lightweight enhanced RSA, and the third performs data encryption using Feistel-based enhanced SIT. This kind of system is designed to deal with security challenges in IoT devices, ensuring adequate data security while reducing the computational footprint using lightweight cryptography. © 2023 River Publishers.

关键词： Authentication

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A Review of Intelligent Configuration and Its Security for Complex Networks

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Chinese Journal of Electronics 2024年第4期33卷 920-947页

作者： Yue ZHAO Bin YANG Fei TENG Xianhua NIU Ning HU Bo TIAN Science and Technology on Communication Security Laboratory School of Computer and Information Engineering Chuzhou University School of Computing and Artificial Intelligence Southwest Jiaotong University School of Computer and Software Engineering Xihua University Cyberspace Institute of Advanced Technology Guangzhou University

Complex networks are becoming more complex because of the use of many components with diverse technologies. In fact, manual configuration that makes each component interoperable has breed latent danger to system security. There is still no comprehensive review of these studies and prospects for further research. According to the complexity of component configuration and difficulty of security assurance in typical complex networks, this paper systematically reviews the abstract models and formal analysis methods required for intelligent configuration of complex networks, specifically analyzes, and compares the current key technologies such as configuration semantic awareness, automatic generation of security configuration, dynamic deployment, and verification evaluation. These technologies can effectively improve the security of complex networks intelligent configuration and reduce the complexity of operation and maintenance. This paper also summarizes the mainstream construction methods of complex networks configuration and its security test environment and detection index system, which lays a theoretical foundation for the formation of the comprehensive effectiveness verification capability of configuration security. The whole lifecycle management system of configuration security process proposed in this paper provides an important technical reference for reducing the complexity of network operation and maintenance and improving network security.

关键词： Complex networks Intelligent configuration Configuration security Software defined network Lifecycle management

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An AI-Enabled Smart Healthcare Application for Remote Patient Monitoring System 2

An AI-Enabled Smart Healthcare Application for Remote Patien...

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2nd International Conference on Recent Trends in Microelectronics, Automation, Computing, and Communications systems, ICMACC 2024

作者： Abdullah, A Sheik Pooja, A. Poovithazhan, K. Anjali, K. Kavya, R. School of Computer Science and Engineering Vellore Institute of Technology Chennai India Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350366570

This research presents a groundbreaking healthcare application aimed at revolutionizing patient care and improving the overall healthcare experience. The application is developed through an extensive research and development process that leverages cutting-edge technologies to address critical challenges faced by healthcare providers and patients alike. By seamlessly integrating advanced features and functionalities, this application holds the potential to revolutionise the way healthcare is given and received. This, outlines a health care application that aims to improve patient care, facilitate interaction between the healthcare professionals and patients and enhance overall healthcare management. The application provides a comprehensive platform for patients, doctors and healthcare providers to access and manage medical records, schedule appointments, receive personalized health recommendations, and securely communicate in real-time. It incorporates advanced features such as AI-powered symptom analysis, medication reminders, and telemedicine capabilities. The application's user-friendly interface, combined with robust data privacy and security measures, ensures a seamless and confidential healthcare experience for all users. By leveraging technology, this health care application strives to revolutionize the way healthcare is delivered and accessed, ultimately improving patient outcomes and healthcare efficiency. © 2024 IEEE.

关键词： Electronic health record

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Converting SAR Images to Optical Images Using CycleGAN 2

Converting SAR Images to Optical Images Using CycleGAN

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2nd International Conference on Emerging Trends in information technology and engineering, ic-ETITE 2024

作者： Siddiqui, Aman Ali Soorya Prakash, K. Ambastha, Swayam School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India School of Electronics and Communication Engineering Vellore Institute of Technology Vellore Tamil Nadu Vellore India

ISBN: (纸本)9798350328202

This paper explores the application of CycleGAN, a generative adversarial network, to perform unpaired image-to-image translation between Synthetic Aperture Radar (SAR) images and optical images. This research leverages an unpaired dataset of SAR and optical images, training the CycleGAN model to generate realistic optical images from previously unseen SAR data. The implications of this work are significant for remote sensing applications, where optical images may be more advantageous for specific tasks. The study demonstrates the potential of unpaired data in addressing the challenge of SAR to optical image translation. © 2024 IEEE.

关键词： Synthetic aperture radar

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Real-Time Fire Detection in Unmanned Ground Vehicles Integrating YoloV5 and AWS IoT

Real-Time Fire Detection in Unmanned Ground Vehicles Integra...

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2023 International Conference on System, Computation, Automation and Networking, ICSCAN 2023

作者： Banerjee, Saptajit Das, Ronodeep Rathinam, Rajesh Dhanalakshmi, R. School of Computer Science and Engineering Vellore Institute of Technology Chennai India Centre for Cyber Physical Systems School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350315127

This paper aimed at the development of a remotely controllable 4-wheeled unmanned ground vehicle (UGV) capable of transmitting realtime sensor data and live video feeds over the internet. The central innovation lies in the utilization of an advanced object detection model, to detect fires within the live video feed. By leveraging a Raspberry Pi 4 with 2GB RAM, AWS IoT Core, MQTT, HTTP, and TCP protocols, the UGV achieves seamless data exchange. AWS IoT Core serves as both MQTT broker and subscriber, with the UGV as publisher for sensor readings and subscriber for control commands. The web app acts as the publisher for control commands, generating signals based on user input. The Remote. it software plays a pivotal role in facilitating a cloud-based HTTP service for live video streaming, thereby significantly enhancing global accessibility. Moreover, diverse remote operation methods including keyboard, onscreen, and speech controls, provide flexible means for user interaction. The integration of YOLOv5, Tensorflow, and OpenCV yields a robust fire detection capability, effectively triggering SMTPbased email alerts. This system effectively showcases the seamless fusion of robotics, IoT, web technologies and machine learning, thereby contributing to safety enhancement and inspiring a wave of future advancements. © 2023 IEEE.

关键词： Web services

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Voting Ensemble: Performance Improvement for Intrusion Detection System 3

Voting Ensemble: Performance Improvement for Intrusion Detec...

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

作者： Rajathi, C. Rukmani, P. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350372120

In the present digital world, security has become increasingly challenging. Many organizations rely on security mechanisms to safeguard their resources from intrusion. However, previous research indicates that despite the implementation of several security measures, organizations still face security breaches. Therefore, organizations must adopt an effective security mechanism. One such mechanism is the Intrusion Detection System (IDS), which examines network traffic to protect an organization from potential attacks. To handle the large volume of network traffic, a well-organized classification system is necessary. Machine learning classification, among the various approaches used in research, provides an effective and efficient solution for detecting intrusion and enhancing security. Despite significant efforts, IDS still faces challenges due to the nature of the concept drifting. To mitigate this issue, a hybrid ensemble approach is proposed. This approach combines the Majority Rule Ensemble (MRE) and Probability Consensus Ensemble (PCE). The ensemble is constructed using various classification algorithms such as Logistic Regression (LR), Naive Bayes (NBC), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF). Each algorithm was individually built on the training dataset. The hybrid ensemble, which employs the MRE and PCE as aggregation methods, is then prepared by fitting it to the dataset. The proposed model's effectiveness is assessed using Network Security Laboratory - Knowledge Discovery and Databases (NSL-KDD) and a real-time testbed dataset. The results from the proposed hybrid ensemble show the improvement in the model's performance and the adaptability to the drifting nature of the intrusion dataset compared to a standalone model. © 2024 IEEE.

关键词： Network security

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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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Analyzing Sentiments: A Comprehensive Study of Roberta-Based Sentiment Analysis on Twitters 4

Analyzing Sentiments: A Comprehensive Study of Roberta-Based...

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4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Krishnamoorthy, A. Sundhar, K.A. Naveen Kumar, V. Karthik, V. Vellore Institute of Technology School of Computer Science & Engineering Tamil Nadu India Vellore Institute of Technology Department of Computer Science Engineering Tamil Nadu India

ISBN: (数字)9798350364729

ISBN: (纸本)9798350364729

Social media platforms have become essential avenues for individuals to express opinions and emotions across diverse topics such as products, events, and policies, using both text and emojis. Understanding these sentiments is crucial, yet manually analyzing the vast data on platforms like Twitter is impractical. Automated sentiment analysis tools, exemplified by Roberta, offer a solution. Roberta, an evolving language model, employs advanced bidirectional learning to predict sentiment labels accurately. This study focuses on sentiment analysis on Twitter, leveraging Roberta to autonomously categorize tweets into positive, negative, or neutral sentiments. We utilizing natural language processing and machine learning algorithms for dataset preprocessing, incorporating sources like Kaggle and ensuring data integrity. Assessment metrics encompassing accuracy, precision, recall, and F1-score, in addition to confusion metrics, assess model performance. According to our findings, machine learning powered sentiment analysis models are dependable, even in the face of challenges and more so when processing Twitter data sets. The study gives direction on how this kind of model can be of help in analyzing sentiments expressed through social networking sites thus assisting scholars and professional Consultancy in the field. © 2024 IEEE.

关键词： Tweets

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