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

作者： Oberoi, Vansh Gautam Karthik, V. Dheeba, J. Raja Singh, R. Vellore Institute of Technology School of Computer Science and Engineering Tamil Nadu Vellore India Vellore Institute of Technology School of Electrical Engineering Tamil Nadu Vellore India Automotive Research Center Vellore Institute of Technology School of Electrical Engineering Tamil Nadu Vellore India

ISBN: (纸本)9798350370249

Industrial drive systems play a critical role in modern manufacturing processes, and their reliable operation is essential for productivity. These systems often consist of multiple interconnected control units that communicate through transmission control protocols. The security of these protocols remains vulnerable to attacks if the network is compromised. In this article, the security challenges faced by industrial electrical drive system is investigated and the strategies to enhance their resilience against cyber threats are proposed. The intended drive system is realized with a 2.2 kW squirrel cage induction motor and two-level voltage source inverter controlled using the F28379D microcontroller. The microcontroller is interfaced with the computer through USB for deploying the control program, as well as to monitor and tune the drive parameters. The focus lies on the computer, which provides speed commands to the microcontroller. By implementing these strategies, an effective intrusion detection system is tailored to the unique requirements of industrial electrical drive systems, safeguarding them against service disruption attacks. © 2024 IEEE.

关键词： Microcontrollers

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Explorative Deployment of Fine-Tuned Large Language Model for On-Site computerized Numeric Control Machine Operator Assistance

Explorative Deployment of Fine-Tuned Large Language Model fo...

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2024 IEEE Silchar Subsection Annual Conference, SILCON 2024

作者： Kanimozhi, S. Sriker, Y.S. Vasan, Vinod School of Computer Science and Engineering Vellore Institute of Technology Chennai India Vellore Institute of Technology School of Mechanical Department Chennai India

ISBN: (纸本)9798331540821

In a dynamic workshop environment focused on optimal machine utilization and minimal downtime, it is imperative that a greater emphasis is laid on the tools required to localize faults in essential equipment on the shop floor. One of the major detractors of productivity is the requirement of repeatedly cross-referencing equipment user manuals, consulting consumer portals, and acquiring trained personnel for assistance. This presents an opportunity to blend the intelligence of AI with an emphasis on simplicity of use in a machine shop. CNCGPT is a fine-tuned LLM (Large Language Model) that aims to address machine specific queries that conventional LLM models currently deployed en masse may not be able to ascertain. This model has been tailored to serve as a companion for operators on the shop floor, providing consistent, high-quality responses without tiring. This study aims to present an overview of the model's architecture and an account of a real-time operational assessment conducted during the deployment of the model for assistance with the operation of a specific CNC machine. © 2024 IEEE.

关键词： computerized Numeric Control (CNC) Generative Pre-trained Transformer (GPT) Large Language Model (LLM) Model Fine Tuning Operational Companion User Assistance

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Revolutionizing Smart Agriculture: Burning Number Analysis and Linear Regression for Optimal Sensor Activation in 3-Ary n-Cube Networks

Revolutionizing Smart Agriculture: Burning Number Analysis a...

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2024 International Conference on Integrated Circuits, Communication, and Computing Systems, ICIC3S 2024

作者： Rajeshwari, S. Rajesh, M. School of Advanced Sciences Vellore Institute of Technology Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350364088

This study introduces a novel approach by combining burning number analysis and supervised machine learning, specifically linear regression, to optimize sensor placement in large agricultural fields modeled as a 3-ary n-cube network. In this unique integration, each node represents a location equipped with environmental sensors, and edges signify wireless communication links. The burning number analysis identifies critical sensor nodes, strategically activating them for efficient information propagation throughout the network. Simultaneously, linear regression is employed to predict and optimize the activation of key sensors, maximizing coverage and data dissemination. This approach aims to enhance network efficiency, providing valuable insights for precision agriculture practices such as crop management, irrigation scheduling, and resource allocation. Through this innovative method, the study not only contributes to the development of adaptive solutions for sustainable farming but also showcases the potential of combining graph theory-based burning number analysis with linear regression for optimizing sensor networks in complex agricultural landscapes. © 2024 IEEE.

关键词： Crops

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A Double Layer Encryption for Communication using Cryptographic Algorithms 2

A Double Layer Encryption for Communication using Cryptograp...

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

作者： Ragavan, K. Mohankumar, B. Vivekrabinson, K. Arun, R. Arumuga School of Computer Science and Engineering Vellore Institute of Technology Vellore India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu Krishnankoil India

ISBN: (纸本)9798350328202

Cryptography is a fundamental field in computer science that focuses on securing communication and protecting sensitive information from unauthorized access. It involves the use of mathematical algorithms and techniques to transform plaintext into ciphertext, rendering it unintelligible to anyone without the proper decryption key. Cryptographic systems play a crucial role in ensuring confidentiality, integrity, and authenticity of data in various domains, including finance, healthcare, e-commerce, and communication networks. In this paper, a double-layer encryption method is proposed that uses the Vigenere and Polybius ciphers to secure communication. The Vigenere cipher is a classical method that uses a keyword to encrypt plaintext, while the Polybius cipher is another classical cipher that uses a grid to map letters to two-digit numbers to encrypt data. By combining these two ciphers, the proposed algorithm aims to enhance the security of the data by introducing a more complex and secure encryption method. The proposed method is implemented using Python programming language with web implementation. The web implementation of the algorithm uses Flask, a Python web framework. This allows users to encrypt and decrypt their data through a user-friendly interface, making it more accessible and practical for real-world use. Based on the obtained results demonstrate that the proposed method provides a higher level of security compared to using each cipher individually, without significantly impacting the encryption and decryption speed. Overall, the proposed double-layer encryption method can be a useful tool for secure communication in various applications. It contributes to the field of cryptography by introducing a novel approach that enhances data security without compromising usability. © 2024 IEEE.

关键词： Cryptography

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Autism Detection using InceptionV3 and Security using Blockchain 8

Autism Detection using InceptionV3 and Security using Blockc...

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

作者： Annie Silviya, S.H. Seetha, R. Raja, S. Sriman, B. Rupesh, S. Gopala Krishnan, J. Rajalakshmi Institute of Technology Computer Science Engineering Chennai India Vellore Institute of Technology Computer Science Engineering and Information Systems Vellore India Panimalar Engineering College Computer Science and Engineering Chennai India Institute of Science and Technology Computer Science and Engineering Vel Tech Rangarajan Dr Sagunthala R&d Chennai India Rajalakshmi Institute of Technology Computer and Communication Engineering Chennai India

ISBN: (纸本)9798350367904

The mental health and well-being of children are critical components of their overall development and future success. In India, only 1 in 6 8 children are diagnosed with autism, since monitoring and addressing the mental health issues among children is a challenge because of various factors. Studies have proven that autism detection can be made possible with the assessment of specific facial features that are used as primary markers for the proposed model. The proposed solution aims to leverage technological advancements, such as deep learning, for detecting autism in children using the aforementioned markers. The model gathers data on the children's facial features and predicts any underlying mental disorder. Since the application collects and analyzes sensitive information from the users, an additional security feature is implemented in the proposed solution using blockchain. Key features of the solution include standardized assessment tools, real-time tracking capabilities, personalized interventions, and seamless communication channels between parents, caregivers, and mental health professionals. © 2024 IEEE.

关键词： Diseases

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Phishtor: Phishing Detection Using Machine Learning

Phishtor: Phishing Detection Using Machine Learning

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2024 International Conference on Recent Innovation in Smart and Sustainable technology, ICRISST 2024

作者： Rangasamy, Gokul Primya, T. Kumar, Gurupriya Sivakumar, Harinath Kanagaraj, G. Sathish Kumar, D. Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India Kumaraguru College of Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India Vellore Institute of Technology School of Computer Science Engineering and Information Technology Tamil Nadu Vellore India

ISBN: (纸本)9798350309140

Nowadays, the majority of people rely heavily on the internet. Online transactions, online sign-ups, and online purchasing are used by everyone. One kind of threat to websites is phishing, which is an illegal activity that involves obtaining user data, including credit/debit card numbers, passwords, and login credentials. An efficient phishing detection method based on ensemble learning is presented in this study. The technique proposed, which combines three classifiers (K-Nearest Neighbours, KNN, Support Vector Machines, and Gradient Boosting Classifiers, GBC) to achieve the highest accuracy performance, has been shown to yield positive results overall (97.4%). The proposed technique uses feature extraction for processing the Uniform Resource Locator (URL) and predicts the website whether it's phishing or not. Therefore, it is preferable and used to employ the suggested technique to identify phishing using machine learning. © 2024 IEEE.

关键词： Phishing

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From Tweets to Insights: BERT-Enhanced Models for Cyberbullying Detection

From Tweets to Insights: BERT-Enhanced Models for Cyberbully...

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2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems, ICETSIS 2024

作者： Sen, Madhura Masih, Jolly Rajasekaran, Rajkumar School of Computer Science and Engineering Vellore Institute of Technology Vellore India Bml Munjal University Gurgaon India

ISBN: (纸本)9798350372229

The rapid rise of social media usage, particularly during the COVID-19 pandemic, has amplified the prevalence of cyberbullying, necessitating effective detection and prevention measures. This research explores the application of sentiment analysis (SA) and deep learning models, specifically utilizing Bidirectional Encoder Representations from Transformers (BERT) in combination with Convolutional Neural Network (CNN) and Multi-Layer Perceptron (MLP), to automatically detect cyberbullying in Twitter posts. The study utilizes a Hate Speech and Offensive Language dataset, employing data preprocessing techniques and exploratory data analysis to address imbalances and understand the dataset's intricacies. The proposed architecture demonstrates superior performance, achieving an accuracy range of 87.2% to 92.3%, outperforming existing methods such as Naive Bayes, Support Vector Machine (SVM), Deep Neural Network (DNN), and CNN. The research contributes to the ongoing efforts to develop robust cyberbullying detection systems, emphasizing the need for proactive measures in the face of escalating online harassment incidents, especially in the COVID-19 era. © 2024 IEEE.

关键词： COVID-19 Cyberbullying Deep Learning Natural Language Processing Sentiment Analysis

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A Novel Framework for QR Code Detection and Decoding from Obscure Images using YOLO Object Detection and Real-ESRGAN Image Enhancement Technique 14

A Novel Framework for QR Code Detection and Decoding from Ob...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Edula, Vinay Ammisetty, Kalyan Kotha, Aakash Ravipati, Deepthi Davuluri, Aakashnag Arulmozhivarman, P. Vellore Institute of Technology Computer Science Vellore India Vellore Institute of Technology Electronics Engineering Vellore India SRM University Computer Science Kattankulathur India Vellore Institute of Technology Centre for Clean Environment Vellore India

ISBN: (纸本)9798350335095

QR codes have become increasingly popular in various applications, such as inventory management, advertising, and payment systems. However, detecting and decoding QR codes from blurry or unclear images can be challenging due to various factors, such as low image resolution, noise. The research aims to propose an efficient approach for detecting and decoding QR codes from unclear images using the You Only Look Once (YOLO) object detection model and deep super-resolution techniques implemented through Real-ESRGAN [1]. Authors trained and evaluated five different YOLOv8 object detection models with varying sizes and parameters to detect and crop the QR code from the input image. Based on the evaluation results, authors selected the model which achieved the best performance in terms of precision and recall. The cropped QR code based on YOLOv8 bounding box, is then enhanced using real-ESRGAN [1], which applies deep super-resolution techniques to improve the quality of the image. Finally, the enhanced QR code is decoded using three QR code utility libraries in Python, namely pyzbar, zxing, and opencv, which are executed in parallel using multithreading for efficient retrieval in less time. The results after testing demonstrates the effectiveness of proposed approach in accurately detecting and decoding QR codes from blurry or unclear images. The proposed approach can be used in various fields, such as inventory management, advertising, and payment systems, to provide a seamless user experience. © 2023 IEEE.

关键词： Object detection

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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 PV-Powered Microcontroller-Based Agricultural Robot Utilizing GSM technology for Crop Harvesting and Plant Watering

A PV-Powered Microcontroller-Based Agricultural Robot Utiliz...

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2023 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2023

作者： Nuhel, Ahsan Kabir Sazid, Mir Mohibullah Paul, Dipta Hasan, Ekramul Roy, Priyadarshini Hriddhi Sinojiya, Fenish Prafulbhai Electrical and Electronics Engineering Bangladesh Vellore Institute of Technology Computer Science and Engineering Vellore India Vellore Institute of Technology Department of Mechanical Engineering Vellore India

ISBN: (纸本)9798350398748

A solar-powered, intelligent grass cutter and pesticide application system that uses microcontrollers, sonar sensors, solar panels, and GSM technology is being proposed as a solution to the issue of high labor costs in the agricultural sector of Bangladesh. This is done in order to achieve exquisite cost management. The initial objective is to design a system that, through utilizing a robot controlled by a micro controller, will make the laborious work of our farmers and entrepreneurs more feasible. This machine can trim the grass, apply pesticides, and simultaneously provide the plant with water. Solar panels are used to extract power from the sun to charge the device. At long last, GSM technology has been integrated, turning this device into one that can be remotely controlled using a straightforward SMS protocol. © 2023 IEEE.

关键词： Microcontrollers

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