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6th International Conference on Pattern Analysis and Intelligent Systems, PAIS 2024

作者： Arbaoui, Ahmed Abdelatif, Sahraoui Derdour, Makhlouf Dept. of Computer Science Tebessa12000 Algeria Echahid Cheikh Larbi Tebessi University Lamis Laboratory Dept. of Computer Science Tebessa12000 Algeria University of Larbi Ben m'Hidi Liaoa Laboratory Dept. of Mathematics And Computer Science Oum El Bouaghi04000 Algeria

ISBN: (纸本)9798350350265

The implementation of network slicing services, designed to fulfill the diverse requirements of each Vehicle-to-Everything (V2X) use case, has significantly facilitated the expansion of the Internet of Vehicles (IoV). Three critical communication needs for the V2X ecosystem are ensured by network slicing: ultra-low latency and high reliability, enhanced mobile broadband, and massive machine-type communication (mMTC) support. In addition, network scalability, high mobility, and large density are taken into account. It is noteworthy that the utilization of network slicing technology has evolved beyond its fundamental objectives. In this context, the application of network cutting technology extends its objectives to encompass aspects beyond the essential requirements. This review aims to scrutinize and categorize scientific research endeavors within this domain, according to the specific objectives underlying the adoption of network slicing technology in the IoV ecosystem. © 2024 IEEE.

关键词： Vehicle to Everything

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Trust Based Cooperative Secure Routing Protocols for Internet of Things (IoT) 2

Trust Based Cooperative Secure Routing Protocols for Interne...

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

作者： Mekala, Sagar Ramji, Bhukya CVR College of Engineering Dept. of Computer Science and Engineering Hyderabad India Kakatiya Institute of Technology and Science Dept. of Computer Science and Engineering Hanmakonda India

ISBN: (纸本)9798350366570

The Internet of Things has rapidly emerged and continues to create services, software, sensors-embedded devices, and protocols. IoT allows physical objects to communicate, exchange information, and make decisions while performing critical jobs. The Internet of Things is enhanced by wireless sensor networks, which serve as a continuous layer. To gain adoption and spread, Internet of things-based sensor networks must overcome serious general and specialized risks and technological hurdles. The main drawbacks of wireless sensor networks are insufficient resources. Internet of Things (IOT)-based Wireless Sensor Networks have issues in energy consumption, longevity, throughput, routing, and security. The goal of this effort was to enhance network longevity, throughput, packet latency/loss, and better encounters with hostile nodes. Consider wireless energy harvesting in the proposed three-layer cluster-based wireless sensor network routing protocol to enhance network lifespan. The proposed approach is a Trust based Cooperative Secure Routing Protocol (TCSRP) technique with security system to obtain malicious activities of objects and to slant them into blacklist. Finally, experiments were done to test the methodology. It outperforms most traditional routing protocols like AODV, DSR, and DSDV. © 2024 IEEE.

关键词： Network layers

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Evaluating Various Techniques for Twitter Sentiment Analysis for Election Results 1

Evaluating Various Techniques for Twitter Sentiment Analysis...

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1st International Conference on Pioneering Developments in computer science and Digital Technologies, IC2SDT 2024

作者： Mishra, Prerna Punia, Divya Sikka, Geeta Sharma, Ajay K Sikka, Kushagra NIT Delhi Dept. of Computer Science and Engineering India University of Florida Dept. of Computer Science and Engineering Gainsville United States

ISBN: (纸本)9798350365016

The Indian General Election of 2019 is proof of the rise in political content on social media platforms, reflecting user attitudes and behaviors towards the electoral process. This paper examines the collective behavior of Twitter users during the election period and assesses the feasibility of election prediction through social media analysis in India. This work presents weekly sentiment analysis on election-related Twitter content. It analyzes how social media content can be used as a reliable factor of public sentiment during elections and has recourse to be a predictive parameter alongside other factors. The simulation results show that VADER outperforms various state-of-the-art techniques with a mean absolute error of 1.345 for candidate-wise predictions, significantly outperforming Textblob (5.445), Pattern analyzer (5.135), and pre-trained BERT (28.335) while yielding similar results for party-wise and alliance-wise analyses. © 2024 IEEE.

关键词： Tweets

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Multiple Approaches Towards Authentication Using Keystroke Dynamics 2

Multiple Approaches Towards Authentication Using Keystroke D...

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2nd International Conference on Machine Learning and Data Engineering, ICMLDE 2023

作者： Arsh, Aditya Kar, Nirmalya Das, Smita Deb, Subhrajyoti Dept. of Computer Science & Engineering NIT Agartala Tripura India Dept. of Computer Science & Engineering ICFAI University Tripura India

In the realm of global computer security, safeguarding sensitive data and computer systems stands as an ongoing challenge. Striking the delicate balance between convenient access for legitimate users and thwarting imposter attacks is imperative. Traditional authentication methods, primarily reliant on username and password schemes, have been the longstanding cornerstone of cyber system authentication. Yet, this conventional approach is marred by inherent vulnerabilities, including password exchange, shoulder surfng, brute force attacks, dictionary attacks, speculation, phishing, and emerging threats. In response to these shortcomings, biometrics technology has emerged as a reliable alternative for authentication and verification, with Keystroke Dynamics being a well-established method. This research advances Keystroke Dynamics-based authentication through cutting-edge technologies, including machine learning, deep learning, and neural networks. A quantitative assessment demonstrates remarkable results - Machine Learning achieves 99.9% accuracy with the Random Forest model, Deep Learning combines CNN with GRU to reach 99.31% accuracy, and the DBN model registers at 98.01%. In the realm of neural networks, Bi-CNN and FFM-NN excel, with accuracy rates of 96.8% and 94.7%, respectively. These findings lay the foundation for robust, secure authentication systems, ensuring the protection of sensitive data and computer assets in our interconnected world. © 2024 Elsevier B.V.. All rights reserved.

关键词： Authentication

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Automated Exam Script Checking using Zero-Shot LLM and Adaptive Generative AI

Automated Exam Script Checking using Zero-Shot LLM and Adapt...

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2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Himi, Shinthi Tasnim Tanzila Monalisa, Natasha Sultana, Shirin Afrin, Anika Hasib, Khan Md Bangladesh University of Business and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh Jahangirnagar University Dept. of Computer Science and Engineering Dhaka Bangladesh Brac University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798331529765

The manual grading of exam scripts is a labor-intensive process, often plagued by subjectivity and inconsistency. This study presents an innovative approach to automating the evaluation of exam scripts using the Large Language Model (LLM) integrated with Zero-Shot Learning (ZSL) and Generative AI. Our system leverages the capabilities of GPT-4 to generate and evaluate answers, applying similarity measures to ensure accuracy and fairness in grading. The model's adaptability through ZSL allows it to assess new questions without additional training. The system also incorporates iterative refinement techniques to enhance the quality of standard answers over time. Performance evaluations demonstrate a low error rate, with an average relative error of 1.29% for annotated questions and 1.67% for non-annotated questions compared to human graders. The performance underscores the automated system's transformative potential in revolutionizing educational assessments, offering a consistent, fair, and highly efficient alternative to traditional grading methods. © 2024 IEEE.

关键词： Zero-shot learning

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Effective Machine Learning Model for Disease Recognition and Categorization of Mango Leaves

Effective Machine Learning Model for Disease Recognition and...

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2024 International Conference on Computational Intelligence and Computing Applications, ICCICA 2024

作者： Kumar, Sandeep Gupta, Bhupesh Grover, Richa Chhabra, Mohit Computer Science & Engineering Dept. Ambala Mullana India Panipat Institute Of Engineering & Technology Computer Science & Engineering Dept. India

ISBN: (纸本)9798350306446

Maintaining the health and production of mango farms depends on the early detection of illnesses in mango trees. Despite the vital role agriculture plays, it remains an industry receiving limited attention from the machine-learning community. A noteworthy challenge is the lack of standardized and openly accessible agricultural datasets, impeding researchers from fully leveraging the potential of advanced computational prediction tools. The study focuses on employing deep learning and image processing techniques for diagnosing prevalent mango leaf diseases in India. The goal is to enhance both the quality and quantity of agricultural production by implementing effective disease control measures upon identifying plant diseases. The automation of plant disease detection proves particularly beneficial, reducing the monitoring efforts required by large farms. Rapid identification of leaf diseases is crucial, given that leaves serve as a plant's primary source of nutrition. Notably, none of the seven different mango leaf illnesses that have been observed in India have ever been the subject of earlier research. In this study, we provide a lightweight Resnet50 model that can reliably classify healthy mango leaves and the seven discovered illnesses on mango leaves. The performance of our proposed lightweight Resnet-50 model is evaluated against many pre-trained models, such as Exception, Resnet101, and VGG16. The findings reveal that the lightweight Resnet-50 model achieves the highest testing accuracy, reaching an impressive 95%. This underscores the model's effectiveness in automating the identification of mango leaf diseases, offering a promising solution for improving agricultural practices in India. © 2024 IEEE.

关键词： Diseases

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Enhancing Quantum Operations: Random Forests for Anomaly Detection and Alert Generation 7

Enhancing Quantum Operations: Random Forests for Anomaly Det...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Anoop, M. Nithya, B. SRM Institute of Science and Technology Dept. of Computer Science and Applications Chennai India Hindustan Institute of Technology and Science Dept. of Computer Applications Chennai India

ISBN: (纸本)9798350372816

A promising approach for enhancing the dependability and security of quantum processes is the integration of Random Forests, a traditional machine learning technique, with quantum anomaly detection and alert production. This approach involves training Random Forest models to inspect quantum data and spot abnormalities such as variations in quantum states, entanglement patterns, and the results of quantum processes. When anomalies are found, alarms are activated, categorized by severity, and sent out via several notification channels. Operators of quantum systems can act quickly by applying techniques for adaptive control, error correction, or system reinitialization. The rigorous preservation of historical data makes post-incident study and ongoing system improvement possible. This novel strategy strengthens security while simultaneously improving the performance of quantum systems, particularly in situations involving quantum communication. To guarantee its efficiency in the continually changing quantum landscape, careful design and ongoing monitoring are essential. © 2024 IEEE.

关键词： Quantum communication

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5G User Equipment (UE) Positioning and Localization Estimation using Machine Learning

5G User Equipment (UE) Positioning and Localization Estimati...

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

作者： Survase, Sanket Diwan, Raman Lal, Kumari Nidhi Kumar, Shishupal VNIT Nagpur Dept. of Computer Science and Engineering Nagpur India IIIT Nagpur Dept. of Computer Science and Engineering Nagpur India

ISBN: (纸本)9798350378092

The emergence of 5G technology has ushered in a new era of connectivity, making communications faster and cheaper. One of the main challenges of 5G networks is accuracy and efficiency, which are important for many applications such as driver less cars, smart cities and virtual reality. This research paper explores the integration of machine learning techniques to improve the accuracy and reliability of 5G infrastructure. The plan uses rich data generated by 5G networks, combining various parameters such as signal strength, flight time and angle of arrival. Machine learning algorithms are used to process this data and create good models for the correct location. The system is designed to adapt to dynamic and complex urban environments where traditional methods often encounter limitations. The main contribution of this research includes the development of a new machine learning-based distribution algorithm suitable for 5G networks, a comprehensive evaluation of performance comparison with the existing system, and evaluation of recommendations for feasibility and implementation. The results showed significant improvements in accuracy and reliability, demonstrating the potential of machine learning to revolutionize the 5G workplace. © 2024 IEEE.

关键词： Adversarial machine learning

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An Information-Theoretically Secure Method to Encrypt One-Time Pads Utilizing Cellular Automata 12

An Information-Theoretically Secure Method to Encrypt One-Ti...

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12th IEEE International Conference on Advanced Computing, ICoAC 2023

作者： Kumaresan, G. Central University of Tamil Nadu School of Mathematics and Computer Science Dept. of Computer Science Tamil Nadu Thiruvarur India

ISBN: (纸本)9798350318210

The public cloud lacks security and integrity, so customers are most concerned about it. The current authentication method must be modified to meet these difficulties, even though cloud computing's technological and financial benefits are crucial for information-enabled services. This study provides a two-level selection-based One-Time Pad encryption method for the public cloud to achieve the avalanche distribution requirements and perplexity aspects. It was observed that combining non-uniform cellular automata with reversible cellular automata methods provides a reliable master key for the public cloud. The experimental findings demonstrate that the suggested strategy has plenty of key space to resist attacks using traditional brute force and particular plaintext. Furthermore, cloud data execution times are quicker. Analytical theory confirms the potent security and appropriate range of the recommended CA-based random key generation mechanism. © 2023 IEEE.

关键词： Cellular automata

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Deep Learning Assisted Intelligent Human computer Interaction for Next Generation Internet Applications

Informatica (Slovenia)

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Informatica (Slovenia) 2024年第2期48卷 145-146页

作者： Hasan, Al Mehedi Hwang, Yong Seok Shin, Jungpil Dept. of Computer Science and Engineering Rajshahi University of Engineering and Technology Bangladesh Kwangwoon University Seoul Korea Republic of School of Computer Science and Engineering The University of Aizu Aizuwakamatsu Japan

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