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

作者： Gupta, Shreya Gupta, Deepa Indian Institute of Technology Delhi Department of Mathematics Delhi India Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

The advent of Large Language Models (LLMs), has largely transformed global interactions with artificial intelligence. These models, though useful in tasks ranging from translation to text generation, have now permeated everyday life, thereby raising concerns about their potential misuse, particularly in educational institutions. Despite measures like access restrictions implemented by educational institutions, AI based plagiarism has continued to grow, with existing plagiarism detection tools exhibiting limited efficacy in curbing the problem. This paper addresses the need for better AI detection algorithms, focusing on the detection of subtler forms of plagiarism facilitated by LLMs. Leveraging insights from existing studies, the research explores the efficacy of various boosting based machine learning models and dimensionality reduction techniques in improving the accuracy of detecting AI-augmented essays. © 2024 IEEE.

关键词： AI in Education ChatGPT Natural Language Processing plagiarism detection

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SVM Machine Learning Model for Detection of Etherlock Vulnerability in Solidity Smart Contracts 6

SVM Machine Learning Model for Detection of Etherlock Vulner...

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6th International Conference on Blockchain computing and Applications, BCCA 2024

作者： Almobaideen, Wesam Alnuman, Rashed Sajid, Tayyab Hasan, Qusai Department of Electrical Engineering and Computing Sciences Rochester Institute of Technology Dubai Dubai United Arab Emirates Department of Computer Science The University of Jordan Amman Jordan

ISBN: (纸本)9798350351538

Blockchain is a disruptive technology that changed how financial operations and ownership exist over the internet. With the implementation of smart contracts in blockchains, facilitation of transactions without intermediaries is possible and fully automated. Smart contracts allow relatively safe transactions and transfer of ownership, but as with any piece of software, it proved to have vulnerabilities and challenges. Unfortunately, considering that smart contracts hold and facilitate valuable asset and funds transfer, the damage caused by exploitation of these contracts is devastating with billions of dollars’ worth of cryptocurrency and other assets lost or stolen up until now. One attack on Ethereum smart contracts is the Etherlock attack, which exploits bad logic in the code to stall the transaction indefinitely, effectively sealing user funds. In this paper, we propose a high-accuracy Support Vector Machine model for detection of Etherlock vulnerability in Solidity smart contracts. The model is developed into a standalone tool and achieves 98.44% accuracy accompanied with 87% recall using a generated dataset of Smart Contracts from the Ethereum blockchain. Our promising results still show the necessity of more sophisticated accurate detection models for protection of crypto assets on the blockchain. © 2024 IEEE.

关键词： Smart contract

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Feature Enhancer Segmentation Network (FES-Net)for Vessel Segmentation

Feature Enhancer Segmentation Network (FES-Net)for Vessel Se...

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2023 International Conference on Digital Image computing: Techniques and Applications, DICTA 2023

作者： Khan, Tariq M. Arsalan, Muhammad Iqbal, Shahzaib Razzak, Imran Meijering, Erik School of Computer Science & Engineering Unsw Sydney Australia Qatar University Kindi Center for Computing Research Doha2713 Qatar Department of Electrical and Computer Engineering Islamabad Pakistan

ISBN: (纸本)9798350382204

Diseases such as diabetic retinopathy and age-related macular degeneration pose a significant risk to vision, highlighting the importance of precise segmentation of retinal vessels for tracking and diagnosis of progression. However, existing vessel segmentation methods that heavily rely on encoder-decoder structures struggle to capture contextual information about retinal vessel configurations, leading to challenges in reconciling semantic disparities between encoder and decoder features. To address this, we propose a novel feature enhancement segmentation network (FES-Net) that achieves accurate pixel-wise segmentation without requiring additional image enhancement steps. FES-Net directly processes the input image and utilizes four prompt convolutional blocks (PCBs) during downsampling, complemented by a shallow upsampling approach to generate a binary mask for each class. We evaluate the performance of FES-Net on four publicly available state-of-the-art datasets: DRIVE, STARE, CHASE, and HRF. The evaluation results clearly demonstrate the superior performance of FES-Net compared to other competitive approaches documented in the existing literature. © 2023 IEEE.

关键词： Image enhancement

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Classification of Offensive Tweet in Marathi Language Using Machine Learning Models 5th

Classification of Offensive Tweet in Marathi Language Usin...

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5th International Conference on Computational Intelligence in Communications and Business Analytics, CICBA 2023

作者： Kumari, Archana Garge, Archana Raj, Priyanshu Kumar, Gunjan Singh, Jyoti Prakash Alryalat, Mohammad Department of Computer Science and Engineering National Institute of Technology Patna Bihar Patna India Al-Balqa Applied University Salt Jordan

ISBN: (纸本)9783031488757

Offensive language identification is essential to make social media a safe and clean place to share one’s view. In this work, a model is proposed to automatically classify offensive tweets into offensive and not offensive classes of low-resource language. Marathi is spoken in Western India. Marathi being a low-resource language, lacks a comprehensive list of stopwords and proper stammer. To fill this gap, we created a list of stopwords for stopword removal and a list of suffixes to identify the root word in the Marathi language. Two different methods, Label Vectorizer and term frequency-inverse document frequency (TF-IDF) Vectorizer, are used to extract features from the text and then these features are used with six different conventional machine learning classifiers to classify a Marathi tweet into offensive or non-offensive. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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Twitter sentiment analysis using conditional generative adversarial network

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International Journal of Cognitive computing in engineering 2024年第1期5卷 161-169页

作者： Mahalakshmi, V. Shenbagavalli, P. Raguvaran, S. Rajakumareswaran, V. Sivaraman, E. Department of Information Technology Sengunthar College of Engineering Tiruchengode Tiruchengode637205 India Assistant Professor Karunya Institute of Technology and Sciences Coimbatore India Assistant Professor Department of Computational Intelligence School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur Kanchipuram Chennai Tamil Nadu 603203 India Assistant Professor Department of Computer Science and Design Erode Sengunthar Engineering College Tamil Nadu Thuduppathi India Department of Electrical and Computer Engineering Curtin University Miri Malaysia

Sentiment analysis, which aims to extract information from textual data indicating people's ideas or attitudes about a particular problem, has developed into one of the most exciting study issues in natural language processing (NLP) with the development of social media. Twitter is a social network with an extensive audience that expresses their thoughts and opinions clearly and readily. Due to the prevalence of slang phrases and incorrect spellings in short phrase styles, Twitter data analysis is more challenging than data analysis from other social networks. Automated feature selection still has several limitations, such as higher computing costs that rise with the number of characteristics. Deep learning, which is self-learned and more accurate at processing vast amounts of data, is utilized to overcome these challenges. This paper introduces a conditional generative adversarial network (GAN) for Twitter sentiment analysis, whereas a convolutional neural network (CNN) has been used to extract traits from Twitter data. Compared to existing works, the proposed work has outperformed in accuracy, recall, precision, and F1 score. The suggested method is the most accurate, with a classification accuracy of 93.33 %. © 2024

关键词： Sentiment analysis

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Two-Stage Incentive Mechanism Based on a Cooperative Mode: A Stackelberg Game Approach

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IEEE Internet of Things Journal 2025年第13期12卷 24722-24737页

作者： Wang, Long Wang, Yingjie Teng, Haojun Jiao, Xiuzhen Sun, Meimei Yang, Jishen Cai, Zhipeng Yantai University School of Computer and Control Engineering Yantai264005 China China Judicial Big Data Research Institute Co. Ltd China Naval Aviation University China Georgia State University Department of Computer Science AtlantaGA30303 United States

With the explosive growth of mobile data, Mobile Crowd Sensing (MCS) has become a popular paradigm for large-scale data collection. The difficulty of data collection and the gaps in workers’ sensing capabilities are key factors to consider in worker recruitment and task assignment. To address these issues, we designed a two-stage cooperative incentive mechanism. In the first stage, a shelving level is introduced to assess task difficulty. Tasks are divided into high-quality and low-quality groups based on quality scores, while workers are categorized into high-ability and low-ability groups based on their historical performance. The Improved Chaotic Particle Swarm Optimization (ICPSO) algorithm is then applied to generate optimal task combinations. In the second stage, we address benefit distribution among cooperating workers by introducing time-dependent rewards and employing Stackelberg game theory to analyze the optimal completion time for both high-ability and low-ability workers. This analysis determines the optimal reward distribution and ability value allocation, with proof of the existence and uniqueness of a Nash equilibrium. Comparative experiments conducted on real-world datasets demonstrate that our cooperative task mechanism outperforms existing task bundling methods, validating its rationality and effectiveness. © 2014 IEEE.

关键词： Nash equilibrium

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Deep Learning and Quantum-Enabled Cloud Platform Approach on Optimized Crop Health Predictions 5th

Deep Learning and Quantum-Enabled Cloud Platform Approach on...

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5th Doctoral Symposium on Computational Intelligence, DoSCI 2024

作者： Singh, Jagendra Thakur, Hardeo Kumar Sharma, Neetu Alsahlanee, Abbas Thajeel Rhaif Sahunthala, S. Shaik, Nazeer Alliance School of Advanced Computing Alliance University Bangalore India School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Computer Science and Engineering Galgotias University Greater Noida India Department of Computer Science and Information Technology University of Salahaddin-Erbil Erbil Iraq Department of Artificial Intelligence and Data Science Rajalakshmi Institute of Technology Tamil Nadu Chembarambakkam India Department of Computer Science and Engineering Srinivasa Ramanujan Institute of Technology—Autonomous Anantapur India

ISBN: (纸本)9789819763177

This study is an innovative investigation into the field of plant disease detection and plant health prediction using state-of-the-art machine learning models and Quantum Support Vector Machines (QSVM) for feature extraction. A diverse dataset of 700 images, including a variety of plant species and disease states, served as the foundation for this study. The study evaluated the performance of four machine learning models: CNN (ConvNet), RNN (Recurrent), KNN (k-Nearest Neighbors), and SVM (Support Vector Machine) for accurately identifying and classifying plant diseases. Convolutional neural network (CNN) model unexpectedly prevailed, achieving a remarkable accuracy rate of 98.77%. Accuracy, recall, and F1 score of the model further demonstrated its propensity for disease diagnosis. QSVMs were used to extract intricate features from pictures of sick leaves in order to increase the models’ precision and prognostication ability. The findings have significant impact for agriculture. And the sophisticated toolkit they offer has provided an important step forward in proactive disease management for better crop health. Carrying out an early intervention for disease can result in higher crop output, lower agricultural losses, and improved global food security. The QSVM’s application of quantum computing principles also opens up fresh frontiers for creating machine learning applications in different industries. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Support vector machines

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Implementing a Robust and Scalable Detect-and-Avoid Algorithm for UAV Navigation in ROS 15

Implementing a Robust and Scalable Detect-and-Avoid Algorith...

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15th International Conference on Information, Intelligence, Systems and Applications, IISA 2024

作者： Meimetis, Dimitrios Daramouskas, Ioannis Lappas, Vaios Perikos, Isidoros Kapoulas, Vaggelis Paraskevas, Michael Nkua Department of Aerospace Science & Technology Athens Greece Computer Technology Institute and Press 'Diophantus' Patras Greece UoP Applied Mechanics Lab Patras Greece UoP Computer Engineering and Informatics Department Patras Greece Computer Technology Institute and Press 'Diophantus' University of Peloponnese Electrical and Computer Engineering Dept. Patras Greece

ISBN: (纸本)9798350368833

Unmanned aerial vehicles (UAVs) have garnered significant attention from the research community during the last decade, due to their diverse capabilities and potential applications. One of the most critical functions that drones must execute efficiently is navigation in real-world environments. This paper presents a decentralized approach for enabling unmanned aerial vehicles (UAVs) to navigate safely in unknown environments and avoid obstacles. Leveraging the Optimal Reciprocal Collision Avoidance (ORCA) algorithm, implemented in the Robot Operating System (ROS), our method facilitates conflict detection and resolution in 2D environments. Through simulations using ROS, Gazebo, and Iris drones, we validate the effectiveness of our approach in scenarios with initial trajectory conflicts. Our work addresses the pressing need for UAVs to autonomously plan and execute safe flights, laying the groundwork for enhanced UAV capabilities in various real-world applications. The simulation results demonstrate the efficiency and robustness of our approach. © 2024 IEEE.

关键词： Drones

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Deep learning and ensemble methods for anomaly detection in ICS security

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International Journal of Information Technology (Singapore) 2025年第3期17卷 1761-1775页

作者： Hossain, Md. Alamgir Hasan, Tahmid Karovic, jr, Vincent Abdeljaber, Hikmat A. M. Haque, Md. Alimul Ahmad, Sultan Zafar, Aasim Nazeer, Jabeen Mishra, B.K. Department of Computer Science and Engineering State University of Bangladesh South Purbachal Kanchan Dhaka 1461 Bangladesh Department of Computer Science and Engineering Prime University Dhaka Bangladesh Department of Information Systems Faculty of Management Comenius Bratislava Odbojárov 10 Bratislava 25 82005 Slovakia Department of Computer Science Faculty of Information Technology Applied Science Private University Amman 11931 Jordan MEU Research Unit Middle East University Amman 11831 Lebanon Department of Computer Science Veer Kunwar Singh University Bihar Ara India Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj 11942 Saudi Arabia University Center for Research and Development (UCRD) Department of Computer Science and Engineering Chandigarh University Gharuan Punjab Mohali 140413 India Department of Computer Science Aligarh Muslim University U.P Aligarh 202002 India P.G. Department of Physics Veer Kunwar Singh University Bihar Ara India

This research addresses the escalating threats to industrial control systems by introducing a novel approach that combines deep learning for feature selection with a robust ensemble-based classification technique to enhance anomaly detection. Our method utilizes a tailored autoencoder architecture to efficiently select features, followed by a Random Forest classifier to ensure reliable and generalizable detection of anomalies. Evaluated on the HAI Security Dataset, the proposed approach demonstrates exceptional performance, with significant improvements in detecting anomalous activities in ICS. The results highlight the method’s potential to enhance ICS security by providing a scalable and adaptable framework that evolves with emerging threats. This study not only advances cybersecurity methodologies for industrial systems but also lays the groundwork for future research, emphasizing the need for innovative and effective security solutions in the face of evolving cyber challenges. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： Anomaly detection in ICS Cyber-resilience in critical infrastructure Deep learning for feature selection Industrial control systems security Random forest classifier for ICS

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Deep Learning Advances in Brain Tumor Classification: Leveraging VGG16 and MobileNetV2 for Accurate MRI Diagnostics 4

Deep Learning Advances in Brain Tumor Classification: Levera...

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Alex David, S. Begum, Almas Kalilulah, S. Ismail Ruth Naveena, N. Rajathi, K. Hemalatha, D. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Saveetha School of Engineering Chennai India Dr. M. G. R. Educational and Research Institute Department of Computer Science and Engineering Chennai India Hindustan Institute of Technology & Science Department of Mathematics Chennai India

ISBN: (纸本)9798331508845

These modern deep learning models, such as VGG16 and MobileNetV2 will be utilized in this study to classify Brain tumors from a unique specialized MRI dataset The dataset consists of brain tumor original and augmented images as a two-class (yes/no) classification problem domains, where yes indicates the presence of a tumor while learns no corresponds to its absence. Two models VGG16 and MobileNetV2 support deep convolutional layer operations, well known for their very deep architecture;the other one is optimized over efficiency with depth-wise separable convolutions and is used to evaluate their performance in accurately classifying MRI images. The models use similar data preprocessing and regularization techniques;however, their performance metrics are different. Accuracy, precision, recall, and F1-score are the evaluation metrics for each of the five models to understand how well these differentiate between tumor images versus non-tumor images. This study is designed to improve the ability of brain tumor detection, thereby assisting in more reliable diagnosis and clinical decision-making. © 2024 IEEE.

关键词： Convolutional neural networks

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