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International Conference on Biosignals, Images and Instrumentation (ICBSII)

作者： S. Mangai A. Supriya V. Kavitha S. Viveka S. Maheswari S. Govindaraj Department of Biomedical Engineering Velalar College of Engineering and Technology Erode India Department of Data Science and Business systems SRM Institute of Science and Technology Chennai India Department of Computing Technologies SRM Institute of Science and Technology Chennai India

ISBN: (数字)9798331513863

ISBN: (纸本)9798331513870

The quick rise in diabetes cases globally underscores the essential need for technology-driven, innovative management solutions. The project gives the introduction about the cutting-edge in AI-based chatbot tailored for diabetic patients. The combination of Support Vector Machine (SVM), K-Nearest Neighbors (KNN), results the chatbot to provide highly accurate, personalized recommendations to improve self-management. The added advantage is the inclusion of voice-based interaction which offers enhanced accessibility, especially for the elders and visually impaired persons. This study demonstrates about the ability of the chatbot to interpret the complex health data, delivers the real-time insights, and promote adherence to the treatment plans. The advancement in the fusion of machine learning algorithms and voice interaction secures a dynamic and user-friendly experience for diabetes management.

关键词： Support vector machines Accuracy Medical services Machine learning Transforms Nearest neighbor methods Chatbots Diabetes Diseases Biomedical imaging

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A Survey on Cyber Attack Detection: Techniques, datasets, and Challenge

A Survey on Cyber Attack Detection: Techniques, Datasets, an...

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Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE), International Conference on

作者： G Geetha Manjula Rajagopal K Purna Chand Department of Computing Technologies School of Computing (Research Scholor) SRM Institute of Science and Technology Kattankulathur Tamilnadu Department of Computing Technologies School of Computing (Assistant Professor) SRM Institute of Science and Technology Kattankulathur Tamilnadu Department of Computer Science and Engineering(Data Science) (Professor) B V Raju Institute of Technology Narsapur India

Networking technology is expanded along with cyber threats. Cyber threats are the instances which adversely influences the functional operations of the system or network. It results in various issues such as illegal access, data leakage, data stolen, DDoS attacks, etc. To overcome such issues, several intrusion detection systems are implemented over the past years. Though, these intrusion detection systems exhibit several limitations while trying to achieve excellent efficiency in attack detection. In this survey, totally 50 cyber-attack detection based research works are analysed under several perspectives. The analysis is done on several IDS techniques implemented in existing works. The reviewed learning-based approaches are explained in two major categories as ML and DL approaches. Here, the ML algorithms are further classified into three types: supervised, unsupervised and reinforcement learning approaches. Moreover, the existing works are also analysed in accordance with optimization algorithms and it is classified as metaheuristic and stochastic algorithms. In which, population-based algorithms and swarm-based algorithms are the categories classified under metaheuristic algorithms. In addition, the analyses are also performed on datasets usage and cyber-attack mitigation models. Furthermore, the analysis on existing works is done on performance analysis. And it is analysed under performance of cyber-attack detection models with specific description about maximum and minimum performance attained by recent works on cyber-attack detection models. Finally, the chronological review and challenges involved in existing cyber-attack detection models are discussed.

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Pose-Transformation and Radial Distance Clustering for Unsupervised Person Re-identification 32

Pose-Transformation and Radial Distance Clustering for Unsup...

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32nd British Machine Vision Conference, BMVC 2021

作者： Seth, Siddharth Sonth, Akash Chakraborty, Anirban Visual Computing Lab Department of Computational and Data Sciences Indian Institute of Science Bangalore India

Person re-identification (re-ID) aims to tackle the problem of matching identities across non-overlapping cameras. Supervised approaches require identity information that may be difficult to obtain and are inherently biased towards the dataset they are trained on, making them unscalable across domains. To overcome these challenges, we propose an unsupervised approach to the person re-ID setup. Having zero knowledge of true labels, our proposed method enhances the discriminating ability of the learned features via a novel two-stage training strategy. The first stage involves training a deep network on an expertly designed pose-transformed dataset obtained by generating multiple perturbations for each original image in the pose space. Next, the network learns to map similar features closer in the feature space using the proposed discriminative clustering algorithm. We introduce a novel radial distance loss, that attends to the fundamental aspects of feature learning - compact clusters with low intra-cluster and high inter-cluster variation. Extensive experiments on several large-scale re-ID datasets demonstrate the superiority of our method compared to state-of-the-art approaches. © 2021. The copyright of this document resides with its authors.

关键词： Clustering algorithms

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Unveiling the Digital Shadows: A Comprehensive Exploration of Network Forensics Methodologies

Unveiling the Digital Shadows: A Comprehensive Exploration o...

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Emerging Research in Computational science (ICERCS), International Conference on

作者： Dhanalakota Sai Jyothika Nandini B. Lanitha H. Azath Ganeshan R Sajjad Ahmed School of Computing Science and Engineering VIT Bhopal University Kothrikalan Sehore India Department of Artificial Intelligence and Data Science Karpagam Academy of Higher Education Coimbatore India School of Computing Science and Engineering VIT Bhopal University Sehore India

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534974

The Internet has experienced significant growth in attacks that affect network security features such as confidentiality, integrity, and availability. Network security and network forensics work together to ensure system integrity, availability, and confidentiality. Network security measures prevent unau-theorized access, attacks, and breaches, while network forensics investigates and analyses the aftermath of security incidents. Network forensics refers to the process of collecting, analyzing, and preserving digital evidence from network activity in order to investigate and respond to security incidents, breaches, or other malicious activities. It involves reconstructing the events to determine attack methods, compromised data, and potential perpetrators. It lies at the intersection of digital forensics, incident response and network security. This paper is going to analyse the methodologies which are used in network forensics and application of new technologies in each network forensics methodology through ten figures. This paper critically examines network forensics methodologies, focusing on the evolution of these methods and the integration of emerging technologies. It assesses new strategies and discusses inherent issues and limitations of existing techniques, emphasizing the need for cutting-edge techniques to enhance the effectiveness of network forensic processes.

关键词： Protocols Scientific computing Forensics Scalability Prevention and mitigation Organizations Network security Threat assessment Stakeholders Monitoring

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Gesture Showdown: Rock Paper Scissors with AI Vision

Gesture Showdown: Rock Paper Scissors with AI Vision

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Computational, Communication and Information Technology (ICCCIT), International Conference on

作者： Hemal Babu H Yeshwanth. C. D Shravan. N K. Regin Bose Department of Artificial Intelligence and Data science Rajalakshmi Institute of Technology Chennai India Department of AI&DS and Cloud Computing Rajalakshmi Institute of Technology Chennai India

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512972

This paper presents a gesture-based RockPaper-Scissors game that leverages computer vision and hand gesture recognition to enable intuitive human-computer interaction. Utilizing MediaPipe's pre-trained hand tracking module, the system detects and identifies 21 hand landmarks in real-time to classify gestures as rock, paper or scissors based on angular relationships between key points. OpenCV is used for webcam integration and video frame preprocessing, ensuring seamless interaction. The game compares the recognized gesture with a computer-generated move, dynamically displaying the results and resetting after each round for continuous play. The system identifies gestures through precise angle calculations, differentiating between closed fists, open palms and extended fingers. Sample outputs demonstrate real-time gameplay scenarios, highlighting gesture recognition accuracy and user engagement. Future enhancements include refining recognition accuracy, adding multiplayer functionality, introducing customizable gestures, and deploying the game on web and mobile platforms for broader accessibility. This project showcases the potential of integrating AI and computer vision in creating engaging and user-friendly applications.

关键词： Hands Human computer interaction Computer vision Accuracy Webcams Tracking Games Gesture recognition Rocks Real-time systems

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MVMoE: multi-task vehicle routing solver with mixture-of-experts 24

MVMoE: multi-task vehicle routing solver with mixture-of-exp...

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Proceedings of the 41st International Conference on Machine Learning

作者： Jianan Zhou Zhiguang Cao Yaoxin Wu Wen Song Yining Ma Jie Zhang Chi Xu College of Computing and Data Science Nanyang Technological University Singapore School of Computing and Information Systems Singapore Management University Singapore Department of Information Systems Eindhoven University of Technology The Netherlands Institute of Marine Science and Technology Shandong University China College of Computing and Data Science Nanyang Technological University Singapore and Singapore Institute of Manufacturing Technology (SIMTech) Agency for Science Technology and Research (A*STAR) Singapore

Learning to solve vehicle routing problems (VRPs) has garnered much attention. However, most neural solvers are only structured and trained independently on a specific problem, making them less generic and practical. In this paper, we aim to develop a unified neural solver that can cope with a range of VRP variants simultaneously. Specifically, we propose a multitask vehicle routing solver with mixture-of-experts (MVMoE), which greatly enhances the model capacity without a proportional increase in computation. We further develop a hierarchical gating mechanism for the MVMoE, delivering a good trade-off between empirical performance and computational complexity. Experimentally, our method significantly promotes zero-shot generalization performance on 10 unseen VRP variants, and showcases decent results on the few-shot setting and real-world benchmark instances. We further conduct extensive studies on the effect of MoE configurations in solving VRPs, and observe the superiority of hierarchical gating when facing out-of-distribution data. The source code is available at: https://***/RoyalSkye/Routing-MVMoE.

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A Conceptual Evaluation of the data as Partner (DAP) Framework

A Conceptual Evaluation of the Data as Partner (DAP) Framewo...

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IEEE International Smart Cities Conference (ISC2)

作者： Sonia Hassan Drishty Sobnath Esther Snell Olufemi Isiaq Department of Science and Engineering Solent University Southampton England Department of Computer and Data Science and AI Creative Computing Institute Universiy of Arts London

This paper explores the innovative data As Partner (DAP) philosophy as a transformative approach to organisational data strategy, drawing upon established concepts of information value, technology acceptance, and inclusivity for people with disabilities (PwD) in smart cities. The study conducted a comprehensive systematic review, after meticulous screening, 20 eligible papers were selected, forming the foundation for deriving the key components of the DAP framework. Subsequently, a qualitative focus group study engaged 18 participants from diverse sectors to evaluate the practical applicability of the DAP conceptual framework. Thematic analysis of the focus group sessions revealed five key themes: practical value, pragmatic performance, ethical perspective, clarity of the framework, and enhancement tools. These findings offer valuable insights for refining the DAP framework and optimising its integration into real-world scenarios, in empowering individuals in smart cities, especially those with disabilities. Future research should explore larger and more diverse samples to generalise findings and assess the framework's long-term impact through longitudinal evaluations. By adopting user-centred methodologies, the DAP framework can be further enhanced as a strategic tool, empowering data to actively participate in organisational decision-making processes.

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Deep Learning Driven - Pest Classification and Sustainable Agriculture 3

Deep Learning Driven - Pest Classification and Sustainable A...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Kumar, M. Sirish Usha, R. Reddy, B. Rama Kantha Ramachandra, S. Prakash, P. Jaya Swetha, B. Mohan Babu University School of Computing Dept. of Data Science India Madanapalle Institute of Technology and Science Madanapalle India Sri Venkateswara College of Engineering Tirupati India Mother Theresa Institute of Engineering and Technology Palamaner India Sri Venkateswara College of Engineering Department of It Tirupati India Mohan Babu University Dept. of Master of Computer Applications India

ISBN: (纸本)9798331509675

This study introduces a system designed to identify pests in crops and classify them as either beneficial or harmful. The project begins by providing a comprehensive overview of various pest identification methods, analyzing their strengths and limitations. To address the problem, a categorization approach is proposed that incorporates convolutional neural networks (CNN) to build the pest identification and classification model. The model was trained using a dataset containing images of different pest species. Extensive testing was conducted on a large dataset, and the system's performance was validated against multiple traditional classification models. The proposed framework achieved a classification accuracy of 90%, outperforming conventional methods. This research highlights agriculture's vital role in the economy and underscores the potential of advanced technologies in supporting sustainable farming practices. © 2025 IEEE.

关键词： Agriculture Sustainability Classification Accuracy Classification Model Convolutional Neural Network (CNN) Pest Classification

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Effective and Imperceptible Adversarial Textual Attack Via Multi-objectivization

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ACM Transactions on Evolutionary Learning and Optimization 2024年第3期4卷 1-23页

作者： Liu, Shengcai Lu, Ning Hong, Wenjing Qian, Chao Tang, Ke Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen College of Engineering South Tower 1088 Xueyuan Avenue Guangdong Shenzhen518055 China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong999077 Hong Kong National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen 10F Zhizhen Building Canghai Campus 3688 Nanhai Avenue Guangdong Shenzhen China National Key Laboratory for Novel Software Technology School of Artificial Intelligence Nanjing University Nanjing International College Area Xianlin Campus 163 Xianlin Avenue Jiangsu Nanjing China

The field of adversarial textual attack has significantly grown over the past few years, where the commonly considered objective is to craft adversarial examples (AEs) that can successfully fool the target model. However, the imperceptibility of attacks, which is also essential for practical attackers, is often left out by previous studies. In consequence, the crafted AEs tend to have obvious structural and semantic differences from the original human-written text, making them easily perceptible. In this work, we advocate leveraging multi-objectivization to address such an issue. Specifically, we reformulate the problem of crafting AEs as a multi-objective optimization problem, where the attack imperceptibility is considered as an auxiliary objective. Then, we propose a simple yet effective evolutionary algorithm, dubbed HydraText, to solve this problem. HydraText can be effectively applied to both score-based and decision-based attack settings. Exhaustive experiments involving 44,237 instances demonstrate that HydraText consistently achieves competitive attack success rates and better attack imperceptibility than the recently proposed attack approaches. A human evaluation study also shows that the AEs crafted by HydraText are more indistinguishable from human-written text. Finally, these AEs exhibit good transferability and can bring notable robustness improvement to the target model by adversarial training. © 2024 Copyright held by the owner/author(s).

关键词： Multiobjective optimization

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An agent-based model of citation behavior

arXiv

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arXiv 2025年

作者： Chacko, George Park, Minhyuk Ramavarapu, Vikram Grama, Ananth Robles Granda, Pablo Warnow, Tandy Siebel School of Computing and Data Science Grainger College of Engineering University of Illinois Urbana-ChampaignIL United States Department of Computer Science Purdue University United States

Whether citations can be objectively and reliably used to measure productivity and scientific quality of articles and researchers can, and should, be vigorously questioned. However, citations are widely used to estimate the productivity of researchers and institutions, effectively creating a ‘grubby’ motivation to be well-cited. We model citation growth, and this grubby interest using an agent-based model (ABM) of network growth. In this model, each new node (article) in a citation network is an autonomous agent that cites other nodes based on a ‘citation personality’ consisting of a composite bias for locality, preferential attachment, recency, and fitness. We ask whether strategic citation behavior (reference selection) by the author of a scientific article can boost subsequent citations to it. Our study suggests that fitness and, to a lesser extent, out degree and locality effects are influential in capturing citations, which raises questions about similar effects in the real world. © 2025, CC BY.

关键词： Autonomous agents

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