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International Conference on Computing and Networking Technology (ICCNT)

作者： Dhamini R Nijgal Stebin George Parameswaran Subramanian Dept. of Computer Science Christ University Bangalore India Dept. of Data Science Christ University Bangalore India

Face recognition technology has been extensively used in multiple verticals of security, surveillance, and human-computer interaction. Conventional techniques including manual sign-ins, identity cards, or biometric verification have been used by traditional attendance systems. Face recognition systems have, however, become a popular way to track attendance, thanks to developments in computer vision and machine learning. The construction of an attendance registration application is the main topic of this research study, which also offers a thorough overview of facial recognition attendance systems. This study seeks to provide light on the benefits, drawbacks, and potential applications of these fast-developing technologies. Face recognition technology may be integrated into attendance systems to increase productivity, accuracy, and user comfort. However, issues like privacy worries and technological constraints must be resolved. With predicted future improvements in machine learning algorithms and hardware capabilities, face recognition attendance systems look to have a bright future. This research article adds to a deeper understanding and successful application of facial recognition technology in attendance systems by examining these features.

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Image to Audio Using LLM for Blind People

Image to Audio Using LLM for Blind People

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data, Computation and Communication (ICDCC), International Conference on

作者： Kumaresan T Geetha A Rajeshkumar G Benita Gracia Thangam J Malathi Eswaran Thivya Rajkumar Dept. of Computer Science and Engineering Bannari Amman Institute of Technology Sathyamangalam India Dept. of Computer Science and Engineering Presidency University Bangalore India Dept. of Artificial Intelligence and Data science Velalar college of Engineering and Technology Erode India Dept. of Computer Technology Kongu engineering College Perundurai India

ISBN: (数字)9798331532956

ISBN: (纸本)9798331532963

Blind people face many difficulties while engaging with their surroundings. This project is all about creating innovative solutions for the blind people by providing the information in the format of audio. Image-to-Audio transformation system specifically designed for natural language understanding using Large Language Model (LLM).The system will capture a picture or video of the situation present in front of the blind person and then provide descriptions about the images. The objective is to provide guidelines for blind people by understanding their surroundings through an LLM model and provide audio response. The CLIP model analyzes the objects, scenes, text etc.. and provides relevant information. The extracted visual data is then passed through GTTs (Google-Text-To-Speech) to create rich audio descriptions. It provides accurate and detailed representation of the visual content, allowing blind people to understand their surroundings. The project involves integrating convolutional neural networks and Large Language Model (e.g GPT-4) technologies to create a real-time image-to-text system for blind individuals. The CNN model is used to calculate the distance of the object from the user. We are integrating the Google Assistants to open the app automatically by the blind people with help of their voice.

关键词： Visualization Navigation Large language models Blindness Real-time systems Natural language processing Internet Convolutional neural networks Multimedia communication Monitoring

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ShoppingGPT: A GPT-based Product Recommendation Dialogue System 4

ShoppingGPT: A GPT-based Product Recommendation Dialogue Sys...

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4th IEEE International Conference on Pattern Recognition and Machine Learning, PRML 2023

作者： Yu, Renjie Guan, Yujing Zhan, Yuancheng Guangxi University of XingJian College of Science and Liberal Arts Dept. of Computer and Information Technology Nanning China Guangxi University of Finance and Economics Dept. of School of Big Data and Artificial Intelligence Nanning China Technology of NanNing University NanNing University Dept. Global Institute of Software Nanning China

ISBN: (纸本)9798350324303

This article introduces a shopping recommendation dialogue system called ShoppingGPT, which aims to help people get comprehensive product recommendations based on individual needs and preferences. The system can leverage existing large-scale generative pre-trained models with the ability to communicate and understand human language to provide more detailed and comprehensive product recommendations through multiple conversations with users. By constructing the shopping recommendation dataset and using the large-scale language model fine-tuning methods, we successfully fine-tuned on a large-scale language model and transformed the shopping recommendation task into a sequential labeling task to adapt to our shopping recommendation dialogue system. And we design a specific method for unlabeled prompt, which uses the basic language knowledge of large-scale language models to output product information for unsupervised annotation for product recommendation. In the experiment, we evaluate several existing language model fine-tuning techniques and explore the generalization ability of the ShoppingGPT system on unlabeled samples. We also compared models before and after fine-tuning to see what the models learned from the data. In the discussion section, we provide a detailed analysis of the advantages and limitations of ShoppingGPT and its potential applications in the shopping field. In addition, we explore the potential security challenges that ShoppingGPT can pose and discuss how to address them. Finally, we discussed how to improve ShoppingGPT to make it perform better and more secure. © 2023 IEEE.

关键词： Large dataset

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Scorigami: Simulating the Distribution and Assessing the Rarity of National Football League Scores

Scorigami: Simulating the Distribution and Assessing the Rar...

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Simulation Winter Conference

作者： Liam Moyer Jameson Railey Andrew Daw Samuel C. Gutekunst Dept. of Computer Science Bucknell University Lewisburg PA USA Dept. of Computer Science Stevens Institute of Technology Hoboken NJ USA Dept. of Data Sciences and Operations University of Southern California Los Angeles CA USA Dept. of Computer Science and Mathematics Bucknell University Lewisburg PA USA

ISBN: (数字)9798331534202

ISBN: (纸本)9798331534219

NFL Scorigamis have a cult-like following, and occur whenever a game ends in a new, never-before-seen score. While substantial research has gone into simulating and predicting NFL game scores, most work has been in relation to winner prediction and betting spreads. We analyze a Poisson random variable model for the distribution of NFL game scores and show that it fails to incorporate important game dynamics. Through an analysis of extensive play-by-play data, we extend this to a non-stationary, state-dependent Poisson process model. This latter model more closely fits real NFL score data, and we use it in NFL score simulations to forecast likely future Scorigamis.

关键词： Analytical models Simulation Games Predictive models Nonhomogeneous media data models Real-time systems Random variables Sports

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Image to Audio Using LLM for Blind People 1

Image to Audio Using LLM for Blind People

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1st International Conference on data, Computation and Communication, ICDCC 2024

作者： Kumaresan, T. Geetha, A. Rajeshkumar, G. Benita Gracia Thangam, J. Eswaran, Malathi Rajkumar, Thivya Bannari Amman Institute of Technology Dept. of Computer Science and Engineering Sathyamangalam India Presidency University Dept. of Computer Science and Engineering Bangalore India Velalar college of Engineering and Technology Dept. of Artificial Intelligence and Data science Erode India Kongu engineering College Dept. of Computer Technology Perundurai India

ISBN: (纸本)9798331532956

关键词： Image retrieval

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Cybersecurity for Industrial IoT: A Review on Vulnerabilities and Attacks 8

Cybersecurity for Industrial IoT: A Review on Vulnerabilitie...

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8th International Conference on Electronics, Materials Engineering and Nano-Technology, IEMENTech 2025

作者： Kumari, Reetu Kumar, Sunil Amandeep Dutta, Kamlesh Kumari, Aparna Guru Jambheshwar University of Science and Technology Dept. of Computer Science & Engineering Haryana Hisar India Guru Jambheshwar University of Science and Technology Dept. of AI & Data Science Haryana Hisar India National Institute of Technology Hamirpur Dept. of Computer Science & Engineering Himachal Pardesh India Institute of Technology Nirma University Dept. of Computer Science & Engineering Gujarat Ahmedabad India

ISBN: (纸本)9798331520762

Internet has evolved from a network of connecting people to a network of connecting things, leading to a more complex and sophisticated network of Industrial Things, known as Industrial IoT (IIoT) today. This evolution has revolutionized the traditional Industrial system into real-time smarter systems that enhanced overall efficiency and productivity. Despite its benefits, the interconnected nature of IIoT, expanded the attack surface, exposing the systems to new crucial and complex cybersecurity threats. Several real time attacks on various industries as mentioned in this paper and the increasing cybersecurity cost over the years, clearly states the need of robust security solutions. To deeply understand the cybersecurity aspect, we provided a comprehensive study of attacks and vulnerabilities in this paper. In this paper, we did a indept. study of various IIoT related severe attacks and aspects related to these attacks, like vulnerabilities, impacts, potential solutions etc. We also provided future research directions to help the research community, to work upon and develop more robust and efficient cybersecurity solutions. © 2025 IEEE.

关键词： attacks cybersecurity etc IIoT Industrial IoT IoT vulnerabilities

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Cybersecurity for Industrial IoT: A Review on Vulnerabilities and Attacks

Cybersecurity for Industrial IoT: A Review on Vulnerabilitie...

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International Conference on Electronics, Materials Engineering and Nano-Technology (IEMENTech)

作者： Reetu Kumari Sunil Kumar Amandeep Kamlesh Dutta Aparna Kumari Dept. of Computer Science & Engineering Guru Jambheshwar University of Science and Technology Hisar (Haryana) India Dept. of AI & Data Science Guru Jambheshwar University of Science and Technology Hisar Haryana India Dept. of Computer Science & Engineering National Institute of Technology Hamirpur Himachal Pardesh India Dept. of Computer Science & Engineering Institute of Technology Nirma University Ahmedabad Gujarat India

ISBN: (数字)9798331520762

ISBN: (纸本)9798331520779

关键词： Industries Productivity Reviews Prevention and mitigation Intrusion detection Interconnected systems Real-time systems Internet computer security Industrial Internet of Things

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Multi-view Learning for the Identification of Risky Users in Dynamic Social Networks

arXiv

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arXiv 2025年

作者： Benedetti, Francesco Pellicani, Antonio Pio, Gianvito Ceci, Michelangelo Dept. of Computer Science University of Bari Bari Italy Dept. of Computer Science University of Pisa Pisa Italy Data Science Lab National Interuniversity Consortium for Informatics Rome Italy Dept. of Knowledge Technologies Jožef Stefan Institute Ljubljana Slovenia

Technological progress in the last few decades has granted an increasing number of people access to social media platforms such as Facebook, X (formerly Twitter), and Instagram. Consequently, the potential risks associated with these services have also risen due to users exploiting these services for malicious purposes. The platforms have tools capable of detecting and blocking dangerous users, but they primarily focus on the content posted by users and usually overlook additional factors, such as the relationships among users. Another key aspect to consider is that users’ beliefs and interests evolve over time. Therefore, a user who can be considered safe at one moment might later become malicious, and vice versa. This work describes a novel approach to node classification in temporal graphs, aimed at classifying users in social networks. The method was evaluated on a real-world scenario and was compared to a state-of-the-art system that treats the network as a static entity. Experiments showed that taking into account the temporal evolution of the network, in terms of node features and connections, is beneficial. © 2025, CC BY.

关键词： Federated learning

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Behavioral and Hybrid Anomaly Detection Techniques for Enhanced Fraud Prevention in E-Commerce Transactions

Behavioral and Hybrid Anomaly Detection Techniques for Enhan...

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Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), International Conference on

作者： Sumanth R Praveenkumar S Challagidad L Lakshmaiah Shilpashree S Surekha R Sakhare Jyothi V Kulkarni Dept. of Computer Science Engineering (Data Science) Nagarjuna College Of Engineering and Technnology Bangalore India Dept. of Computer Science Engineering Nagarjuna College Of Engineering and Technnology Bangalore India Dept. of Electronics and Telecommunication Engineering A G Patil Institute Of Technnology Solapur India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

This paper is designed to develop an automated fraud detection system for e-commerce by leveraging user behavior analysis and hybrid anomaly detection models. As e-commerce grows rapidly, fraud, which poses a huge financial risk and damages brand reputation, is also increasing. Modern fraud detection methods are often code-based or business-centric, limiting their ability to transform fraud strategies. This paper proposes a Behavior and Hybrid Anomaly Detection Model (BHADM) which integrates various data, including usage patterns, transaction details, and historical data, to increase accuracy. The BHAD model that combines vulnerability detection, formality testing, and risk scoring is then used to assess the probability of fraud for each transaction. Based on the risk score calculation, the system triggers alerts and suspicious transactions can be flagged or blocked. In addition, a study is ongoing to monitor performance and update the model to adapt to new fraud models. This method not only improves detection capability, but also maintains the performance of the operation, making it suitable for urgent applications in high-frequency e-commerce environments. The application process provides a unique, flexible and effective way to reduce e-commerce fraud transactions.

关键词： Adaptation models Analytical models Accuracy Transforms Fraud Telecommunication computing Electronic commerce Security Anomaly detection Testing

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OSDA-ST: Advancing Open Set Domain Adaptation Through Selective Thresholding in Remote Sensing

OSDA-ST: Advancing Open Set Domain Adaptation Through Select...

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2024 IEEE International Conference on Big data, Bigdata 2024

作者： Wang, Andy Gomes, Rahul Rozario, Papia F. University of Wisconsin Madison School of Computer Data & Information Sciences MadisonWI United States University of Wisconsin-Eau Claire Department of Computer Science Eau Claire United States University of Wisconsin-Eau Claire Dept. of Geography and Anthropology Eau ClaireWI United States

ISBN: (纸本)9798350362480

Segmenting and classifying satellite and aerial images is a crucial task for understanding various environmental, urban, and agricultural phenomena in remote sensing. Traditional segmentation methods often rely on large, labeled datasets, which can be challenging to obtain for every possible domain or geographic region. Unsupervised Domain adaptation (UDA) attempts to solve this problem by transferring pixel-wise semantic knowledge from a labeled source domain to an unlabeled target domain. However, current UDA methods for remote sensing assume a corresponding label space between domains, which is often unrealistic in real-world scenarios where the target domain may contain novel or unknown classes that are absent in the source domain. This paper addresses the limitations associated with Open-Set Domain Adaptation for Semantic Segmentation (OSDA-SS) by proposing OSDA-ST, a framework performing max-logit score (MLS) thresholding, source-only pretraining, and a dynamic source selection score for unknown class estimation. We test these modifications on the remote sensing dataset of Potsdam and Vaihingen for the first time. Compared to state of the art, our methods achieved 39.9 H-score, a significant improvement of 5.82% and 7% on average. Results indicate that these modifications are successful in reducing misclassification of pseudo-labels thereby providing an adaptive approach to thresholding as compared to previous state of the art. © 2024 IEEE.

关键词： Semantic Segmentation

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