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

作者： Anitha P DevaVirdha M Dinesh K Akilesh S Abishek R S Jayanitha Jayaprakash Jayadharani Dept. of Computer Science and Engineering Kumaraguru College of Technology Coimbatore TamilNadu Dept. of Artificial Intelligence and Data Science Kumaraguru College of Technology Coimbatore TamilNadu

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

Vehicle checking along with upkeep are essential in order to assure safety as well as efficiency, but conventional examination techniques may prove to be cumbersome and need technical knowledge. In order to address this issue, an Interactive Augmented Reality (AR) System is proposed to facilitate pre-car real-time inspection and maintenance assistance. The system utilizes AR technology to monitor as well as examine numerous vehicle parts, such as the horn, dashboard lights, sunroof, cruise control, personal memory options, information display, gear system, air conditioning control, and audio control. With an interactive UI, every component is linked to a virtual button with visual and audio cues, as users receive information and instructions in real time. The system also includes comprehensive animated instructions for engine oil changes, providing accurate step-by-step guidance during required maintenance procedures on it. With the inclusion of real-time monitoring and interactive UI elements, the system enhances user understanding. Also, with audiovisual feedback, the system raises engagement, leading to better educated decisions when testing vehicles. The potential result is pretty much a streamlined inspection procedure, totally guaranteeing core vehicle systems remain in full operating condition prior to usage. This process doesn't only boost vehicle maintenance knowledge, it improves strength and performance, which makes it an effective tool for both work and personal use.

关键词： Visualization Manuals Inspection User interfaces Real-time systems User experience Maintenance Automobiles Augmented reality Automotive engineering

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A Blockchain-Based Secure and Transparent Global Radioactive Source Lifecycle Management System

A Blockchain-Based Secure and Transparent Global Radioactive...

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IEEE Southeastcon

作者： Susan Poudel Firdous Kausar Debashis Das Pushpita Chatterjee Sajid Hussain Uttam Ghosh Dept. of Mathematics and Computer Science Fisk University Nashville TN USA Department of Computer Science and Data Science Meharry Medical College Nashville TN USA

ISBN: (数字)9798331504847

ISBN: (纸本)9798331504854

The management of radioactive sources is a critical process that ensures the safe and responsible handling of radioactive materials throughout their lifecycle. These sources require careful management from production to disposal, such as real-time tracking and radiation monitoring to follow everyone's safety rules and protect people and the environment. However, these sources present significant global challenges, especially regarding safety, security, and transparency. Current systems face limitations such as fragmented oversight, lack of accountability, and risks of unauthorized access. To address these limitations, this paper proposes a blockchain-based radioactive source lifecycle management system to manage the lifecycle of radioactive sources. Blockchain's decentralized and tamper-proof ledger secures data throughout the entire lifecycle of radioactive materials. By using smart contracts and access controls, the system ensures that only authorized parties can monitor and verify transactions in real-time, which reduces human error and prevents unauthorized changes to the data. Users can perform key operations such as retrieving source details, transferring ownership, updating source locations, and adding observers to our proposed system. Our experiment in designing and testing a blockchain application has proven the potential for a secure and transparent system that enhances global cooperation in managing radioactive sources. Overall, the proposed system not only addresses current challenges in radioactive source management but also enhances the tracking and monitoring of radioactive materials.

关键词： Access control Radioactive materials Smart contracts Supply chains Production Position measurement Real-time systems Blockchains Safety Testing

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An Agent-Based Approach for Modeling Disease Control Strategies Using the SIR Model

An Agent-Based Approach for Modeling Disease Control Strateg...

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International Conference on New Trends in Computing sciences (ICTCS)

作者： Amera Al-Amery Rabiah Al-Qudah Dept. of Data Science and AI Princess Sumaya Univ. for Technology Amman Jordan Dept. of Computer Science and IT Abu Dhabi University Abu Dhabi UAE

ISBN: (数字)9798331523411

ISBN: (纸本)9798331523428

Understanding infectious disease transmission is essential for reducing pandemic impacts. While previous models have assessed the effects of individual intervention strategies, few have examined their combined impact. This study develops an agent-based model integrating multiple interventions within a traditional SIR framework. This study uses an agent-based SIR model to simulate disease spread and assess control strategies such as face covering and quarantine, represented by five different scenarios. Without interventions, infections peak at 50% of the population. Scenarios show that 10% quarantine compliance reduces peak infections by only 10%, while 50% compliance cuts it by 50%. Preventive strategies significantly reduce this impact; for instance, 50% compliance with masks and quarantine lowers peak infections to under 30% and delays the epidemic peak, providing critical time for healthcare preparedness. These findings guide policymakers in designing adaptive strategies to optimize public health and resources.

关键词： Analytical models Adaptation models Infectious diseases Pandemics Computational modeling Market research Delays Public healthcare Bioinformatics Faces

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Exploring the predictive potential of artificial neural networks in enhancing mechanical properties of derivatives of graphene nanocomposites: a data-driven approach

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International Journal of Information Technology (Singapore) 2025年第2期17卷 999-1005页

作者： Rao, D. Nageswara Dahiya, Mamta Arora, Amit Kumar Jandwani, Paras Verma, Gunjan CSE ACED Alliance University Chikkahagade cross Anekal Bangalore India MAIT Maharaja Agrasen University Himachal Pradesh Solan India Computer Science and Engineering IIT Madras Chennai India Accenture LLP Philadelphia PA United States Dept. of Master of Computer Applications G L Bajaj Institute of Technology and Management Uttar Pradesh Greater Noida India

The given manuscript delves into the realm of materials science and engineering, specifically focusing on the utilization of different derivatives of graphene reinforced in matrix material (DGMM’s) to enhance mechanical properties. Graphene (Gr), with its remarkable characteristics, holds immense promise as reinforcement in composites and nanocomposites (NCs). Using linear regression and artificial neural networks (ANN) framework, this study predicts the weight% of enhanced mechanical properties in DGMMs. By analyzing a dataset comprising various graphene derivatives, matrix materials, processing methods, and resulting enhancements, the ANN model offers valuable insights into the intricate relationships within these NCs. The findings reveal the potential of ANN models in optimizing the design and fabrication processes of DGMMs, thereby accelerating materials development and enabling tailored formulations for specific applications. Also, the given manuscript motivates for future research to enhance the predictive accuracy and broaden the scope of applications for *** confirm the corresponding author is correctly identified and amend if ***, it is *** confirm the inserted city name is correct in Affiliation (3, 4) and amend if ***, it is correct and confirmed © Bharati Vidyapeeth's Institute of computer applications and Management 2024.

关键词： Artificial neural networks Graphene Linear regression Nanocomposites Nanomaterial

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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

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.

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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Incremental SAT-Based Enumeration of Solutions to the Yang-Baxter Equation

arXiv

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arXiv 2025年

作者： Van Caudenberg, Daimy Bogaerts, Bart Vendramin, Leandro KU Leuven Dept. of Computer Science Leuven.AI LeuvenB-3000 Belgium Vrije Universiteit Brussel Dept. of Computer Science BrusselsB-1050 Belgium Vrije Universiteit Brussel Dept. of Mathematics and Data Science BrusselsB-1050 Belgium

We tackle the problem of enumerating set-theoretic solutions to the Yang-Baxter equation. This equation originates from statistical and quantum mechanics, but also has applications in knot theory, cryptography, quantum computation and group theory. Non-degenerate, involutive solutions have been enumerated for sets up to size 10 using constraint programming with partial static symmetry breaking [1];for general non-involutive solutions, a similar approach was used to enumerate solutions for sets up to size 8. In this paper, we use and extend the SAT Modulo Symmetries framework (SMS), to expand the boundaries for which solutions are known. The SMS framework relies on a minimality check;we present two solutions to this, one that stays close to the original one designed for enumerating graphs and a new incremental, SAT-based approach. With our new method, we can reproduce previously known results much faster and also report on results for sizes that have remained out of reach so far. This is an extended version of a paper to appear in the proceedings of the 31st International Conference on Tools and Algorithms for the Construction and Analysis of Systems. © 2025, CC BY.

关键词： Group theory

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Scorigami: Simulating the Distribution and Assessing the Rarity of National Football League Scores 24

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

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Proceedings of the Winter Simulation 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 Depts. of Computer Science and Mathematics Bucknell University Lewisburg PA USA

ISBN: (纸本)9798331534202

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.

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Deep Learning Approaches for Natural Language Understanding

Deep Learning Approaches for Natural Language Understanding

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Saurabh Pratap Singh Rathore Shristi Chamoli Roy T P Ajith Sundaram Neeraj Varshney Vinod Sharma Dept. of Management & Research ICAPSR New Delhi India Dept. of Management Uttaranchal University Dehradun India Dept. of Computer Science & Eng. TKM College of Engineering Kerala India Dept. of Business Amrita Vishwa Vidyapeetham Kochi India Dept. of Computer Eng. & Applications GLA University Mathura India Dept. of Computer Science Jiwaji University Gwalior India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

Deep learning models have really revolutionized the domain of NLU, enabling robots to understand and process human language with high accuracy. These models, which include neural networks like RNNs, CNNs, and Transformers, are quite efficient in extracting complex verbal patterns and contextual information. Recurrent Neural Networks, including LSTM and GRU, are specially proficient in modeling sequences, sentiment analysis, and language translation. Conversely, CNNs are applicable for sentence classification and the extraction of key phrases. The Transformer architecture, especially with models like BERT and GPT, set new benchmarks for NLU by leveraging self-attention mechanisms to improve context understanding and handle long-range dependencies. Deep learning has really advanced tasks such as text classification, question answering, machine translation, among others, which in fact has pushed the boundaries of what machines can achieve in understanding language.

关键词： Deep learning Training Sentiment analysis Analytical models Technological innovation Accuracy Translation Text categorization Transformers Context modeling

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