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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Al Rafi Aurnob M D Rokon Islam Emon Tonmoy Mohajan Dipta Barua Rupom Razzaque Kamruddin Nur Dept. of Computer Science American International University-Bangladesh Dhaka Bangladesh Dept. of Electrical and Electronic Engineering Chittagong University of Engineering and Technology Chittagong Bangladesh Dept. of Computer Science & Engineering International Islamic University Chittagong Chittagong Bangladesh School of Electrical Engineering and Computer Science Queen Mary University of London London United Kingdom

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

The integration of advanced technologies especially with machine learning has the potential to revolutionize animal condition monitoring by improving animal welfare and health status through comprehensive condition monitoring across the agricultural and veterinary domains as well as wildlife conservation. This study focuses on Multiple Additive Regression Trees (MART) and CatBoost as well as the fusion techniques. The proposed FusedMAC framework analyzes complex and high-dimensional data. Stacking fusion techniques aim to provide a comprehensive understanding of animal conditions by integrating diverse data modalities. Also, the use of the explainable artificial intelligence (XAI) methods, called SHAP (Shapley Additive Explanations). This technique ensures the transparency and interpretability of the model’s predictions. Our proposed approach addresses key gaps in previous research, including the need for advanced machine learning integration, robust data fusion and real-world validation. This study contributes to more effective animal management practices by enhancing predictive accuracy and interpretability. Ultimately, improving animal welfare and health outcomes.

关键词： Condition monitoring Additives Animals High dimensional data Prevention and mitigation Wildlife Stacking data integration Predictive models Regression tree analysis

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Applications of AI in Preservation of Kumaoni Culture Through Digital Archival

Applications of AI in Preservation of Kumaoni Culture Throug...

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Disruptive Technologies (ICDT), International Conference on

作者： Vaishali Dev Ansh Dhingra Shashi Kumar Sharma Nishant Koturiya Dept. of Computer Science and Engineering Graphic Era Hill University Bhimtal India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

Culture represents a society with human values and ethics. Without civilization there can be no culture. Thus it is very important to preserve our culture as it represents our values and ethics. Each region has its own characteristics culture which separates it from other regions not just geographically but in terms of traditions, art and literature. The Kumaon region in the Indian state of Uttarakhand lies in the eastern part of the state. It is blessed with a rich culture heritage and nature. The preservation of culture is done through traditional methods. The focus of this study is to use modern Artificial Intelligence techniques to achieve the preservation of culture. The Artificial Intelligence techniques which are used in this paper range from Convolutional Neural Networks, augmentation of data and fine tuning of models. To preserve it is necessary to archive the cultural elements so that it can be easily accessible in the future. The archival process can help in cataloguing the cultural items. By culture we mean traditional arts, such as Aipan, local literature texts and historical structures. The archival process seamlessly integrates the Artificial Intelligence methods to effectively preserve the cultural elements. This process can be further enhanced by fine tuning the Convolutional Neural Networks. This makes the model more robust and accurate than the existing model. The dataset quality can be further improved by using data augmentation techniques. This allows to generate more data from existing dataset. However, with the process comes some challenges like accountability and validity. This study explores these challenges which can help existing researchers in the field of culture and arts.

关键词： Ethics Art Accuracy Linguistics data augmentation data models Cultural differences Convolutional neural networks Artificial intelligence Tuning

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Unified Face Matching and Physical-Digital Spoofing Attack Detection

arXiv

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arXiv 2025年

作者： Kunwar, Arun Rattani, Ajita Dept. of Computer Science and Engineering University of North Texas Denton United States

Face recognition technology has dramatically transformed the landscape of security, surveillance, and authentication systems, offering a user-friendly and non-invasive biometric solution. However, despite its significant advantages, face recognition systems face increasing threats from physical and digital spoofing attacks. Current research typically treats face recognition and attack detection as distinct classification challenges. This approach necessitates the implementation of separate models for each task, leading to considerable computational complexity, particularly on devices with limited resources. Such inefficiencies can stifle scalability and hinder performance. In response to these challenges, this paper introduces an innovative unified model designed for face recognition and detection of physical and digital attacks. By leveraging the advanced Swin Transformer backbone and incorporating HiLo attention in a convolutional neural network framework, we address unified face recognition and spoof attack detection more effectively. Moreover, we introduce augmentation techniques that replicate the traits of physical and digital spoofing cues, significantly enhancing our model robustness. Through comprehensive experimental evaluation across various datasets, we showcase the effectiveness of our model in unified face recognition and spoof detection. Additionally, we confirm its resilience against unseen physical and digital spoofing attacks, underscoring its potential for real-world applications. © 2025, CC BY.

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Improved Sampling Methods for Evaluation of Classification Performance

Improved Sampling Methods for Evaluation of Classification P...

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7th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2025

作者： Lee, Yue-Shi Yen, Show-Jane Tang, Yi-Jie Ming Chuan University Dept. of Computer Science & Information Engineering Taoyuan Taiwan

ISBN: (纸本)9798331507022

To evaluate the classification accuracy of a classification algorithm on a certain dataset, it is necessary to sample training and test data from the original dataset. After building the classification model from the training data, the test data can be used to evaluate the classification accuracy. However, in order to evaluate the classification accuracy of a classification algorithm on an original dataset, it is usually necessary to sample the training/test data many times, build the classification model many times, and then average the accuracies of all the evaluation, which will consume a lot of time and resources. To address this, we propose a sampling method so that the sampled training/test data can replace the results of building multiple models and averaging multiple classification accuracy, that is close to the evaluation results of the original data. Our approach introduces different methods for calculating the similarity of data distributions and incorporates feature weights in the similarity calculation process to select training/test sets that are close to the distribution of the original dataset. © 2025 IEEE.

关键词： Taxonomies

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The All Wheel Speed Balance Control Concept 27

The All Wheel Speed Balance Control Concept

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27th IEEE International Conference on Intelligent Transportation Systems, ITSC 2024

作者： Atheupe, Gael P. Martinez, Didier Monsuez, Bruno Renault Technical Centre Renault Group & Ensta Paris Paris France Renault Technical Centre Renault Group Dept. Chassis Control & Adas Systems Guyancourt France Ensta Paris Dept. Computer Science & Systems Engineering Paris France

ISBN: (纸本)9798331505929

The transition to vehicle electrification introduces new demands on chassis dynamics, paving the way for advances in driving dynamics, safety, and efficiency. A key consideration arises: how are driving torque impulses converted in electric vehicles with multiple power sources? This study introduces a novel approach to address these demands through the integration of state-of-the-art yaw control, using concurrent front/rear axle and front right/front left torque distribution based on the wheel speed balance control concept. Our aim is not necessarily to demonstrate the supremacy of our approach, but rather to present a fresh philosophical perspective on the problem of yaw control in electric vehicles. Our method embodies dynamic load transfer and axle dynamics into the yaw control problem formulation, setting it apart from current methodologies. Through experimental results, we illustrate the effectiveness of our proposed approach regardless of road surface conditions. © 2024 IEEE.

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Evaluation of FinFET in Ultra Low Power ALU 4

Evaluation of FinFET in Ultra Low Power ALU

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4th IEEE International Conference on Design and Test of Integrated Micro and Nano-Systems, DTS 2022

作者： Yadhav, Avinash Rizkalla, Maher Ytterdal, Trond Lee, John J. Balasubramanian, Linknath Surya Gopinath, Anoop Indiana University Purdue University at Indianapolis Dept. of Electrical and Computer Engineering Indianapolis United States Norwegian University of Science and Technology Dept. of Electrical and Computer Engineering Trondheim Norway

ISBN: (纸本)9781665497831

The data path and memory elements are integral hardware components to evaluate the performance of a complex ASIC low power architecture. In this study, a FinFET model was evaluated via 7 nm FinFET process using ASAP PDK and applied to 6T SRAM cells and 32-bit ALU architecture. Devices of 0.207 V threshold were biased at 0.7V supply voltage. The clock router was properly modeled with accurate values of resistances and capacitances for the metal layers. The noise glitch and cross talk within the parallel running interconnects and the driving loads were incorporated in the model. A command set clock uncertainty is also integrated into the model to incorporate the clock jitter and clock skew effects. The architecture was optimized for worst case scenario with typical RC corners. The Non-Linear Delay Model (NLDM) was incorporated for the cell synthesis where a delay that is based on the input transition and output load capacitance was incorporated for better quality of results (QoR). The low power architecture was evaluated for worst case scenario considering typical-typical (TT) corners. A maximum effective device current of 18.26μA with subthreshold slope of 60mV/decade were estimated and considered in the system evaluation. The output ports were loaded with a typical 2fF capacitance. At 1 GHz operation, the internal power, switching power, and leakage power are reported to be 0.2705 mW, 0.7599 mW, and 0.00743 mW, respectively. © 2022 IEEE.

关键词： FinFET

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EMRChain: Electronic Medical Records Management System using Blockchain

EMRChain: Electronic Medical Records Management System using...

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Blockchain and Distributed Systems Security (ICBDS), IEEE International Conference on

作者： Arjun Rajasekharan Rani Koshy Dept. of Computer Science and Engineering College of Engineering Trivandrum Thiruvananthapuram Kerala India Department of Computer Science and Engineering College of Engineering Trivandrum Thiruvananthapuram Kerala India

ISBN: (数字)9798350354348

ISBN: (纸本)9798350354355

Electronic Medical Records (EMRs) are traditionally managed by central authorities, posing significant security risks such as data breaches, limited interoperability, and restricted patient control. This system leverages blockchain technology to address these issues, ensuring data remains immutable, transparent, and highly secure. Utilizing the Ethereum network and IPFS technology, our system provides a decentralized framework for storing and sharing medical records. Healthcare professionals, patients, and authorized parties can seamlessly exchange and access records through Ethereum’s decentralized structure and smart contracts, enabling automated and auditable interactions. To enhance confidentiality, all data uploaded to IPFS undergoes AES 256-bit encryption, with the encrypted Content Identifier (CID) stored on the blockchain. This ensures that intercepted CIDs cannot be accessed without the decryption key. IPFS technology facilitates decentralized storage and retrieval, eliminating the need for a centralized server and improving scalability and resilience. Patients retain full control over their health information, granting or revoking access as necessary. This system ensures secure patient identification, precise access management, and data confidentiality while allowing authorized parties to track and trace record access and modifications. Our blockchain-based EMR management system addresses the inherent risks of traditional centralized systems by providing a secure, transparent, and decentralized solution. By leveraging Ethereum and IPFS, it enhances data security, patient control, and interoperability, thus paving the way for more robust and patient-centric healthcare information management. This approach ensures that healthcare data remains secure, patients maintain control over their information, and authorized parties can effectively manage and access the records.

关键词： data privacy Scalability Smart contracts Process control Blockchains InterPlanetary File System Servers Electronic medical records Interoperability Resilience

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Security and Privacy Challenges in Semantic Communication Networks

Security and Privacy Challenges in Semantic Communication Ne...

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7th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2025

作者： Do, Quang Tuan Won, Dongwook Do, Tung Son Truong, Thanh Phung Cho, Sungrae Chung-Ang University Dept. of Computer Science and Engineering Seoul Korea Republic of

ISBN: (纸本)9798331507022

Semantic communication (SemCom) has emerged as a pivotal advancement in communication systems by focusing on the transmission of task-relevant meaning rather than raw data. This paradigm shift enables efficient communication for intelligent systems but also introduces new security and privacy risks. This paper explores these risks, reviews state-of-the-art countermeasures, and identifies key challenges and future directions in ensuring secure and private semantic communication. By addressing these issues, SemCom can fulfill its potential in domains such as healthcare, IoT, and autonomous systems. © 2025 IEEE.

关键词： Semantic Web

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Benchmarking Network Functionality: Performance Evaluation of SDN Controllers on Different Network Functions

Benchmarking Network Functionality: Performance Evaluation o...

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IEEE Conference on Vehicular Technology (VTC)

作者： Md Fahad Monir Azwad Fawad Hasan Md Mozammal Hoque Tarem Ahmed Fabrizio Granelli Dept. of Computer Science and Engineering Independent University Bangladesh Cullen College of Engineering University of Houston Texas USA Dept. of Information Engineering and Computer Science University of Trento (UniTrento) Trento Italy

ISBN: (数字)9798331517786

ISBN: (纸本)9798331517793

Network Softwarization revolutionizes infrastructure by introducing programmable network functions, leading to scalable, agile, and efficient networks. This innovation facilitates adaptable, secure Next Generation Networks, transforming connectivity. This study conducts a comparative analysis of SDN-based network functions utilizing three different controllers: Open vSwitch (OVS), OpenFlow Reference controller, and RYU, to understand their impact on packet loss and jitter. The OpenFlow Reference controller, primarily used for educational and basic experimental purposes, serves as a baseline. The OVS controller, operating within the Open vSwitch framework in Mininet, offers a practical level of network management capability, more advanced than the OpenFlow Reference controller yet not as comprehensive as the RYU Controller, a production-grade SDN controller. Our research aims to fill the gap in comparative performance analysis of these controllers, particularly in hybrid round-robin-based load balancing and firewall implementations. Recognizing the operational reality of SDN deployments, we explore both reactive and proactive modes, acknowledging that real-world applications like firewalls and load balancers often operate proactively with pre-populated rules, affecting data plane performance. The findings provide insights into each controller’s effectiveness, with OVS and RYU showing enhanced capability in reducing packet loss and jitter compared to the OpenFlow Reference controller. This research not only sheds light on the functional behaviors and performance trade-offs of these controllers but also contributes to the broader SDN and NFV discourse, offering a foundation for future academic and practical advancements in the field.

关键词： Vehicular and wireless technologies Technological innovation Firewalls (computing) Packet loss Bandwidth Switches Jitter Topology Software defined networking Next generation networking

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Convolutional Neural Networks based Enhanced Forest Monitoring System for Early Fire Detection 7

Convolutional Neural Networks based Enhanced Forest Monitori...

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7th International Conference on Communication and Electronics Systems, ICCES 2022

作者： Chatragadda, Adarsh Chalasani, Sai Harsha Vardhan Challa, Nagasupriya Gupta, N. Venkata Ramana Oleti, Prasanna Pravallika Amarendra, K. Prasad. V. Potluri Siddhartha Institute of Technology Dept. of Computer Science Engineering Vijayawada India Koneru Lakshmaiah Education Foundation Dept.of Computer Science Engineering Vijayawada India

ISBN: (数字)9781665496346

ISBN: (纸本)9781665496346

The vital reason behind environmental damage, species extinction, contaminated air is Forest fires. An efficient monitoring system can help us to detect and prevent forest fires such that the damage is prevented. Because wildfires are harder to identify in their initial stages, a quicker and more precise detection approach might help decrease the incidence of damage they did. This research study describe a strategy for automated and advanced fire detection built on such systems with greater level of dependability and require no service or human contact. The proposed study focuses on demonstrating variety of algorithms that are used for the wildfire detection and provide the best algorithm that can be as effective as a resultant. Nevertheless, this research work reviews some of the key deep learning methods such as CNN, VGG19 and DenseNet that can be used to build a forest fire monitoring system. © 2022 IEEE.

关键词： Fires

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