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检索条件"任意字段=International Conference on Advances in Intelligent Control and Computer Engineering"

共 47494 条记录，以下是91-100 订阅

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Blockchain-Enabled Supply Chain Management with Role-Based Access control and AES Encryption

Blockchain-Enabled Supply Chain Management with Role-Based A...

引用

2025 international conference on Computing for Sustainability and intelligent Future, COMP-SIF 2025

作者： Dharshan, J.Y. Amarnath, Arjun R. Reddy, Pothamsetti Sai Srujan Rajagopal, Shinu M. Prashanth, Mrudula Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Amrita School of Engineering Amrita Vishwa Vidyapeetham Department of Mechanical Engineering Bengaluru India

ISBN: (纸本)9798331538538

Blockchain-Enabled Supply Chain Management with Role-Based Access control and AES Encryption will use blockchain technology in order to enable safe, transparent, and decentralized supply chain management. Using smart contracts deployed on Polygon Amoy Testnet, the system ensures data integrity, confidentiality, and traceability while implementing role- based access control (RBAC) in order not to give unauthorized access to data. AES encryption is set forth for secretive treatment of information, thus affording a double-layered security measure for protection against on-chain as well as off-chain transactions. The solution is intended for deployment and testing in both localhost environments for rapid prototyping and the Polygon Amoy Testnet for scalable and distributed implementation. The Polygon Amoy Testnet provides an inexpensive development environment: developers can acquire test tokens (0.807 POL) via the Polygon Faucet for complete testing purposes. Deployment and interactions of smart contracts can be checked via the Polygon Testnet Explorer. The project combines blockchain's immutability with cryptographic security to address traditional supply chain inefficiencies, enhancing trust, scalability, and data accuracy. © 2025 IEEE.

关键词： Authentication

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Improving Face Recognition for Smart Home Security: A Study of Resnet, Siamese Networks, and Attention Mechanisms

Improving Face Recognition for Smart Home Security: A Study ...

引用

2025 international conference on Computing for Sustainability and intelligent Future, COMP-SIF 2025

作者： Sood, Hemaang Nikhil, B.V.S. Dengla, Palak Saeed, Sanya Nagabhushan, S.V. Bms Institute of Technology and Management Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331538538

This study presents a smart doorbell system designed to improve home security through advanced deep learning techniques. At its core, the system utilizes the YOLOv5 (You Only Look Once) algorithm for real-time face detection, ensuring accurate and efficient visitor identification. To further strengthen security, the system employs multifactor authentication (MFA), integrating face recognition alongside speech recognition and or/a manual keypad. The study evaluates three neural network architectures for face recognition-ResNet with cosine similarity, a Siamese network with CNN, and an attention-enhanced Siamese network-to determine the most effective model for secure and reliable authentication. Performance comparisons highlight progressive improvements in accuracy and robustness,. Additionally, a convenient interface enables seamless remote monitoring and control, enhancing both security and convenience. By addressing key limitations of existing smart security systems, this research provides insight into improving intelligent home access control. © 2025 IEEE.

关键词： Authentication

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IoT-based Door Automation and Smart Health Monitoring System 3

IoT-based Door Automation and Smart Health Monitoring System

引用

3rd international conference on intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Chaurasiya, Ruchi Yadav, Shakshi Malik, Swagata Belwal, Meena Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

This project presents the design of an automated access control system that aims to enhance security through the use of an ultrasonic motion sensor and a live heartbeat check. This system makes use of an ultrasonic sensor which detects any motion within a certain radius directing a signal to the controller based on Arduino. The controller is able to communicate with a script written in Python that queries API for the pulse of the user in question. Using these parameters,the system compares the real-time heartbeat of the pulse taken against the thresholds set *** the parameters exceed the range,the control unit within the Arduino will trigger the servo to unlock the door. Another scenario the parameters are within range,the door will remain closed. Such a system is able to increase the control measures in biological information system as it enables people to open the door while maintaining their hands free. The outcome demonstrates that by combining simple hardware components with data processing techniques, a reliable and secure automated access system can be created with minimal manual intervention, improving security while ensuring ease of use. © 2025 IEEE.

关键词： Access control

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IoT based Line of control Monitoring System 2

IoT based Line of Control Monitoring System

引用

2nd international conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Petkar, Taniya Faculty of Engineering and Technology Department of Computer Science And Medical Engineering Maharashtra Wardha442001 India

ISBN: (纸本)9798331505745

This paper presents a novel line-of-control (LoC) monitoring system that leverages the Internet of Things (IoT) to improve border security. The system creates a strong infrastructure for real-time monitoring throughout critical border regions, especially the Line of control, by utilizing IoT devices, sensors, and communication technologies. The coverage, timeliness, and data quality of conventional border surveillance systems are frequently compromised. As opposed to this, the suggested system makes use of a wirelessly networked network of intelligent sensors, such as infrared, acoustic, and video cameras. By utilizing sophisticated image processing algorithms and machine learning to analyze the gathered data, the system is able to differentiate between regular activities and any security risks. To help security staff make decisions quickly, real-time updates are transmitted to a centralized control center. Integration with cloud computing also makes data analysis, retrieval, and storage simpler and guarantees data accessibility for forensic investigations and trend research. © 2025 IEEE.

关键词： Network security

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Design and Implementation of Real-Time Wireless Sensor Network Protocols with Relay Nodes and Localization Methods for Efficient System Performance

Design and Implementation of Real-Time Wireless Sensor Netwo...

引用

2025 international conference on intelligent control, Computing and Communications, IC3 2025

作者： Balan, Gunapriya Pandey, Garima Sirisha, N. Ziara, Sajjad Thethi, H. Pal Alekhya, Juttiga New Horizon College of Engineering Department of Electrical and Electronics Engineering Bangalore India Galgotias university Department of Electrical Electronics & Communication Engineering Uttar Pradesh Gerater Noida203201 India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India College of Medical Technology The Islamic University Radiology Techniques Department Najaf Iraq Lovely Professional University Phagwara India Griet Department of Information Technology Telangana Hyderabad India

ISBN: (纸本)9798331527495

In this research paper we focus on the design and implementation of reliable real time wireless sensor network (WSN) protocols that make use of relay node and proper localization techniques to enhance the performance of the system in real time applications. However, Wireless Sensor Networks (WSNs) offer a very important application in environmental monitoring, industrial control, and smart cities. Maintaining efficient communication protocols and accurate real time localization of sensor nodes, especially in environments with dynamic topologies, is one of the key challenges. This paper presents a robust WSN protocol with relay nodes which achieve both energy efficiency and communication efficiency. Additionally, it examines specialized localization algorithms for use in real time systems that offer enhanced localization precision without sacrificing system performance. Results show the reliability, scalability and energy efficiency of WSNs if utilized in real time applications can be highly improved using the optimized protocols that are coupled with localization techniques. © 2025 IEEE.

关键词： Wireless sensor networks

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AI Principles in Practice with a Learning engineering Framework 17

AI Principles in Practice with a Learning Engineering Framew...

引用

17th international conference on computer Supported Education, CSEDU 2025

作者： Van Campenhout, Rachel Brown, Nick Johnson, Benny VitalSource 227 Fayetteville Street RaleighNC United States

ISBN: (纸本)9789897587467

With the explosion of generative AI, rapid innovation in educational technology can lead to extraordinary advances for teaching and learning-as well AI tools that are ineffective or even harmful to learning. AI should be used responsibly, yet defining responsible AI principles in an educational technology context and how to put those principles into practice is an evolving challenge. Broad AI principles such as transparency, accountability, and human oversight should be paired with education-specific values. In this paper, we discuss the development of AI principles and how to put those principles in practice using learning engineering as a framework, providing examples of the application of responsible AI principles in the context of developing AI-generated questions and feedback. Frameworks to support the rapid development of innovative technology-and the responsible use of AI-are necessary to ground learning tools' efficacy and ensure their benefit for learners. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Educational robots

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Simulation of rotating three bucket loading method 4

Simulation of rotating three bucket loading method

引用

4th international conference on computer Vision, Application, and Algorithm, CVAA 2024

作者： Xiao, Yajing Xi, Qiming Wu, Hui Liu, Jie China Coal Technology & Engineering Group Intelligent Storage & Loading Technology Co. Ltd. Beijing100013 China Huaneng Coal Technology Research Co. Ltd. Beijing100070 China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

A rapid quantitative loading process using a rotating three bucket is proposed and software simulation of the loading process is conducted. Through software simulation, the feeding and unloading of three buckets and buffer buckets during the loading process, as well as the feeding situation of the carriage are studied. Simulations were conducted on the loading process with different vehicle models and loading parameters, and there were no incidents of material scattering or insufficient loading during the entire train loading process. This verifies that the rotating three bucket loading process can adapt to different loading conditions, theoretically verifying the feasibility of the three bucket time-sharing feeding and continuous loading process. © 2025 SPIE.

关键词： control systems Miniaturization Systems modeling

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Integration of Process Optimization and Automation: A Way to AI Powered Digital Transformation 1

Integration of Process Optimization and Automation: A Way to...

引用

1st international conference on advances in computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Kotte, Kulasekhara Reddy Thammareddi, Latha Kodi, Divya Anumolu, Venkateswara Rao Arun Kumar, K. Joshi, Shubham INC SLRI Solutions India India Cyber Security Senior Data Analyst Truist Financial India Technical Architect ERP Solutions- Information Technology India Big Data Architect India Bipin Tripathi Kumaon Institute of Technology Department of Computer Science & Engineering Almora India

ISBN: (纸本)9798331518578

One of the most important factors facilitating AI-powered digital transformation is the combination of process automation and optimisation. Organisations may improve operational efficacy, save costs, and streamline procedures by utilising cutting-edge technology like robotic process automation (RPA), machine learning (ML), and artificial intelligence (AI). While automation seeks to reduce human participation in repetitive and rule-based operations, process optimisation concentrates on improving current business processes to attain improved productivity. The development of intelligent, adaptable, and self-improving systems is made possible by the combination of these two methodologies. Predictive findings, real-time decision-making, and customised customer experiences are further made possible by AI-driven analytics. This connection promotes innovation, scalability, and agility across sectors, giving businesses a competitive edge in the quickly changing digital market. The article examines the main principles, advantages, and difficulties of this shift and provides a road map for businesses looking to automate and optimise processes using AI in order to attain operational excellence and sustainable growth. © 2025 IEEE.

关键词： Decision making

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Cognitive Congestion Alleviation Framework in IoT-Enabled WSN for Next-Gen intelligent Transport Systems via Optimized Capsule Attention Network 3

Cognitive Congestion Alleviation Framework in IoT-Enabled WS...

引用

3rd international conference on intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Mohandas, R. Sudha, Y. Narmatha, P. Venkata Prasad, P. Manikandan, N. Department of Electronics & Communication Engineering Chennai Institute of Technology Tamil Nadu Chennai India Department of Computer Science and Engineering Presidency University Karnataka Bangalore India Department of Computer Science and Engineering Sri Sairam College of Engineering Karnataka Bangalore India Department of Electrical and Electronics Engineering Chaitanya Bharathi Institute of Technology Telangana Hyderabad India Department of Business Administration Kalasalingam Business School Kalasalingam Academy of Research and Education Viruthunagar Krishnankoil India

ISBN: (纸本)9798331527549

The rapid advancement of intelligent Transportation Systems (ITS) has heightened the importance of reliable, real-time data transmission in Wireless Sensor Networks (WSNs). However, congestion in data-heavy Internet of Things (IoT)-enabled networks remains a critical challenge, impacting communication efficiency and decision-making in ITS. In high-density IoT-ITS applications, traditional congestion control methods are insufficient to address the complexity and dynamics of data flows. Existing solutions often fail to adapt in real time to varying traffic loads, leading to delays and data loss. There is a strong need for a congestion alleviation framework capable of intelligently prioritizing data while dynamically managing network resources to ensure seamless information exchange in large-scale deployments. This study presents a Cognitive Congestion Alleviation Framework in IoT-Enabled WSN for Next-Gen intelligent Transport Systems via Optimized Capsule Attention Network (V-CapMiAN-Parr), which combines the Vectorized Adaptive Capsule Neural Network (V-AdCapNet) and Multi-instance Attention Network (MAN), fine-tuned by the Parrot Optimizer (ParrOpt). This integration aims to effectively detect, mitigate, and prevent congestion through an advanced capsule-based attention mechanism with adaptive optimization. The suggested V-CapMiAN-Parr framework demonstrated significant improvements in congestion control, achieving data throughput efficiencies above 99.7%, packet delivery rates above 99.5%, and network reliability reaching 99.2% under high traffic loads. The model’s adaptive weighting mechanism ensures real-time responsiveness and reliability, crucial for next-gen ITS applications. The V-CapMiAN-Parr framework effectively addresses congestion issues in IoT-enabled WSNs, providing a robust, scalable solution for ITS applications. This cognitive framework’s advanced data prioritization and adaptive optimization capabilities are well-suited for enhancing the performanc

关键词： Traffic congestion

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Classifying Human Emotions through EEG data with Machine Learning 1

Classifying Human Emotions through EEG data with Machine Lea...

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1st international conference on intelligent Systems and Computational Networks, ICISCN 2025

作者： Kaur, Gaganjot Gupta, Meenu Kumar, Rakesh Chandigarh University Department of Computer Science and Engineering Punjab India

ISBN: (纸本)9798331529246

The Psychological and neurobiological phenomenon is called an emotion. The capability of human beings to present different expressions clashes with the emotional state of mind. Therefore, it is challenging to identify a human's real emotional state by observing physical appearance. Nowadays, using emotional engineering to predict human emotions is one of the major attempts of Machine Learning (ML). This study aims to investigate the usage of techniques of machine learning for efficient emotion prediction that relies on electroencephalography (EEG) signals. EEG dataset is complex and sometimes noisy, making it difficult to interpret using traditional methods. So, in this research the Kaggle dataset collected through a Muse headband, five distinct ML methods are applied to classify emotions. Performance metrics such as precision, recall, F1 score, support, and accuracy are employed to differentiate between different classification techniques. Random Forest turned out to be the best method due to its high accuracy 99%. Three emotions are predicted based on the results of performance metrics. This study benefits mental health monitoring, neurofeedback therapy, stress management, human-computer interaction, etc. This study lays the groundwork for exploring this field and helps develop more sophisticated control systems in the future. © 2025 IEEE.

关键词： Emotion Recognition

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