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Secure Consumer-Centric Demand Response Management in Resilient Smart Grid as Industry 5.0 Application with Blockchain-Based Authentication

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IEEE Transactions on Consumer Electronics 2024年第1期70卷 1370-1379页

作者： Ayub, Muhammad Faizan Li, Xiong Mahmood, Khalid Shamshad, Salman Saleem, Muhammad Asad Omar, Marwan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu12599 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan The University of Lahore Department of Software Engineering Lahore54590 Pakistan Illinois Institute of Technology Information Technology and Management ChicagoIL60616 United States

The development of the industrial Internet of Things and smart grid networks has emphasized the importance of secure smart grid communication for the future of electric power transmission. However, the current deployment of smart meters and wireless communication methods presents several security concerns that must be addressed to ensure sustainable and resilient smart grid networks for Industry 5.0, with a focus on consumer-centric technologies. In this context, we propose a privacy-focused authentication solution for secure consumer-centric demand response management in smart grid networks, incorporating consumer electronics. Our proposed protocol utilizes blockchain-based authentication to enhance resistance against potential attacks and ensure the integrity of demand response data. Additionally, we have incorporated a PUF to prevent physical attacks. We have conducted informal and formal security analyses to verify the effectiveness of our proposed protocol against various threats and to confirm the session key security. Our comparison study has demonstrated that our protocol provides superior security and added functionalities, with lower computation and communication costs compared to similar protocols. Furthermore, we have measured the computational time for implementing the blockchain component of our protocol while considering the impact of varying numbers of block additions and transactions per block. © 1975-2011 IEEE.

关键词： Authentication

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DNA Cryptography for Secure Multilingual Cloud Messaging 6th

DNA Cryptography for Secure Multilingual Cloud Messaging

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6th International Conference on Smart Computing and Informatics, SCI 2024

作者： Digumarthi, Suseela Singidi, Sugunasri Paladugu, Triveni Mattaparthi, Ramakrishna Chandra Sekhar Reddy, N. Department of Computer Science and Engineering Godavari Institute of Engineering and Technology Andhra Pradesh Rajahmundry India Department of Computer Science and Engineering Dr. M.G.R Educational and Research Institute Maduravoyal Tamil Nadu Chennai India

ISBN: (纸本)9789819619801

Cloud computing stands at the forefront of contemporary advancements in distributed computing, providing a range of data storage, including on-demand services, platform services, and network capabilities. Despite the compelling advantages, many enterprises exhibit hesitancy in embracing cloud services, primarily stemming from apprehensions about data security, given that the data are housed on servers belonging to the cloud service provider. Addressing this concern has prompted researchers worldwide to explore various avenues for fortifying and ensuring the security of data stored in cloud computing. Among these approaches, the bi-directional DNA encryption algorithm is gaining prominence as a notable contender. However, a notable limitation of the current methodology lies in its exclusive focus on the American Standard Code for Information Interchange character set, neglecting the broader scope of non-English users engaging with cloud computing. Consequently, this proposed endeavor seeks to augment the BDEA to accommodate Unicode characters, ensuring a more inclusive and robust security framework. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Distributed cloud

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AMBU-BOT: Effective Accident Preventive System Using IOT

AMBU-BOT: Effective Accident Preventive System Using IOT

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Suganya, P. Varalakshmi, I. Priyadharshini, S. Pradeepa, R. Sathvika, A. Manakula Vinayagar Institute of Technology Department of Computer Science and Engineering Puducherry India St. Joseph's College of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331510022

This paper intends to propose a new robot which overcomes the hurdles of an AED(Automated External Defibrillator) at the nearest Ambubot: A robot, ambulance robot designed and developed that carries an AED for assisting lay rescuers to save patients' lives, as well as Time taken before the ambulance is located in the case of a sudden cardiac arrest. The working of Ambubot could go searching for the victims and also obedient to the human instructions. Population is growing daily, and with it, so is the risk of heart disease also increasing. Even though traffic on our roads is normal for people, it is crucial to admit heart attack victims to hospitals in case of emergency because it can be easy for them to receive an automated external defibrillator at such a young age. Its independent purpose is to save the sufferer. © 2024 IEEE.

关键词： Defibrillators

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A Real-Time Framework for Automatic Sarcasm Detection Using Proposed Tensor-DNN-50 Algorithm 4th

A Real-Time Framework for Automatic Sarcasm Detection Using ...

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4th International Conference on Frontiers in Computing and Systems, COMSYS 2023

作者： Murthy, Jamuna S. Siddesh, G.M. Department of Computer Science and Engineering Ramaiah Institute of Technology Bengaluru India Department of Artificial Intelligence and Data Science Ramaiah Institute of Technology Bengaluru India

ISBN: (纸本)9789819726103

Social media platforms such as Twitter, Facebook, and Instagram are vast repositories of trending global news. They generate an enormous amount of data, offering a valuable resource for both academic researchers and IT marketing teams. These data can be harnessed for various insights and applications. A prominent and pressing research area within this data is the detection of sarcasm and fake news. The internet is rife with misleading and false information, often causing widespread confusion. Current research primarily categorizes news as either real or fake, using machine learning algorithms. However, this binary approach struggles with news that is partially true yet contains elements that could be deemed fake. To address this issue, our work introduces a real-time framework for sarcasm detection, utilizing an innovative deep learning technique called Tensor-DNN-50 for multiclass classification. The TDNN-50 model not only classifies news articles based on authenticity but also quantifies the level of sarcasm within them. Comparative analysis against leading techniques like SVM, att-RNN, and EANN demonstrates the superiority of our proposed algorithm. It achieves an impressive accuracy of 96.4% for two-class classification, 94.32% for four-class classification, and an astonishing 99.1% for seven-class classification. These results underscore the potential of our Tensor-DNN-50 Model in effectively addressing the intricate challenges of sarcasm detection and news classification with unparalleled accuracy across multiple classes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Support vector machines

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Real-Time Facial Recognition System for Secure College Bus Transport using Deep Learning Techniques 3

Real-Time Facial Recognition System for Secure College Bus T...

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

作者： Kavitha, M.N. Saranya, S.S. Pragatheeswari, E. Priya, N. Kaviya Ranjith, S.R. Vanchivel, N. Rahul Kongu Engineering College Department of Computer Science and Design Tamil Nadu India Psg Institute of Technology and Applied Research Department of Computer Science and Engineering Tamil Nadu Coimbatore India Kongu Engineering College Department of Computer Science and Engineering Tamil Nadu Erode India Kongu Engineering College Department of Csd Tamil Nadu India

ISBN: (纸本)9798331509675

The proposed system aims to enhance student transportation security through real-time face detection and recognition. Leveraging the MTCNN framework for accurate face detection and the FaceNet model for reliable face recognition, the system ensures only authorized students can board designated buses by comparing captured faces with a pre-registered database. In case of mismatches, alerts are triggered to notify drivers and authorities. Additionally, parallel implementation of Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) classifiers improves authentication accuracy. The system's deep learning-based architecture ensures robust performance under varying environmental conditions and supports continuous learning for long-term reliability. © 2025 IEEE.

关键词： Students

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IsharaVerse: A Bidirectional, Context-Aware, Multilingual Sign Language System 3

IsharaVerse: A Bidirectional, Context-Aware, Multilingual Si...

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3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Kashish Chelsi Langdi, Ramjas Trivedi, Amit Kumar Thapar Institute of Engineering and Technology Department of Computer Science and Engineeering Patiala India

ISBN: (纸本)9798331523893

IsharaVerse, a Context-Aware Multilingual Sign Language Generation and Translation System, bridges communication gaps between signers (Deaf, Hard of Hearing, Mute communities) and non-signers. It supports Indian Sign Language (ISL), converting between digits, alphabets, words, and sentences. Text/audio input is converted to sign gestures using Rule-Based Mapping, while Sign Language Translation leverages CNN-based Deep Learning. A multilingual module integrates Google Translator, and an Emotion Recognition feature uses a DistilRoBERTa model. Achieving 90% accuracy in sign-to-text and seamless multilingual support, IsharaVerse promotes inclusion in social, educational, and professional contexts. © 2025 IEEE.

关键词： Deep learning

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Superior triethylamine-sensing properties based on SnO2 hollow nanospheres synthesized via one-step process

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Progress in Natural science:Materials International 2024年第4期34卷 682-689页

作者： Mingyue Xu Shu Yan Zia Ur Rehman Jianhua Hou Liangyun Zhai Shengjie Zhu Chuanbao Cao Wanfeng Xie Junyu Zhong Jun Yang State Key Laboratory of Bio-fibers and Eco-textiles School of Electronics and Information College of Materials Science and Engineering College of Chemistry and Chemical Engineering Qingdao University College of Environmental Science and Engineering Yangzhou University State Key Laboratory of Chemical Safety SINOPEC Research Institute of Safety Engineering Co. Ltd. National Registration Center for Chemicals Ministry of Emergency Management Research Center of Materials Science Beijing Institute of Technology State Key Laboratory of Multiphase Complex Systems Institute of Process Engineering Chinese Academy of Sciences

Due to serious harm of triethylamine（TEA） to environmental safety and human health, it is significant to synthesize gas-sensitive materials with high performance for TEA detection. However, it is still a challenge to achieve high-sensitivity detection of TEA at low temperature for a sensor synthesized through an economical and efficient method. In this work, hollow-structured SnO2（HS-SnO2） nanospheres have been fabricated by a facile, low-cost hydrothermal method in one step, which exhibit superior TEA-sensing properties, including not only ultrahigh response（127.75） for 100 ppm TEA, good selectivity, but also fast response and recovery time（17/28 s）, low detection threshold（1 ppm） and robust stability at a relatively low optimum operational temperature of 225°*** excellent gas-sensitizing performances are ascribed to porous hollow structures with rich oxygen vacancies that provide abundant active sites for raising O2adsorption and reaction of TEA and oxygen species. This work offers an effective and economical strategy for fabricating high-performance TEA sensors for industrial applications.

关键词： Hollow-structured SnO_2 nanospheres TEA High response Gas sensors

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Machine Utilization Prediction in Cloud Using Informer Model 4

Machine Utilization Prediction in Cloud Using Informer Model

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Naik, Archana Sooda, Kavitha Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bangalore India B.M.S. College of Engineering Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331529635

This study investigates CPU utilization prediction in cloud environments using the Informer model, a deep learning framework optimized for long-sequence forecasting. By leveraging its ability to capture complex temporal patterns, the Informer model is benchmarked against GRU, LSTM, Prophet, and XGBoost across diverse CPU utilization datasets. Performance is evaluated using RMSE, MSE, MAE, and MAPE metrics. Experimental results demonstrate that the Informer model significantly outperforms the comparison models, excelling in capturing both gradual trends and abrupt fluctuations. Specifically, the Informer achieved a 47.51% reduction in RMSE over GRU, 49.90% over LSTM, 67.36% over Prophet, and 50.46% over XGBoost. These findings underscore the Informer model's superior adaptability and precision, making it a robust solution for dynamic resource utilization prediction in cloud environments. Its predictive accuracy is pivotal for enhancing workload management and optimizing resource allocation. © 2024 IEEE.

关键词： Prediction models

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Machine Learning based Human Stress Detection through Sleep Patterns 5

Machine Learning based Human Stress Detection through Sleep ...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Santhosh Krishna, B.V. Mai, Kiran Aswini, S. Biju, J. Mohanraj, S. Kaliappan, S. New Horizon College of Engineering Department of Computer Science and Engineering Bangalore India Bangalore Technological Institute Department of Computer Science and Engineering Bangalore India Bannari Amman Institute of Technology Information Science and Engineering Erode Tamilnadu Sathyamangalam India KGiSL institute of Technology Artificial Intelligence and Data Science Tamilnadu Coimbatore India Lovely Professional University Division of Research and Development Punjab India

ISBN: (纸本)9798331504403

Stress is a critical factor in overall wellbeing, and understanding its patterns during sleep can provide valuable insights into improving sleep quality and managing stress-related health issues. Stress has become an increasingly prevalent issue in modern society, impacting both mental and physical well-being. The ability to accurately detect stress levels in individuals is crucial for timely intervention and management. Sleep patterns serve as a valuable indicator of stress, as disruptions in sleep quality and duration often correlate with heightened stress levels. Leveraging advancements in machine learning techniques, this study proposes a novel linaer regression based approach to detect human stress by analyzing sleep patterns extracted from wearable devices. The proposed system consists of several key components. Firstly, data collection involves the continuous monitoring of sleep parameters such as sleep stages, heart rate variability, and movement using wearable sensors. Next, feature extraction techniques are applied to transform raw sleep data into informative features that capture relevant aspects of sleep patterns indicative of stress. © 2024 IEEE.

关键词： Sleep research

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Voice command-integrated AR-based E-commerce Application for Automobiles

Voice command-integrated AR-based E-commerce Application for...

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2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2023

作者： Krishnamurthy, Vallidevi Jafrin Rosary, B. Oliver Joel, G. Balasubramanian, Surendiran Kumari, Sakshi Vellore Institute of Technology School of Computer Science and Engineering Chennai India Vellore Institute of Technology School of Mechanical Engineering Chennai India National Institute of Technology Puducherry Department of Computer Science and Engineering Puducherry India

ISBN: (纸本)9798350312126

This research work aims to create an Augmented Reality (AR) based android app that can project the dimensions of an automobile in the real world and recognize voice commands to operate functions like opening car doors and changing colors. The app uses a combination of augmented reality, machine learning technology, Unity game engine, C# script, Google speech recognition API and Vuforia SDK to superimpose images of the car in the real world and allow control through voice commands. The initial focus is on cars, but the solution can also be used to create AR-enabled brochures for marketing companies to enhance sales and provide customers with a better understanding of the product before purchase. © 2023 IEEE.

关键词： Speech recognition

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