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2nd International Conference on Multidisciplinary engineering and Applied Science, ICMEAS 2023

作者： Agajo, James Vishnu, Ram Ov Imoru, Odunayo Thomas, Sadiq Guda, Blessed Amadasun, Kennedy Federal University of Technology Department of Computer Engineering Niger State Minna Nigeria India University of Johannesburg Department of Electrical and Computer Engineering South Africa Nile University Department of Computer Engineering Nigeria School of Computing and Engineering University of Sunderland Department of Electrical/Electronic Engineering United Kingdom

ISBN: (纸本)9798350358834

Network slicing is a key feature of 5G that allows for the creation and running of multiple virtual networks, known as 'slices,' on shared infrastructure. These slices can be customized for specific applications and requirements, and offer greater flexibility than previous network technologies. Additionally, the use of network slicing can generate new revenue opportunities through automation, leading to profitable growth at a large scale. This paper understudied the processes involved in achieving this and thereby proposed a sequence of procedures that will seamlessly realise this, the work first considered Slice Network based on the Business request of the industry vertical, then the aggregation of intent based on the request, thereafter assign slice based on the business request, next a check is carried out if the Network policies are met, the RAN-Slicing is employed, finally, it deploys Network Orchestration, which manages, monitor and grants End-to-End Access, this sequence will help network operators in reducing expenses (OPEX) and capital expenditure (CAPEX). The use of this technology will greatly increase operational efficiency and speed up the rolling out of 5G network services. It will also enable operators to offer a range of new services to customers using the same physical network infrastructure. © 2023 IEEE.

关键词： 5G mobile communication systems

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A Physical-Based Artificial Neural Networks Compact Modeling Framework for Emerging FETs

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IEEE Transactions on Electron Devices 2024年第1期71卷 223-230页

作者： Yang, Ya-Shu Li, Yiming Kola, Sekhar Reddy Reddy National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory The Institute of Communications Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Institute of Communications Engineering Institute of Biomedical Engineering Department of Electronics and Electrical Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan

We report a compact modeling framework based on the Grove-Frohman (GF) model and artificial neural networks (ANNs) for emerging gate-all-around (GAA) MOSFETs. The framework consists of two ANNs;the first ANN constructed with the drain current model not only can capture the main trend of device I - V characteristics but also can predict its variation even when the amount of training data for the ANN is insufficient or outside the range of applied biases. The second one is then designed to improve the model accuracy by further minimizing the errors between the target and the model outputs. We implement the proposed framework to accurately model emerging GAA nanosheet (NS) MOSFETs and complementary FETs (CFETs) without suffering from divergent issues in circuit simulation. In addition, nonphysical behaviors, such as nonzero current at zero bias, do not occur in the modeling framework. Compared to recently reported machine-learning (ML) models, our approach can achieve a similar level of model accuracy with merely 20% amount of the training data. © 1963-2012 IEEE.

关键词： Neural networks

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An Intelligent Big Data Security Framework Based on AEFS-KENN Algorithms for the Detection of Cyber-Attacks from Smart Grid Systems

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Big Data Mining and Analytics 2024年第2期7卷 399-418页

作者： Sankaramoorthy Muthubalaji Naresh Kumar Muniyaraj Sarvade Pedda Venkata Subba Rao Kavitha Thandapani Pasupuleti Rama Mohan Thangam Somasundaram Yousef Farhaoui Department of Electrical and Electronics Engineering CMR College of Engineering&TechnologyHyderabad 501401India Department of Electronics and Communication Engineering Vardhaman College of Engineering KacharamShamshabad 501218India Department of Electronics and Communication Engineering Sreenidhi Institute of Science and TechnologyHyderabad 501301India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai 600062India Department of Electrical and Electronics Engineering Bharat Institute of Engineering and TechnologyHyderabad 501510India Department of Computer Science and Engineering Amrita School of ComputingAmrita Vishwa VidhyapeethamBengaluru 560035India T-IDMS Department of Computer ScienceFaculty of Sciences and TechniquesMoulay Ismail UniversityErrachidia 52000Morocco

Big data has the ability to open up innovative and ground-breaking prospects for the electrical grid,which also supports to obtain a variety of technological,social,and financial *** is an unprecedented amount of heterogeneous big data as a consequence of the growth of power grid technologies,along with data processing and advanced *** main obstacles in turning the heterogeneous large dataset into useful results are computational burden and information *** original contribution of this paper is to develop a new big data framework for detecting various intrusions from the smart grid systems with the use of AI ***,an AdaBelief Exponential Feature Selection(AEFS)technique is used to efficiently handle the input huge datasets from the smart grid for boosting ***,a Kernel based Extreme Neural Network(KENN)technique is used to anticipate security vulnerabilities more *** Polar Bear Optimization(PBO)algorithm is used to efficiently determine the parameters for the estimate of radial basis ***,several types of smart grid network datasets are employed during analysis in order to examine the outcomes and efficiency of the proposed AdaBelief Exponential Feature Selection-Kernel based Extreme Neural Network(AEFS-KENN)big data security *** results reveal that the accuracy of proposed AEFS-KENN is increased up to 99.5%with precision and AUC of 99%for all smart grid big datasets used in this study.

关键词： smart grid Machine Learning(ML) big data analytics AdaBelief Exponential Feature Selection(AEFS) Polar Bear Optimization(PBO) Kernel Extreme Neural Network(KENN)

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Environmental water quality prediction based on COOT-CSO-LSTM deep learning

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Environmental Science and Pollution Research 2024年第42期31卷 54525-54533页

作者： Rajagopal, Sankarasubbu Ganesh, Sundaram Sankar Karthick, Alagar Sampradeepraj, Thangavel Department of Information Technology National Engineering College Tamilnadu Kovilpatti628503 India Department of Computer Science and Engineering R.M.K. College of Engineering and Technology Puduvoyal Tamilnadu Thiruvallur601206 India Renewable Energy Lab Department of Electrical and Electronics Engineering KPR Institute of Engineering and Technology Tamilnadu Coimbatore641407 India Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu Virudhunagar626126 India

Water resource management relies heavily on reliable water quality predictions. Predicting water quality metrics in the watershed system, including dissolved oxygen (DO), is the main emphasis of this work. The enhanced long short-term memory (LSTM) model was suggested to improve the model’s performance. Additionally, a hybrid model was employed to calculate the ideal parameter values for the LSTM model, which helped overcome the nonstationarity, unpredictability, and nonlinearity of the data about the water quality parameters. This model recruited the COOT method. The original weekly water quality values at the Vaigai River, Madurai, Tamil Nadu, India, were tested using the suggested hybrid model. An independent LSTM, the hybrid optimisation method takes its cues from the cuckoo bird’s reproductive strategy and a novel meta-heuristic optimisation technique dubbed COOT, which is based on the behaviour of a flock of coot birds. If implemented, the suggested hybrid model might serve as an alternate framework for water quality prediction, laying the groundwork for basin-wide efforts to manage water quality and control pollutants. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Macroinvertebrates

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Can AI Keep You Safe? A Study of Large Language Models for Phishing Detection 14

Can AI Keep You Safe? A Study of Large Language Models for P...

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2024 IEEE 14th Annual computing and Communication Workshop and Conference, CCWC 2024

作者： Chataut, Robin Gyawali, Prashnna Kumar Usman, Yusuf Quinnipiac University School of Computing and Engineering HamdenCT06518 United States Department of Computer Science and Electrical Engineering MorgantownWV26506 United States

ISBN: (纸本)9798350360134

Phishing attacks continue to be a pervasive challenge in cybersecurity, with threat actors constantly developing new strategies to penetrate email inboxes and compromise sensitive data. In this study, we investigate the effectiveness of Large Language Models (LLMs) in the crucial task of phishing email detection. With the growing sophistication of these attacks, we assess the performance of three distinct LLMs: GPT-3.5, GPT-4, and a customized ChatGPT, against a carefully curated dataset containing both phishing and legitimate emails. Our research reveals the proficiency of LLMs in identifying phishing emails, with each model showing varying levels of success. The paper outlines the strengths and limitations of GPT-3.5, GPT-4, and the custom ChatGPT, illuminating their respective suitability for practical applications in email security. These results underscore the potential of LLMs in effectively identifying phishing emails and their significant implications for enhancing cybersecurity measures and safeguarding users from the risks of online fraud. © 2024 IEEE.

关键词： Electronic mail

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Smart Traffic Control: Adaptive Signal Management Based on Real-time Lane Detection using YOLOv8

Smart Traffic Control: Adaptive Signal Management Based on R...

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2024 IEEE International Conference on Automation and Computation, AUTOCOM 2024

作者： Ravichandran, M. Laxmikant, Kumar Muthu, Annamalai Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India University of Technology & Applied Science Department of Electrical and Electronics Engineering Nizwa Oman

ISBN: (纸本)9798350382723

As cities grow, handling traffic in big urban are-as becomes a huge proble-m. More cars on the road and not enough roads le-ad to heavy traffic jams. This increases trave-l time and harms our environment. Our study tackle-s these problems with a ne-w approach. We use real-time- lane detection with YOLOv8 and adaptable- traffic lights. Using sharp computer vision, our system pinpoints vehicle-s. It adjusts traffic light timings on-the-go to improve traffic flow. Our model works e-xtremely accurately. During the- learning phase, it achieve-d a mAP50 score of 99.3% and a mAP50-95 score of 87.4%. In the te-sting phase, it got a mAP score of 99.2% and a mAP50-95 score of 86.2%. The re-sults highlight how the system can improve city trave-l. It's valuable for city planners and traffic officials. It helps the-m understand smart transportation systems bette-r. © 2024 IEEE.

关键词： computer vision

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ECO-Guard: An Integrated AI Sensor System for Monitoring Wildlife and Sustainable Forest Management 8th

ECO-Guard: An Integrated AI Sensor System for Monitoring Wil...

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8th International Conference on Information and Communication Technology for Competitive Strategies, ICTCS 2023

作者： Nikhilesh Krishna, Ch. Rauniyar, Avishek Kireeti Sai Bharadwaj, N. Raj, Sujay Bharath Valsan, Vipina Suresh, Kavya Ravikumar Pandi, V. Sathyan, Soumya Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9789819994885

In India, 12% of wild animals are threatened with extinction, and human activity threatens more than 80% of East and Southeast Asia's wetlands. Every year, an estimated 400 people are killed by wildlife in India, with Maharashtra alone seeing 88 human deaths and 9258 cattle killings by tigers in 2020. The ECO-Guard is an intelligent device which uses the You Only Look Once (YOLO) model and various sensors and machine learning models with algorithms that support and promote forest monitoring and tourist guidance in forest areas addressing Sustainable Development Goal (SDG) 12 (Life on Land). The paper presents a linear chain-like mesh of devices speeded across bio-area which are used in correlation for intended surveillance. Each device can multimodule apps and tally their sightings automatically to analyze coherence of the situation with the forest rangers, organizations, and figures. It catches from the information, the ill substitutes like human spotting with beasts. It suggests that the advice can be further put into practice by constantly monitoring any closeness of alternatives that match to create an unfavorable combination. Also having to help detect many forest crimes, some including illegal logging, preventing, and deforestation on a large scale. This paper highlights a novel ECO-Guard, an intelligent and autonomous monitoring system which includes artificial intelligence (AI)-enhanced strategies and algorithms to conserve wildlife and forest ecosystems by integrating various hardware and software components. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deforestation

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Assessing Predictor Influence in LSTM Models for Enhanced Solar Energy Forecasting 26

Assessing Predictor Influence in LSTM Models for Enhanced So...

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26th International Symposium on Symbolic and Numeric Algorithms for Scientific computing, SYNASC 2024

作者： Ciobanu, Alexandru-Adrian Bacanin, Nebojsa Sanders, Tilman Stoean, Catalin University of Craiova Department of Computer Science Craiova Romania Singidunum University Faculty of Informatics and Computing Belgrade Serbia FH Muenster Department of Electrical Engineering and Computer Science Steinfurt Germany

ISBN: (纸本)9798331532833

Accurate forecasting of solar energy production is highly important for an adequate integration of renewable energy into the power grid. This study explores the importance of various predictors for enhancing the accuracy of solar energy forecasting using two distinct Long Short-Term Memory models: one that relies solely on historical power production data and past exogenous weather data, and another that additionally incorporates future exogenous weather data. Various settings are tried to improve the results and relatively similar parameters are used by both, yielding more accurate results for the model that also uses information from the future. The results also demonstrate that predictor importance varies significantly between the two models. These findings suggest that different models benefit from distinct predictor combinations and incorporating exogenous data can significantly enhance forecasting performance. © 2024 IEEE.

关键词： Prediction models

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PAC-MC: An Efficient Password-Based Access Control Framework for Time Sequence Aware Media Cloud

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IEEE Transactions on Mobile computing 2025年第7期24卷 5632-5648页

作者： Wang, Haiyan Liu, Penghui Zhong, Xiaoxiong Luo, Fucai Xiao, Bin Yang, Yuanyuan Peng Cheng Laboratory Department of New Networks Shenzhen518000 China Zhejiang Gongshang University School of Computer Science and Technology Hangzhou314423 China Hong Kong Polytechnic University Department of Computing Hong Kong Stony Brook University Department of Electrical and Computer Engineering Stony BrookNY11794 United States

Cloud storage makes it easier for users to access and share data remotely, but it often requires integration with cryptographic technologies to address consumer-oriented applications, such as fine-grained data access, secure data sharing and retrieval. This paper focuses on the fine-grained access problem of media applications based on time sequence, that is, certain critical media applications based on time sequences should ideally be accessible only to authorized clients. The traditional keyword-based searchable encryption (SE) allows effective search and access over encrypted data while preserving data privacy, but most existing solutions do not support temporal access control (i.e., a mechanism that grants access permissions to users within a specified time range). In this paper, we propose PAC-MC, an efficient password-based access control framework for media cloud relying on content control with the time sequence attribute. PAC-MC not only supports multi-keyword search using any monotonic boolean formulas but also allows media owners to control content-encryption keys for different time periods with an updatable password. Furthermore, it supports the self-retrieval of content-encryption keys. In addition, PAC-MC is provably secure under the standard model. Finally, the detailed performance evaluation results and experimental comparisons indicate that PAC-MC is very efficient and outperforms the previous solutions in terms of computation, communication, and storage costs. © 2002-2012 IEEE.

关键词： Media Encryption Access Control Cloud computing Servers Passwords Keyword Search Copyright Protection Streaming Media Performance Evaluation Access Control Content Protection Media Cloud Password Searchable Encryption Time Sequence Access Policy Access Control Framework Computational Cost Cloud computing Communication Cost Storage Cost Encrypted Data Security Proof User Data Hash Function Computational Overhead Secret Key Public Key Access Structure Symmetric Encryption Data Owner Communication Overhead Key Material Encryption Scheme Number Of Keywords Cloud Users Encryption Key Bilinear Pairing Security Model Encrypted File Media Services Single Search L 2 Cache Internet Of Things

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Blockchain Technology and Smart Contracts for Transformative Energy Trading 4

Blockchain Technology and Smart Contracts for Transformative...

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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Valsan, Vipina Sai, P. Sarvan Sri Mukesh, Koraganji Raju, Abbaraju Sree Tharun Sindukuri, Teja Amrita School of Engineering Amrita Vishwa Vidyapeetham Department of Electrical and Electronics Engineering Amritapuri India Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798331518981

The trend in renewable energy calls for safe peer-to-peer(P2P)energy trading in a microgrid. Blockchain technology (BCT) addresses this by providing a secure, tamper-proof ledger that enables transparent and sustainable energy exchange. This paper explores how smart contracts can make energy trading more efficient for microgrid users while securely recording all transactions for easy verification. The architecture designed for these smart contracts ensures reliable mechanisms such as simultaneous submission of bids, user registration, order placement and sorting, and automatic execution of the trading system. The proposed smart contract-based energy trading system improves energy efficiency and flexibility while supporting decentralized control within the microgrid. BCT directly contributes to the Sustainable Development Goal (SDG) 7 (Affordable and Clean Energy) by promoting this technology, increasing renewable energy in the global energy mix, and improving energy efficiency. Furthermore, BCT supports SDG 9 by creating a resilient and sustainable infrastructure through technological innovation. This paper provides a detailed analysis of the P2P trading scenario, smart contract pseudocode, and visualization of the smart contract implementation in Solidity, aiming to navigate the real-world application of P2P energy markets. © 2024 IEEE.

关键词： Smart contract

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