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Enhancing Energy Efficiency with a Dynamic Trust Measurement Scheme in Power Distribution Network

computers, Materials & Continua 2024年第3期78卷 3909-3927页

作者： Yilei Wang Xin Sun Guiping Zheng Ahmar Rashid Sami Ullah Hisham Alasmary Muhammad Waqas Zhejiang Electric-Power Corporation Research Institute Zhejiang310014China Faculty of Information Technology Beijing University of TechnologyBeijing100124China Beijing Trusty Cloud Technology Co. Ltd.Beijing100022China Department of Computer Science and Engineering Ghulam Ishaq Khan Institute of Engineering Sciences and EngineeringTopi23640Pakistan Department of Computer Science Shaheed Benazir Bhutto UniversitySheringalUpper Dir18050Pakistan Department of Computer Science College of Computer ScienceKing Khalid UniversityAbha61421Saudi Arabia School of Computing and Mathematical Science Faculty of Engineering and ScienceUniversity of GreenwichLondonSE109LSUK School of Engineering Edith Cowan UniversityPerth6027Australia

The application of Intelligent Internet of Things(IIoT)in constructing distribution station areas strongly supports platform transformation,upgrade,and intelligent *** sensing layer of IIoT comprises the edge convergence layer and the end sensing layer,with the former using intelligent fusion terminals for real-time data collection and ***,the influx of multiple low-voltage in the smart grid raises higher demands for the performance,energy efficiency,and response speed of the substation fusion ***,it brings significant security risks to the entire distribution substation,posing a major challenge to the smart *** response to these challenges,a proposed dynamic and energy-efficient trust measurement scheme for smart grids aims to address these *** scheme begins by establishing a hierarchical trust measurement model,elucidating the trust relationships among smart IoT *** then incorporates multidimensional measurement factors,encompassing static environmental factors,dynamic behaviors,and energy *** comprehensive approach reduces the impact of subjective factors on trust ***,the scheme incorporates a detection process designed for identifying malicious low-voltage end sensing units,ensuring the prompt identification and elimination of any malicious ***,in turn,enhances the security and reliability of the smart grid *** effectiveness of the proposed scheme in pinpointing malicious nodes has been demonstrated through simulation ***,the scheme outperforms established trust metric models in terms of energy efficiency,showcasing its significant contribution to the field.

关键词： IIoT trusted measure energy efficient

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An Approach to Radiotherapy Treatment Planning based on Machine Learning Algorithms 4

An Approach to Radiotherapy Treatment Planning based on Mach...

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4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Divya, Komati Bhargavi, C.H. Abirami, M. Arjun, Ponnaganti Padmaja, Pabbathi Dhanwanth, Batini Panimalar Institute of Technology Department of Computer Science and Engineering Chennai India Department of Computer Science and Engineering Chennai India St.Peter's Institute Of Higher Education And Research Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350385793

Customizing radiation treatments for each patient is a formidable obstacle in the fight against cancer. Because they rely on human intervention and generalization, traditional methods often provide less-than-ideal results and unwanted side effects. Utilizing patient-specific anatomical and tumor-specific data, this research analyzes the revolutionary possibilities of machine learning for planning radiation therapy. The goal of this research work is to forecast radiation doses, identify tumor boundaries, and reduce the exposure to healthy tissue using Deep Learning (DL), Support Vector Machine (SVM) and Random Forest (RF) models. Radiation planners must prioritize data heterogeneity, model interpretability, and clinical validation if machine learning has to be widely used. Improved patient outcomes and quality of life may be achieved via the use of Machine Learning (ML) to improve the radiation efficacy and accuracy, as shown in this research study that synthesizes existing research. © 2024 IEEE.

关键词： Radiotherapy

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Holt-Winters Algorithm to Predict the Stock Value Using Recurrent Neural Network

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Intelligent Automation & Soft computing 2023年第1期35卷 1151-1163页

作者： M.Mohan P.C.Kishore Raja P.Velmurugan A.Kulothungan Department of Computer Science&Engineering SRM UniversitySonepatHaryanaIndia Department of Electronics&Communication Engineering SRM UniversitySonepatHaryanaIndia Department of Computing Technologies SRM Institute of Science and TechnologyChennaiIndia Department of Computer Science&Engineering SRM Institute of Science and TechnologyModinagarGhaziabadIndia

Prediction of stock market value is highly risky because it is based on the concept of Time Series forecasting system that can be used for investments in a safe environment with minimized chances of *** proposed model uses a real time dataset offifteen Stocks as input into the system and based on the data,predicts or forecast future stock prices of different companies belonging to different *** dataset includes approximatelyfifteen companies from different sectors and forecasts their results based on which the user can decide whether to invest in the particular company or not;the forecasting is done for the next *** model uses 3 main concepts for forecasting *** one is for stocks that show periodic change throughout the season,the‘Holt-Winters Triple Exponential Smoothing’.3 basic things taken into conclusion by this algorithm are Base Level,Trend Level and Seasoning *** value of all these are calculated by us and then decomposition of all these factors is done by the Holt-Winters *** second concept is‘Recurrent Neural Network’.The specific model of recurrent neural network that is being used is Long-Short Term Memory and it’s the same as the Normal Neural Network,the only difference is that each intermediate cell is a memory cell and retails its value till the next feedback *** third concept is Recommendation System whichfilters and predict the rating based on the different factors.

关键词： Stock market stock market prediction time series forecasting efficient market hypothesis National stock exchange India smoothing observation trend level seasonal factor

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Groundbreaking taxonomy of metaverse characteristics

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Artificial Intelligence Review 2025年第8期58卷 1-50页

作者： Sadeghi-Niaraki, Abolghasem Rahimi, Fatema Azlan, Nur Alya Emanuelle Binti Song, Houbing Ali, Farman Choi, Soo-Mi Department of Computer Science and Engineering and Convergence Engineering for Intelligent Drone XR Research Center Sejong University Seoul Korea Republic of BaltimoreMD21250 United States Department of Applied AI School of Convergence College of Computing and Informatics Sungkyunkwan University Seoul03063 Korea Republic of

The Metaverse, a dynamic and immersive virtual realm, has captured the imagination of researchers and enthusiasts worldwide. This survey paper aims to introduce a groundbreaking taxonomy for the characteristics of the Metaverse offering a structured and adaptable framework that extends beyond existing categorizations by incorporating dynamic transformations. Unlike prior taxonomies, which often focus on fixed attributes, our approach emphasizes the dynamic evolution of Metaverse characteristics. Through an extensive review of published literature, this study explores key technological, social, economic, and ethical dimensions of the Metaverse. It introduces a process-oriented classification based on 23 distinct characteristics, including immersification, spatiotemporalification, interactification, persistentification, presentification, personification, unification, imaginification, economification, uncertaintification, and credification. By mapping these evolving aspects, we provide a structured and future-proof foundation for understanding the Metaverse’s continuous development. This survey establishes a new standard for comprehensiveness and innovation, shedding light on the diverse facets that have been explored in literature. Through this novel taxonomy, we provide a detailed map of the current landscape and offer insights that pave the way for future research and development in this burgeoning digital frontier. © The Author(s) 2025.

关键词： Comprehensive analysis Futuristic exploration Metaverse characteristics Paradigm-shifting taxonomy Revolutionary categorization

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Analysis of various Classification Techniques using CNN models for the Detection of Alzheimer's Disease using MRI images 14

Analysis of various Classification Techniques using CNN mode...

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14th International Conference on computing Communication and Networking Technologies, ICCCNT 2023

作者： Hemkiran, S. Sudha Sadasivam, G. Pradeepa, T. Samukthaa, R.S. Ajay Raj, R. Psg Institute of Technology and Applied Research Department of Computer Science and Engineering Coimbatore India Psg College of Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350335095

Alzheimer's disease (AD) is a brain disorder that is associated with memory loss and is typically observed in elderly and aging individuals. This condition is irreversible in nature. Neural networks have shown better performance than traditional machine learning algorithms when it comes to analyzing high-dimensional data from Magnetic Resonance Images (MRI) brain images. This is an approach for processing such data. Detection of Alzheimer's disease is essential because the early treatment can control the progression of the disease in a slower pace. In this work, the proposed model used different models of convolutional neural networks (CNN) techniques to process the MRI images of the brain and classified them into different classes. The proposed model detects AD using neural network feature extraction and classification methods based on the pre-processed datasets. Here, in the proposed model, various algorithms such as Resnet-50, DenseNet-169 and Hyperparameter tuning of Convolutional Neural Network are utilized to find the better performing model in terms of accuracy when compared with other traditional techniques. © 2023 IEEE.

关键词： Image classification

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Weighted Average Ensembled Probability Pipeline of Pre-trained CNN Models for Hand Gesture Classification 15

Weighted Average Ensembled Probability Pipeline of Pre-train...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Gunti, Rahul Rohan Reddy, B. Swathi, Gunti Sharath Kumar Reddy, G. Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai601103 India Vellore Institute of Technology School of Computer Science and Engineering Vellore India

ISBN: (纸本)9798350370249

The automatic detection of sign language from hand gesture images is fundamental for effective human-computer interaction, especially for individuals with hearing and speech disorders. Achieving accurate detection and classification of sign language gestures is paramount. However, accurately recognizing these gestures frequently presents challenges due to dull backgrounds, variations in gesture orientation, and fluctuations in lighting conditions. Previous approaches tried to overcome these challenges but still the model performance lacks recognition accuracy and visual quality. To address this need for accuracy, we proposed a Weighted Average Ensembled Probability Pipeline composed of EfficientNetB7, ResNet-50, and Xception models. Leveraging the Extensive Hand Gesture Recognition Database (HGRD), comprising 20,000 images, our individual models achieved notable accuracies: 96.498 % for EfficientNetB7, 93.2 % for ResNet-50, and 90.175 % for Xception. Through ensemble learning, we integrate these models to attain an impressive overall accuracy of 98.95 %. This research contributes to robust gesture recognition system and underscores the significance of ensemble techniques in enhancing performance for Gesture recognition systems. © 2024 IEEE.

关键词： Gesture recognition

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Efficient Identification of Waste in Water Bodies with YOLO V7-Object Detection 1

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4th International Conference on Smart Systems: Innovations in computing, SSIC 2023

作者： Sivakumar, Deena Annamalai, R. Rohith, N.D. Department of Computer Science and Engineering Sri Ramachandra Institute of Higher Education and Research Porur Chennai India Department of Computer Science and Engineering Annamalai University Chidambaram India Amrita School of Computing AmritaVishwaVidyapeetham Chennai India

ISBN: (数字)9789819736904

ISBN: (纸本)9789819736898

Marine pollution is made up of waste and debris, the majority of which comes from land and is thrown or blown into the water. The ecology, the health of all living things, and international financial organizations are all negatively impacted by this pollution. The threat of marine pollution to civilization is one of the biggest. All manufactured items, the majority of which are made of plastic and end up in the water, are considered marine trash. This material builds up as a result of littering, storm gusts, and poor waste management, and in 80% of the cases, it originates from sources on land. In this study, we suggest an improved object identification model based on deep learning to find waste in water bodies and protect marine life. In order to locate waste in water bodies and save marine life, we propose an improved deep learning-based object detection model to address this problem. YOLOv7 performs faster and more accurately than any other object detectors between 5 and 160 frames per second. Large datasets of images of marine trash are used to train this model and is able to accurately identify a wide variety of objects, including plastic bags, bottles, straws, and other debris. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Debris

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NeuroAI: A Cascaded CNN Model for High-Accuracy Autism Detection Using Neuroimaging Data 4

NeuroAI: A Cascaded CNN Model for High-Accuracy Autism Detec...

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Hema, V. Gowri, S. Sathyabama Institute of Science and Technology School of Computing Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331508845

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition that impacts social communication, behavior, and cognitive functions. Early detection of autism is crucial for timely intervention, which can significantly improve developmental outcomes for individuals. This paper presents a novel methodology for detecting Autism Spectrum Disorder (ASD) using a Cascaded Convolutional Neural Network (CCNN) model. The proposed CCNN model processes neuroimaging data to detect patterns indicative of autism. The dataset used comprises 6,214 samples from the Autism Brain Imaging Data Exchange (ABIDE). Key preprocessing steps include resizing images to 224x224 pixels, noise reduction using a Median Filter, image cropping to focus on the region of interest (ROI), and contrast enhancement using histogram equalization. The CCNN architecture involves multiple convolutional layers, followed by max-pooling and fully connected layers, culminating in a Softmax layer for classification. The model is trained using the Adam optimizer with binary cross-entropy loss, achieving an accuracy of 93.19% on the test set, outperforming traditional models such as SVM, Random Forest, and Logistic Regression. © 2024 IEEE.

关键词： Median filters

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MOCBOA:Multi-Objective Chef-Based Optimization Algorithm Using Hybrid Dominance Relations for Solving engineering Design Problems

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computer Modeling in engineering & sciences 2025年第4期143卷 967-1008页

作者： Nour Elhouda Chalabi Abdelouahab Attia Abdulaziz S.Almazyad Ali Wagdy Mohamed Frank Werner Pradeep Jangir Mohammad Shokouhifar Computer Science Department University of M’silaIchebiliaM’sila28000Algeria Computer ScienceDepartment Mohamed El Bachir El IbrahimiUniversity of Bordj BouArreridjBordj BouArreridj34000Algeria Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11931Jordan Faculty of Mathematics Otto-von-Guericke UniversityMagdeburg39016Germany University Centre for Research and Development Chandigarh UniversityMohali140413India Department of Electrical and Computer Engineering Shahid Beheshti UniversityTehran14399Iran DTU AI and Data Science Hub(DAIDASH) Duy Tan UniversityDa Nang550000Vietnam

Multi-objective optimization is critical for problem-solving in engineering,economics,and *** study introduces the Multi-Objective Chef-Based Optimization Algorithm(MOCBOA),an upgraded version of the Chef-Based Optimization Algorithm(CBOA)that addresses distinct *** approach is unique in systematically examining four dominance relations—Pareto,Epsilon,Cone-epsilon,and Strengthened dominance—to evaluate their influence on sustaining solution variety and driving convergence toward the Pareto *** comparison investigation,which was conducted on fifty test problems from the CEC 2021 benchmark and applied to areas such as chemical engineering,mechanical design,and power systems,reveals that the dominance approach used has a considerable impact on the key optimization measures such as the hypervolume *** paper provides a solid foundation for determining themost effective dominance approach and significant insights for both theoretical research and practical applications in multi-objective optimization.

关键词： Multi-objective optimization chef-based optimization algorithm(CBOA) pareto dominance epsilon dominance cone-epsilon dominance strengthened dominance

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Smart Hybrid Mini-Xception Model and Eyebrow Displacement-Based Stress Level and Emotion Detection System 12th

Smart Hybrid Mini-Xception Model and Eyebrow Displacement-Ba...

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12th International Conference on Recent Trends in computing, ICRTC 2024

作者： Bhuvana, S. Anantha Prabha, P. Deva Priya, M. Christy Jeba Malar, A. Karthick, S. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India Department of Computer Science and Engineering PSG Institute of Technology and Applied Research Tamilnadu Coimbatore India Department of Computer Science and Engineering Karpagam College of Engineering Tamilnadu Coimbatore India Department of Information Technology Sri Krishna College of Technology Tamilnadu Coimbatore India Department of Computer Science and Engineering Delhi - NCR Campus SRM Institute of Science and Technology Uttar Pradesh Ghaziabad India

ISBN: (纸本)9789819789450

Stress is a state of mental or emotional strain due to adversative or challenging situations. A human may undergo bad life experiences or events, and it is a significant issue to be dealt in today's society. It comes as a result of family situations, health concerns, work environments and people's behavior among other things. It affects people of various ages, including children, teenagers, middle-aged people and senior citizens. It has serious consequences affecting human body and life. These problems can be addressed by detecting mental stress at an early stage. The main goal of the propounded system is to identify human stress using a pre-trained Mini-Xception model. Additional measures such as eye moments, mouth activity and head motion are employed for determining the level of human stress. FER2013 dataset is used and the system is tested for all kinds of emotions. Human stress level is experimented by capturing video using webcam. From the experimental outcomes, it is evident that the proposed system offers better performance in terms of Precision, Accuracy, Specificity and Sensitivity in contrast to traditional methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Eyebrow displacement FER2013 dataset Mini-Xception model Stress detection

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