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Smart Grid Security Framework for Data Transmissions with Adaptive Practices Using Machine Learning Algorithm

computers, Materials & Continua 2025年第3期82卷 4339-4369页

作者： Shitharth Selvarajan Hariprasath Manoharan Taher Al-Shehari Hussain Alsalman Taha Alfakih School of Built Environment Engineering and ComputingLeeds Beckett UniversityLeedsLS13HEUK Department of Computer Science and Engineering Chennai Institute of TechnologyChennai602109India Centre for Research Impact & Outcome Chitkara UniversityChandigarh140401India Department of Electronics and Communication Engineering Panimalar Engineering CollegeChennai600123India Department of Self-Development Skill Common First Year DeanshipKing Saud UniversityRiyadh11362Saudi Arabia Department of Computer Science College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Information Systems College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia

This research presents an analysis of smart grid units to enhance connected units’security during data *** major advantage of the proposed method is that the system model encompasses multiple aspects such as network flow monitoring,data expansion,control association,throughput,and *** addition,all the above-mentioned aspects are carried out with neural networks and adaptive optimizations to enhance the operation of smart grid ***,the quantitative analysis of the optimization algorithm is discussed concerning two case studies,thereby achieving early convergence at reduced *** suggested method ensures that each communication unit has its own distinct channels,maximizing the possibility of accurate *** results in the provision of only the original data values,hence enhancing *** power and line values are individually observed to establish control in smart grid-connected channels,even in the presence of adaptive settings.A comparison analysis is conducted to showcase the results,using simulation studies involving four scenarios and two case *** proposed method exhibits reduced complexity,resulting in a throughput gain of over 90%.

关键词： Machine learning power systems security smart grid

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The Power of PDF-to-Audio Summaries in Modern Learning 4

The Power of PDF-to-Audio Summaries in Modern Learning

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Kawde, Dhanshri Mendhe, Sameer Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Engineering Maharashtra Sawangi Wardha442001 India

ISBN: (纸本)9798331523923

PDF -to-audio based summarization technology is designed to enhance information accessibility and efficiency by transforming long written reports into short, audible summaries. AI, NLP, and TTS technologies that have developed recently have made it possible to extract main points without losing context. Yet, current systems have various shortcomings such as inability to process intricate PDF layouts like tables, charts, and annotations, contextual summarization inaccuracies, robotic or unhuman speech synthesis, and limited customization. Furthermore, the absence of multilingual support and easy integration into various platforms hinders wider adoption. These issues are addressed in this paper with a better approach that employs sophisticated NLP methods, context-sensitive summarization algorithms, and adaptive TTS systems. Our goal is to create a strong, user-oriented solution that provides greater summarization accuracy, personalized speech output, and improved accessibility for a variety of users, such as professionals, students, and the disabled. © 2025 IEEE.

关键词： Web Design

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A Near-Field 3D Parameter Estimation Method Based on a Symmetric Enhanced Nested Array

A Near-Field 3D Parameter Estimation Method Based on a Symme...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Yu, Linke Chen, Hua Xue, Dingfan Liu, Wei Tian, Ye Wang, Gang Faculty of Electrical Engineering and Computer Science Ningbo University China Department of Electrical and Electronic Engineering The Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9798350368741

In this paper, a high-precision three-dimensional (3-D) near-field (NF) localization method is proposed under an underdetermined case based on a symmetric enhanced nested array (SENA). Firstly, the symmetry of the array and the fourth-order cumulant (FOC) are utilized to construct the equivalent virtual far-field (FF) reception data. Then, a gridless sparse and parametric approach (SPA), combined with an l1-SVD based pairing procedure, is used to obtain estimates for two paired angles. Finally, a one-dimensional (1-D) spectral estimator is applied to obtain the estimate of range parameter. Simulation results show the effectiveness of the proposed method. © 2025 IEEE.

关键词： cross array near-field source localization spatial-temporal underdetermine estimation

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High-accuracy face recognition with the MRYOLO-SCO algorithm: combining residual networks and YOLOv2 for improved precision

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Signal, Image and Video Processing 2025年第7期19卷 1-12页

作者： Ettiyappan, Anitha palani, Hari kumar Duraipandian, Kavitha Selvaganapathy, Nandhini Department of Information Technology Loyola - ICAM College of Engineering and Technology Nungambakkam Chennai600034 India Department of Computer Science and Engineering MIT School of Computing MIT Art Design and Technology University Loni Kalbhor Pune India Department of computer science and engineering Sathyabama Institute of Science and Technology Chennai India Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Chengalpattu603203 India

Face recognition in real-world scenarios presents significant challenges due to variations in lighting conditions, occlusions, pose changes, and low-resolution images. To address these challenges, this study proposes a Modified Residual YOLO Network-based Single Candidate Optimization algorithm, which integrates a Modified Residual Network with YOLOv2 to improve recognition accuracy while maintaining computational efficiency. Unlike conventional YOLO models, the proposed framework introduces bipolar feature extraction, enabling the network to capture both high-level global features and low-level local details, thus improving robustness against occlusions and illumination variations. Atailored cost function is introduced to minimize bounding box localization errors and classification inaccuracies, incorporating weighted penalties to enhance model precision. Additionally, a Single Candidate Optimization algorithm is employed to fine-tune hyperparameters, ensuring faster convergence and lower computational complexity. The performance of the proposed technique is evaluated using the benchmark datasets and the simulation results that the proposed framework outperforms existing YOLO-based models, achieving an accuracy of 98.8%, a recall of 98.0%, and an F1-score of 98.1%while maintainingalower computational cost than YOLOv3, YOLOv4, and YOLOv5. The experimental results also indicate that the tailored cost function reduces misclassification errors by 6.1% and accelerates convergence by 20%, making the proposed framework highly suitable for real-time face recognition applications in resource-constrained environmentssuch asIoT-based surveillance and smart security systems. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Face recognition

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Depth-aware Image Captioning with Bus Dataset for Visually Impaired Individuals 24

Depth-aware Image Captioning with Bus Dataset for Visually I...

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8th International Conference on computer science and Artificial Intelligence, CSAI 2024

作者： Xiang, Wang Sanqian, Li Risa, Higashita Jie, Li Hongwu, Qin Jiang, Liu Department of Electronic and Information Engineering Changchun University Changchun China Research lnstitute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

ISBN: (纸本)9798400718182

Visually impaired individuals face difficulties in public transportation, especially in recognizing bus numbers. Though existing image captioning method can provide some bus scene information via voice broadcasting, precisely recognizing bus number while analyzing complex scenes remains challenges. To overcome this bottleneck, we propose a novel Text based Depth-aware image Captioning network (TDCnet) that facilitates a deeper understanding of the spatial relationships and geometric configurations of objects within bus scenes. Specially, we first carefully construct a new real-world dataset of bus scene integrated with bus number of LED strobe effect, to ensure generality and representations in practice. Then, we incorporate the depth information into the object and optical character recognition (OCR) feature for exploiting the spatial geometric relationships and capturing high-level semantic information. Finally, the experimental results demonstrate the necessity of constructing the Buscap dataset by comparing it with the public Textcap dataset. Besides, the comparison with other competing benchmark models verifies the effectiveness and practicability of the proposed TDCnet method. © 2024 Copyright held by the owner/author(s).

关键词： Bus transportation

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Applications of Topological Data Analysis with Morphology Operators on Hypergraphs

Applications of Topological Data Analysis with Morphology Op...

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International Conference on Mathematics of Intelligent Computing and Data science, ICMICDS 2022

作者： Ramkumar, P.B. Dhanya, P.M. Department of Mathematics Rajagiri School of Engineering and Technology Kerala Kakkanad India Department of Computer Science and Business Systems Rajagiri School of Engineering and Technology Kerala Kakkanad India

ISBN: (纸本)9789819615049

A hypergraph is a type of graph, H=(V,E) where V is the set of nodes and E is the set of hyperedges;where one edge consists of may nodes. Morphological operators are nonlinear operators which can be applied on images, graphs, hypergraphs etc. Dilation and erosion are morphological operators and other operators like opening, closing can be obtained by repeated application of these operators. Topological space consists of a set X together with topology τ defined on X, which satisfies the axioms namely Nullset, arbitrary union of opensets and finite intersection of opensets. Such a topological space S=(X,T) is compatible with a hypergraph H if X=VH∪EH where VH and EH are the vertex set and edge set respectively. The morphological operators induces several topology on hypergraph with its compatible topological space S=(X,T). Neighborhood of a node is defined in many ways which can be considered as open sets in the corresponding topological space. The purpose of this paper is to apply topological properties on hypergraphs using morphological operators. Separation axiom and closure function are illustrated using hypergraph structure. The work is extended to crime analysis, text processing and patient disease analysis. Topological feature extraction is done on Barcodes created from crime hypergraphs. This gives a better visualization of crime analysis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Graph theory

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AI-Powered Clinical Decision Support systems (CDSS): Challenges, Benefits, Applications, and Future Directions 2

AI-Powered Clinical Decision Support Systems (CDSS): Challen...

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2nd International Conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Yesankar, Prajyot Puri, Chetan Gote, Pradnyawant M. Faculty of Engineering and Technology Department of Computer Science and Design Maharashtra Wardha442001 India

ISBN: (纸本)9798331505745

AI-based Clinical Decision Support systems are a revolutionary innovation in the healthcare sector, using artificial intelligence to improve diagnostic precision, optimize treatment planning, and enhance patient outcomes. This paper discusses the challenges, benefits, applications, and future directions of AI-driven CDSS. Some of the key challenges are data quality, integration with EHRs, ethical concerns, and explainability issues. Despite these challenges, benefits such as efficiency improvements, reduced clinician workload, and the possibility of tailored medicine are substantial. In addition, the paper depicts diverse applications across oncology, cardiology, and primary care. The paper further stresses the directions: validation of robustness, user-friendly interfaces, and equitable deployment to achieve maximal utilization of AI-CDSS in global health care systems. Besides this, the integration of multimodal data and improvement in predictive analytics will help to revolutionize preventive care. Moreover, ethical practices with AI will bring forth trust and adoption. Such an all-inclusive review signifies the pivotal role AI will play in shaping clinical decision-making for the future. © 2025 IEEE.

关键词： Personalized medicine

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Economical Camera-based Measuring Scheme for Roundabout Traffic

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Periodica Polytechnica Transportation engineering 2025年第2期53卷 235-241页

作者： Kovács, Tamás Kovács, Lóránt Department of Information Technology GAMF Faculty of Engineering and Computer Science John von Neumann University Izsáki út 10. KecskemétH-6000 Hungary

Counting the turning movements in a four-leg roundabout is a challenging task and often executed by vehicle recognition and tracking on traffic videos. In order to obtain accurately all the 12 flow values of the origin-destination (O-D) matrix we need to measure arbitrary chosen 12 linearly independent flow values. However, the larger the area to be observed the more cameras are needed because the good pixel resolution of the vehicles and the proper camera inclination angles are essential. Here we propose a novel measuring scheme, which observes only two roundabout legs instead of observing all the four, as a result obtaining all the 12 flow values requires a reduced number of cameras. The directly measured flow values and the mathematical method of determining the O-D matrix are given in details. © 2025 Budapest University of Technology and Economics. All rights reserved.

关键词： Object recognition

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Optimizing task scheduling in cloud environments: a hybrid golden search whale optimization algorithm approach

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Neural Computing and Applications 2025年 1-23页

作者： Acharya, Biswaranjan Panda, Sucheta Das, Satyabrata Majhi, Santosh Kumar Gerogiannis, Vassilis C. Kanavos, Andreas Department of Computer Science and Engineering Veer Surendra Sai University of Technology Odisha Burla India Bilaspur India Department of Digital Systems University of Thessaly Larissa Greece Department of Informatics Ionian University Corfu Greece

Managing fluctuating workloads and optimizing resource utilization in cloud environments pose significant challenges, particularly in fields requiring real-time data processing, such as healthcare. This paper introduces a novel hybrid metaheuristic technique, the Golden Search Whale Optimization Algorithm (GSWOA), specifically designed for scheduling independent dynamic biomedical data. GSWOA merges the strengths of Golden Search Optimization (GSO) and Whale Optimization Algorithm (WOA), optimizing numerical function optimization and achieving a balance between exploration and exploitation. The algorithm’s effectiveness was assessed using MATLAB by applying standard benchmark functions and further evaluated on a real-world biomedical dataset within the CloudSim environment. The performance evaluations demonstrate that GSWOA significantly outperforms existing metaheuristic and traditional scheduling techniques, achieving a 42.71% increase in resource utilization and a 14.17% reduction in makespan compared to conventional methods. These results highlight GSWOA’s potential to enhance scheduling efficiency substantially in cloud computing infrastructures, suggesting it is a powerful tool for complex task allocations. Future research will explore the scalability of GSWOA and its applicability across other data-intensive sectors. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Scheduling algorithms

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Optimized ensemble deep learning approach for accurate breast cancer diagnosis using transfer learning and grey wolf optimization

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Evolving systems 2025年第2期16卷 1-17页

作者： Hassan, Esraa Saber, Abeer El-Sappagh, Shaker El-Rashidy, Nora Faculty of Artificial Intelligence Kafrelsheikh University Kafr Elsheikh Egypt Information Technology Department Faculty of Computers and Artificial Intelligence Damietta University Damietta34517 Egypt Faculty of Computer Science and Engineering Galala University Suez435611 Egypt

Breast cancer (BC) is one of the most significant threats to women’s health worldwide, affecting one in eight women and causing over 42,250 deaths in 2024. Early detection plays a crucial role in improving patient outcomes, with mammography being the primary screening method most health organizations recommend. This paper highlights the potential of deep learning (DL) in enhancing diagnostic Accuracy and speed. However, despite the promising results of DL models, the increasing complexity of breast cancer data presents a significant challenge in selecting the most relevant features. We propose a robust DL model incorporating transfer learning (TL) and grey wolf optimization (GWO) to enhance BC diagnosis. The architecture is developed by leveraging multiple deep neural networks, including ResNet and Inception, based on convolutional neural networks (CNNs). These networks are fine-tuned using a mammographic image dataset to optimize model weights and parameters for higher classification accuracy. Additionally, the Wisconsin Breast Cancer Dataset (WBCD) is utilized with GWO to refine feature selection further. Experimental results demonstrate that the proposed ensemble method improves robustness, generalization, and detection rates while minimizing false positives and negatives. Large-scale dataset evaluation yielded a precision of 0.942, sensitivity of 0.982, Accuracy of 0.965, and an area under the curve (AUC) value of 0.971. These findings suggest that the proposed framework could significantly enhance patient care and improve healthcare service organization and management. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Diseases

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