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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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Advanced Machine Learning Models for electrical Fault Detection and Classification in Transmission Lines 3

Advanced Machine Learning Models for Electrical Fault Detect...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Deepika, Dugganapalli Charan, Maridinapalli Madhu Nossam, Sri Chakradhar Manitha, P.V. Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

With the fast development of power systems, efficient methods for fault detection and classification are needed to maintain the stability, safety and efficiency of the systems. In particular, this paper investigates advanced machine learning techniques for fault detection and classification in transmission lines using supervised learning algorithms, including Support Vector Machines (SVM), Decision Trees, Random Forests, Naïve Bayes and Linear Discriminant Analysis (LDA). A dataset consisting of three phase currents and voltages from MATLAB simulations of normal and faulty system operations are used to study the problem. These models classify faults to be single line to ground, line to line and the three phase faults. Performance evaluation is done by metrics such as accuracy, precision, recall, F1 score, and AUC with respect to ROC analysis. Decision Trees are of high reliability and have good computational efficiency for fault detection among the models. The results presented in this research point to the importance of machine learning in reducing fault diagnosis circuit in order to achieve a more intelligent and resilient power transmission systems. © 2025 IEEE.

关键词： Power transmission lines

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Predicting Customer Lifetime Value in E-commerce: A Data-Driven Approach to Enhance Customer Retention Strategies 3

Predicting Customer Lifetime Value in E-commerce: A Data-Dri...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Dey, Sonali Radha, D. Kirthika Devi, V.S. Department of Computer Science Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

Predicting Customer Lifetime Value (CLV) is one of the most critical tasks that businesses undertake in order to improve customer retention and optimize marketing strategies. The present paper proposes a predictive model for CLV using several machine learning techniques. The features considered include customer demographics, purchasing behavior, and transaction history. All these features were preprocessed using feature engineering and missing value handling. Several regression models, including Linear Regression, Decision Tree, and Random Forest Regressor, were compared in terms of predictive performance. The study includes the decision trees,hash tables as advance data structures such that they can handle large datasets effectively. Of the ones tested, Random Forest Regressor proved to be superior to the others, having the lowest Mean Squared Error (MSE) and highest R2 score, thereby validating its robustness in identifying the complex patterns within the data. The work highlights the time and space complexities of different models. This study contributes to the application of machine learning in customer analytics and provides a foundation for future research to integrate more diverse data sources and enhance model performance through hyperparameter optimization. Future work will also explore real-time prediction capabilities and the integration of multimodal data to further refine CLV forecasting. © 2025 IEEE.

关键词： Sales

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Optimizing Hiring Practices: A Machine Learning Approach for Candidate Selection 3

Optimizing Hiring Practices: A Machine Learning Approach for...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Dogiparthy, Naga Datta Radha, D. Kirthika Devi, V.S. Department of Computer Science and Engineering Amrita school of computing Amrita Vishwa Vidhyapeetham Bengaluru India Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidhyapeetham Bengaluru India

ISBN: (纸本)9798331527549

Recruitment is one of the most crucial factors in shaping efficient and high-performing teams within organizations. However, the traditional recruitment processes are often plagued by biases and subjective judgments that lead to inconsistent and suboptimal hiring decisions. This project uses machine learning(ML) to make recruitment more objective, efficient, and data-driven. By taking the age, gender, education level, experience, interview scores, and personality traits of the applicants as key features, the model predicts candidate suitability without bias. The methodology uses robust preprocessing, Chi-Square feature selection, and the SMOTE technique to overcome class imbalance in the dataset. The proposed system uses various different machine learning algorithms like CatBoost, KD-Trees, Random Forest, and Logistic Regression and many more. All these models are trained and fine-tuned with GridSearchCV for hyperparameter optimization. Among them, the best model was CatBoost with 95% accuracy. This work shows how machine learning can change the recruitment game for organizations to make unbiased, data-driven hiring decisions and ultimately increase workforce efficiency and organizational success. © 2025 IEEE.

关键词： Adversarial machine learning

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Credit Card Fraud Detection(CFD) using GNN and Ensemble Techniques 3

Credit Card Fraud Detection(CFD) using GNN and Ensemble Tech...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Vinay, B.V.S.N.S. Radha, D. Kirthika Devi, V.S. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidhyapeetham Bengaluru India Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidhyapeetham Bengaluru India

ISBN: (纸本)9798331527549

Credit card fraud is an essential problem in the economic industry;thus, its detection is solved with the help of the developed methods in order to minimize the overall loses and to improve the confidence of clients. Towards this end, this paper presents a novel framework that applies GNNs in combination with ensemble learning to accurately identify credit card fraud. GNNs are used for cardinality, representing various relationships between transactions, cardholders, and merchants where complicated types of forgery are identified and are beyond the capability of conventional approaches. Ensemble learning further improves the accuracy of the system in order to reduce high false positive rates by using multiple models to solve the problem of imbalanced classes that are characteristic of the COVID-19 data. The effectiveness of the proposed framework is thus compared with several state-of-the-art malicious account detection techniques and various highly relevant real credit card transaction datasets show the proposed framework attains better precision, recall, F1-score, scalability. Thus, this strategy is much more suitable for ongoing and unspecific problem addressing in an unobstructed and ambiguous environment, such as credit card fraud. © 2025 IEEE.

关键词： Crime

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Adaptive Traffic Signal Control System Using Object Detection Based Approach 4

Adaptive Traffic Signal Control System Using Object Detectio...

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4th International Conference on Robotics, electrical and Signal Processing Techniques, ICREST 2025

作者： Khan, Irfan Hasan, Mehedi Islam, Shamsul Reza, Suman Reza, Tuhin Efaz, Erteza Tawsif Faridpur Engineering College Department of Computer Science & Engineering Faridpur Bangladesh Louisiana State University Division of Electrical & Computer Engineering Baton RougeLA United States

ISBN: (纸本)9798331530938

In Bangladesh, most four-legged intersections use a static-timed or manually-controlled traffic signaling approach, which creates irrefutable congestion and requires human involvement. This paper proposes an adaptive traffic control system utilizing the YOLO object detection framework within CNNs to address this issue. The system captures and frames images using cameras facing every road at a roundabout to detect and count vehicles in real-time for providing lights based on the traffic conditions, prioritizing emergency vehicles such as ambulances, firetrucks, and police-car while also maintaining queues. It sets the signal duration considering road condition. Using a dataset of 10,462 images across 12 classifications of automobiles and a transfer learning approach in YOLOv8, the model achieved a mAP50 score of 0.90. This automated method reasonably enhances traffic flow management by dynamically adjusting signal lights without human intervention. © 2025 IEEE.

关键词： Adaptive control systems

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Forecasting Bitcoin Price Trends: Integrated Machine Learning with Market Trends 3

Forecasting Bitcoin Price Trends: Integrated Machine Learnin...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Samir Ahamed, S. Radha, D. Kirthika Devi, V.S. Department of Computer Science And Engineering Amrita school of computing Amrita Vishwa Vidhyapeetham Bengaluru India Department of Electrical and Electronics Engineering Amrita school of Engineering Amrita Vishwa Vidhyapeetham Bengaluru India

ISBN: (纸本)9798331527549

Bitcoin price prediction remains a complicated task in financial markets since price fluctuations are characterized by high variability, non-linear and dependent on different trends. The common models of forecasting can be disruptive because they do not readily accommodate the breadth of the data and the dynamics of the marketplace. The paper presents a new approach to predicting Bitcoin price shifts based on state-of-the-art data structures and both machine learning and technical analysis. The formative historical price datasets and large scale features are organized and manipulated in an efficient manner with the aid of data structures as well as the temporal and non-linear trends in prices are captured by ARIMA,Exponential smoothing and Random. Also, trends of moving averages are used to boost the model’s competency to analyze the sentiments of the market. The proposed framework was put through a test on real world Bitcoin price data and it was shown to provide better results as compared to the traditional methods with better accuracy and visualization. Besides that, our method enhances the accuracy of the forecast, while the deployment of the method allows developing a realtime Bitcoin price prediction model that can be useful for traders and investors. The integration of machine learning and technical analysis gives a powerful and reliable tool to forecast the Bitcoins price trends. © 2025 IEEE.

关键词： Prediction models

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Privacy-Aware Secure Data Auditing for Cloud-based Intelligence of Things Environment

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IEEE Internet of Things Journal 2025年第11期12卷 15288-15303页

作者： Ullah, Fasee Pun, Chi-Man Mohmand, Muhammad Ismail Mahendran, Rakesh Kumar Khan, Arfat Ahmad Alhammad, Sarah M. Rodrigues, Joel J. P. C. Farouk, Ahmed Universiti Teknologi PETRONAS Computer and Information Sciences department Perak Darul Ridzuan Seri Iskandar32610 Malaysia Avenida da Universidade University of Macau China Sarhad University of Science and IT Department of Computer Science and IT Peshawar Pakistan Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Khon Kaen University College of Computing Department of Computer Science Khon Kaen40002 Thailand Princess Nourah Bint Abdulrahman University College of Computer and Information Sciences Department of Computer Sciences Riyadh11671 Saudi Arabia Amazonas State University Manaus Brazil Hurghada University Faculty of Computers and Artificial Intelligence Department of Computer Science Hurghada Egypt

Cloud-based Intelligence of Things is significant for Augmented Enterprise Management Systems. Data integrity auditing is challenging in the intelligence of things environment, mainly when the newer versions in the public cloud environment update existing encrypted data. The related literature on cloud-based intelligence relies on encrypted data uploading or locally handling encryption and decryption using user keys. Considering the security risk, storage constraints at the edge, and realtime environment, both approaches have limited applicability in the intelligence of things environment. This paper presents the Privacy-Aware Secure Data Auditing (PASDA) framework at the cluster head for online data integrity verification. Specifically, the users hide data files by the blinding process with a generation of their corresponding signatures, which achieves data auditing by utilizing homomorphic techniques. A novel automated self-triggering/ Self-auditing-based data integrity auditing system is proposed, which detects the changes made in the cloud-stored data and sends alert messages to the trusted primary cloud server and users. A data dynamics method is developed containing a timestamp with a pointer to store multiple versions of the same file without signatures re-generation for the whole same file. The user is revoked due to prolonged absence or detection of the missed behaviour with system or service expiry. With these data dynamics, the proposed PASDA framework allows CH to regenerate signatures of the revoked user using its membership key for cloud-based stored data access and data integrity auditing. In-depth security analysis and extensive simulations based on comparative performance evaluation attest to the benefits of the proposed PA. © 2014 IEEE.

关键词： Data integrity

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Weibull probability distribution based on four years wind speed data using nine numerical methods

Weibull probability distribution based on four years wind sp...

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International Conference on Innovation and Technological Advances for Sustainability, ITAS 2023

作者： Badawi, Ahmed Ali, Hassan Ismail, Nasser A. Zyoud, Alhareth Yusoff, Siti Hajar Al-Rimawi, Ashraf Ghanem, Wasel College of Engineering Technology University of Doha for Science and Technology Doha Qatar Department of Electrical and Computer Engineering Birzeit University Birzeit Ramallah Palestine Department of Electrical and Computer Engineering International Islamic University Malaysia Kuala Lumpur Malaysia

ISBN: (纸本)9781032803722

Wind energy potential can be estimated in a specific area using the Probability density function (PDF). PDF is used for the mean wind speed data on daily basis. This paper represents the analysis of the climatology of the mean wind speed data for Askalan city on the Mediterranean coastal plain of Palestine. A selection of the PDF had been implemented using nine numerical analysis methods to represent the cumulative distribution function and Weibull probability distribution. The two main parameters of the Weibull shape factor are k = 1.7848 and the scale factor parameter c = 4.3642 ms-1. The study assessment finds out the characteristics of wind energy conversion and results confirm that it can produce energy using small-scale wind turbines. Matlab software was used to implement the numerical methods and extract the wind characteristics. © 2025 The Author(s).

关键词： Windmill

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Effect of Low-Energy Proton Radiation on the Degradation of Silicon Carbide Schottky Diodes

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Journal of Electronic Materials 2025年 1-9页

作者： Baba, Tamana Hasbullah, Nurul Fadzlin Javed, Yasir Khan, Zafar Iqbal Sulaiman, Nurul Nabiilah Department of Electrical and Computer Engineering Kulliyyah of Engineering International Islamic University Malaysia Selangor Kuala Lumpur Malaysia College of Computer Science and Information Sciences Prince Sultan University P.O. Box 66833 Riyadh11586 Saudi Arabia

Silicon carbide (SiC) Schottky diodes are gaining traction for their potential in high-voltage, high-temperature, and radiation-resistant applications. While superior to traditional silicon (Si) diodes, their radiation hardness requires further exploration, particularly at lower energies. Although high-energy radiation undoubtedly degrades devices, understanding the impact of lower-energy radiation is crucial for real-world applications. This study employed 8 MeV proton radiation, considered relatively low energy in radiation physics, to investigate the response of SiC Schottky diodes. Interestingly, the irradiated devices displayed a slight decrease in reverse leakage current, indicating improved blocking ability. Encouragingly, both forward current and capacitance remained relatively unaffected, with a minor change in the ideality factor. Microscopy confirmed these findings by revealing no surface-level defects or leakage current hotspots, solidifying the exceptional resilience of SiC Schottky diodes to even lower-energy radiation. © The Minerals, Metals & Materials Society 2025.

关键词： Silicon carbide

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