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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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An Enhanced Framework for Churn Prediction using Stratifed Bagging and Stacked Multi-Output Random Forests 3

An Enhanced Framework for Churn Prediction using Stratifed B...

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

作者： Bhardwaj, Anay 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

Customer churn prediction is an important task in customer relationship management because it helps businesses know who is at risk of leaving and retain such at-risk *** and time-efficient churn prediction is essential to identify the factors driving churn, enabling businesses to implement timely and effective retention strategies.A framework is proposed which combines stratified bagging with multi-output Random Forest and stacked feature transformation, to enhance prediction accuracy. This approach balances the dataset, trains a custom Random Forest using stratified bagging, and applies stacked learning using Logistic Regression on transformed features. Tested on real-world data, the model demonstrated 99% accuracy, showing improvements over the classic Random Forest in precision, recall, and prediction time. © 2025 IEEE.

关键词： Sales

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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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Smart Home Automation with Smart Metering Using ZigBee Technology and Deep Belief Networks - Optimization Using MB-ABCOA Algorithm 2

Smart Home Automation with Smart Metering Using ZigBee Techn...

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2nd IEEE International Conference on Contemporary Computing and Communications, InC4 2024

作者： Navaz, K. Mano Raja Paul, M. Yamuna Bee, J. Kavitha, S. Bhuvanesh, A. Mariappan, R. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Nehru Institute of Engineering and Technology Department of Electrical and Electronics Engineering Coimbatore India PSN College of Engineering and Technology Department of Computer Science and Engineering Tirunelveli India Sri Eshwar College of Engineering Department of English Tamil Nadu Coimbatore India PSN College of Engineering and Technology Department of Electrical and Electronics Engineering Tirunelveli India

ISBN: (纸本)9798350383652

In the domain of smart homes and smart metering, ZigBee technology plays a pivotal role within the energy efficiency of homes, albeit facing challenges such as packet transmission delays and uneven energy consumption among nodes. To address these issues while emphasizing energy efficiency, we propose the Energy-efficient ZigBee Directionally Opportunistic Short-cut Trees Routings (EP-ZDOSTR), which optimizes network lifetimes and throughput by prioritizing energy-efficient packet transmission. Complemented by a hybrid Monarchy Butterfly and Artificial Bees Colonies Optimizations Algorithms (MB-ABCOA) approach, EP-ZDOSTR demonstrates significant improvements in network performances metric, including higher throughputs, increased packet delivery ratios, reduced transmission delays, and minimized energy consumption. Comparisons with existing methods like ZigBee shortcut tree routing (ZSTR), ZigBee opportunistic shortcut tree routing (ZOSTR), and Energy Efficient shortcut tree routing (EPSTR) validate its superiority in enhancing overall network efficiency and reliability. Furthermore, integrating ZigBee with Deep Belief Networks (DBNs) holds promise for further enhancing network intelligence, enabling predictive analysis of energy usage patterns and optimization of network operations, thereby advancing the capabilities of smart home in the context of smart homes and metering systems within the IoT landscape. © 2024 IEEE.

关键词： Zigbee

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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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Optimisation and Evaluation of Breadth First Search with oneAPI/SYCL on Intel FPGAs: From Describing Algorithms to Describing Architectures 24

Optimisation and Evaluation of Breadth First Search with one...

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12th International Workshop on OpenCL and SYCL, IWOCL 2024

作者： Olgu, Kaan Kenter, Tobias Nunez-Yanez, Jose McIntosh-Smith, Simon School of Electrical & Electronics Engineering University of Bristol United Kingdom Paderborn Center for Parallel Computing Department of Computer Science Paderborn University Germany Dept. Electrical Engineering Linkoping University Sweden

ISBN: (纸本)9798400717901

FPGAs are often used in scientific fields to process graph algorithms due to their energy efficiency, reconfigurability, and fine-grained parallelism. However, these algorithms face challenges in memory access patterns, scalability, and programmability. The SYCL2020 implementation in the Intel oneAPI toolchain supports FPGA targets alongside SYCL2020 features like modern C++ with a single-source offloading to improve programmability. This study analysed the Breadth-First Search algorithm on Stratix 10 FPGA with the Intel oneAPI toolchain. The implementation was done in two phases. At first, we applied the typical optimisations proposed in the official guidelines alongside an automatic cache to achieve proper pipelining and improve random memory accesses performance. However, limitations occurred with fine-grained parallelism, and it was competitive only to some related work that utilised hardware-description languages or established high-level synthesis tools. For the second phase, we added bit-level representations of data in memory, banking in on-chip memory, and fine-grained control over parallel data streams. The second implementation was generally superior or on par with all compared designs, outperforming other works in 10 out of 15 tested datasets, including various synthetic RMAT and real-world datasets, with a peak performance of 1021 MTEPS. © 2024 Owner/Author.

关键词： Field programmable gate arrays (FPGA)

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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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Digital Twin-Based Automated Fault Diagnosis in Industrial IoT Applications

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computers, Materials & Continua 2023年第4期75卷 183-196页

作者： Samah Alshathri Ezz El-Din Hemdan Walid El-Shafai Amged Sayed Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science and Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Computer Science Department Security Engineering LabPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Electrical Energy Engineering College of Engineering&TechnologyArab Academy for Science Technology&Maritime TransportSmart Village CampusEgypt

In recent years, Digital Twin (DT) has gained significant interestfrom academia and industry due to the advanced in information technology,communication systems, Artificial Intelligence (AI), Cloud Computing (CC),and Industrial Internet of Things (IIoT). The main concept of the DT isto provide a comprehensive tangible, and operational explanation of anyelement, asset, or system. However, it is an extremely dynamic taxonomydeveloping in complexity during the life cycle that produces a massive amountof engendered data and information. Likewise, with the development of AI,digital twins can be redefined and could be a crucial approach to aid theInternet of Things (IoT)-based DT applications for transferring the data andvalue onto the Internet with better decision-making. Therefore, this paperintroduces an efficient DT-based fault diagnosis model based on machinelearning (ML) tools. In this framework, the DT model of the machine isconstructed by creating the simulation model. In the proposed framework,the Genetic algorithm (GA) is used for the optimization task to improvethe classification accuracy. Furthermore, we evaluate the proposed faultdiagnosis framework using performance metrics such as precision, accuracy,F-measure, and recall. The proposed framework is comprehensively examinedusing the triplex pump fault diagnosis. The experimental results demonstratedthat the hybrid GA-ML method gives outstanding results compared to MLmethods like LogisticRegression (LR), Na飗e Bayes (NB), and SupportVectorMachine (SVM). The suggested framework achieves the highest accuracyof 95% for the employed hybrid GA-SVM. The proposed framework willeffectively help industrial operators make an appropriate decision concerningthe fault analysis for IIoT applications in the context of Industry 4.0.

关键词： Automated fault diagnosis control system ML AI CC IIoT digital twins genetic algorithm GA-ML technique

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Accelerating Mobile Edge Generation (MEG) by Constrained Learning

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IEEE Transactions on Cognitive Communications and Networking 2025年第3期11卷 1854-1869页

作者： Xu, Xiaoxia Liu, Yuanwei Mu, Xidong Xing, Hong Nallanathan, Arumugam Queen Mary University of London School of Electronic Engineering and Computer Science LondonE1 4NS United Kingdom The University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong School of Electronics Electrical Engineering and Computer Science BelfastBT3 9DT United Kingdom The Hong Kong University of Science and Technology IoT Thrust Guangzhou Guangzhou511453 China Hong Kong University of Science and Technology Department of ECE Hong Kong Kyung Hee University Department of Electronic Engineering Gyeonggi-do Yongin-si17104 Korea Republic of

A novel mobile edge generation (MEG) framework is proposed for low-latency image generation on mobile devices. Exploiting a latent diffusion model (LDM) distributed across the edge server (ES) and the user equipment (UE), only low-dimension features need to be transmitted for creating artificial intelligence generative content (AIGC). Two novel modules, namely dynamic diffusion and feature merging, are conceived to compress the diffusion model and transmitted features, respectively. By jointly optimizing compression rates of denoising steps and feature merging, the image quality maximization problem is formulated subject to latency and energy consumption constraints. To address this problem in dynamic channel conditions, a low-complexity compression protocol is developed. First, a backbone LDM architecture is learned by offline distillation to support various compression options. Then, compression rates are predicted in online environment specific to channel and task features. To solve the resultant constrained Markov Decision Process (MDP), a constrained variational policy optimization (CVPO) based MEG algorithm, MEG-CVPO, is further developed to learn constraint-guaranteed optimization. Numerical results demonstrate that: 1) The proposed framework improves image distortions while reducing over 40% latency compared to conventional generation schemes. 2) MEG-CVPO stringently guarantee constraints and realizes a flexible trade-off between generation qualities and overheads. © 2015 IEEE.

关键词： Markov processes

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IoT-Based Portable ECG and Heart Rate Monitoring System with Minimal Cost 1

IoT-Based Portable ECG and Heart Rate Monitoring System with...

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1st International Conference on AIML-Applications for engineering and Technology, ICAET 2025

作者： Tonmoy, Achinta Brata Roy Sultan, Selim Bristy, Suparna Rani University of Wyoming Electrical Engineering and Computer Science Department LaramieWY United States Department of Electrical and Electronics Engineering Jain Bangalore India Mawlana Bhashani Science and Technology University Department of Information and Communication Technology Tangail Santosh1902 Bangladesh

ISBN: (纸本)9798350355611

Cardiovascular disease is a major cause of mortality globally, and early detection through electrocardiograms (ECG) is crucial for effective treatment. ECGs measure the heart's electrical impulses, enabling detection of various cardiac irregularities. In recent years, there has been a push to develop low-cost, IoT-based portable ECG monitoring systems to make healthcare more accessible. However, existing solutions are often constrained by limited Bluetooth connectivity (under 100 meters) or rely on expensive processors like Raspberry Pi, which restricts use in low-resource settings. This research proposes a novel IoT-based ECG monitoring system designed to be both affordable and efficient, overcoming these limitations with a minimal hardware setup. The system utilizes the ESP8266 module as a cost-effective microcontroller, enabling real-time ECG data transmission over the internet and allowing healthcare professionals to monitor patients remotely without range restrictions. At a cost of only ∃16, this system reduces expenses by 88.49% compared to similar existing models, offering a practical, scalable solution for telemedicine. The system architecture includes an ECG detecting node, IoT cloud platform, and a user-friendly interface, with the AD8232 sensor gathering ECG data processed by the ESP8266 and transmitted to a secure cloud platform(Ubidots). Authorized healthcare providers can access this data, with alerts for any detected irregularities, supporting timely intervention. The system's performance has been validated on multiple patients, achieving a 92% accuracy that aligns well with clinical ECG standards. This low-cost and precise IoT-based ECG monitoring technology signifies a significant progression in telemedicine, facilitating efficient remote monitoring and broadening access to vital healthcare services. © 2025 IEEE.

关键词： Telemedicine

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