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A novel approach to deriving adaboost classifier weights using squared loss function for overlapping speech detection

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Multimedia Tools and Applications 2025年 1-28页

作者： Asbai, Nassim Bounazou, Hadjer Zitouni, Sihem Department of Electronics Faculty of Sciences and Technology Mohamed El Bachir El Ibrahimi University Bordj Bou Arreridj City34030 Algeria Speech Com. & Signal Proc. Lab. Faculty of Electrical Engineering USTHB Bab Ezzouar City16111 Algeria Department of Computer Science Faculty of Mathematics and Computer Science Mohamed El Bachir El Ibrahimi University Bordj Bou Arreridj City34030 Algeria

In this paper, we address the complex problem of detecting overlapping speech segments, a key challenge in speech processing with applications in speaker diarization, automatic transcription, and multi-speaker recognition systems. Traditional approaches, often relying on exponential loss functions within the AdaBoost framework, struggle to maintain robustness in noisy or imbalanced data environments. To enhance detection accuracy, we propose a novel AdaBoost variant—SLFAdaBoost—utilizing a squared loss function specifically tailored for overlapping speech detection. This model demonstrates superior stability and convergence, significantly outperforming conventional methods. Experimental evaluation on the NIST 2005 corpus revealed that SLFAdaBoost achieves an accuracy range of 92.5% to 98.6%, with F1 scores between 95.4% and 99.2%, notably surpassing standard AdaBoost and Random Forest classifiers. Additionally, our model shows resilience in noisy conditions, maintaining precision and recall metrics at higher noise levels than other classifiers. These results underscore SLFAdaBoost's capacity to handle intricate data patterns, offering a more robust solution for real-world speech processing applications where overlapping segments are prevalent. This contribution provides an efficient, high-performing model, advancing the capabilities of ensemble methods in complex audio environments. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Speech recognition

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Artificial Neural Network-Based Inverter Control and MPPT for Enhanced Performance of Hybrid Microgrids With PV and Battery Storage 15

Artificial Neural Network-Based Inverter Control and MPPT fo...

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15th International Renewable Energy Congress, IREC 2025

作者： Rajab, Z. Elrashid, M. Elhashane, M. Gomez-Romero, J. Khalil, A. Seliman, A. Mohamed, F. Benghazi University Faculty if Engineering Electrical and Electronic Department Libya University of Granada Department of Computer Science and Artificial Intelligence Spain Technical University of Denmark Dtu Engineering Technology Denmark Omar Al-Mukhtar University Faculty of Engineering Electrical Department Libya Authority of Natural Science Research and Technology Tripoli Libya

ISBN: (纸本)9798331516772

Today's power system network has become more complex, with greater responsibilities and challenges in providing a secure, reliable, and high-quality energy supply to communities. A smaller entity of the electrical network, known as a Microgrid (MG), has become more popular in recent times to enhance the reliability and security of energy supply during any energy crisis in the utility network. Microgrids (MG) can provide clean energy through renewable resources such as photovoltaic (PV) systems, wind, and energy storage, offering an effective solution for power supply in both remote and urban residential communities. To ensure the stable operation of a standalone microgrid based on a photovoltaic system, it is essential to implement various mechanisms and control strategies simultaneously for optimal efficiency. This paper introduces a complete model of a hybrid microgrid consisting of a PV source, an Artificial Neural Network (ANN)-based Maximum Power Point Tracking (MPPT) system, a storage unit, a DC to AC converter with an ANN controller, and both DC and AC loads. The performance of the controller under various ambient conditions and load variations is tested. The ANN controller demonstrates good performance in different situations. © 2025 IEEE.

关键词： Hybrid power

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Enhancing PCOS diagnosis accuracy with modified Giza Pyramids Optimizer, SLOGPO, and PCOSNet: advanced classification and optimization techniques

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Cluster Computing 2025年第5期28卷 1-27页

作者： Aldosary, Abdallah El-Shafai, Walid Mohamed, Ehab Mahmoud Emara, Heba M. Department of Computer Engineering College of Engineering in Wadi Addwasir Prince Sattam bin Abdulaziz University Wadi Addwasir11991 Saudi Arabia Department of Computer Science Prince Sultan University Riyadh11586 Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Electrical Engineering College of Engineering in Wadi Addawasir Prince Sattam Bin Abdulaziz University Wadi Addawasir11991 Saudi Arabia Department of Electronics and Electrical Communications Engineering High Institute of Electronic Engineering Ministry of Higher Education Bilbis-Sharqiya44621 Egypt

Polycystic Ovary Syndrome (PCOS) is a prevalent endocrine disorder affecting women during their reproductive years, characterized by a range of hormonal imbalances and reproductive dysfunctions. Accurate diagnosis of PCOS is crucial, as it serves as the foundation for timely interventions and personalized management strategies. In this context, this paper presents a comprehensive evaluation of two advanced classification models: the Stacked Learning Optimized by Giza Pyramids Optimizer (SLOGPO) and PCOSNet, a model leveraging 1-Dimensional Convolutional Neural Networks (CNN) integrated with a BORUTA-based feature selection framework. The study employs three distinct datasets, each containing a variety of physical and clinical parameters. The SLOGPO model, which combines stacked learning, the Improved Giza Pyramids Optimizer (IGPO), and BORUTA for feature selection, demonstrated strong generalization capabilities, achieving accuracies of 96% on Dataset I, 96% on Dataset II, and 95% on Dataset III. In contrast, the PCOSNet model, based on a 1D CNN architecture with BORUTA, achieved accuracies of 94% on Dataset I, 93% on Dataset II, and 84% on Dataset III. These results underscore the superior generalization ability of the SLOGPO model across varied datasets, while the performance of PCOSNet indicates areas for further improvement, particularly in handling diverse datasets. This study provides valuable insights into the impact of feature selection techniques and dataset variability on the performance of PCOS detection models. The findings emphasize the importance of evaluating models across multiple datasets to assess their generalizability and robustness, laying the groundwork for future research in PCOS detection and related medical applications. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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Phasmatodea Population Evolution Algorithm Based on Spiral Mechanism and Its Application to Data Clustering

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computers, Materials & Continua 2025年第4期83卷 475-496页

作者： Jeng-Shyang Pan Mengfei Zhang Shu-Chuan Chu Xingsi Xue Václav Snášel College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China School of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing210044China Department of Information Management Chaoyang University of TechnologyTaichung41349TaiwanChina Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of TechnologyFuzhou350118China Faculty of Electrical Engineering and Computer Science VŠB-Technical University of OstravaOstrava70833Czech Republic

Data clustering is an essential technique for analyzing complex datasets and continues to be a central research topic in data *** clustering algorithms,such as K-means,are widely used due to their simplicity and *** paper proposes a novel Spiral Mechanism-Optimized Phasmatodea Population Evolution Algorithm(SPPE)to improve clustering *** SPPE algorithm introduces several enhancements to the standard Phasmatodea Population Evolution(PPE)***,a Variable Neighborhood Search(VNS)factor is incorporated to strengthen the local search capability and foster population ***,a position update model,incorporating a spiral mechanism,is designed to improve the algorithm’s global exploration and convergence ***,a dynamic balancing factor,guided by fitness values,adjusts the search process to balance exploration and exploitation *** performance of SPPE is first validated on CEC2013 benchmark functions,where it demonstrates excellent convergence speed and superior optimization results compared to several state-of-the-art metaheuristic *** further verify its practical applicability,SPPE is combined with the K-means algorithm for data clustering and tested on seven *** results show that SPPE-K-means improves clustering accuracy,reduces dependency on initialization,and outperforms other clustering *** study highlights SPPE’s robustness and efficiency in solving both optimization and clustering challenges,making it a promising tool for complex data analysis tasks.

关键词： Phasmatodea population evolution algorithm data clustering meta-heuristic algorithm

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FAS for Secure and Covert Communications

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IEEE Internet of Things Journal 2025年第11期12卷 18414-18418页

作者： Yao, Junteng Xin, Liangxiao Wu, Tuo Jin, Ming Wong, Kai-Kit Yuen, Chau Shin, Hyundong Ningbo University faculty of Electrical Engineering and Computer Science Ningbo315211 China Nanyang Technological University School of Electrical and Electronic Engineering Singapore639798 Singapore University College London Department of Electronic and Electrical Engineering Torrington Place WC1E 7JE United Kingdom Kyung Hee University Department of Electronic Engineering Gyeonggi-do Yongin-si17104 Korea Republic of

This letter considers a fluid antenna system (FAS)-aided secure and covert communication system, where the transmitter adjusts multiple fluid antennas' positions to achieve secure and covert transmission under the threat of an eavesdropper and the detection of a warden. This letter aims to maximize the secrecy rate while satisfying the covertness constraint. Unfortunately, the optimization problem is non-convex due to the coupled variables. To tackle this, we propose an alternating optimization (AO) algorithm to alternatively optimize the optimization variables in an iterative manner. In particular, we use a penalty-based method and the majorization-minimization (MM) algorithm to optimize the transmit beamforming and fluid antennas' positions, respectively. Simulation results show that FAS can significantly improve the performance of secrecy and covertness compared to the fixed-position antenna (FPA)-based schemes. © 2025 IEEE.

关键词： Convex optimization

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Advanced brain tumor segmentation using DeepLabV3Plus with Xception encoder on a multi-class MR image dataset

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Multimedia Tools and Applications 2025年 1-22页

作者： Saifullah, Shoffan Dreżewski, Rafal Yudhana, Anton Faculty of Computer Science AGH University of Krakow Krakow30-059 Poland Department of Informatics Universitas Pembangunan Nasional Veteran Yogyakarta Yogyakarta55281 Indonesia Department of Electrical Engineering Universitas Ahmad Dahlan Yogyakarta55191 Indonesia

Accurate segmentation of brain tumors from Magnetic Resonance Imaging (MRI) scans presents notable challenges. This particularly in differentiating tumors from surrounding tissues with similar intensity. This study utilizes the DeepLabV3Plus model with an Xception encoder to address these challenges. We evaluated the model on a dataset of 3064 MR images, which included meningioma, glioma, and pituitary tumors. The model achieved high segmentation performance, with an accuracy of 0.9991, Dice Similarity Coefficient (DSC) of 0.9529, and Jaccard Index (JI) of 0.9133. It also demonstrated substantial boundary precision, recording an Average Symmetric Surface Distance (ASSD) of 0.84 mm and a Hausdorff Distance (HD) of 2.3 mm. These metrics surpass those of existing state-of-the-art models. While the model performed well with well-defined tumor boundaries, it faced challenges segmenting tumors with ambiuous edges. These findings suggest significant potential for enhancing diagnostic accuracy in neuro-oncology. Future work will focus on enhancing the model’s sensitivity to subtle tumor features and expanding the dataset to include a broader range of tumor characteristics. Additionally, incorporating multimodal imaging data could further improve segmentation precision. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Magnetic resonance imaging

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Guest editorial: Electromagnetic modeling exploiting fields and circuits

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COMPEL - The International Journal for Computation and Mathematics in electrical and Electronic engineering 2025年第3期44卷 271-273页

作者： Petrun, Martin Faculty of Electrical Engineering and Computer Science University of Maribor Maribor Slovenia

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Energy Harvesting Jammer-Aided Covert Communications in Wireless Multi-Relay IoT Systems

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IEEE Internet of Things Journal 2025年第11期12卷 17443-17455页

作者： Lv, Hao Yang, Bin Sun, Xiuwen Gao, Chan Gui, Bao Taleb, Tarik School of Computer Science and Technology of Anhui University Hefei230000 China Chuzhou University School of Computer and Information Engineering Chuzhou239000 China Xi'an University of Posts and Telecommunications National Engineering Research Center for Secured Wireless School of Cybersecurity Xi'an710121 China Ruhr University Bochum Faculty of Electrical Engineering and Information Technology Bochum44801 Germany

This paper investigates covert communications in a multi-relay Internet of Things (IoT) system with multiple energy harvesting jammers, where a transmitter (Alice) attempts to covertly transmit confidential messages to its destination (Bob) through relay forwarding, while a warden (Willie) detects the existence of Alice's transmission. Specifically, we employ a harvesting-then-jamming protocol with which the jammers first harvest energy from Alice and then send jamming signals to interfere with Willie's detection. We propose a relay and jammer selection strategy, namely quality of service (QoS)-aware selection, and use the random selection strategy as a comparison strategy. Under these two selection strategies, we derive the optimal detection threshold and minimum detection error probability at Willie, respectively. We then model the covert throughput performance and obtain the maximum covert throughput by jointly optimizing covert transmit power and jamming transmit power. Extensive numerical results are provided to illustrate the impacts of system parameters on covert throughput performance. © 2014 IEEE.

关键词： Jamming

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Real-Time Testing of Microgrid Control Algorithms: Development of Experimental Setup 29

Real-Time Testing of Microgrid Control Algorithms: Developme...

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29th International Conference on Information Technology, IT 2025

作者： Forcan, Miodrag Dobrić, Goran Abed, Amir Žarković, Mileta Ikić, Marko Forcan, Jovana Barragán-Villarejo, Manuel Maza-Ortega, José María De Paula Garcia-López, Francisco University of East Sarajevo Faculty of Electrical Engineering East Sarajevo71123 Bosnia and Herzegovina University of Belgrade School of Electrical Engineering Belgrade11120 Serbia University of East Sarajevo Faculty of Philosophy Department of Computer Science and Systems East Sarajevo71420 Bosnia and Herzegovina University of Sevilla Department of Electrical Engineering Seville41004 Spain

ISBN: (纸本)9798331517649

This paper provides an overview of microgrid control strategies, examining differences between centralized and decentralized approaches, and focusing on classical hierarchical control schemes as well as the structure of three-layer microgrid control systems. It also explores Hardware-in-the-Loop modeling, a crucial approach for microgrid simulation, with particular emphasis on devices supporting microgrid modeling. The paper primarily focuses on the design and implementation requirements of an experimental setup for real-time testing of microgrid control algorithms. The setup incorporates Hardware-in-the-Loop device, a power amplifier, a power converter, a battery energy storage system, and a digital controller. It is specifically designed to handle both power and information (control) signals, facilitating a comprehensive approach to evaluating and validating microgrid control strategies in real-world conditions. A detailed analysis of the setup elements and their interactions is also provided. © 2025 IEEE.

关键词： Hardware-in-the-loop simulation

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Ontology-Driven Semantic Web Framework with Sentiment Analysis for Risk Assessment and Diagnosis of Diabetes Mellitus 4

Ontology-Driven Semantic Web Framework with Sentiment Analys...

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

作者： Muruganantham, A. Velmurugan, R. Kumar, R. Rajan, M. Sundar Sivaraj, V. Karnataka Bengaluru India Arba Minch Institute of Technology Arba Minch University Faculty of Electrical and Computer Engineering Ethiopia Karpagam Academy of Higher Education Department of Computer Science Coimbatore India

ISBN: (纸本)9798331523923

This paper uses semantic web and ontology techniques to predict the risk analysis of patients with diabetes mellitus. The data is collected from patients through personal interaction and by accessing their previous medical history. However, during personal interactions, medical staff often fail to gather all the necessary details for an accurate diagnosis, and patients may lose their previous medical records, which hinders doctors' ability to gain a full understanding of the problem. To address this issue, ontology techniques are employed, and additional details are collected from patients, such as their food habits, smoking history, alcohol consumption, sexual activity, and cardiovascular health. Using the collected data, ontology and semantic web technologies are applied to predict the risk analysis of diabetic patients. The results are visualized in a graph, providing a more comprehensive and accurate assessment of the patients' health risks. © 2025 IEEE.

关键词： Electronic health record

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