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4th International Symposium on Instrumentation, Control, Artificial Intelligence, and Robotics, ICA-SYMP 2025

作者： Krinsukont, Thosapon Sirithanapipat, Taweedej National Science and Technology Development Agency National Electronics and Computer Technology Center Pathumthani Thailand Kasetsart University Department of Mechanical Engineering Bangkok Thailand

ISBN: (纸本)9798331505400

This paper presents an experiment and results of the modified CNN algorithm, it was developed by combining a compact 1D convolution neural network with a tuned signal filter (low-pass filter in this experiment). The algorithm was specifically designed for signal shape detection, prediction, and classification. An experiment aimed to evaluate the created algorithm's structure and signal filter to ensure its suitability for vibration signal data analysis. A testbed was built using an inverter motor equipped with various unbalanced conditions by increasing screws. Four-class unbalanced conditions were studied compared with the normal condition. Each conditions produced the distinct signal shape, with slight differences, making them suitable for evaluating the proposed algorithm's performance. The results demonstrate the satisfied performance of the algorithm, which effectively classifies vibration signals unbalance-3 (precision 54%), unbalance-4 (precision 77%) and a group of normal, unbalance-1, and unbalance-2. The group of normal, unbalance-1, and unbalance-2 can't be easily classified due to the tiny differences in the signal shapes, caused by small mass variations of the unbalanced screw unbalance-1, and unbalance-2. In conclusion, the algorithm proved satisfied effectiveness for its intended purpose of vibration signal classification. © 2025 IEEE.

关键词： Testbeds

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Sustainable AI Systems for Monitoring and Predicting Wildfires in Vulnerable Forest Regions

Sustainable AI Systems for Monitoring and Predicting Wildfir...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Raj, T. Selvin Retna Raj, J. Relin Francis Krishnan, R. Santhana Balamuralikrishnan, G. Vikkiramapandian, D. Jothi, J. Nirmal Dmi College of Engineering Department of Electronics and Communication Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Scad College of Engineering of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India Psna College of Engineering and Technology Department of Information Technology Dindigul India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Avadi Chennai India

ISBN: (纸本)9798331509828

Wildfires pose significant threats to ecological systems, human safety, and economic stability, especially in vulnerable forest regions. To address this critical issue, a sustainable AI-driven framework, WiSEFire (Wildfire Surveillance and Estimation), is proposed for real-time monitoring and prediction of wildfires. The framework integrates a hybrid approach, utilizing Random Forest for feature analysis and Gated Recurrent Units (GRU) for temporal fire prediction. The system processes multi-source data, including satellite imagery, IoT sensors, and meteorological information, to enhance detection accuracy and prediction reliability. Simulation experiments were conducted using benchmark datasets to evaluate WiSEFire against state-of-the-art algorithms, including Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. Performance metrics such as accuracy, latency, and detection efficiency were analyzed. The results demonstrated that WiSE Fire outperformed existing methods, achieving superior detection precision and reduced latency while maintaining energy efficiency. This research highlights the potential of sustainable AI frameworks for mitigating wildfire risks and improving emergency response capabilities © 2025 IEEE.

关键词： Convolutional neural networks

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Microgrid digital twins: concepts and their controlling through multi-agent systems

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International Journal of High Performance Systems Architecture 2023年第4期11卷 225-236页

作者： Negi, Gaurav Singh Mishra, Anupama Gupta, Mukul Kumar Saxena, Nitin Kumar Saini, DilipKumar Jang Bahadur Joshi, Kapil Electrical and Electronics Engineering Uttaranchal Institute of Technology Uttaranchal University Dehradun248007 India Department of Computer Science and Engineering Himalayan School of Science and Technology Swami Rama Himalayan University Uttarakhand Dehradun248140 India School of Engineering University of Petroleum and Energy Studies Dehradun248006 India Department of Electronics & Instrumentation Engineering MJP Rohilkhand University UP Bareilly243006 India Electrical and Electronics Engineering KIET Group of Institutions Ghaziabad201206 India Electrical and Computer Engineering University of Denver Denver80210 United States Department of Computer Science and Engineering School of Engineering and Technology Pimpri Chinchwad University Pune411044 India Department of CSE Uttaranchal Institute of Technology Uttaranchal University Dehradun248007 India

Real-time grid analysis is made possible by the electric digital twin grid, which combines history and present data to convey system status and project future circumstances. Cooperative smart agents that can solve problems bigger than one agent's scope make up a multi-agent system (MAS). A micro-grid digital twin (MGDT) uses real-time data interchange and high-fidelity models to simulate a microgrid’s operation. Hybrid energy storage systems are managed by agents who maximise renewable resources and minimise expenses. Microgrids combine sensing, control, and intelligence technology in utility networks. Plant management, communication protocols, and power system operations are all made easier with MAS-based control. For dependable microgrid operation, dispersed energy resources are coordinated by MAS. In order to handle complexity and improve resilience and control of the energy system, this study investigates the application of MAS in microgrid control. Energy management and agent communication in microgrids are the main topics of MAS research. Integrating microgrid digital twins with MAS holds promise for advancing energy systems. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Multi agent systems

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Fusion of Forward and Backward LSTMs for Effective Occlusion Reconstruction in Gait Sequences

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SN computer Science 2025年第3期6卷 1-20页

作者： Jain, Manav Chattopadhyay, Pratik Paul, Abhishek Jain, Jinesh Department of Computer Science and Engineering Indian Institute of Technology (Banaras Hindu University) Uttar Pradesh Varanasi 221005 India Department of Electronics and Communication Engineering National Institute of Technology West Bengal Durgapur 713209 India

Gait-based person identification in the presence of occlusion is a challenging problem and research work in this domain is still in its infancy. Some methods that have been developed in the past are based on certain assumptions such as gait features over a cycle following a Gaussian, or a complete cycle can be reconstructed from multiple occluded cycles. Recent deep neural network-based approaches to occlusion handling also mostly focus on making feature-level reconstruction instead of frame-level reconstruction, and thus their effectiveness is likely to suffer if several frames in a cycle are corrupted due to occlusion. There exists a single work on LSTM-based occlusion reconstruction which predicts frames based on the previous unoccluded/reconstructed frames only and does not utilize the complete sequence information effectively. In this paper, we improve the existing work on occlusion reconstruction by performing a two-way prediction using LSTMs and finally combining the two predictions. Our reconstruction model is based on (i) a forward LSTM that performs reconstruction in the forward direction by predicting each occluded frame from a few previous unoccluded or already reconstructed frames and (ii) a backward LSTM that carries out reconstruction in the backward direction by predicting each occluded frame from a few succeeding unoccluded or already reconstructed frames. To train the LSTMs, an extensive gallery set is constructed from the CASIA-B and the OU-ISIR LP data, and the mean-square loss between the corresponding generated and target frames is minimized. Next, a fusion network combines the predictions from the two LSTMs to generate the final reconstructed frame corresponding to each occluded frame. Evaluation of our approach has been performed using synthetically occluded sequences generated from the OU-ISIR LP, OU-ISIR MVLP and CASIA-B data and real occluded sequences present in the TUM-IITKGP and GREW data. The effectiveness of the proposed reconstructio

关键词： Gait recognition Long short term memory Occlusion detection and reconstruction Residual block

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Deep ensemble optimal classifier-based software defect prediction for early detection and quality assurance

Journal of Umm Al-Qura University for Engineering and Archit...

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Journal of Umm Al-Qura University for engineering and Architecture 2025年 1-12页

作者： Srinivas, Kalyanapu Janapati, Krishna Chaithanya Sai Kiran, Mungalched Sudheer Reddy, Davu Vinay, Thota Vamshi Yashwanth, Choula Department of Computer Science and Engineering Dhanekula Institute of Engineering and Technology Andhra Pradesh Vijayawada India Department of Electronics and Communication Engineering Vardhaman College of Engineering Telangana Hyderabad India Department of Computer Science and Engineering (Data Science) Malla Reddy Engineering College and Management Sciences Medchal Telangana Kistapur India

Software Defect Prediction (SDP) is critical in identifying fault-prone modules during the software development life cycle, enhancing software quality, and reducing maintenance costs. However, existing SDP models face significant challenges, including high false positive rates and severe class imbalance between defect-prone and non-defect modules, which hinder practical model training and accurate defect detection. To overcome these challenges, this study proposes a robust and efficient SDP architecture that integrates several advanced techniques to enhance predictive accuracy and model reliability. The methodology begins with comprehensive data preprocessing, incorporating normalization to standardize feature scales and outlier detection to eliminate noise and improve data quality. To address the inherent class imbalance in the dataset, the minority oversampling by synthetic data (MOSD) technique is employed to generate synthetic samples for the minority class, thus achieving a more balanced distribution. Following this, an Adaptive sequential K-best (ASKB) feature selection strategy is implemented to identify and retain the most relevant features, effectively reducing dimensionality while preserving critical information. Finally, the weighted random forest (WRF) classifier is utilized, which assigns appropriate weights to training instances, enabling improved classification performance, especially for minority class instances. The proposed methodology achieved an accuracy of 99.119%, a precision of 99.431%, a recall of 99.122%, and an F1-score of 99.333%. © The Author(s) 2025.

关键词： Adaptive sequential K-best Class imbalance Data preprocessing Feature selection Minority oversampling Predictive modeling Software defect prediction Synthetic data Weighted random forest

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Classification of Electroencephalogram Signals Using LSTM and SVM Based on Fast Walsh-Hadamard Transform

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computers, Materials & Continua 2023年第6期75卷 5271-5286页

作者： Saeed Mohsen Sherif S.M.Ghoneim Mohammed S.Alzaidi Abdullah Alzahrani Ashraf Mohamed Ali Hassan Electronics and Communications Engineering Department Al-Madinah Higher Institute for Engineering and TechnologyGiza12947Egypt Department of Artificial Intelligence Engineering Faculty of Computer Science and EngineeringKing Salman International University(KSIU)South Sinai46511Egypt Department of Electrical Engineering College of EngineeringTaif UniversityTaif21944Saudi Arabia Department of Electronics and Communications Engineering October High Institute for Engineering and Technology6th of October CityGiza12596Egypt

Classification of electroencephalogram(EEG)signals for humans can be achieved via artificial intelligence(AI)***,the EEG signals associated with seizure epilepsy can be detected to distinguish between epileptic and non-epileptic *** this perspective,an automated AI technique with a digital processing method can be used to improve these *** paper proposes two classifiers:long short-term memory(LSTM)and support vector machine(SVM)for the classification of seizure and non-seizure EEG *** classifiers are applied to a public dataset,namely the University of Bonn,which consists of 2 classes–seizure and *** addition,a fast Walsh-Hadamard Transform(FWHT)technique is implemented to analyze the EEG signals within the recurrence space of the ***,Hadamard coefficients of the EEG signals are obtained via the ***,the FWHT is contributed to generate an efficient derivation of seizure EEG recordings from non-seizure EEG ***,a k-fold cross-validation technique is applied to validate the performance of the proposed *** LSTM classifier provides the best performance,with a testing accuracy of 99.00%.The training and testing loss rates for the LSTM are 0.0029 and 0.0602,respectively,while the weighted average precision,recall,and F1-score for the LSTM are 99.00%.The results of the SVM classifier in terms of accuracy,sensitivity,and specificity reached 91%,93.52%,and 91.3%,*** computational time consumed for the training of the LSTM and SVM is 2000 and 2500 s,*** results show that the LSTM classifier provides better performance than SVM in the classification of EEG ***,the proposed classifiers provide high classification accuracy compared to previously published classifiers.

关键词： Electroencephalogram LSTM SVM fast Walsh-Hadamard transform seizure accuracy sensitivity specificity

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CGFSSO: the co-operative guidance factor based Salp Swarm Optimization algorithm for MPPT under partial shading conditions in photovoltaic systems

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International Journal of Information technology (Singapore) 2024年 1-16页

作者： Raj, S. Antony Elakkiya, E. Rajmohan, Shathanaa Samuel, G. Giftson Electrical and Electronics Engineering St Ann’s College of Engineering and Technology Andhra Pradesh Chirala India Computer Science and Engineering SRM University Andhra Pradesh Amaravati India Ramanujan Computing Centre College of Engineering Guindy Campus Anna University Chennai India Electrical and Electronics Engineering Arifa Institute of Technology Nagapattinam India

The increasing adoption of solar photovoltaic (PV) power generation stems from its renewable and eco-friendly attributes. However, conventional Maximum Power Point Tracking (MPPT) methods encounter difficulties in efficiently harnessing power from PV systems under Partial Shading Conditions (PSC). During PSC, these systems exhibit fluctuating power outputs due to shading, leading to challenges in identifying the Global Maximum Power Point (GMPP). The presented research introduces a pioneering Co-Operative Guidance factor based Salp Swarm Optimization algorithm (CGFSSO) tailored for MPPT in PSC scenarios within PV systems. The CGFSSO method focuses on precise GMPP localization with minimized oscillations by enhancing the update mechanism and effectively exploring the expansive search space. To assess its efficacy, the proposed CGFSSO approach undergoes comparison against conventional MPPT techniques, Fuzzy logic and Optimization based MPPT methods through rigorous simulation studies. The results underscore the CGFSSO method's exceptional performance in precisely tracking the GMPP and improving MPPT power efficiency when contrasted with established methodologies. This study signifies a promising stride towards optimizing power extraction from PV systems operating under demanding partial shading conditions. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Complex partial shaded conditions Maximum Power Point Tracking Photovoltaic systems Renewable energy sources Salp Swarm Optimization

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An Iterative PRISMA Review of GAN Models for Image Processing, Medical Diagnosis, and Network Security

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computers, Materials & Continua 2025年第2期82卷 1757-1810页

作者： Uddagiri Sirisha Chanumolu Kiran Kumar Sujatha Canavoy Narahari Parvathaneni Naga Srinivasu Department of Computer Science and Engineering Prasad V Potluri Siddhartha Institute of TechnologyVijayawada520007India Department of Computer Science and Engineering Koneru Lakshmaiah Education FoundationVaddeswaram522302India Department of Electronics and Communication Engineering Sreenidhi Institute of Science and TechnologyHyderabad501301India Amrita School of Computing Amrita Vishwa VidyapeethamAmaravati522503India Department of Teleinformatics Engineering Federal University of CearáFortaleza60455-970Brazil Inti International University Nilai71800Malaysia

The growing spectrum of Generative Adversarial Network (GAN) applications in medical imaging, cyber security, data augmentation, and the field of remote sensing tasks necessitate a sharp spike in the criticality of review of Generative Adversarial Networks. Earlier reviews that targeted reviewing certain architecture of the GAN or emphasizing a specific application-oriented area have done so in a narrow spirit and lacked the systematic comparative analysis of the models’ performance metrics. Numerous reviews do not apply standardized frameworks, showing gaps in the efficiency evaluation of GANs, training stability, and suitability for specific tasks. In this work, a systemic review of GAN models using the PRISMA framework is developed in detail to fill the gap by structurally evaluating GAN architectures. A wide variety of GAN models have been discussed in this review, starting from the basic Conditional GAN, Wasserstein GAN, and Deep Convolutional GAN, and have gone down to many specialized models, such as EVAGAN, FCGAN, and SIF-GAN, for different applications across various domains like fault diagnosis, network security, medical imaging, and image segmentation. The PRISMA methodology systematically filters relevant studies by inclusion and exclusion criteria to ensure transparency and replicability in the review process. Hence, all models are assessed relative to specific performance metrics such as accuracy, stability, and computational efficiency. There are multiple benefits to using the PRISMA approach in this setup. Not only does this help in finding optimal models suitable for various applications, but it also provides an explicit framework for comparing GAN performance. In addition to this, diverse types of GAN are included to ensure a comprehensive view of the state-of-the-art techniques. This work is essential not only in terms of its result but also because it guides the direction of future research by pinpointing which types of applications require some

关键词： GAN CGAN WGAN DCGAN image analysis

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Effectiveness of Almost Sub Frame Approach as Inter Cell Interference Management in LTE-A Macro - Pico Scenario 9

Effectiveness of Almost Sub Frame Approach as Inter Cell Int...

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9th IEEE International Conference for Convergence in technology, I2CT 2024

作者： Jember, Kehali A. Abose, Tadele A. Kassahun, Ermias Madhav, T. Venu Adama Science And Technology University Department Electronics And Communication Engineering Adama Ethiopia Mettu University Department Electrical And Computer Engineering Ethiopia Gondar University Department Electrical And Computer Engineering Gondar Ethiopia

ISBN: (纸本)9798350394474

Heterogeneous networks are promising solutions for enhancing network performance of LTE-A mobile networks by deploying small cells within the area of the serving macro cells. The goal of deploying such networks is to offload data from the congested macro cells towards the small cells. However, cross tier interference is a serious issue, which is introduced due to the transmitted power difference between Macro cell node and small cell node. Hence, cell age users are mainly affected by high power interference originated from Macro node. The paper develops enhanced inter-cell interference coordination using Almost Blank Sub-Frame (ABS) to improve user throughput. This can be achieved by stopping data transmission on in certain sub frame of macro cell while Pico users are scheduled at the same frame. computer simulation results depict that the performance of Pico cell users is highly degraded, especially for cell edge users. The ABS technique improves cell edge user throughput of Pico cell by 95% and center cell user throughput by 75.25%. © 2024 IEEE.

关键词： Heterogeneous networks

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An Effective Biomedical Named Entity Recognition by Handling Imbalanced Data Sets Using Deep Learning and Rule-Based Methods

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SN computer Science 2023年第5期4卷 650页

作者： Archana, S.M. Prakash, Jay Singh, Pramod Kumar Ahmed, Waquar Computer Science and Engineering National Institute of Technology Kerala Calicut India Computer Science and Engineering ABV-Indian Institute of Information Technology and Management Madhya Pradesh Gwalior India Electronics and Communication Engineering National Institute of Technology Kerala Calicut India

Biomedical named entity recognition (Bio-NER) is an important and fundamental task for biomedical text mining. Bio-NER has various applications, especially in Natural Language Processing, and its performance heavily impacts dependent tasks. However, the class imbalance problem is the major issue for an effective Bio-NER. Over the past few years, rule-based methods and deep learning-based methods have been widely used. However, they are not able to effectively handle class imbalance issues. Therefore, we proposed a hybrid method by taking advantage of both methods for Bio-NER, which is named Hybrid Bio-NER (HBio-NER). Furthermore, we propose a training data format called ‘single_entity-O’, which reduces the class imbalance problem. Data preprocessing and ‘single_entity-O’ data representation format improve the class balance, further improving the HBio-NER system performance. HBio-NER identifies the named entities using the Bidirectional Long Short-Term Memory (Bi-LSTM) model and named entity boundaries (begin, inside of entity mentions) using rules. The effectiveness of the proposed method is tested on two data sets (NCBI disease and CHEMDNER chemical data sets) with different word representation models (GloVe, Word2Vec and FastText). The comparative analysis indicates that significant performance gain is achieved by the proposed method especially for disease named entity recognition. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Biomedical named entity recognition Biomedical text mining Deep learning Imbalanced data handling Natural language processing

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