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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Messaoudi, Maher Kameli, Sayed Mohammad Refaat, Shady S. Abu-Rub, Haitham Trabelsi, Mohamed Department of Electrical and Computer Engineering Texas A&M University at Qatar Doha Qatar School of Physics Engineering and Computer Science University of Hertfordshire Hatfield United Kingdom Electronics and Communications Engineering Department Kuwait College of Science and Technology Kuwait

ISBN: (纸本)9781665464543

Discharges, such as partial discharges (PDs) and corona discharges (CDs) are the most common faults that occur in insulation materials used in high voltage (HV) equipment. A high repetition rate of discharge activity indicates the severity of the defects that shorten the lifetime of electrical equipment, leading to insulation failure. To solve this, this paper proposes an efficient classification technique for corona discharge defect intensity using features obtained from statistical parameters such as the ignition voltage of CDs. The Recurrent Neural Network (RNN) is proposed to identify the intensity of corona discharges. A comprehensive experimental evaluation is conducted, to demonstrate the capabilities of the proposed solution. The exceptional predictive abilities of the long short-term memory (LSTM) method are the primary benefit of the proposed approach presented, with a potential for enhancing the performance of CD detection systems. The obtained results demonstrate the accuracy of the proposed model, indicating its potential for deployment in practical applications. The innovative approaches utilized in this paper will help engineers and operators quickly determine the severity (sharpness and curvature) of the protrusions or surface defects that cause CDs, solely based on measurements of the ignition voltage. © 2024 IEEE.

关键词： Long short-term memory

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EnsemBEP: A Robust Ensemble Model for Enhanced B-cell Epitope Prediction 8

EnsemBEP: A Robust Ensemble Model for Enhanced B-cell Epitop...

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8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Karthiga, M. Shiv Navin, V.B. Cibin Kumar, R.M. Sathiya, B. Satheesh Kumar, A. Priyanga, M.A. Bannari Amman Institute of Technology Department of Computer Science and Engineering Tamilnadu Sathyamangalam India Nandha Engineering College Department of Computer Science and Engineering Tamilnadu Erode India

ISBN: (纸本)9798350367904

B-cell epitope prediction has found its use in understanding B-cell recognition spots on a number of antigens that is vital in vaccine design and immunotherapy. These fields urgently need powerful prediction models, but the existing methods often fail to satisfy the requirement of both high sensitivity and high specificity. In response, this manuscript presents an ensemble model named EnsemBEP that combines Random Forest, Gradient Boosting, and XGBoost for improving the performance of B-cell epitope prediction. New model for epitope prediction, named EnsemBEP shows better performance in comparison with other models including EpiDope and BepiPred-2.0. The proposed EnsembEP model demonstrates superior performance compared to existing models such as EpiDope and BepiPred-2.0. Through rigorous evaluation, EnsemBEP achieved an AUC of 0.945, accuracy of 0.885, sensitivity of 0.823, specificity of 0.926, and an MCC of 0.743. All these findings point out that EnsemBEP yield better results than competing models thereby confirming that it is more accurate and reliable in predicting B-cell epitopes. The results provide evidence for the significance of ensemble learning methodologies in attaining higher predictive performance in immunoinformatic. © 2024 IEEE.

关键词： Prediction models

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Removing Speckle Noise from Synthetic-Aperture Radar Images Using Artificial Intelligence Techniques 11

Removing Speckle Noise from Synthetic-Aperture Radar Images ...

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11th International Symposium on Telecommunication, IST 2024

作者： Moein, Fateme Taban, Mohammad Reza Isfahan University of Technology Department of Electrical and Computer Engineering Isfahan Iran

ISBN: (纸本)9798350356250

In recent times, Synthetic-Aperture Radar (SAR) images have gained significant importance due to their extensive use in both military and civilian applications. However, these images are often compromised by speckle noise, which severely hampers their interpretability and utility across various domains. This paper explores a range of image denoising techniques, with a particular focus on deep learning-based approaches, while also considering traditional methods. Given the critical nature of the subject, real SAR images have been rigorously analyzed. The study emphasizes that speckle noise in real SAR images is far more detrimental than in simulated scenarios, revealing the limitations of artificial intelligence algorithms when used in isolation for denoising tasks. To address this challenge, the proposed algorithm in this paper first preprocesses the real data using classical methods before applying a neural network for denoising. Both quantitative metrics and visual assessments demonstrate that this hybrid approach not only outperforms standard denoising methods but also preserves critical information in SAR images more effectively. © 2024 IEEE.

关键词： Radar imaging

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Spatiotemporal Representation Learning on Event Stream 7

Spatiotemporal Representation Learning on Event Stream

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7th International Symposium on Autonomous Systems, ISAS 2024

作者： Yang, Beibei Li, Weiling Jiang, Guangyu Liu, Zhigang Zhong, Yurong Fang, Yan Dongguan University of Technology School of Computer Science and Technology Dongguan China Kennesaw State University Department of Electrical and Computer Engineering MariettaGA United States

ISBN: (纸本)9798350363173

The spatiotemporal correlation of events obtained by event camera contains the operational laws of moving targets. For deeper understanding events, an effective spatiotemporal representation learning-based model is desired. Note that events are dense in time and sparse in space, how to capture the global correlations of all events in the event stream simultaneously while well addressing the sparse input is a challenging task. To fulfil this task, an Elastic Net-incorporated tensor network (ENTN) model is proposed in this work with twofold ideas: a) utilizing a fully-connected 3rd-order tensor network for tensor decomposition to implement spatiotemporal representation learning on event stream, b) incorporating Elastic-net regularization to perform better feature selection. Experimental results indicate that ENTN can represent the spatiotemporal correlation of events in an event stream with high quality. © 2024 IEEE.

关键词： Tensors

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Blockchain Integration in Emergency Medical Transportation: Advancing Security and Coordination for Critical Care Scenarios 5th

Blockchain Integration in Emergency Medical Transportation: ...

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5th Doctoral Symposium on Computational Intelligence, DoSCI 2024

作者： Rawat, Abhishek Suryavanshi, Raghuraj Singh Nagar, Vishal Yadav, Divakar Department of Computer Science and Engineering Pranveer Singh Institute of Technology Uttar Pradesh Kanpur India Department of Computer Science and Engineering Institute of Engineering and Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819771776

Blockchains improve safety and faster teamwork for emergency medical transport in critical care. Emergency medical transport is not the only healthcare sector that has become better and different because of current blockchains. This report also illustrates how blockchains can alter the safety, integrity, and effectiveness of emergency medical transport operations, including a wide-ranging examination of these technologies’ consequences on the healthcare sector. Traditional Emergency Medical Transportation Systems face different challenges, and this paper provides a framework for a complete understanding of the possible benefits and creative solutions that Blockchain offers to the healthcare industry. This paper has given a compelling business example for applying Blockchain to enhance Emergency Medical Transportation. It gives valuable perspectives on smart steps that can be used to improve care and well-being and streamline pool offerings and a feasibility rate that could help to create an effective outcome for Emergency Medical Transport Services with the usage of Blockchain. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Blockchain Healthcare industry Medical Data History Transportation

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Deep Learning-Based Classification of Conference Paper Reviews: Accept or Reject? 23th

Deep Learning-Based Classification of Conference Paper Revie...

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23rd International Conference on Intelligent Systems Design and Applications, ISDA 2023

作者： Prama, Tabia Tanzin Biswas, Al Amin Anwar, Md Musfique Department of Computer Science and Engineering Jahangirnagar University Dhaka Bangladesh Department of Computer Science and Engineering Bangabandhu Sheikh Mujibur Rahman University Kishoreganj Bangladesh

ISBN: (纸本)9783031647758

Peer review plays a crucial role in ensuring that papers published in conferences are as true, valid, and accurate as possible. As part of the publication process, the scholarly work of an academic author undergoes review by domain experts, reflecting the commitment to uphold integrity. With the growing volume of scholarly submissions at the conference, the classification of the paper reviews through an automated classification system has become a pressing requirement. The primary aim of this research is to develop an automated classification system for conference paper reviews into two categories: accept and reject. However, the objective of this paper is twofold: developing an automated classification system and explaining the decision-making process through explainable artificial intelligence (XAI). First, to develop an automated classification system, we present a deep learning-based framework. Next, to explain the results produced by deep learning (DL) models, Local Interpretable Model-Agnostic Explanations (LIME), SHapley Additive exPlanations (SHAP), and Anchor Explainer have been applied. Among the applied DL techniques, the Bi-GRU-LSTM-CNN model attained the highest accuracy of 95.33% which indicates that the proposed methodology is able to replicate the human decision-making process in an effective manner. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Decision making

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Cloud-Enabled Bio-Inspired Optimization: A Scalable Solution for Complex Real-World Problems 3

Cloud-Enabled Bio-Inspired Optimization: A Scalable Solution...

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

作者： Shreya Sakthi, U.R. Sruthi, S. Shruti, S.G. Rajganesh, N. Department of Computer Science and Engineering Sri Venkateswara College of Engineering Sriperumbudur India

ISBN: (纸本)9798331527549

Bio-inspired optimization algorithms, encompassing swarm intelligence, neural networks, fuzzy logic, and evolutionary algorithms, are powerful tools for addressing complex real-world optimization problems. However, their effectiveness is often constrained by the computational demands of training and execution, which surpass the capabilities of traditional computing platforms—particularly when dealing with massive datasets or intricate optimization challenges. These limitations restrict the practical applicability of such algorithms. This work introduces a framework that leverages cloud computing as a scalable and efficient platform to overcome these computational barriers. By integrating bio-inspired optimization techniques with the elastic and high-performance resources of cloud environments, this research demonstrates significant improvements in both the scope and efficiency of these algorithms. Experimental results validate the proposed approach, showcasing enhanced performance, reduced execution time, and the ability to solve larger and more complex real-world problems, thereby unlocking the full potential of bio-inspired optimization in practical applications. © 2025 IEEE.

关键词： Cloud platforms

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A Stochastic Biofilm Disruption Model Based on Quorum Sensing Mimickers

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IEEE Transactions on Molecular, Biological, and Multi-Scale Communications 2023年第3期9卷 346-350页

作者： Gulec, Fatih Eckford, Andrew W. Department of Electrical Engineering and Computer Science York University TorontoONM3J 1P3 Canada

Quorum sensing (QS) mimickers can be used as an effective tool to disrupt biofilms which consist of communicating bacteria and extracellular polymeric substances (EPS). In this paper, a stochastic biofilm disruption model based on the usage of QS mimickers is proposed. A chemical reaction network (CRN) involving four different states is employed to model the biological processes during the biofilm formation and its disruption via QS mimickers. In addition, a state-based stochastic simulation algorithm is proposed to simulate this CRN. The proposed model is validated by the in vitro experimental results of Pseudomonas aeruginosa biofilm and its disruption by rosmarinic acid as the QS mimicker. Our results show that there is an uncertainty in state transitions due to the effect of the randomness in the CRN. In addition to the QS activation threshold, the presented work demonstrates that there are underlying two more thresholds for the disruption of EPS and bacteria, which provides a realistic modeling for biofilm disruption with QS mimickers. © 2023 IEEE.

关键词： Random processes

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A Study of Contrastive Learning Methods for Bengali Social Analysis 6

A Study of Contrastive Learning Methods for Bengali Social A...

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6th International Conference on electrical engineering and Information and Communication Technology, ICEEICT 2024

作者： Maisha, Jannatim Swarnali, Farhana Hossain Iftikar, M. Saymon Islam Ahammed, Sarkar Bulbul Shah, Faisal Muhammad Ahsanullah University of Science and Technology Department of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350385779

Across various domains, Contrastive Learning (CL) has already proven to be a powerful technique but using the Bengali language in the domain of Natural Language Processing (NLP) its' application is still unexplored. In this research, we introduce the implementation of CL in Bengali NLP by presenting a comprehensive benchmark study on two distinct datasets: Bengali Hate Speech, and Rokomari Book Review. The efficiency of the supervised contrastive techniques is emphasized by our methodology. We have implemented a contrastive learning technique through Bangla Bert. The superiority of supervised contrastive learning techniques over traditional Cross Entropy (CE) methods has been showcased by our result. Detailed experiments reveal performance variations across datasets, models, and hyperparameters but ensure the superiority of CL techniques over CE. Our findings show the significance of contrastive learning methods in low-resource settings, contributing to the advancement of Bengali NLP research. © 2024 IEEE.

关键词： Learning systems

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Secure and Efficient Deduplication for Multimedia Sharing in Privacy-Sensitive Cloud Ecosystems 3

Secure and Efficient Deduplication for Multimedia Sharing in...

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

作者： Suresh, A. Murali, Erasappa Anjali Sri, V. Sumana, E. Sathwik, Gopaluni Venkata Sai Devendra Reddy, P. Department of Computer Science and Engineering Siddharth Institute of Engineering & Technology Puttur India

ISBN: (纸本)9798331527549

Current cloud-based systems face the challenge of managing storage and eliminating redundant data associated with the exponentially growing multimedia content, where numerous semantically similar but nonidentical files are included. In this paper we introduce a novel secure deduplication framework for encrypted cloud storage. This proposed system employs similarity-preserving hashing, fuzzy PoW protocol, and attribute-based encryption to perform efficient deduplication without compromising security and providing ownership verification. Security and deduplication rate scalability are improved over brute-force and ownership-faking attacks by dynamically varying thresholds based on file characteristics. Experimental evaluation shows that a 48% average deduplication rate and resistance to common attack vectors make it suitable for real-world cloud storage scenarios. The framework fills critical gaps in existing approaches and provides an innovative solution for dealing with similarity-aware data deduplication in privacy-sensitive environments. © 2025 IEEE.

关键词： Cloud storage

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