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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Md. Ismiel Hossen Abir Nurul Bashar Md. Shaek Ahmed Hasibul Islam Peyal Dept. of Computer Science & Engineering International Standard University Dhaka Bangladesh Dept. of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

The accurate identification of diseases based on patient symptoms and demographic data is a critical area of healthcare research, with the potential to significantly improve patient outcomes. In this study, we employed machine learning algorithms, specifically the XGBClassifier, to classify diseases based on a dataset containing patient symptoms and demographic information such as age, gender, fever, cough, fatigue, and other health indicators. To ensure model interpretability and transparency, we incorporated Explainable AI (XAI) techniques like SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations), which allow us to understand and interpret the feature contributions to the model’s predictions. The results demonstrated that the XGBClassifier achieved an accuracy of 81.42%, outperforming other machine learning models tested. This study emphasizes the importance of combining XAI with machine learning for disease classification, offering greater transparency in the decision-making process, which is vital in healthcare settings.

关键词： Support vector machines Electric potential Accuracy Machine learning algorithms Explainable AI Medical services Predictive models Fatigue Convolutional neural networks Diseases

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Enhancing Concrete Performance: Machine Learning Models for Predicting and Classifying Tensile Strength

Enhancing Concrete Performance: Machine Learning Models for ...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Mayaz Uddin Gazi Nahidul Islam Rupom Gourab Bose Durjoy Gourab Roy Nafiz Nahid Md Mehrab Hossain Md. Titumir Hasan Dept. of Civil Engineering Sylhet Engineering College Sylhet Bangladesh Computer Science & Engineering Dept. Leading University Sylhet Bangladesh Computer Science & Engineering Dept. Sylhet Engineering College Sylhet Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Accurate prediction and detection of concrete’s mechanical properties, such as tensile strength are critical for optimizing structural performance. For performing this study, a comprehensive dataset, including cement content, water-cement ratio, aggregate types, and additives were utilized. This study introduces a novel approach that integrates Machine Learning (ML) techniques and novel proposed model to predict and classify the tensile strength of concrete, combining both tasks for a more Comprehensive evaluation. Advanced models, such as XGBoost and ANNs, were fine-tuned using hyperparameter optimization to enhance predictive accuracy. The models demonstrated significant improvements over traditional regression methods, with XGBoost achieving an R 2 of 0.9993 and the lowest MSE (0.0129) and RMSE (0.0013). For classification, the proposed ANN model achieved 99.89% accuracy. This study introduces a novel machine learning framework integrating prediction and classification tasks, Although, the research not only showcases the superior performance of these models but also highlights their potential to reduce the need for extensive physical in concrete engineering. By integrating machine learning with feature engineering, this study paves the way for more efficient, data-driven design processes and broader applications in the construction materials science.

关键词： Materials science and technology Accuracy Machine learning Artificial neural networks Predictive models Hyperparameter optimization Concrete Mechanical factors Tuning Overfitting

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Blockchain Integration for Robust Intellectual Property Protection in New Product Development (NPD) 3

Blockchain Integration for Robust Intellectual Property Prot...

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3rd International Conference on Electronics and Renewable systems, ICEARS 2025

作者： Chinnasamy, P. Subashini, B. Nijanthan, Nijanthan Madasamy Raja, G. Swetha, Devulapally Sreenivasulu Reddy, L. School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu India School of Computing Srmist Department of Data Science and Business Systems Kattankulathur India Paavai Engineering College Tamilnadu Namakkal India Paavai Engineering College Tamilnadu Namakkal India Vaagdevi Engineering College Dept. of Computer Science and Engineering Warangal Dist. Telangana Bolli Kunta506005 India School of Advanced Sciences Kalasalingam Academy of Research and Education Department of Mathematics Tamilnadu Krishnankoil India

ISBN: (纸本)9798331509675

The rapid advancement of Blockchain technology has led to its widespread application in several domains of digital activities, such as e-government concerns and monetary security. This article presents a smart contract-based Blockchain solution for protecting intellectual property rights in new product development. The proposed solution effectively tackles the challenges associated with the confidentiality of personally identifiable information, efficiency in storing transaction-related information, as well as cooperation and monitoring in IPR. Initially, we create a mathematical model based on smart contract enabled Blockchain and intelligent contracts. Subsequently, we develop algorithms for Blockchain generation, and intelligent contracts. The system is suitable for protecting IPR in a blockchain network, by implementation of distributed identity authentication and real-time extensible storage. These features enhance the security and expandability of circuit copyright protection based on blockchain technology. The experimental results demonstrate that the suggested algorithm has decreased the cost for communication and enhanced the effectiveness of storing information and administration. Furthermore, it demonstrates resilience against many common attacks such as double-spending assaults, while maintaining low cost and overhead. Additionally, it exhibits a superior level of security in comparison to three other competing algorithms. © 2025 IEEE.

关键词： Smart contract

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Smith Predictor Based Fractional LFC Strategy for Storage Supported Thermal Power System Amid Communication Dead Time and Cyber Threats

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IEEE Transactions on Industry Applications 2025年第3期61卷 4515-4529页

作者： Kumar, Deepak Raja, G. Lloyds Alkhatib, Mohamed Deep, Akash Kumar Muduli, Utkal Ranjan United Arab Emirates University Department of Electrical and Communication Engineering Abu Dhabi Al Ain15551 United Arab Emirates National Institute of Technology Department of Electrical Engineering Patna800005 India Khalifa University APEC Dept. of Electrical Engineering and Computer Science Abu Dhabi127788 United Arab Emirates

Communication dead time (CDT) amid integration of renewable energy sources (RESs) and energy storage systems (ESSs) into modern power system networks can result in instabilities, phase lags and sluggish corrective action when dealing with frequency and tie-line power deviations. To compensate CDT, a Smith predictor based Fractional Order Proportional-Integral-Derivative with Integrator (FPIDI) controller is analytically designed for a dual-area thermal power system (TPS) with integrated RES, battery ESS, flywheel ESS and Ultra-capacitors. The FPIDI controller is designed using a modified internal model control (IMC) theory that is based on the frequency-relocated fractional-order model of the TPS. This modified IMC method eliminates the need of phase-lag inducing IMC filter. The performance-robustness tradeoff is enhanced by appropriate selection of the single tuning parameter (frequency-relocation factor (ψ)) by minimizing the integral of time-weighted absolute value of area control errors. The proposed controller is validated both by simulation and hardware-in-loop implementation to show its practical feasibility. © 1972-2012 IEEE.

关键词： Hardware-in-the-loop simulation

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Machine Learning Based Phishing Website Detection System - Natural Language Processing Approach 2

Machine Learning Based Phishing Website Detection System - N...

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2nd International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Patni, Jagdish Chandra Naik, S. Vinay Harika, K. Bahadure, Nilesh Bhaskarrao Verma, Kamal Kant Dept. of CSE Alliance School of Advanced Computing Alliance University Bengaluru India Department of Computer Science and Engineering School of Technology GSFC University Gujarat Vadodara India School of Computer Science IILM University Greater Noida India

ISBN: (纸本)9798331530389

Natural language processing (NLP) methods can be used to identify phishing websites in addition to static and dynamic features. Phishing sites frequently include certain phrases, misspellings, or misleading text patterns in their material, as well as other visual cues and language that match those of authentic websites. Through the examination of textual components such anchor texts, meta-descriptions, and homepage titles. The proposed model is able to comprehend the linguistic traits that are frequently seen in phishing websites using NLP-based features like sentiment analysis, keyword matching, and phrase frequency to increase model's sensitivity. In addition, we use real-time URL parsing to detect possible dangers as users come across them, guaranteeing prompt notifications and lowering the possibility that phishing attempts would be successful. Our study emphasizes how crucial it is to keep improving and upgrading phishing detection models because attackers usually change their strategies. © 2025 IEEE.

关键词： Phishing

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Advanced Medical Question and Answering Chatbot Powered by Llama-2 For Precis Health Answer 2

Advanced Medical Question and Answering Chatbot Powered by L...

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2nd International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Patni, Jagdish Chandra Anup, S.B. Swetha Chitra, K.S. Poornima, B.M. Raviprakash, B.P. Kant Verma, Kamal Dept. of CSE Alliance School of Advanced Computing Alliance University Bengaluru India Department of Computer Science and Engineering School of Technology GSFC University Gujarat Vadodara India School of Computer Science IILM University Greater Noida India

ISBN: (纸本)9798331530389

LLaMA-2 model, which is known for having the best capabilities in natural language processing at the core of our system. With respect to symptom diagnosis, it ranked among the most accurate in being able to give the chances of as many conditions possible as per the information that it receives from the user. Creating custom responses to unique user queries all the time, which may potentially increase user satisfaction and engagement. We have tested and perfected our chatbot to ensure that the capabilities stood out at a level of accuracy as high as 85 percent-showing outstanding care in the matters of healthcare assistance issues under numerous medical topics. Our work adds advanced features with the improvement techniques to the conventional medical Q and A chatbot. The new deep learning chatbot is not only great for symptom diagnosis but also uses its capabilities to provide context-aware responses based on user health history, lifestyles, and preferences with adjustment of its advice for a more personal healthcare experience. © 2025 IEEE.

关键词： Chatbot LLaMA-2 model medical Question and Answers Natural language processing (NLP) symptom identification

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Key Exchange in the Quantum Era: Evaluating a Hybrid System of Public-Key Cryptography and Physical-Layer Security 59

Key Exchange in the Quantum Era: Evaluating a Hybrid System ...

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59th Annual Conference on Information sciences and systems, CISS 2025

作者： Staat, Paul Dorpinghaus, Meik Sheikholeslami, Azadeh Paar, Christof Fcllwcis, Gerhard Goeckel, Dennis Max Planck Institute for Security and Privacy Bochum Germany Technische Universität Dresden Vodafone Chair Mobile Communications Systems Dresden Germany Suffolk University Math and Computer Science Department Boston United States University of Massachusetts Dept. Electrical and Computer Engineering Amherst United States

ISBN: (纸本)9798331513269

Today's information society relies on cryptography to achieve security goals such as confidentiality, integrity, authentication, and non-repudiation for digital communications. Here, public-key cryptosystems play a pivotal role to share encryption keys and create digital signatures. However, quantum computers threaten the security of traditional public-key cryptosystems as they can tame computational problems underlying the schemes, i.e., discrete logarithm and integer factorization. The prospective arrival of capable-enough quantum computers already threatens today's secret communication in terms of their long-term secrecy when stored to be later decrypted. Therefore, researchers strive to develop and deploy alternative *** this work, we evaluate a key exchange protocol based on combining public-key schemes with physical-layer security, anticipating the prospect of quantum attacks: If a powerful quantum attacker cannot immediately obtain a private key, legitimate parties have a window of short-term secrecy to perform a physical-layer jamming key exchange (JKE) to establish a long-term shared secret. Thereby, the protocol constraints the computation time available to the attacker to break the employed public-key cryptography. In this paper, we outline the protocol, discuss its security, and point out challenges to be resolved. © 2025 IEEE.

关键词： Quantum computers

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Quantum Key Distribution Protocol with Enhanced Eavesdropper Detection Ability

Quantum Key Distribution Protocol with Enhanced Eavesdropper...

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Devices for Integrated Circuit (DevIC)

作者： Tamal Deb Jyotsna Kumar Mandal Deeptanu Sen Dept. of Computer Science Barasat College Kolkata India Dept. of Computer Science and Engineering University of Kalyani Kalyani India

ISBN: (数字)9798350391107

ISBN: (纸本)9798350391114

Quantum key distribution protocols printed a prominent mark in the old path of digital communication by making it even more secure. Unlike its classical counterpart, quantum key distribution protocols exploit the key principles of quantum elements, such as superposition and entanglement, to expose the intrusion in an efficient way. This paper presents a prepare and measure quantum key distribution protocol that detects the eavesdropper more efficiently by inflating bit mismatch between the sender and receiver. The proposed work achieves bit mismatch up to 62.5 percent while Eve is present in the quantum communication channel. The improvement is remarkable in comparison with the classical BB84 protocol, which has a bit mismatch up to 37.5 percent in presence of Eve.

关键词： Integrated circuits Protocols Quantum entanglement Receivers Digital communication Quantum key distribution Quantum communication

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Faster R-CNN in Healthcare and Disease Detection: A Comprehensive Review

Faster R-CNN in Healthcare and Disease Detection: A Comprehe...

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2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

作者： Tian, Jiawei Lee, Seungho Kang, Kyungtae Major in Bio Artificial Intelligence Hanyang University Dept. of Computer Science and Engineering Ansan-si Korea Republic of Hanyang University Dept. of Applied Artificial Intelligence Ansan-si Korea Republic of Hanyang University Dept. of Artificial Intelligence Ansan-si Korea Republic of

ISBN: (纸本)9798331510756

This review examines the applications, challenges, and prospects of Faster Region-based Convolutional Neural Networks (Faster R-CNN) in healthcare and disease detection. Through a meta-analysis of Web of science literature from 2017 to 2024, we provide insights into the evolving landscape of Faster R-CNN in medical contexts. The algorithm can be applied to medical image analysis across various modalities, including radiography, computed tomography, magnetic resonance imaging, ultrasound, microscopy, and endoscopy. Its applications extend to optical RGB images for dermatological and surgical uses, as well as broader healthcare areas such as posture detection, medication recognition, and assistive technologies. Despite its success, Faster R-CNN faces challenges in handling subtle abnormalities, addressing class imbalance in medical datasets, ensuring result interpretability, and managing patient privacy. Our analysis reveals dominant application areas, technological advancements, and integration trends with other artificial intelligence technologies. The review highlights Faster R-CNN's significant impact on improving diagnostic accuracy and healthcare delivery while acknowledging the need for continued research to address limitations. Emerging directions include real-time disease detection and advancements in personalized medicine, underscoring Faster R-CNN's potential to further transform healthcare practices and patient outcomes. © 2025 IEEE.

关键词： computerized tomography

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Knee Osteoarthritis detection and classification using a customized CenterNet with DenseNet201 3

Knee Osteoarthritis detection and classification using a cus...

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3rd International Conference on Intelligent systems, Advanced Computing, and Communication, ISACC 2025

作者： Shirisha, N. Shilpa, K. Meghana, C.H. Rahman, Mohammed Abdul MLR Institute of Technology Dept. of Computer Science and Engineering Hyderabad India MLR Institute of Technology Dept. of Computer Science and Engineering Nalgonda India

ISBN: (纸本)9798331523893

Knee osteoarthritis (KOA) is a prevalent joint disorder diagnosed using imaging modalities like MRI, CT scans, and X-rays, with X-rays being the most cost-effective. Early detection is crucial for effective management. This study presents an automated deep learning approach to detect and classify KOA severity based on the Kellgren-Lawrence (KL) grading system using single posteroanterior standing knee X-ray images. Utilizing the Osteoarthritis Initiative dataset, we employed transfer learning to fine-tune DenseNet-201, enhancing model performance. Additionally, knowledge distillation was applied to reduce computational complexity while maintaining accuracy. Our model achieved over 95% accuracy on both testing and cross-validation datasets, outperforming existing methods. This approach offers a reliable tool for early KOA diagnosis and grading, potentially aiding clinical decision-making © 2025 IEEE.

关键词： Diagnostic radiography

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