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Maximizing Performance in Wireless Mobile Ad-Hoc Networks Through Unconventional Routing Strategies

SN computer science 2025年第2期6卷 1-13页

作者： Singh, Satyanand Sreelakshmy, R. Bhagyalakshmi, L. Shekhar, Himanshu Thanuja, Annamaraju Suman, Sanjay Kumar School of Electrical & Electronics Engineering Fiji National University Suva Fiji Dept. of ECE Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Tamil Nadu Chennai India Rajalakshmi Engineering College Tamil Nadu Chennai India Dept. of ECE Hindustan Institute of Technology and Science Tamil Nadu Chennai India Dept. of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur India Dept. of ECE and Dean R & D St. Martin’s Engineering College Telangana Secunderabad India

A Mobile Ad-Hoc Network (MANET) comprises wireless mobile nodes without a fixed infrastructure or central control. Nodes communicate within range, with distant nodes acting as intermediaries. Frequent connection failures occur due to node mobility. This work aims to enhance a novel MANET routing algorithm and assess its impact on selected multipath protocols. Prioritizing paths with higher residual energy optimizes battery usage, minimizing route lifetimes and communication delays. Energy and link quality considerations led to the development of an Energy and Link aware Ad hoc On-Demand Multipath Distance Vector (AOMDV) protocol, outperforming traditional AOMDV in energy efficiency. Additionally, a Reliable Link AOMDV protocol predicts connection failures based on received signal strength, pre-emptively switching paths to maintain data transfer reliability, ultimately improving packet transfer efficiency and productivity. These enhancements in route discovery contribute to overall routing quality improvement. Compared to AOMDV, EQL-simulation AOMDV’s results show that it uses 87% less energy when selecting a reliable link. The novel finding is that enhanced AOMDV protocols prioritizing energy and link quality improve energy efficiency and reliability in MANETs, reducing communication delays and maintaining stable data transfer through pre-emptive path switching. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： AOMDV EQL-AOMDV MANET REL-AOMDV

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Advancing Drug Discovery through a Deep Learning-Based Protein-Ligand Interaction Prediction Tool 3

Advancing Drug Discovery through a Deep Learning-Based Prote...

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3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Hunge, Sudhir S. Yadao, Aman Rajendra Shewate, Varsha Tekdas Lalsare, Prakash Namdeorao Prasannan, Shiji Ramulu, Kothuri Parashu Chintamani College of Science Pg Dept. of Chemistry Maharashtra Pombhurna India Shriram Arts And Science Junior College Dept. Of Chemistry Maharashtra Ramtek India Chintamani College of Science Dept. of Chemistry Maharashtra Pombhurna India Shri Prabhu Ramchandra Junior College Dept. of Chemistry Nanda Tah Korpana Maharashtra India Faculty of Management Studies Parul Institute of Management and Research Parul University Gujarat Vadodara India Indur Institute Of Engineering And Technology Dept. of Computer Science and Engineering India

ISBN: (纸本)9798331515911

In recent years, deep learning has emerged as a promising approach to advance drug discovery by accurately predicting interactions between proteins and small molecules, or ligands. The identification of these interactions is essential in developing drugs, as it informs us about a molecule's potential therapeutic efficacy. Traditional methods, like molecular docking, often struggle with accuracy and computational efficiency, limiting their application in high-throughput settings. In this paper, we propose a novel deep learning-based tool designed to predict protein-ligand interactions with enhanced precision and scalability. Our model leverages convolutional and graph neural networks to capture the complex, three-dimensional nature of these interactions. Using a large, curated dataset, we train and test the model to optimize binding affinity prediction, employing metrics such as mean squared error and accuracy to validate performance. The results show that our approach outperforms conventional techniques and recent machine learning models, demonstrating a significant improvement in predictive reliability. This tool has potential applications in screening vast compound libraries in early-stage drug discovery, reducing time and costs associated with experimental trials. By providing more accurate insights into protein-ligand affinities, our method represents a step forward in computational drug design, with implications for developing targeted therapies across various disease areas. © 2025 IEEE.

关键词： Graph neural networks

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Unpaired Shadow Removal: Enhancing Attention to Shadow Areas via Dropkey

Unpaired Shadow Removal: Enhancing Attention to Shadow Areas...

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

作者： Yang, Jaewon Shin, Ukcheol Cho, Donghyeon Chungnam National University Dept. of Electronics Engineering Daejeon Korea Republic of Robotics Institute Carnegie Mellon University Pittsburgh United States Hanyang University Dept. of Computer Science Seoul Korea Republic of

ISBN: (纸本)9798331510756

Shadows appear in image areas where an object obstructs the light path. These areas, having lower values than non-shadow areas, degrade image quality and lead to issues in object recognition and segmentation. Supervised shadow removal depends on datasets with shadow and shadow-free image pairs, sparking increased interest in unpaired techniques. However, unpaired shadow removal faces challenges in accurately focusing on shadow areas without direct supervisory signals from ground truth. Consequently, shadows are not effectively removed, and non-shadow areas may suffer from unintended distortions. In this paper, we introduce a transformer-based network designed to identify shadow areas accurately, leveraging global context through spatial and channel attention mechanisms. Additionally, in the training phase, the transformer network is trained to precisely concentrate on shadow areas using a domain classifier and a dropkey mechanism, which randomly drops features of the keys to enhance focus. Our method is tested across several benchmark datasets for shadow removal, such as ISTD, ISTD+, SRD and WSRD, demonstrating better performance compared to current unpaired approaches. © 2025 IEEE.

关键词： Image segmentation

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Investigation of the Coupling Characteristics of Wheel Polygon and Rail Corrugation in High-Speed Railways 3

Investigation of the Coupling Characteristics of Wheel Polyg...

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3rd International Conference on Rail Transportation, ICRT 2024

作者： Gao, Wanlin Yin, Yue Bu, Han Wang, Shiqi Cui, Xiaolu Dept. of Computer Science and Technology Chongqing Jiaotong Univ. Chongqing China Dept. of Rail Transit Chongqing Vocational College of Transportation Research State Univ. Chongqing China Dept. of Mechanical and Electrical Engineering and Vehicle Engineering Chongqing Jiaotong Univ. Chongqing China

ISBN: (数字)9780784485941

ISBN: (纸本)9780784485941

Wheel polygon and rail corrugation are common types of periodic wear in the wheel-rail system. This research investigates their interaction in high-speed railways using friction coupling vibration theory. A finite element model of the wheel-rail system is developed to study friction coupling vibrations in straight sections of long slopes. The study identifies the causes of 22nd-order wheel polygonization and rail corrugation with a 130 mm wavelength and creates wear models for both. The effects of their interaction on friction coupling vibrations are analyzed, with an emphasis on frequency-dependent coupling characteristics. Results indicate that saturated wheel-rail creep forces in long slopes easily trigger friction coupling vibrations, which are worsened by both wheel polygon and rail corrugation, with wheel polygon having a more pronounced impact. These findings offer insights for improving wheel-rail system design and maintenance in high-speed railways. Future research should aim to develop advanced techniques to mitigate the negative effects of wheel polygon and rail corrugation on system performance and safety. © ASCE.

关键词： Railroads

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Event Identification and Chronology Model of Ramayana

Event Identification and Chronology Model of Ramayana

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International Conference on Electronics, Materials engineering and Nano-Technology (IEMENTech)

作者： Apurba Paul Rishabh Mishra Nayan Padia Dipankar Das Dept. of Computer Science & Engineering IEM Kolkata UEM Kolkata Kolkata India Dept. of Computer Science & Engineering IEM Kolkata Kolkata India Dept. of Computer Science & Engineering Jadavpur University Kolkata India

ISBN: (数字)9798331520762

ISBN: (纸本)9798331520779

The paper introduces an event chronology model for Hindu mythological texts, with a focus on the Ramayana's Bala-kanda. It captures and structures sentence-level events based on their chronological order to better understand narrative progression and character interactions. A manually crafted dataset of 1217 events, involving 214 characters across 75 chapters, is used to generate a knowledge graph. The graph organizes events by sequence, linking characters, actions, and chapters within the storyline. The event chronology is mapped by establishing relationships between events, ensuring they are connected in the proper sequence to reflect the narrative flow. This approach enables a deeper understanding of how each event contributes to the overarching story, providing insights into both the individual actions of characters and their significance within the broader mythological narrative. Transformer-based models (BERT, DistilBERT, and RoBERTa) were utilized for predicting subsequent events, with RoBERTa showing the highest accuracy in preserving the correct event chronology. This structure is key to analyzing the sequential development of the Ramayana's storyline.

关键词： Accuracy Annotations Refining Knowledge graphs Manuals Predictive models Transformers Guidelines

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PhishVQC: Optimizing Phishing URL Detection with Correlation Based Feature Selection and Variational Quantum Classifier

PhishVQC: Optimizing Phishing URL Detection with Correlation...

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International Symposium on Advanced Computing and Communication (ISACC)

作者： Md. Farhan Shahriyar Gazi Tanbhir Abdullah Md Raihan Chy Mohammed Abdul Al Arafat Tanzin Md. Jisan Mashrafi Dept. of Computer Science and Engineering World University of Bangladesh Dhaka Bangladesh Dept. of Computer Science and Engineering Southern University Bangladesh Chittagong Bangladesh Dept. of Computer Science and Engineering Brac University Dhaka Bangladesh

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Phishing URL detection is crucial in cybersecurity as malicious websites disguise themselves to steal sensitive information. Traditional machine learning techniques struggle to perform well in complex real-world scenarios due to large datasets and intricate patterns. Motivated by quantum computing, this paper proposes using Variational Quantum Classifiers (VQC) to enhance phishing URL detection. We present PhishVQC, a quantum model that combines quantum feature maps and variational ansatzes such as RealAmplitude and EfficientSU2. The model is evaluated across two experimental setups with varying dataset sizes and feature map repetitions. PhishVQC achieves a maximum macro average F1-score of 0.89, showing a 22% improvement over prior studies. This highlights the potential of quantum machine learning to improve phishing detection accuracy. The study also notes computational challenges, with execution wall times increasing as dataset size grows.

关键词： Uniform resource locators Quantum computing Correlation Accuracy Phishing Computational modeling Machine learning Feature extraction Intelligent systems

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PhishVQC: Optimizing Phishing URL Detection with Correlation Based Feature Selection and Variational Quantum Classifier

arXiv

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arXiv 2025年

作者： Farhan Shahriyar, Md. Tanbhir, Gazi Chy, Abdullah Md Raihan Tanzin, Mohammed Abdul Al Arafat Jisan Mashrafi, Md. Dept. of Computer Science and Engineering World University of Bangladesh Dhaka Bangladesh Dept. of Computer Science and Engineering Southern University Bangladesh Chittagong Bangladesh Dept. of Computer Science and Engineering Brac University Dhaka Bangladesh

—Phishing URL detection is crucial in cybersecurity as malicious websites disguise themselves to steal sensitive information. Traditional machine learning techniques struggle to perform well in complex real-world scenarios due to large datasets and intricate patterns. Motivated by quantum computing, this paper proposes using Variational Quantum Classifiers (VQC) to enhance phishing URL detection. We present PhishVQC, a quantum model that combines quantum feature maps and variational ansatzes such as RealAmplitude and EfficientSU2. The model is evaluated across two experimental setups with varying dataset sizes and feature map repetitions. PhishVQC achieves a maximum macro average F1-score of 0.89, showing a 22% improvement over prior studies. This highlights the potential of quantum machine learning to improve phishing detection accuracy. The study also notes computational challenges, with execution wall times increasing as dataset size grows. Copyright © 2025, The Authors. All rights reserved.

关键词： Phishing

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Federated Learning in Healthcare: A Privacy-Preserving Approach to Predictive Analytics

Federated Learning in Healthcare: A Privacy-Preserving Appro...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Reddy, M. Jithender Kotiyal, Arnav Lakhanpal, Sorabh Badhoutiya, Arti Mounika, T. Jayanthi, Muthuswamy Vasavi College of Engineering Dept. of Computer Science and Engineering Telangana Hyderabad India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Lovely Professional University Punjab Phagwara India GLA University Department of Electrical Engineering Mathura India MLR Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India New Horizon College of Engineering Department of Electronics and Communication Engineering Bangalore India

ISBN: (纸本)9798331512965

Technology such as machine learning in healthcare has pushed privacy concerns to the point that the handling of sensitive patient data has become paramount. In the context of FL, predictive analytics without data sharing between institutions can be performed through the process. Next, we explore the application of FL in healthcare in order to maintain data privacy while obtaining robust predictive models. FL utilizes aggregated decentralized data from multiple healthcare providers to reduce the chance of data breaches and continue to be compliant with rigorous regulatory frameworks, like HIPAA and GDPR, while doing so. In this work, we evaluate the model performance of FL models in predicting different types of health outcomes in comparison with traditional centralized models. We show that FL can achieve similar predictive accuracy while maintaining data privacy, which makes the choice of FL as a viable alternative to privacy sensitive healthcare environments. Second, applying FL is very challenging: there are communication overhead, data heterogeneity, and security risks. This study also considers ways to minimize these challenges. The results demonstrate the promise of Federated Learning as an innovative paradigm for applying predictive analytics in healthcare, indicative of a robust shift toward more secure, efficient and privacy preserving healthcare systems. © 2025 IEEE.

关键词： Federated learning

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Integrated Framework for Equitable Healthcare AI: Bias Mitigation, Community Participation, and Regulatory Governance

Integrated Framework for Equitable Healthcare AI: Bias Mitig...

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International Conference on Communication Systems and Network Technologies (CSNT)

作者： Joydeep Chandra Ramanjot Kaur Rashi Sahay Dept. of Computer Science and Engineering Chandigarh University Mohali Punjab India Dept. of Computer Science and Engineering MRIIRS Faridabad India

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

Artificial intelligence (AI) has revolutionized healthcare by enhancing diagnostic precision, personalizing treatments, and optimizing clinical workflows. However, algorithmic biases arising from imbalanced training data dis-proportionately affect marginalized populations, perpetuating health disparities. For instance, a widely-used predictive model underprioritized Black patients by equating healthcare costs with illness severity, restricting access to essential care. To address these challenges, this paper introduces an integrated framework combining adversarial debiasing, reinforcement learning (RL)-driven threshold optimization, synthetic data augmentation, and community engagement strategies. Empirical evaluations using the MIMIC-III dataset demonstrate near-perfect Area Under the ROC Curve (AUC = 0.994) alongside significant reductions in demographic parity (DP = 0.000) and error rate differences across subgroups. While the RL-optimized threshold ensures no missed diagnoses, it increases false positives, highlighting the trade-offs between fairness and precision. This work underscores the importance of synthesizing technical, regulatory, and community-driven approaches to promote equitable AI deployments. By embedding participatory design principles and aligning with emerging governance frameworks like the European Union’s AI Act, the proposed methodology offers a scalable pathway toward transparent, accountable, and high-impact healthcare AI solutions.

关键词： Ethics Prevention and mitigation Training data Medical services Reinforcement learning Prediction algorithms Optimization Standards Synthetic data Guidelines

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Classifying white blood cells using combining different convolutional neural networks

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Multimedia Tools and Applications 2025年 1-24页

作者： Toptaş, Murat Toptaş, Buket Hanbay, Davut Software Eng. Dept. Engineering and Natural Science Faculty Bandırma Onyedi Eylül University Balıkesir Turkey Computer Eng. Dept. Engineering Faculty Inonu University Malatya44280 Turkey

White blood cells are warrior cells that protect the human body against external factors. Each of these warrior cells performs a distinct task, making every piece of information about them highly valuable in the medical field. In this article, a classification framework for the four known types of white blood cells is proposed. It is hoped that the classification of these types will contribute to the prediction of diseases such as AIDS, malaria, leukemia, and many others. In the proposed method, images of white blood cells from the Blood Cell Classification and Detection dataset were used as input to Convolutional Neural Networks. The feature vectors extracted using these Convolutional Neural Network architectures were combined into a single vector. A Minimum Redundancy Maximum Relevance algorithm was then employed to identify the most effective features within the feature vector. Experiments were conducted using these selected features, and the analysis of each experiment was reported in detail. The Support Vector Machines classifier achieved an accuracy of 98.63% in classifying white blood cell types by combining features from multiple deep learning architectures. The experimental results demonstrated that the features obtained from different layers of the Convolutional Neural Networks had varying impacts on the classification performance. © The Author(s) 2025.

关键词： Convolutional neural networks

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