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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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Homomorphic Encryption in Quantum Computing

Homomorphic Encryption in Quantum Computing

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Devices for Integrated Circuit (DevIC)

作者： Surojit Mondal Bikash Debnath Jadav Chandra Das Debashis De Dept. of Computer Science & Engineering Jadavpur University Kolkata India Dept. of Computer Science & Engineering Asansol Engineering College West Bengal India Dept. of Information Technology MAKAUT Kolkata India Dept. of Computer Science and Engineering MAKAUT Kolkata India

ISBN: (数字)9798350391107

ISBN: (纸本)9798350391114

Homomorphic encryption (HE) is a groundbreaking cryptographic method that allows calculations on encrypted data without the need for decryption, thus safeguarding privacy and security. This article investigates how to implement homomorphic encryption with QISKIT, a free-to-use quantum computing framework. The research investigates the theoretical underpinnings of HE, its use in quantum computing, and its importance for privacy-preserving calculations. Moreover, the study covers adder circuits based on quantum logic gates, their realization with QISKIT, and the prospects for improving computational security in cloud environments. The results stress the practical viability of HE in quantum environments, underscoring its possible uses in secure data processing and privacy-enhancing technologies.

关键词： Data privacy Cloud computing Quantum computing Technology Quantum mechanics Logic gates Cryptography Homomorphic encryption Adders Stress

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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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Behavioral Analytics for Predictive Modeling of Mental Health Disorders: A Review of Machine Learning Techniques and Challenges

Behavioral Analytics for Predictive Modeling of Mental Healt...

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2025 International Conference on Intelligent Control, Computing and Communications, IC3 2025

作者： Vats, Prashant Kshirsagar, Pravin R. Upreti, Kamal Lalit, Keshav Tak, Tan Kuan Mahajan, Shubham Manipal University Jaipur Dept. of Computer Science and Engg Jaipur India J D College of Engineering & Management Department of Electronics & Telecommunication Engineering Nagpur India Dept. of Computer Science Delhi NCR India Manipal Institute of Technology Dept. of Computer Science and Engg Karnataka Manipal India Singapore Institute of Technology Singapore Amity University Gurugram India

ISBN: (纸本)9798331527495

Mental health issues, including anxiety, stress, and depression, may remain untreated until they escalate to a severe level. The issues significantly impact an individual's overall well-being and productivity. Timely identification is crucial for the effectiveness of both intervention and therapy. The application of machine learning techniques makes behavioral analytics a powerful tool for mental health disease prediction modeling. By analyzing behavioral data, this technology facilitates the early detection of various illnesses. This work aims to provide a thorough overview of the use of machine learning techniques, including models that employ Deep structured learning as well as both unsupervised as well as supervised learning, to behavioral data, including activity levels, speech patterns, and facial movements, in order to identify signs of mental health. The benefits and drawbacks of a broad range of machine learning algorithms are examined, with a focus on how these computer algorithms may be applied to identify patterns linked to illnesses like stress, anxiety, and emotional depression. This study looks into the problems that this business encounters as well. These difficulties include combining behavioral data with extra environmental issues and physiological features from the immediate surroundings, the necessity for large and diverse datasets, the need for security of information, and the capacity to understand models. © 2025 IEEE.

关键词： Supervised learning

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Artificial Intelligence for Green Agriculture and Sustainable Resource Management: A Comprehensive Overview

Artificial Intelligence for Green Agriculture and Sustainabl...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Eman Alduweib Waseem Alromema Essa Hezzam Dept. of Computer Science University of Petra Amman Jordan Dept. of Computer Science and Information Applied College Taibah University Medina Saudi Arabia Dept. of Information Systems College of Computer Science and Engineering Taibah University Medina Saudi Arabia

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Artificial Intelligence (AI) is a revolutionary technology disrupting the process of finding solutions for various sustainability challenges. Specifically, AI is having a significant influence on many sectors, especially agriculture, energy, waste management, and biodiversity conservation. This paper offers a comprehensive overview of AI's applications in sustainable resource management, with a special focus on green agriculture as a case study, in which the application of AI greatly enhances the efficiency of resource use, crop yield, and environmental conservation efforts. This overview describes the potential application of AI to address performance concerns (ethical value, energy use, and data quality deficiency), and proposes the future research directions such as the explainable AI, circular economies, and the integration of AI with the Internet of Things (IoT) to guide researchers and policymakers in exploiting AI to enhance sustainability in key domains.

关键词： Waste management Ethics Explainable AI Data integrity Transforms Resource management Internet of Things Biodiversity Artificial intelligence Sustainable development

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A Comparative Analysis of Deep Learning Based Approaches for DeepFake Identification

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Procedia computer science 2025年 259卷 482-493页

作者： Krity Duhan Abhishek Kajal Dept. of Computer Science and Engineering GJUS&T Hisar 125001 India

One of the major concerns in the present global environment is the creation and utilization of deepfakes. In this work, we have examined the issues and challenges that are created by the deepfake in the security and surveillance mechanisms. Deepfakes are used for by passing the facial identification or biometrics system that are normally used as surveillance mechanisms. Previously the only issue was to find the authentic person through the photographs that they have put on their identity proofs but nowadays it is extended to find the forged pics that are manipulated with the usage of AI based methods like deepfakes. Deepfakes are used in the current environments for bypassing the security by impersonation and providing false information and thus become a source of danger especially in the politics arena and entertainment landscape. Deep learning has the tendency to mitigate the impact of deepfakes to considerable extent by incorporating the same in the techniques develop for the identification of deepfakes. This paper provides the comprehensive review of the deepfake related work that has been done by researchers with the usage of deep learning approaches which could identify the fake images, videos to significant extent in various contexts along with the mechanism used for deepfake creation and identification in general. Moreover, the comparative analysis of the existing techniques has been done considering a range of factors like dataset used, technique used, accuracy, AUC, data type used for deepfake identification etc. It has been inferred that majority of the researchers have used the FaceForensics++, Celeb-DF, DFDC dataset and have utilized CNN technique for the deepfake identification primarily on images.

关键词： Deepfake CNN GAN Accuracy Deep learning Dataset AUC

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Bone Fracture Classification in X-ray Images: A Deep Learning Approach Leveraging Transfer Learning

Bone Fracture Classification in X-ray Images: A Deep Learnin...

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

作者： Md. Sabbir Ahammed Md. Musfiqur Rahman Mridha Md. Taufiq Khan Md. Jamil Chaudhary Md. Fatin Nibbrash Nakib Sakib Imtiaz Md. Ashikuzzaman Ashik Dept. of Computer Science & Engineering Varendra University Rajshahi Bangladesh Dept. of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Bone fracture is a very common medical issue faced by medical practitioners, and fracture detection is quite crucial for treatment. Till now, the most popular diagnosing method for bone fracture is X-ray imaging. However, in many cases, an X-ray image of a fractured bone is prone to human error. Advancements in artificial intelligence (AI) have demonstrated the potential to achieve high accuracy in tasks like X-ray image classification of fractured bone. In this work, we have incorporated a deep learning-based approach to facilitate the diagnosis of bone fracture. We have utilized different pre-trained state-of-the-art (SOTA) convolutional neural networks (CNN), namely, DenseNet, VGG, ResNet, Xception, and EfficientNet as feature extractors, to extract higher-level feature space representations of X-ray images. We have incorporated multiple classifiers, namely Logistic Regression, Random Forest, Linear Regression, XGBoost, and a custom feed-forward network (FFN) to discriminate among the higher-level representations extracted by the aforementioned pre-trained feature extractors. Among the several feature extractor and classifier combinations we have experimented with, DenseNet169 combined with the custom FFN produced the best results, reporting an overall accuracy of 99.48%, Receiver Operating Characteristic Area Under the Curve (ROC-AUC) score of 99.99%, precision of 99.48%, recall of 99.48%, and F1 score of 99.48%. The results demonstrate that combining an appropriate pretrained SOTA CNN model and a classifier can achieve high classification accuracy in the bone-fracture X-ray image classification task. Such a method previews a promising avenue in helping medical practitioners to achieve better diagnostic performance with patient benefit.

关键词： Deep learning Accuracy Transfer learning Feature extraction Bones Convolutional neural networks X-ray imaging Medical diagnostic imaging Random forests Image classification

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Multi-head Attention Fusion Network for Mpox Screening Using Skin Lesion Image Analysis

Multi-head Attention Fusion Network for Mpox Screening Using...

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

作者： Suparna Sarker Jahid Hasan Rony Ashifur Rahman Momotaz Begum Dept. of Computer Science and Engineering Dhaka University of Engineering & Technology Gazipur Dept. of Computer Science and Engineering Bangladesh University of Business and Technology Dhaka

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Monkeypox also known as Mpox is an infectious disease where screening is a vital task for early detection and effective management, particularly during recent outbreaks. Screening using skin lesions is a quick and convenient method. However, it can be challenging to differentiate Mpox from other diseases like chickenpox and measles in the early stages because their skin lesions can appear similar. This study introduces a multi-head Attention Based Fusion Network (MAFNet) to analyze skin lesion images for Mpox detection. The model achieves an accuracy of 89.81%, surpassing traditional architectures like DenseNet, VGG16, and ResNet. Using the multiple head attention mechanisms the proposed model enhances its focus on critical features within skin lesions and more accurately differentiates among various dermatological conditions. Besides that the model is trained not only on Mpox and healthy skin but also along with four other classes of similar diseases, enhancing its ability to generalize effectively. By the identification of Mpox without explicit clinical diagnosis and easy to use web app, this model helps early detection of Mpox patients. Such early identification is particularly valuable in high-traffic areas such as airports, where rapid decision-making is crucial. This approach aims to help prevent future pandemics similar to Covid-19, by promoting timely public health interventions.

关键词： Deep learning Image analysis Attention mechanisms Pandemics Measles Skin Lesions Public healthcare Standards Medical diagnostic imaging

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