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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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DISTANCE BETWEEN SPECIES BY CONFUSION OPERATORS OF MULTI-CLASS CLASSIFIERS

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UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2025年第1期2025卷 171-182页

作者： Tarbă, Nicolae Irimescu, Ionela N. Pleavă, Ana M. Scarlat, Eugen N. Mihăilescu, Mona Doctoral School Computer Science and Engineering Department Faculty of Automatic Control and Computers National University of Science and Technology POLITEHNICA Bucharest Romania Applied Sciences Doctoral School National University of Science and Technology POLITEHNICA Bucharest Romania CAMPUS Research Center National University of Science and Technology POLITEHNICA Bucharest Romania Physics Dept National University of Science and Technology POLITEHNICA Bucharest Romania Physics Dept Research Center for Applied Sciences in Engineering National University of Science and Technology POLITEHNICA Bucharest Romania

We introduce a method to evaluate the similarities between classes of objects based on the confusion matrices coming from the multi-class machine learning (ML) predictors that operate in the vector space generated by the classes. Operation in the confusion domain allows us to use the distance as evaluation metric of the closeness of the relevant properties carried by the classes. We applied the method to semiquantitatively evaluate the effects of X-ray and carbon ion radiation on cultured SW1353 chondrosarcoma cells with respect to the non-irradiated reference. We prove that it is possible to draw useful information on the strength of cells nuclei changes. © 2025, Politechnica University of Bucharest. All rights reserved.

关键词： Radiation effects

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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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A Deep Learning Approach for Classifying Retinal Diseases and Diabetic Retinopathy Stages to Enhance Early Detection in Diagnosis

A Deep Learning Approach for Classifying Retinal Diseases an...

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

作者： Shifa Tasmiah Tisha Sharmistha Chanda Tista Md Nazmul Hoq Dept. of Computer Science and Engineering Chittagong University of Engineering & Technology Chattagram Bangladesh Dept. of Computer Science and Engineering Concordia University Montreal Canada

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Diabetic Retinopathy (DR) is a common consequence of diabetes mellitus, resulting in retinal lesions that can adversely affect vision. If undetected and mistreated, diabetic retinopathy can ultimately result in blindness. This study concentrates on creating an enhanced model for the precise classification of retinal illnesses, particularly Diabetic Retinopathy. The suggested model increases the precision and effectiveness of disease classification by utilising deep learning methods and attention mechanisms. The study analyses the performance of many pre-trained models, such as ResNet152, EfficientNetB7, and MobileNetV3Large, using a varied dataset of retinal pictures. The ResNet152 model, enhanced with the Spatial Attention Module, achieved the best results in retinal disease classification, with an accuracy of 87.65(%), precision of 89.88(%), recall of 90.69(%), and F1-score of 88.58(%). This performance surpasses other models and demonstrating the effectiveness of attention modules in improving classification accuracy and robustness. The study also highlights the competitive performance of the suggested model by contrasting it with earlier studies in the field. All things considered, this research advances the classification of retinal diseases and may improve patient treatment and ophthalmology diagnostic precision.

关键词： Deep learning Diabetic retinopathy Accuracy Transfer learning Retina Software Robustness Medical diagnostic imaging Diseases Residual neural networks

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Animated Virtual Therapist in Virtual Reality Environment for People with Parkinson's Disease 7

Animated Virtual Therapist in Virtual Reality Environment fo...

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7th IEEE International Conference on Artificial Intelligence and eXtended and Virtual Reality, AIxVR 2025

作者： Li, Wenting Agada, Ruth Yan, Jie Washington University Biomedical Engineering Dept. St. Louis United States Bowie State University Department of Computer Science Bowie United States

ISBN: (纸本)9798331521578

This study investigates the effectiveness of virtual rehabilitation using a 3D animated virtual therapist delivering the LSVT-BIG program for people with Parkinson's disease (PwPD). Integrating an animated virtual therapist in a VR environment aims to overcome accessibility barriers, especially for individuals in remote areas. This VR-enhanced system provides immersive experiences, real-time feedback, and posture correction through computer vision, along with progress-tracking via personalized reports. This approach offers potential improvements in accessibility, engagement, and motivation, which may contribute to enhanced quality of life for individuals with Parkinson's disease. © 2025 IEEE.

关键词： Virtual environments

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Unique MS Lesion Identification from MRI

Unique MS Lesion Identification from MRI

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Medical Imaging 2025: Image Processing

作者： Rivas, Carlos A. Zhang, Jinwei Wei, Shuwen Remedios, Samuel W. Carass, Aaron Prince, Jerry L. Image Analysis and Communications Laboratory Dept. of Electrical and Computer Engineering Johns Hopkins University BaltimoreMD21218 United States Dept. of Computer Science Johns Hopkins University BaltimoreMD21218 United States

ISBN: (纸本)9781510685901

Unique identification of multiple sclerosis (MS) white matter lesions (WMLs) is important to help characterize MS progression. WMLs are routinely identified from magnetic resonance images (MRIs) but the resultant total lesion load does not correlate well with EDSS∗;whereas mean unique lesion volume has been shown to correlate with EDSS. Our approach builds on prior work by incorporating Hessian matrix computation from lesion probability maps before using the random walker algorithm to estimate the volume of each unique lesion. Synthetic images demonstrate our ability to accurately count the number of lesions present. The takeaways, are: 1) that our method correctly identifies all lesions including many that are missed by previous methods;2) we can accurately separate confluent lesions;and 3) we can accurately capture the total volume of WMLs in a given probability map. This work will allow new more meaningful analyses to be computed from WMLs in brain MRIs. © 2025 SPIE.

关键词： Ferrimagnetic resonance

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