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

作者： Amira Hossain Anamul Sakib Al Shahriar Uddin Khondakar Pranta Jesika Debnath Md Tanvir Rahman Tarafder Sazzadul Islam Shamim Forhad Md. Redwan Ahmed Rezaul Haque Shafiur Rahman Dept. of Computer Science Westcliff University Irvine CA USA Dept. of Business Administration International American University CA USA Dept. of Computer Science Wright State University Dayton OH USA Dept. of Information Technology Westcliff University Irvine California Dept. of Computer Science and Engineering BRAC University Dhaka Bangladesh Dept. of Electrical and Electronics Engineering American International University Bangladesh Dept. of Computer Science and Engineering East West University Dhaka Bangladesh Dept. of Software Engineering Daffodil International University Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Early and accurate segmentation of oral cancer is essential for timely diagnosis and treatment. Traditional methods like visual inspections and biopsies are often subjective and costly, which can hinder early detection. To improve segmentation accuracy for binary and multiclass classification, we propose a transformer-based ensemble model that combines Vision Transformer (ViT), Data-efficient Image Transformer (DeiT), Swin Transformer, and BEiT. This ensemble utilizes self-attention mechanisms for better feature extraction and spatial representation. Our study employs two datasets: the MOD dataset (463 images of oral diseases) and a histopathological dataset (1,224 images of oral squamous cell carcinoma and normal epithelium). We applied extensive preprocessing and augmentation techniques, such as grayscale conversion, binary thresholding, and Contrast Limited Adaptive Histogram Equalization (CLAHE), to enhance image quality and model generalization. The performance evaluation showed that our ensemble model outperformed individual architectures, achieving an Intersection over Union (IoU) of 0.9601 and a Dice Coefficient of 0.9598 for binary segmentation, and IoU of 0.9587 and Dice Coefficient of 0.9575 for multiclass segmentation. A comparative analysis with state-of-the-art models confirmed the effectiveness of our approach. These results demonstrate the potential of transformer-based ensemble learning for oral cancer diagnosis, presenting a scalable tool for clinical applications. Future work will focus on expanding dataset diversity, optimizing computational efficiency, and integrating real-time inference for improved usability in healthcare.

关键词： Image segmentation computer vision Adaptation models Visualization Accuracy Computational modeling Transformers Cancer detection Real-time systems Cancer

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Refractive Index Biosensor–Based Detection of Mycobacterium Tuberculosis Using Sea Lion Political Optimizer and Deep Learning

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Plasmonics 2025年 1-19页

作者： Ragavan, V. K. Senthil Nithya, N.S. Vengala, Anantharamaiah Subramanian, Balambigai Ambhika, C. Sri Shanmugha College of Engineering and Technology Tamil Nadu Sankari Salem (D.T.) 637 304 India Department of Software Systems School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of C S E K L E F Andhra Pradesh Vaddeswaram Guntur 522502 India Dept of ECE Karpagam College of Engineering Tamil Nadu Coimbatore India Department of AIML R.M.D Engineering College RSM Nagar Tamil Nadu Kavarapetai India

Mycobacterium tuberculosis (MTB) detection is a major global health concern, requiring accurate and reliable diagnostic tools. According to the World Health Organization (WHO), the global burden of TB is staggering, with approximately 10 million new cases reported annually, leading to a devastating 1.5 million fatalities worldwide every year. The timely detection and treatment of TB are critical components in the fight against this disease, as they play a vital role in preventing its transmission, minimizing the risk of complications and deaths, and enhancing the overall quality of life for affected individuals. Hence, this study presents a novel Sea Lion Political Optimizer_Deep Maxout Network (SLnPO_DMN) for detecting MTB, leveraging a robust processing pipeline to achieve exceptional accuracy. The proposed pipeline involves a series of meticulous steps, including data pre-processing by Min–Max normalization to scale the data to a common range. To enhance model generalizability, data augmentation is carried out by Bootstrap, generating additional training samples by resampling the original data with replacement. The augmented data is then fed into a DMN, optimized by the SLnPO, a hybrid algorithm combining the strengths of sea lion optimization (SLnO) and political optimizer (PO). The SLnPO_DMN model is trained to identify and learn intricate patterns within the data, ultimately attaining optimal performance and demonstrating exceptional capability in detecting Mycobacterium tuberculosis with high accuracy and reliability. The SLnPO_DMN is evaluated using a comprehensive set of metrics, including accuracy, sensitivity, and specificity. The results demonstrate the effectiveness of the SLnPO_DMN model achieving an accuracy (99.12%), sensitivity of (98.74%), and specificity of (98%). © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Deep maxout network Min–Max normalization Mycobacterium tuberculosis Political optimizer Sea lion optimization

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Hybrid Vision Transformer Model for Accurate Prostate Cancer Classification in MRI Images

Hybrid Vision Transformer Model for Accurate Prostate Cancer...

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

作者： Jesika Debnath Arafath Bin Mohiuddin Al Shahriar Uddin Khondakar Pranta Anamul Sakib Amira Hossain Md. Moniruzzaman Shanto Shamim Forhad Md. Redwan Ahmed Rezaul Haque Shafiur Rahman Dept. of Computer Science Westcliff University Irvine CA USA Department of Collage of Engineering and Technology Westcliff University CA USA Dept. of Computer Science Wright State University Dayton USA Department of Business Administration International American University CA USA Dept. of Computer Science and Engineering East West University Dhaka Bangladesh Dept. of Electrical and Electronics Engineering American International University Bangladesh Dept. of Software Engineering Daffodil International University Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Prostate cancer is a widespread disease in men worldwide, posing challenges in early detection due to its diverse nature and the limitations of current diagnostic techniques. In this study, we have developed a new method that combines CNNs with Vision Transformers (ViTs) to improve the classification of prostate cancer using MRI images. To address data imbalance and enhance model robustness, we used dual synthetic oversampling techniques and data augmentation methods. Our preprocessing pipeline reduced noise, preserved edges, and improved local contrast without amplifying noise. For feature extraction, we utilized the GrayLevel Co-occurrence Matrix (GLCM) and shape-based features to represent complex MRI data better. Among the evaluated architectures, ConvNeXt showed the best performance. Using SMOTE, it achieved an F1-score of 97.21% and a MCC of 95.32%. With ADASYN, performance improved further, reaching an F1-score of 98.82% and an MCC of 97.86%. Additionally, we created a web-based application for realtime analysis of prostate MRI scans, which can aid in clinical decision-making. These results demonstrate the potential of our approach to improve the accuracy of prostate cancer diagnosis and provide an interpretable model for clinical use.

关键词： computer vision Accuracy Magnetic resonance imaging Noise Pipelines Transformers Feature extraction Robustness Prostate cancer Resilience

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Real-Time Traffic Sign Detection for Autonomous Vehicles Using YOLOv11

Real-Time Traffic Sign Detection for Autonomous Vehicles Usi...

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

作者： Md Shahabub Alam Al Sani Md Tanvir Islam Ayan Kumar Ghosh Avijit Chowdhury Department of Computer Science Universität Potsdam Potsdam Germany Dept. of Industrial & Production Engineering Jashore University of Science and Technology Jashore Bangladesh Department of Software Sungkyunkwan University Suwon Republic of Korea Department of Mechanical Engineering Chittagong University of Engineering and Technology Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Reliable detection of traffic signs plays a critical role in autonomous vehicles and driver-assistance technologies, especially when operating under diverse and complex real-world conditions. This study investigates the effectiveness of various YOLO-based object detection frameworks, ranging from YOLOv5 to the newly proposed YOLOv11, in addressing the task of traffic sign recognition. The dataset includes varying class configurations and challenging conditions such as low-light and noise, enabling a comprehensive analysis of model robustness. YOLOv11 outperformed prior versions, achieving the highest mAP of 0.961, establishing it as a state-of-the-art (SOTA) solution. The study also conducted ablations to analyze the impact of batch size, data augmentation, and adverse conditions on model performance. Notably, YOLOv11 demonstrated strong resilience to noise and low-light scenarios, maintaining high accuracy even under dense conditions. Comparisons with existing models further validated its superiority in terms of precision and consistency. These results highlight YOLOv11’s potential for real-world traffic sign detection tasks, where reliability is critical.

关键词： YOLO Analytical models Adaptation models Electric potential Accuracy Noise Robustness Real-time systems Autonomous vehicles Resilience

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Phoneme Classification in Imagined Speech Using Explainable Machine Learning

Phoneme Classification in Imagined Speech Using Explainable ...

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International Winter Workshop on Brain-computer Interface (BCI)

作者： Sejin Kim Hyunwook Kang Ji-Hoon Jeong Minji Lee Dept. Artificial Intelligence The Catholic University of Korea Bucheon South Korea Dept. Electrical and Computer Engineering Inha University Incheon South Korea Dept. Computer Science Chungbuk National University Cheongju South Korea Dept. Biomedical Software Engineering The Catholic University of Korea Bucheon South Korea

ISBN: (数字)9798331521929

ISBN: (纸本)9798331521936

Imagined speech is gaining attention as a next-generation paradigm for brain-computer interfaces in terms of its intuitiveness in communication. Many studies have focused on classifying imagined words as the basis of communication, but some studies on phoneme classification based on imagined speech have begun to be conducted in terms of the fact that combinations of phonemes can further expand sound images. In addition, explainability is a major issue in classification results in machine learning, but there have been few studies that analyzed the explainability of imagined speech classification. In this study, we examined explainability using SHAP for phoneme classification based on imagined speech. FEIS dataset was used, and 16 phonemes were classified using the XGBoost classifier. In 21 subjects, the average accuracy was 40.36 %, the average precision was 41.7%, the average recall was 40.34%, and the average F1-score was 40.7%. In addition, when looking at Sub 04, which had the highest performance, the SHAP values were high in AF3 and F7 channels, which correspond to Wernicke's area related to speech. In this regard, we could observe the feature importance of this area. These results prove that Wernicke's area actually plays an important role in classifying phoneme units in imagined speech, and can provide important insights to improve the performance of imagined speech for intuitive communication based on brain-computer interface in the future.

关键词： Accuracy Machine learning algorithms Closed box Machine learning Predictive models Brain modeling Brain-computer interfaces Classification algorithms Reliability Next generation networking

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Rare and Common Types of Retinal Disease Recognition Using Ensemble Deep Learning

Rare and Common Types of Retinal Disease Recognition Using E...

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

作者： Jesika Debnath Al Shahriar Uddin Khondakar Pranta Arafath Bin Mohiuddin Amira Hossain Anamul Sakib Md. Moniruzzaman Shanto Shamim Forhad Abdullah Al-Sakib Rezaul Haque Shafiur Rahman Dept. of Computer Science Westcliff University Irvine CA USA Dept. of Computer Science Wright State University Dayton USA Department of Collage of Engineering and Technology Westcliff University CA USA Department of Business Administration International American University CA USA Dept. of Computer Science and Engineering East West University Dhaka Bangladesh Dept. of Electrical and Electronics Engineering American International University Bangladesh Dept. of Information Technology Westcliff University CA USA Dept. of Software Engineering Daffodil International University Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Detecting retinal diseases is crucial for preventing vision loss, but it comes with challenges, such as data imbalance and accurately classifying rare conditions. This study introduces a new ensemble transfer learning model that combines EfficientNetB7, SE-ResNet, DenseNet-169, and NASNet to improve the classification of common and rare retinal diseases. The ensemble uses a probability averaging approach to maximise each architecture’s strengths. Essential preprocessing techniques such as Global Contrast Normalization (GCN) and Total Variation Denoising (TVD) were used to enhance image quality. The model was trained and evaluated on the Retinal Fundus Multi-Disease Image Dataset (RFMiD) 2.0, achieving 98.65% accuracy and an F1-score of 0.96% for common diseases and 99.10% accuracy with an F1-score of 0.97 for rare diseases, outperforming individual models significantly. The ensemble has been integrated into a web-based application that allows clinicians to upload retinal images for instant diagnostic feedback, enabling real-time analysis and visual insights. Despite its strengths, challenges such as high computational demands and reliance on high-resolution photos remain. Future work will improve computational efficiency through model compression techniques and extend the model to other imaging modalities, such as Optical Coherence Tomography (OCT). In conclusion, this study presents a robust and scalable solution for retinal disease classification, potentially improving early detection and providing real-time support in clinical settings, particularly in resource-limited environments.

关键词： Deep learning Visualization Accuracy Computational modeling Optical coherence tomography Transfer learning Imaging Retina Real-time systems Diseases

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Staging of Different Tumor by Utilizing Laser Guidance (@405 nm) in CT Scan Images Along with Statistical Analysis

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Lasers in engineering 2025年第4-6期59卷 309-325页

作者： Amjad, M. Ullah, H. Siddique, M.A. Majid, M.A. Aleem, A. Akhtar, M. Gilanie, G. Abdullah-Al-Wadud, M. Farhan, M. Akram, A. Batool, Z. Nazir, A. Biophotonics Imaging Techniques Laboratory Institute of Physics The Islamia University of Bahawalpur 63100 Pakistan Department of Computer Science Faculty of Computing The Islamia University of Bahawalpur Bahawalpur Pakistan Dept. of Software Engineering College of Computer and Information Sciences King Saud University Riyadh11543 Saudi Arabia School of Earth Sciences and Engineering Hohai University Nanjing China

Early detection of lung cancer, liver cancer (HCC), and pancreatitis is crucial for effective treatment. This study assessed the use of laser guidance CT scans for diagnosis and staging. 26 patients of CT scan which were collected from BVH and civil hospital Bahawalpur, Pakistan. Visible light with a 405 nm wavelength is used. Results showed staging of lung cancer (2 at stage 1, 2 at stage 2, 3 at stage 4) and HCC (stages 1 to 4a). Pancreatic diseases detected included acute pancreatitis (44%), chronic pancreatitis (22%), gallstones (22%), and pancreatic tumors (11%). Alkaline phosphatase, SGOT, and blood urea levels were notably elevated in all three cancers compared to controls. CT scans show promise in identifying cancer stages and diagnosing pancreatitis, aiding in tailored treatment. Statistical analysis shows that alkaline phosphatase emerges as a potential biomarker for these conditions. ©2025 Old City Publishing, Inc.

关键词： Image analysis

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Enhanced Healthcare Through Metaverse Integration: Explainable AI and Blockchain for Trust, Security, and Immersive Experiences

Enhanced Healthcare Through Metaverse Integration: Explainab...

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

作者： Bilal Ibrahim Sowan Hazem Qattous Mohammad Fasha Ahmad Altarawneh Dept. of Bus. Intell. & Data Analytics University of Petra Amman Jordan Dept. of Software Engineering Princess Sumaya University for Technology Amman Jordan Dept. of Computer Science Brunel University of London London UK

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

The metaverse, a dynamic blend of virtual and augmented reality, holds extraordinary promise for reshaping the healthcare landscape. By integrating advanced technologies such as virtual reality (VR), artificial intelligence (AI), blockchain, and digital twins, the metaverse introduces transformative opportunities in patient care, medical education, and healthcare operations. These tools foster immersive, personalized, and data-driven environments, significantly improving diagnostic precision, therapeutic strategies, and training methodologies. This paper provides a framework to utilize this potential while navigating key concerns, including systems scale, user customization, and ethical adequacy. In that case, the framework's goal should be eliminating existing limitations and barriers within healthcare systems by utilizing state-of-the-art technologies with real-world applications. The framework involves virtual consultation sites that enable provider-patient interactions, intelligent virtual agents with specific care recommendations, blockchain integration for data integrity, and dynamic VR segments that are functional in delivering healthcare education and training. The study focuses on scenario-based modeling and the use of pilot deployment in various healthcare settings to test for practicability and feasibility. The research also emphasizes that ethical issues and inclusion during the design and implementation of the innovations should also consider society's values regarding the medical field and promote equal access to healthcare. This framework proves a comprehensive blueprint for continuous progress and places the metaverse as one cornerstone in the global healthcare system.

关键词： Training Ethics Technological innovation Solid modeling Metaverse Medical services Blockchains Digital twins Security Medical diagnostic imaging

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Optimizing Spot Instance Reliability and Security Using Cloud-Native Data and Tools

arXiv

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arXiv 2025年

作者： Saqib, Muhammad Malhotra, Shubham Mehta, Dipkumar Jangid, Jagdish Yashu, Fnu Dixit, Sachin Texas Tech University Dept. of Computer Science United States Rochester Institute of Technology Dept. of Software Engineering United States C.K.Pithawalla College of Eng. and Tech India Infinera Corp United States Stony Brook University Dept. of Computer Science United States Stripe Inc United States

This paper presents "Cloudlab," a comprehensive, cloud-native laboratory designed to support network security research and training. Built on Google Cloud and adhering to GitOps methodologies, Cloudlab facilitates the creation, testing, and deployment of secure, containerized workloads using Kubernetes and serverless architectures. The lab integrates tools like Palo Alto Networks firewalls, Bridgecrew for "Security as Code," and automated GitHub workflows to establish a robust Continuous Integration/Continuous Machine Learning pipeline. By providing an adaptive and scalable environment, Cloudlab supports advanced security concepts such as role-based access control, Policy as Code, and container security. This initiative enables data scientists and engineers to explore cutting-edge practices in a dynamic cloud-native ecosystem, fostering innovation and improving operational resilience in modern IT infrastructures. Copyright © 2025, The Authors. All rights reserved.

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Evaluating Fault Tolerance and Scalability in Distributed File Systems: A Case Study of GFS, HDFS, and MinIO

arXiv

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arXiv 2025年

作者： Malhotra, Shubham Yashu, Fnu Saqib, Muhammad Mehta, Dipkumar Jangid, Jagdish Dixit, Sachin Rochester Institute of Technology Dept. of Software Engineering United States Stony Brook University Dept. of Computer Science United States Texas Tech University Dept. of Computer Science United States C.K.Pithawalla College of Eng. and Tech India Infinera Corp United States Stripe Inc United States

Distributed File Systems (DFS) are essential for managing vast datasets across multiple servers, offering benefits in scalability, fault tolerance, and data accessibility. This paper presents a comprehensive evaluation of three prominent DFSs—Google File System (GFS), Hadoop Distributed File System (HDFS), and MinIO—focusing on their fault tolerance mechanisms and scalability under varying data loads and client demands. Through detailed analysis, how these systems handle data redundancy, server failures, and client access protocols, ensuring reliability in dynamic, large-scale environments is assessed. In addition, the impact of system design on performance, particularly in distributed cloud and computing architectures is assessed. By comparing the strengths and limitations of each DFS, the paper provides practical insights for selecting the most appropriate system for different enterprise needs, from high-availability storage to big data analytics. Copyright © 2025, The Authors. All rights reserved.

关键词： Scalability

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