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A Novel Differential Exchange Parallel Great Wall Construction Algorithm for Efficient Multilevel Image Segmentation

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IEEE Access 2025年 13卷 83794-83814页

作者： Pan, Jeng-Shyang Zhang, Rongli Chu, Shu-Chuan Sui, Xiao Watada, Junzo Shandong University of Science and Technology College of Computer Science and Engineering Qingdao266590 China Chaoyang University of Technology Department of Information Management Taichung City41349 Taiwan Nanjing University of Information Science and Technology School of Artificial Intelligence Nanjing210044 China Shimonoseki City University Daigakucho School of Data Science Yamaguchi Shimonoseki751-8510 Japan

The Great Wall Construction Algorithm (GWCA) is an innovative meta-heuristic algorithm for solving engineering challenges. Parallel strategies are an effective approach to enhancing the performance of algorithms. In this paper, parallel technology is introduced into GWCA by proposing a new communication strategy named Differential Exchange, which is combined with GWCA to design the Parallel Great Wall Construction Algorithm (PGWCA). The goal is to avoid local optima and improve the algorithm's performance. The performance of the proposed PGWCA has been critically compared with seven other meta-heuristic algorithms on 10, 30, and 50 dimensions, respectively, using the CEC 2017 benchmark function suite. In addition, the PGWCA algorithm has been used to solve the multilevel image threshold segmentation problem by segmenting eight images and evaluating the segmentation results using three metrics: PSNR, SSIM, and FSIM. The results show that out of 29 test functions, PGWCA achieved the best (minimum) value 17, 20, and 16 times, respectively. In the image threshold segmentation experiments, the best performance was achieved 60 times out of all 72 evaluated comparisons. The experimental results show that PGWCA achieves better performance in identifying the optimal solution and improving the convergence performance, and has good performance in image segmentation. The code is available for download at the following link: https://***/drive/folders/1I-5k-IzTP7cUvZqdo7YV-jOz1Zfka42j?usp=sharing. © 2013 IEEE.

关键词： Image segmentation

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CS2023: computer science Curriculum Guidelines: A New Liberal Arts Perspective

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ACM Inroads 2025年第1期16卷 40-52页

作者： Holland-Minkley, Amanda Barnard, Jakob E. Barr, Valerie Braught, Grant Davis, Janet Reed, David Schmitt, Karl Tartaro, Andrea Teresco, James D. Department of Computing and Information Studies Washington & Jefferson College 60 S. Lincoln St. WashingtonPA15301 United States Department of Computer Science and Technology University of Jamestown 6401 College Ln. JamestownND58405 United States Department of Computer Science Bard College 30 Campus Road Annandale-on-HudsonNY12504 United States Department of Computer Science Dickinson College P.O. Box 1773 CarlislePA17013 United States Department of Computer Science Whitman College 345 Boyer Ave. Walla WallaWA99362 United States Department of Computer Science Design & Journalism Creighton University Hitchcock 209 OmahaNE68178 United States Department of Computing and Data Analytics Trinity Christian College 6601 West College Drive Palos HeightsIL60463 United States Department of Computer Science Furman University 3300 Poinsett Hwy GreenvilleSC29690 United States Department of Computer Science Siena College 515 Loudon Rd. LoudonvilleNY12211 United States

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Automated AI-Powered Fruit Identification Using Convolutional Neural Network

Automated AI-Powered Fruit Identification Using Convolutiona...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Sai Tejasree, K. Surendran, R. Rajakumar, B. Gomathi, R.M. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India J.N.N Institute of Engineering Department of Artificial Intelligence and Data Science Chennai India School of Computing Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509828

Clever system that can look at pictures of fruits and figure out what kind of fruit each picture shows. AI algorithms like deep learning, which is like giving the Machine learning model a crash course in fruit recognition. This method teaches the ML Model we clearly explained the Model using application's agent using PEAS and the application's task environment using the 6 dimensions by showing it tons of fruit images, so over time, it gets really good at spotting the differences and similarities between, say, a banana and a grape. We also used another technique called pattern recognition, which helps the computer pay attention to specific details like the fruit's color, shape, size and texture. Overcoming multiple obstacles in order to automatically identify the type of fruit from the picture. The variety of images is influencing the color, texture, and shape of many different types of fruits. When it came to fruit picture detection, Convolutional Neural Network (CNN) Algorithm outperformed standard support-vector-machine-based approaches using handcrafted features in terms of accuracy also, it is a lot faster to implement for new fruits. By integrating deep learning and pattern recognition techniques such as Convolutional Neural Network Algorithm we have got the accuracy of 84%, our system efficiently identified different fruit types from images, demonstrating the power and effectiveness of our methods. The goal of our project is to create a tool that can quickly and correctly identify many types of fruits in photos, which could be useful for things like sorting fruits in a grocery store or helping people learn about different fruits by using Convolutional Neural Network Algorithm. This is not just about teaching a computer to recognize fruits;it is about making technology that can understand and interact with the world in a way that is helpful to us. © 2025 IEEE.

关键词： Convolutional neural networks

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Swin Transformer and Momentum Contrast (MoCo) in Leukemia Diagnostics: A New Paradigm in AI-Driven Blood Cell Cancer Classification

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IEEE Access 2025年 13卷 82441-82466页

作者： Jain, Eshika Kaushik, Pratham Kukreja, Vinay Ati, Modafar Hariharan, Shanmugasundaram Ahuja, Vandana Bhattacherjee, Abhishek Kumar Kaushal, Rajesh Chitkara University Institute of Engineering and Technology Chitkara University Centre for Research Impact and Outcome Punjab Rajpura140401 India Abu Dhabi University Computer Science and Information Technology College of Engineering Abu Dhabi United Arab Emirates Vardhaman College of Engineering Department of Artificial Intelligence and Data Science Telangana Hyderabad501218 India Swami Vivekanand Institute of Engineering and Technology Department of Computer Science and Engineering Punjab Banur140601 India Lovely Professional University Department of Computer Science Engineering Punjab Phagwara144411 India Chitkara University Institute of Engineering and Technology Chitkara University Department of Computer Applications Punjab Rajpura140401 India

Acute Lymphoblastic Leukemia (ALL) is a fast-growing blood cancer that requires prompt diagnosis for effective treatment. Automated image diagnostics offer potential solutions but often lack clinical robustness. Despite their widespread use in medical imaging, Convolutional Neural Networks (CNNs) struggle to differentiate morphologically similar ALL subtypes due to limited context and feature discrimination. Moreover, integrating contrastive self-supervised learning with hierarchical attention-based models remains underexplored in hematologic malignancy classification. This study aims to develop a robust, automated classification model for ALL subtypes using peripheral blood smear images, employing advanced feature extraction through the Swin Transformer framework, combined with Momentum Contrast (MoCo) for contrastive learning and a Bidirectional Encoder Transformer for classification. The Swin Transformer’s patch-based embedding and multi-level attention enhance feature discrimination across ALL subtypes, while MoCo generates distinct embeddings, minimizing overlap between cell types. BiET is employed to classify the refined feature vectors, leveraging self-attention mechanisms to improve classification accuracy. The model achieved an overall classification accuracy of 92.5%, with the precision of 90.3%, a recall of 91.1%, and an F1-score of 90.7% across four classes (Benign, Malignant Early Pre-B, Malignant Pre-B, and Malignant Pro-B). Class-specific performance metrics indicate that Malignant Pre-B achieved the highest F1-score of 92.4%. The MoCo framework reduced contrastive loss from 0.5 to 0.097 for benign cells, enhancing feature discrimination. An ablation study revealed that omitting the dynamic queue decreased accuracy by 5%, underscoring its importance for effective feature learning. This approach can be extended to other hematologic malignancies, with potential for further improvement using larger datasets and real-time diagnostic workflows to support p

关键词： Diseases

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Enhancing Organizational Performance: Synergy of Cyber-Physical Systems, Cloud Services, and Crowdsensing

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International Journal of Crowd science 2025年第1期9卷 44-55页

作者： Al-Qerem, Ahmad Ali, Ali Mohd Nabot, Ahmad Jebreen, Issam Alauthman, Mohammad Alangari, Someah Aburub, Faisal Aldweesh, Amjad Computer Science Department Faculty of Information Technology Zarqa University Zarqa13110 Jordan Communications and Computer Engineering Department Faculty of Engineering Al-Ahliyya Amman University Amman19328 Jordan Department of Information Security University of Petra Amman11196 Jordan Department of Computer Science College of Science and Humanities Dawadmi Shaqra University Shaqra11911 Saudi Arabia Department of Business Intelligence and Data Analytics University of Petra Amman11196 Jordan College of Computing and Information Technology Shaqra University Shaqra11911 Saudi Arabia

In the contemporary business landscape, software has evolved into a strategic asset crucial for organizations seeking sustainable competitive advantage. The imperative of ensuring software quality becomes evident as low-quality software systems pose formidable challenges to organizational performance. This study delves into the profound impact of three key dimensions of information system quality on organizational performance—information quality (IQ), quality of service (QoS), and software quality (SQ). Anchored in the DeLone and McLean information system (IS) success model, a quantitative questionnaire was administered to 360 industry experts and academics. Rigorous data analysis, employing exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and structural equation modeling (SEM), revealed significant positive effects of all three quality dimensions on organizational performance. Among these dimensions, software quality emerged as the most influential, showcasing substantial total effects, closely followed by information and service qualities. The study underscores the tangible value derived from strategic investments in enhancing software, information, and service quality. Elevating these facets manifests as a catalyst for improved organizational performance, empowering decision-makers with accurate and timely information while enhancing user satisfaction with the system. This research contributes significantly to the IS success literature by empirically validating the synergistic relationship between information quality, service quality, software quality, and organizational outcomes. The systematic analysis offered in this study goes beyond theoretical validation, providing actionable insights for managers. The findings guide the prioritization of quality initiatives and resource allocation, enabling organizations to maximize competitive advantage. As a future research direction, investigating moderator influences and exploring alternate qualit

关键词： data quality

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A Mathematical and Dynamical Model for Analyzing H-Shaped PM Vernier Motor for Electric Motorcycle Mid-Drive Applications

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IEEE Transactions on Energy Conversion 2025年第1期40卷 529-543页

作者： Ghods, Mehrage Faiz, Jawad Bazrafshan, Mohammad Amin Gorginpour, Hamed Toulabi, Mohammad Sedigh University of Tehran School of Electrical and Computer Engineering College of Engineering Tehran*** Iran Persian Gulf University Department of Intelligent Systems Engineering and Data Science Bushehr*** Iran Oakland University Department of Electrical and Computer Engineering RochesterMI48309 United States

The permanent magnet (PM) Vernier machines enhance torque density and decrease cogging torque compared to conventional permanent magnet synchronous motor. This paper presents a novel fractional-slot H-shaped PM Vernier machines, which incorporates unique design for the PM housing and rotor core construction. These designs are specifically optimized for the small-scale electric vehicles application. The choice of the PM arrangement is based on the combination of V-shaped and spoke-array PM topologies. The aim is to achieve a higher torque density than V-shaped PM machines, while reducing cogging torque compared to spoke-array PM machines. By using this methodology, leakage fluxes in the terminal region of the PMs reduce, resulting in an augmentation of both flux-linkage and power factor. A prototype 680 W motor was fabricated with 12 slots and 8 poles to verify the accuracy and effectiveness of the proposed model. Mathematical model and dynamical analysis are proposed for the analytical prediction of machine performance. Due to the unique shape feature of the machine, the conformal mapping approach is applied to design the rotor core. To validate the model, the predicted performance of the motor was compared with those obtained by finite element analysis and experiments. © 1986-2012 IEEE.

关键词： Permanent magnets

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Improving Antibody-Antigen Interaction Prediction Through Flexibility with ESMFold

Improving Antibody-Antigen Interaction Prediction Through Fl...

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18th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2025

作者： Joubbi, Sara Maccari, Giuseppe Ciano, Giorgio Micheli, Alessio Milazzo, Paolo Medini, Duccio Department of Computer Science University of Pisa Pisa Italy Data Science for Health Lab Fondazione Toscana Life Sciences Siena Italy

Antibodies are essential proteins in the immune system due to their capacity to bind to specific antigens. They also play a critical role in developing vaccines and treatments for infectious diseases. Their complex structure, with variable regions for antigen binding and flexible hinge regions, presents challenges for accurate computational modeling. Recent advancements in deep learning have revolutionized protein structure prediction. Despite these advancements, predicting interactions between antibodies and antigens remains challenging, mainly due to the flexibility of antibodies and the dynamic nature of binding events. This study uses fingerprint-based methodologies that incorporate ESMFold confidence scores as a flexibility feature to model Ab-Ag interactions. Our methodology shows how including flexibility has improved Ab-Ag interactions by 3%, achieving an AUC-ROC of 91%. © 2025 by SCITEPRESS– science and Technology Publications, Lda.

关键词： Vaccines

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A study on Electronic Health Records (EHR) in India: Security challenges, initiatives and blockchain integration 4th

A study on Electronic Health Records (EHR) in India: Securit...

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4th International Conference on Computational Methods in science and Technology, ICCMST 2024

作者： Bhargava, Misha Mahi, Samarjit Rao, Vidya Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal India

ISBN: (纸本)9781032911571

With its vast population and extensive healthcare requirements, India is moving toward digitalizing its health records. Safeguarding Electronic Health Records (EHRs) is crucial given the increase in security breaches and attacks, with India’s healthcare sector at the forefront. This study provides an overview of EHR security in India by exploring the EHR adoption rate in India and the barriers to its use. It focuses on the significant security challenges they bring, and the initiatives currently being implemented in India, such as the Ayushman Bharat Digital Mission (ABDM) and the recently released National Accreditation Board for Hospitals (NABH) Digital Health Standards. Integrating Blockchain in current EHR systems to resolve the observed challenges is then explored. The findings of this study show the urgency of integrating emerging technologies such as blockchain which have the potential for patient-controlled data sharing and data immutability possibly within the framework of the ABDM. This study aims to serve as a valuable resource for healthcare professionals, researchers and other regions seeking to understand and improve EHR security. © 2025 the Author(s).

关键词： Electronic health record

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Psychological Impact of Internet Blackouts: A Case Study With Machine Learning-Based Stress Analysis

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IEEE Access 2025年 13卷 83505-83527页

作者： Islam, Mohammad Ariful Islam, Tahidul Hossain, Gahangir Mofazzal Hossain, M. The University of Melbourne Department of Computing and Information Systems MelbourneVIC3010 Australia Bangladesh University of Engineering and Technology Department of Computer Science and Engineering Dhaka1000 Bangladesh University of North Texas Department of Data Science DentonTX76205 United States Southeast University Department of Electrical and Electronic Engineering Dhaka1208 Bangladesh

Access to the internet has become a vital part of modern life, especially for communication and essential services. However, during politically sensitive times, internet blackouts can disrupt daily routines, leading to significant psychological impacts. This study explores the mental health effects of the internet shutdown imposed during the Bangladesh Quota Movement in July 2024, when the government cut off access to control information flow. The blackout hindered communication, financial transactions, and critical services, amplifying stress, feelings of isolation, and emotional distress. A survey of 2,085 participants was conducted to assess the behavioral, emotional, and psychological consequences, particularly in academic, work, and social settings. Stress levels among respondents varied from minimal to extreme, reflecting widespread mental distress. To classify these stress levels, machine learning models;Decision Tree (DT), Random Forest (RF), Bernoulli Naive Bayes (BNB), Logistic Regression (LR), eXtreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM) were applied. The SVM model outperformed others, achieving high precision (99.33%), recall (99.00%), F1-score (99.33%), and accuracy (99.49%). This study underscores the urgent need for mental health support during such crises, particularly in low- and middle-income countries like Bangladesh, where mental health care is often neglected. These findings are aligned with Sustainable Development Goal (SDG 3), "Good Health and Well-Being," stressing the importance of mental health interventions in fostering resilience, well-being, and social stability during crises. © 2013 IEEE.

关键词： Support vector machines

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Comparative Model Evaluation of Lightweight Transformer-Based and CNN-Based Architechture for Degenerative Lumbar Spine Classification

Comparative Model Evaluation of Lightweight Transformer-Base...

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Advancement in data science, E-learning and Information Systems (ICADEIS), International Conference on

作者： Jennifer Ardelia Limicia Wilbert Chandra Alexander Agung Santoso Gunawan Jeffrey Junior Tedjasulaksana Data Science Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331513320

ISBN: (纸本)9798331513337

Low back pain is a leading cause of disability globally, is often associated with degenerative lumbar spine conditions. Accurate diagnosis of these conditions is critical but challenging due to the subjective nature of MRI interpretation and the weak correlation between imaging findings and symptoms. Thus, the purpose of this study is to evaluate the performance of CNN-based architectures (VGG-16, EfficientNetB0, EfficientNetV2) and transformer-based architecture specifically Vision Transformers (ViT) in classifying lumbar spine conditions as normal, moderate, or severe. Using the RSNA 2024 Lumbar Spine Degenerative Classification dataset. The models are evaluated using the ROC-AUC and PRAUC as the performance metrics due to the dataset imbalance. Results indicate that EfficientNetB0 achieved the highest overall performance, with an average ROC-AUC of 0.784 and PR-AUC of 0.528, demonstrating strong adaptability to imbalanced datasets. EfficientNetV2 also performed competitively, while VGG-16 showed moderate effectiveness. The Vision Transformer (ViT), however, underperformed due to its reliance on larger datasets and challenges in capturing fine-grained spatial features. The findings highlight the potential of EfficientNet-based models for accurate and efficient lumbar spine diagnostics. This study underscores the potential of advanced deep learning approaches in improving diagnostic workflows for degenerative lumbar spine conditions.

关键词： computer vision Adaptation models Accuracy Magnetic resonance imaging Soft sensors Spine Transforms Transformers data models Convolutional neural networks

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