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SSRN 2024年

作者： Hao, Jia Deng, Ruofan Jia, Liangyue Li, Zuoxuan Industrial and Systems Engineering Laboratory Beijing Institute of Technology Beijing100081 China Yangtze Delta Region Academy of Beijing Institute of Technology Jiaxing314019 China Key Laboratory of Industry Knowledge & Data Fusion Technology and Application Ministry of Industry and Information Technology Beijing Institute of Technology Beijing100081 China

Automatic optimization algorithms play an important role in vehicle body lightweight design;however, the existing methods are still inefficient and result in excessive iterations that escalate both time and costs. Current interactive optimization strategies partially mitigate this issue but fall short in offering a broad spectrum of manipulation points and auxiliary information model, essential for optimizing design efficiency and accuracy. As such, we introduce a novel approach, "Human-in-the-Loop based method for Vehicle Body Lightweight Design" (HIL-VBLD). This method seamlessly integrates human decision-making with optimization algorithms to reduce unproductive iterations. HIL-VBLD comprises two key components: (1) a novel interaction mode offering multiple manipulation points including constraint modification, algorithm switching, and selection of solutions of interest (SOI);(2) A comprehensive auxiliary information model aids in decision-making for designers. Our analysis underscores HIL-VBLD's efficacy, evidenced by a 54.5% reduction in iteration cycles for genetic algorithm using SOI selection. Algorithm switching led to a 4.5% mass reduction, circumventing local optimum pitfalls associated with gradient algorithms. Additionally, the auxiliary information model contributed to a further 1.25% mass reduction, enhancing optimization robustness. Compared to conventional automatic algorithm switching strategies, HIL-VBLD maintains equivalent accuracy with 23.9% fewer iterations. © 2024, The Authors. All rights reserved.

关键词： Genetic algorithms

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Cross-Cultural Insights in Health: Integrating Machine Learning and Deep Learning for Early Predictive Diabetes Detection

Cross-Cultural Insights in Health: Integrating Machine Learn...

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International Conference on Computer and information Technology (ICCIT)

作者： Tonmoy Roy H.M. Nahid Kawsar Iffat Rimi Md. Zehan Alam Department of Data Analytics and Information Systems Utah State University Logan Utah USA Department of Applied Mathematics Noakhali Science and Technology University Bangladesh Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh Department of Mathematics University of Dhaka Dhaka Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

Diabetes, a potentially life-threatening condition if undetected, necessitates an early and accurate diagnosis. This study evaluates traditional machine learning (ML) models and deep neural networks (DNNs) for diabetes prediction, utilizing the Pima Indians Diabetes dataset (PIDD) and the Sylhet Diabetes Hospital dataset (SDHD). We optimized six ML models—Logistic Regression, Decision Trees, Random Forests, Gradient Boosting, Support Vector Machines, and K-Nearest Neighbors—using GridSearchCV and ensemble learning, and deployed DNNs with varied train-test splits (90-10%, 80-20%, and 70-30%). Evaluations on accuracy, precision, recall, F1-score, and AUC-ROC highlighted RF’s outstanding performance after GridSearchCV on the SDHD with Accuracy: 98%, Precision: 100%, Recall: 96%, F1 Score: 98%, AUC: 100% and on PIDD with Accuracy: 76%, Precision: 63%, Recall: 80%, F1 Score:70%, AUC:81%. This underscores advanced ML’s effectiveness in handling complex datasets and enhancing early diabetes detection strategies.

关键词： Support vector machines Accuracy Hospitals Artificial neural networks Predictive models Prediction algorithms Diabetes Ensemble learning Regression tree analysis Random forests

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Enhancing Transfer Learning for Medical Image Classification with SMOTE: A Comparative Study

Enhancing Transfer Learning for Medical Image Classification...

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International Conference on Computer and information Technology (ICCIT)

作者： Md Zehan Alam Tonmoy Roy H.M. Nahid Kawsar Iffat Rimi Department of Mathematics University of Dhaka Dhaka Bangladesh Department of Data Analytics and Information Systems Utah State University Logan Utah USA Department of Applied Mathematics Noakhali Science and Technology University Bangladesh Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

This paper explores and enhances the application of Transfer Learning (TL) for multilabel image classification in medical imaging, focusing on brain tumor class and diabetic retinopathy stage detection. The effectiveness of TL—using pre-trained models on the ImageNet dataset—varies due to domain-specific challenges. We evaluate five pre-trained models—MobileNet, Xception, InceptionV3, ResNet50, and DenseNet201—on two datasets: Brain Tumor MRI and APTOS 2019. Our results show that TL models excel in brain tumor classification, achieving near-optimal metrics. However, performance in diabetic retinopathy detection is hindered by class imbalance. To mitigate this, we integrate the Synthetic Minority Over-sampling Technique (SMOTE) with TL and traditional machine learning(ML) methods, which improves accuracy by 1.97%, recall (sensitivity) by 5.43%, and specificity by 0.72%. These findings underscore the need for combining TL with re-sampling techniques and ML methods to address data imbalance and enhance classification performance, offering a pathway to more accurate and reliable medical image analysis and improved patient outcomes with minimal extra computation powers.

关键词： Diabetic retinopathy Accuracy Sensitivity Transfer learning Brain tumors Brain modeling Reliability Medical diagnostic imaging Image classification Residual neural networks

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Blending Ensemble Learning for Enhanced Arrhythmia Classification Utilizing 12-Lead ECGs

Blending Ensemble Learning for Enhanced Arrhythmia Classific...

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IEEE International Conference on Research, Innovation and Vision for the Future

作者： Hai-Chau Le Hai-Long Nguyen Huu Cam Nguyen Quy Nam Tran Chien Trinh Nguyen Department of Data Engineering Posts and Telecom. Inst. of Tech. Hanoi Vietnam Data and Intelligent Systems Labs. Posts and Telecom. Inst. of Tech. Hanoi Vietnam Faculty of Information Technology Dai Nam University Hanoi Vietnam Department of Signals and Systems Posts and Telecom. Inst. of Tech. Hanoi Vietnam

ISBN: (数字)9798331505073

ISBN: (纸本)9798331505080

The rise in heart-related diseases has driven the development of advanced techniques for identifying irregular heart conditions, and with the progress in artificial intelligence and signal processing, automated arrhythmia classification using machine learning and electrocardiograms (ECG) has become increasingly effective and widely utilized by healthcare professionals. This paper presents an efficient machine learning solution for robust arrhythmia classification using 12-lead electrocardiograms (ECGs) by leveraging blending ensemble learning. Our developed blending model utilizing six base models-Adaptive boosting, Extreme gradient boosting, Decision trees, K-nearest neighbors, Random forest, and Support vector machine-with a Logistic regression meta-model, demonstrates enhanced efficiency in classifying arrhythmias based on 12-lead ECGs. This approach not only exploits the unique strengths of each base model but also captures diverse predictive patterns, which are combined by the LR meta-model into a refined and cohesive output. The use of LR as the meta-model enhances interpretability and generalization, reducing the risk of overfitting and optimizing overall performance. Experimental results demonstrate that the proposed blending ensemble outperforms conventional notable works in terms of accuracy and offers a robust and effective solution for accurate arrhythmia classification, i.e. up to 97.2% accuracy, supporting clinical decision-making.

关键词： Accuracy Machine learning algorithms Arrhythmia Electrocardiography Predictive models Nearest neighbor methods Boosting Vectors Ensemble learning Overfitting

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Out-of-Distribution Fault Diagnosis of Industrial Cyber-Physical systems Based on Orthogonal Anchor Clustering With Adaptive Balance

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical systems 2025年 3卷 48-60页

作者： Liu, Ruonan Hu, Puyuan Zhao, Siheng Sun, Zhijian Han, Te Pang, Zhibo Zhang, Weidong Shanghai Jiao Tong University Department of Automation Shanghai200240 China National Key Laboratory of Marine Engine Science and Technology Shanghai201108 China Crossocean of Suzhou Technology Suzhou215121 China Beijing Institute of Technology Center for Energy and Environmental Policy Research School of Management Beijing100081 China ABB Corporate Research Sweden Department of Automation Technology Vasteras722 26 Sweden KTH Royal Institute of Technology Department of Intelligent Systems Stockholm114 28 Sweden Henan University Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information Engineering Kaifeng475001 China Shanghai Jiaotong University Department of Automation Shanghai200240 China

Given the critical role of rotating machinery in industrial cyber-physical systems (ICPS), ensuring their reliable operation is essential for the stability and safety of ICPS. Deep neural networks have demonstrated competitive results for intelligent fault diagnosis, which are usually trained via the historical data of all fault modes. However, in real engineering, it is usually difficult to collect samples and exhaust all failures during the training stage. As a result, out-of-distribution fault diagnosis (OOD-FD) becomes a more realistic problem that requires the methods to not only accurately diagnose the known faults, but also effectively recognize unknown ones. Therefore, a novel orthogonal anchor clustering with focal attention (FA-OAC) is proposed in this paper for OOD-FD. Firstly, an orthogonal anchor clustering (OAC) algorithm is proposed to fix the class center of each fault mode orthogonally and distinguish the known and unknown faults at the class level. Then, because the identifiability of different fault modes changes a lot in OOD-FD, the focal attention mechanism is applied to dynamically adjust the attention to different fault modes according to the distance loss of OAC, thus addressing the identifiability imbalance problem. To verify the effectiveness of the proposed method, different OOD-FD tasks are designed based on two rotating machinery datasets. The experimental results and comparison with state-of-the-art fault diagnosis methods have demonstrated that the proposed method has improved the OOD-FD performance greatly and therefore provides an effective tool in real engineering. © 2023 IEEE.

关键词： Rotating machinery

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Improving Prediction Quality of Face Image Preference Using Combinatorial Fusion Algorithm 16th

Improving Prediction Quality of Face Image Preference Using...

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16th International Conference on Brain Informatics, BI 2023

作者： Zhang, Zihan Schweikert, Christina Shimojo, Shinsuke Hsu, D. Frank Laboratory of Informatics and Data Mining Department of Computer and Information Science Fordham University YorkNY10023 United States Division of Computer Science Mathematics and Science St. John’s University QueensNY11439 United States Division of Biology and Biological Engineering/Computation and Neural Systems California Institute of Technology PasadenaCA91125 United States

ISBN: (纸本)9783031430749

When two face images are shown to a subject, the subject’s preference choice is influenced by many factors. Although it is a challenging task, one way to detect a subject’s preference is to analyze the eye movement gaze cascade in the recorded video sequence together with a set of extracted attributes (features). Combinatorial fusion algorithm (CFA) is a new information fusion and machine learning paradigm for combining multiple scoring systems using rank-score characteristic (RSC) function and cognitive diversity (CD). In this paper, we apply CFA in the analysis of the eye movement gaze cascade sequence. In particular, we characterize each of the attributes and measure the diversity between two attributes using RSC function and CD respectively. The novelty of the combinatorial fusion approach is the new paradigm of incorporating both score function and rank function as well as using both score combination and rank combination. In addition, our results demonstrate that weighted rank combination has some advantage over weighted score combination when the weight is measured using diversity strength. Since diversity strength depends only on cognitive diversity between two attributes but not on any specific sequence in the eye movement gaze cascade, our result facilitates learning and modeling in an unsupervised manner. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： information fusion

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Normalization of Stain and Scan Distortions for Red Blood Cell Analysis on Manually Prepared Smears Using Deep Learning

SSRN

引用

SSRN 2024年

作者： Elmanna, Mohamed Elsafty, Ahmed Ahmed, Yomna Rushdi, Muhammad Morsy, Ahmed A. Department of Biomedical Engineering and Systems Faculty of Engineering Cairo University Cairo University St Giza12613 Egypt PathOlOgics LLC for processed medical data and tele-pathology services Egypt School of Information Technology New Giza University Giza12256 Egypt

Background: The introduction of the digital pathology realm has provided numerous opportunities in computer-aided diagnosis (CAD) and many tools that can assist pathologists. Such valuable tools include the utilization of advances in machine learning techniques, including deep learning (DL). In hematopathology, however, staining variations and distortions caused by digital pathology scanners during image digitization pose challenges to DL-based automated digital pathology systems. These challenges are the most likely cause for CAD performance ***: In this work, we investigate the effects of stain and scan normalization on red blood cell (RBC) image analysis systems. The normalized images are thus used for DL-based RBC segmentation and ***: A highly diverse dataset of more than 100K RBCs named 100K-RBC-PathOlOgics was used for RBC segmentation and classification. Then, large simulated/synthetic datasets were created to reflect the wide data variability among scanners, staining protocols, and imaging conditions. Subsequently, an autoencoder with a DL architecture was trained using the simulated data to perform normalization of stain and scan distortions. The cell segmentation and classification stages were realized using U-Net and EfficientNetB0 architectures, respectively. The proposed scheme was validated on real and simulated datasets from four different scanners to assess the impact of autoencoder-based normalization on RBC classifier ***: The results show that our DL-based normalizer can significantly improve the RBC classification performance (in terms of the F1-score) against the RBC classifier without image normalization. The improvements are especially significant on test images collected from the two scanners with the low-quality images, namely, Scanner 3 (with a p-value of [[EQUATION]]) and Scanner 4 (with a p-value of [[EQUATION]]).Conclusions: The experimental results highlight the importance of RBC im

关键词： Computer aided diagnosis

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Interactive Blood Bank Repository: Django AWS Approach

Interactive Blood Bank Repository: Django AWS Approach

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Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), International Conference on

作者： E. Sujatha V. Manickavasagan. S. Lakshminarayanan R. Ani Minisha K.S. Rekha V. Loganathan Department of Computer Science and Engineering Saveetha Engineering College Tamil Nadu Department of Computer Science and Business Systems Sri Sairam Engineering College Tamil Nadu Department of Computer Science and Engineering Sri Sairam Engineering College Tamil Nadu Department of Information Technology Saveetha Engineering College Tamil Nadu Department of Artificial Intelligence and Data Science Vel Tech Multi Tech Dr RR and Dr SR Engineering College Tamil Nadu

Everyday, there are a growing number of people who require blood transfusions and there is no substitute for human blood. The secure blood donors are voluntary non-remunerated blood donors from risk populations. Donors in India and across the globe are three types namely, Voluntary donors, Professional donors and replacement donors. The deadline-driven web framework that supports Django's clean, practical design and speedy development. The existing system has a manual work restriction. This has restrictions on securely controlling the task for timely results declaration, and has a significant resource consumption. Web-based blood donation is the coordination of the supply and demand of blood through an online portal. The main goal of this research study is to give blood donors and blood searchers an online platform and to develop an engaging method of connecting the two groups. The suggested system provides a great deal of information regarding blood types, blood supply, and blood donation specifics to raise awareness among the public. Additionally, other system features like password-based security protection, report generation, chat functionality, and stock shortages can even improve the management tasks by streamlining them.

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Deep Learning Segmentation and Classification of Red Blood Cells Using a Large Multi-Scanner dataset

arXiv

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arXiv 2024年

作者： Elmanna, Mohamed Elsafty, Ahmed Ahmed, Yomna Rushdi, Muhammad Morsy, Ahmed Department of Biomedical Engineering and Systems Faculty of Engineering Cairo University Cairo University St Giza12613 Egypt PathOlOgics LLC for Processed Medical Data and Tele-pathology Services Egypt School of Information Technology New Giza University Giza12256 Egypt

Digital pathology has recently been revolutionized by advancements in artificial intelligence, deep learning, and high-performance computing. With its advanced tools, digital pathology can help improve and speed up the diagnostic process, reduce human errors, and streamline the reporting step. In this paper, we report a new large red blood cell (RBC) image dataset and propose a two-stage deep learning framework for RBC image segmentation and classification. The dataset is a highly diverse dataset of more than 100K RBCs containing eight different classes. The dataset, which is considerably larger than any publicly available hematopathology dataset, was labeled independently by two hematopathologists who also manually created masks for RBC cell segmentation. Subsequently, in the proposed framework, first, a U-Net model was trained to achieve automatic RBC image segmentation. Second, an EfficientNetB0 model was trained to classify RBC images into one of the eight classes using a transfer learning approach with a 5×2 cross-validation scheme. An IoU of 98.03% and an average classification accuracy of 96.5% were attained on the test set. Moreover, we have performed experimental comparisons against several prominent CNN models. These comparisons show the superiority of the proposed model with a good balance between performance and computational cost. © 2024, CC BY.

关键词： Image classification

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Enhancing Communication Efficiency and Training Time Uniformity in Federated Learning through Multi-Branch Networks and the Oort Algorithm

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Algorithms 2024年第2期17卷 52页

作者： Juan, Pin-Hung Wu, Ja-Ling Department of Computer Science and Information Engineering National Taiwan University Taipei106 Taiwan Graduate Institute of Networking and Multimedia National Taiwan University Taipei106 Taiwan Center for Data Intelligence: Technologies Applications and Systems National Taiwan University Taipei106 Taiwan

In this study, we present a federated learning approach that combines a multi-branch network and the Oort client selection algorithm to improve the performance of federated learning systems. This method successfully addresses the significant issue of non-iid data, a challenge not adequately tackled by the commonly used MFedAvg method. Additionally, one of the key innovations of this research is the introduction of uniformity, a metric that quantifies the disparity in training time amongst participants in a federated learning setup. This novel concept not only aids in identifying stragglers but also provides valuable insights into assessing the fairness and efficiency of the system. The experimental results underscore the merits of the integrated multi-branch network with the Oort client selection algorithm and highlight the crucial role of uniformity in designing and evaluating federated learning systems. © 2024 by the authors.

关键词： Efficiency

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