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Fusion learning methods for the age-related macular degeneration diagnosis based on multiple sources of ophthalmic digital images 2

Fusion learning methods for the age-related macular degenera...

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2nd international conference on Electrical, Electronics, and Information engineering, EEIE 2023

作者： Wang, Mini Han Zhou, Junjie Huang, Chengde Tang, Zhoujie Yu, Xiangrong Hou, Guanghui Yang, Jie Yuan, Qingting Chong, Kelvin K.L. Huang, Lina Department of Ophthalmology & Visual Sciences Faculty of Medicine The Chinese University of Hong Kong Hong Kong999077 Hong Kong The Faculty of Data Science City University of Macau 999078 China Zhuhai People's Hospital Zhuhai Hospital Affiliated with Jinan University Zhuhai519000 China The Department of AI and Big Data Application Zhuhai Institute of Advanced Technology Chinese Academy of Sciences Zhuhai519000 China Perspective Technology Group Zhuhai519000 China Beijing Institute of Technology Zhuhai China Aier Eye Hospital of Zhuhai Zhuhai519000 China College of Artificial Intelligence Chongqing Industry & Trade Polytechnic Chongqing408000 China Business School Hong Kong University of Science and Technology Hong Kong999077 Hong Kong Company Ltd. Guangdong Shenzhen518000 China Jinan University Affiliated Shenzhen Eye Hospital Shenzhen China

ISBN: (纸本)9781510672840

Age-related Macular Degeneration (AMD) is one of the major causes of elders' vision losses, and therefore its early screening and treatment are the most efficient way to reduce the risk of blindness. AI-based methods based on ophthalmic images have great potential for AMD diagnosis. However, low levels of accuracy, robustness, and explainability are challenges for AI approaches to be clinically applied. Traditionally unsupervised methods (Hierarchical Clustering and K-Means) and supervised methods (SVM, VGG-16, and ResNet), are used for AI-based AMD detection using different image datasets. However, single data sources and single models are not able to reflect the real data distribution, thus leading to low accuracy and robustness. Thus, this study proposes a multi-data source fusion method and a multi-model fusion approach for detecting AMD. Based on Optical Coherence Tomography (OCT), Fundus Autofluorescence (FAF), regular color fundus photography (CFP), and Ultra-Wide field Fundus (UWF) images, the multi-data source fusion method preprocesses and enhances each type of data, extracts features using unsupervised ML models, combines and normalizes them, and learns a model using a multi-layer perception (MLP) algorithm. The multi-model fusion method builds the model using different supervised machine learning and deep learning algorithms and adopts a voting mechanism for the model selection and optimization. Findings show that the proposed methods achieve higher accuracy and robustness than the traditional methods. © 2024 SPIE.

关键词： Image fusion

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Classification of ECG Signals Using Machine learning for Detection of Cardiac Abnormalities

Classification of ECG Signals Using Machine Learning for Det...

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learning and Technology conference (L&T)

作者： Nawar Alimam Alhassan Alsaggaf Enfel Barakat Electrical and Computer Engineering Effat University Jeddah Saudi Arabia

ISBN: (数字)9798331532215

ISBN: (纸本)9798331532222

Cardiovascular diseases (CVDs) are the leading cause of mortality worldwide, with arrhythmias contributing significantly to these statistics. This study proposes a machine learning framework for automated ECG classification, focusing on Premature Ventricular Contraction (PVC), Atrial Fibrillation (AFib), and Ventricular Tachycardia (VTach). Normal electrocardiogram (ECG) signals from the MIT-BIH Arrhythmia database were integrated with synthetically generated abnormal signals, which were derived using mathematical models and implemented in MATLAB. Signals were segmented into 1-second intervals, with features extracted including P-wave amplitude, QRS width, and T-wave frequency. A Random Forest classifier achieved 0.88 accuracy, with high precision and recall for each class, including normal, atrial fibrillation, and ventricular ectopic beats, demonstrating balanced performance across all categories. The incorporation of synthetic data addressed dataset imbalances, particularly improving detection of minority classes like VTach. The framework demonstrates potential for real-time implementation in wearable devices and telemedicine platforms. Future work will focus on expanding the dataset and exploring deep learning techniques to enhance clinical applicability.

关键词： Accuracy Arrhythmia Atrial fibrillation Electrocardiography Feature extraction Real-time systems Heart rate variability Wearable devices Random forests Synthetic data

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End-to-End Fault Tolerant Control of Discrete Event System Using Recurrent Neural Networks 2

End-to-End Fault Tolerant Control of Discrete Event System U...

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2nd international Mobile, intelligent, and Ubiquitous Computing conference, MIUCC 2022

作者： El-Mahdy, Mostafa H. Maged, Shady A. Awad, Mohammed I. Ain Shams University Mechatronics Engineering Dept. Giza Egypt

ISBN: (纸本)9781665466776

Industry 4.0 is concerned with how the global production and supply network operates through the ongoing automation of traditional manufacturing systems which depends on closed-loop control system components. These components are prone to faults, especially in sensors. so, a fault-tolerant control framework is needed to deal with these kinds of faults. Many researchers work on developing more intelligence data driven fault-tolerant control techniques instead of traditional ones which need a complete sophisticated model. This paper proposes a learning fault tolerant control of discrete event systems subjected to sensors faults, based on recurrent neural networks. The proposed network has both previous and current sensors signals as inputs and current actuators signals as outputs. The new data driven fault tolerant control replaces the diagnosis and control reconfiguration block function in the traditional one. A case study that integrates automated pneumatic material handling is used to demonstrate the proposed approach. Factory I/O simulator is integrated with MATLAB based on the digital twin concept to simulate and verify the proposed approach. Results demonstrate that the proposed method is capable of handling sensors faults in faultless and various faulty scenarios. © 2022 IEEE.

关键词： Recurrent neural networks Fault tolerant systems Fault tolerant control Ubiquitous computing Sensor systems Production facilities Mathematical models

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Accuracy Study Based on Different Preprocessing Machine learning Approaches

Accuracy Study Based on Different Preprocessing Machine Lear...

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IEEE SSD international Multi-conference on Systems, Signals and Devices

作者： Muteeah Aljawarneh Rim Hamdaoui Rim Kilani Ahmed Zouinkhi Mohamed Naceur Abdelkrim MACS Research Laboratory National Engineering School of Gabes Gabes Tunisia Research Laboratory: MACS National Engineering School of Gabes Gabes Tunisia

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

With the ever-increasing data in the world, artificial intelligence (AI) is predicted to be an essential tool for converting huge data into meaningful data. The goals of machine learning (ML) are to identify complicated patterns in multi-dimensional data and utilize uncovered patterns to classify processes or predict data. Prediction accuracy is the accustomed measure of success in ML applications. In this paper, the effect of using a machine learning approach on accuracy value is studied. We compared three feature selection algorithms, namely IndFeat, MRMR, and ANOVA algorithms. The SVM classifier is applied to the selected features in order to compare the three algorithms and examine the effect of data reduction on the accuracy value. The simulation results showed that the ANOVA algorithm is better than the MRMR and IndFeat algorithms.

关键词： Support vector machines Training Machine learning algorithms Accuracy Simulation Machine learning Feature extraction Prediction algorithms Classification algorithms Analysis of variance

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Web engineering with Human-in-the-Loop 22nd

Web Engineering with Human-in-the-Loop

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22nd international conference on Web engineering (ICWE)

作者： Ustalov, Dmitry Pavlichenko, Nikita Tseytlin, Boris Baidakova, Daria Drutsa, Alexey Toloka Luzern Switzerland

ISBN: (纸本)9783031099175;9783031099168

Modern Web applications employ sophisticated Machine learning models to rank news, posts, products, and other items presented to the users or contributed by them. To keep these models useful, one has to constantly train, evaluate, and monitor these models using freshly annotated data, which can be done using crowdsourcing. In this tutorial we will present a portion of our six-year experience in solving real-world tasks with human-in-the-loop pipelines that combine efforts made by humans and machines. We will introduce data labeling via public crowdsourcing marketplaces and present the critical components of efficient data labeling. Then, we will run a practical session, where participants address a challenging real-world Information Retrieval for e-Commerce task, experiment with selecting settings for the labeling process, and launch their label collection project on real crowds within the tutorial session. We will present useful quality control techniques and provide the attendees with an opportunity to discuss their annotation ideas. Methods and techniques described in this tutorial can be applied to any crowd-sourced data and are not bound to any specific crowdsourcing platform.

关键词： Crowdsourcing

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On The Understanding of Customer Purchase Behavior: A Case Study on the Computer Systems E-Commerce Website 22

On The Understanding of Customer Purchase Behavior: A Case S...

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22nd international conference on Computational Science and Its Applications, ICCSA 2022

作者： Adak, Tahir Enes Sahin, Yunus Zaval, Mounes Aktas, Mehmet S. Casper Computer Systems Research and Development Center Istanbul Turkey Yildiz Technical University Computer Engineering Dept. Istanbul Turkey

ISBN: (纸本)9781665455848

The tremendous growth of the E-commerce industry over the last decade drew attention to the importance of understanding customers' behaviors and modeling user-system interactions to maximize customers' satisfaction by providing them the best user experience and gaining new customers to the e-commerce website. Within the scope of this study, software architecture _that can help us understand customers' purchase behaviors and then make a binary prediction based on their interactions with the system to determine whether a computer configuration will be bought_ is proposed and explained thor-oughly. For this purpose, machine learning algorithms are utilized to correctly model complex user-system interactions. A prototype of the proposed software architecture is developed using the latest technologies, and experimental studies are performed on top of this prototype in order to understand how successful our software architecture is. The conducted experimental results prove the efficiency of the proposed software architecture in real-world data, with 83% and 88% scores for the accuracy and F1-score metrics, respectively. © 2022 IEEE.

关键词： Software architecture

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Towards Multi-Label Open Set Diagnosis for Medical Images 22

Towards Multi-Label Open Set Diagnosis for Medical Images

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22nd IEEE international Symposium on Biomedical Imaging, ISBI 2025

作者： Pang, Shijie Zhao, Yuze Zhao, Rui-Wei Feng, Rui Academy for Engineering and Technology Fudan University Shanghai China School of Computer Science Fudan University Shanghai Key Laboratory of Intelligent Information Processing Shanghai China

ISBN: (纸本)9798331520526

In real clinical scenarios, diagnosis algorithms may face the complex concurrent presence of multiple common, rare, or even unknown disease conditions. In particular, rare disease categories may be underrepresented or even absent from the class-imbalanced training set, leaving safe and trustworthy diagnostic decision-making extremely challenging. In this work, we first formalize the medical image diagnosis as a multi-label open set recognition under class-imbalanced training data and propose a novel Multi-label Open set Diagnosis (MOSD) solution to address this problem. Our MOSD first employs a Balanced Multi-label data Augmentation (BMDA) module to augment multi-label image data to alleviate the class imbalance in model training more effectively. Then it also boasts a Multi-label Evidential Deep learning (MEDL) module with tailored learning objectives to better estimate the uncertainty for multi-label open set prediction. Experimental results on the ODIR-5K benchmark successfully demonstrate its superiority in both close set known class classification and open set unknown class detection. © 2025 IEEE.

关键词： evidential learning multi-label classification open set recognition uncertainty estimation

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T Line and C Line Detection and Ratio Reading of the Ovulation Test Strip Based on Deep 22nd

T Line and C Line Detection and Ratio Reading of the Ovulati...

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22nd international conference on intelligent data engineering and automated learning

作者： Wang, Libing He, Li Li, Junyu Zeng, Deane Wen, Yimin Guilin Univ Elect Technol Guangxi Key Lab Image & Graph Intelligent Proc Guilin 541004 Peoples R China Guangxi Police Coll Nanning 530028 Peoples R China Easy Healthcare Corp Guangzhou 510000 Peoples R China

ISBN: (纸本)9783030916077;9783030916084

The ovulation test strip is a tool for ovulation detection. At present, many APPs have been developed to analyze the photos taken from ovulation test strips to read the T/C ratio for detecting the level of luteinizing hormone (LH) in human urine. However, in the process of detecting the T and C lines, the background colors of the area near the T line or C line may be red, the T and C lines are sometimes fuzzy, and the colors of the T and C lines are sometimes distributed unevenly. In these cases, these APPs will be confronting difficulty to accurately detect the T and C lines and further read their ratio. To solve these problems, we proposed a method that consists of two steps. The first step is to use Mask R-CNN to locate the T and C lines, and the second step is to use a trained Pseudo-Siamese ratio reading network (PSRRNet) to read the ratio value based on the output results of the first step. The proposed PSRRNet consists of a Pseudo-Siamese network for simultaneously extracting the features of the T and C lines and a fully connected network to use the extracted features to predict the T/C ratio value. The experimental results illustrated that the proposed method suits to handle ovulation test strip reading.

关键词： Ovulation test strip Mask R-CNN Deep learning

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ScanCloud: Holistic GPR data Analysis for Adaptive Subsurface Object Detection 22

ScanCloud: Holistic GPR Data Analysis for Adaptive Subsurfac...

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22nd IEEE international conference on Information Reuse and Integration for data Science (IRI)

作者： Omwenga, Maxwell M. Wu, Dalei Liang, Yu Huston, Dryver Xia, Tian Univ Tennessee Chattanooga Dept Comp Sci & Engn Chattanooga TN 37403 USA Univ Vermont Dept Mech Engn Burlington VT 05405 USA Univ Vermont Dept Elect & Biomed Engn Burlington VT 05405 USA

ISBN: (纸本)9781665438759

The conventional ground penetrating radar (GPR) data analysis methods, which use piecemeal approaches in processing the GPR data formulated in variant formats such as A-Scan, B-Scan, and C-Scan, fail to provide a global view of underground objects on the fly to adapt the operations of GPR systems in the field. To bridge the gap, in this paper, we propose a novel GPR data analysis approach termed "ScanCloud" which is focused on the whole in situ GPR dataset rather than on individual A-Scans, B-Scans or C-Scans. We also study the integration of ScanCloud and a deep reinforcement learning method called deep deterministic policy gradient (DDPG) to adapt the operation of GPR system. The proposed method is evaluated using GPR modeling software called GprMax. Simulation results show the efficacy of ScanCloud and the adaptive GPR system enabled by the integration of ScanCluod and DDPG.

关键词： Autonomous Cognitive GPR deep reinforcement learning subsurface sensing object reconstruction 3D ScanCloud

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A Machine learning Based automated Model for Managing Student Dropout

A Machine Learning Based Automated Model for Managing Studen...

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international conference on Software engineering Research, Management and Applications (SERA)

作者： Partha Ghosh Arnab Charit Hindol Banerjee Debanwesa Bandhu Agniv Ghosh Ankita Pal Takaaki Goto Soumya Sen Department of Computer Science and Engineering Academy of Technology Adisaptagram India Faculty of Information Sciences and Arts Toyo University Saitama Japan A.K.Choudhury School of Information Technology University of Calcutta Kolkata India

ISBN: (数字)9798350391343

ISBN: (纸本)9798350391350

Addressing the persistent challenge of student dropout, particularly prevalent in developing countries like India, Bangladesh, etc. are of paramount importance. Factors such as poverty, natural calamities, and early marriages exacerbate this issue. High student dropout rates can negatively impact a country by diminishing its economic productivity, increasing social inequalities, and perpetuating a cycle of poverty. Addressing dropout issues requires comprehensive strategies to ensure a skilled and educated workforce, fostering societal well-being and global competitiveness. This research focuses on analysing comprehensive data on students who have dropped out. Thereafter, a machine learning based methodology is used to discern the underlying causes of student attrition in various schools. Furthermore, it allows for efficient monitoring of the state's educational landscape, with the ability to drill down to granular levels when necessary to identify specific regional challenges. The effectiveness of this approach is validated through the utilization of real-world datasets.

关键词： Productivity Disasters Machine learning Developing countries Monitoring Software engineering

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