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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Shihuan He Zhihui Lai Heng Kong College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Breast and Thyroid Surgery Baoan Central Hospital of Shenzhen Shenzhen China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Medical image segmentation is a challenging task especially when dealing with unlabeled data. It has been proven that the use of complementary information for co-training is effective for medical image segmentation. We find that high-confidence regions are easy to segment, but low-confidence regions are difficult to correctly segment, which results in poor segmentation performance. Therefore, accurate segmentation in low-confidence regions is the effective way to enhance the performance of medical image segmentation. To achieve this goal, we propose a novel co-training strategy named low-confidence heterogeneous pixel-prototype contrastive learning with uncertainty-guide cross supervision for semi-supervised medical image segmentation. Specifically, an uncertainty-guided cross supervision module is firstly designed to estimate the confidence score of predictions from multiple outputs and then perform cross supervision between high-confidence regions. Moreover, we design a low-confidence heterogeneous prototype contrastive learning, which builds the relationship between low-confidence pixel and heterogeneous prototype to mine discriminative information in low-confidence regions so as to improve the segmentation performance. Extensive experiments on medical image datasets demonstrate that our method outperforms state-of-the-art methods in segmentation results.

关键词： Image segmentation Accuracy Biological system modeling Prototypes Contrastive learning Bioinformatics Biomedical imaging

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Effectiveness of Using Deep Learning for Blister Blight Identification in Sri Lankan Tea

Effectiveness of Using Deep Learning for Blister Blight Iden...

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2023 International Research Conference on Smart Computing and Systems engineering, SCSE 2023

作者： Hewawitharana, G.H.A.U. Nawarathne, U.M.L.A. Hassan, A.S.F. Wijerathna, Lochana M. Sinniah, G.D. Vidhanaarachchi, Samitha P. Wickramarathne, Jagath Wijekoon, Janaka L. Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka Tea Research Institute of Sri Lanka Plant Pathology Division Talawakelle Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka Military Technological College Systems Engineering Department Muscat Oman

ISBN: (纸本)9798350341454

Ceylon tea industry faces a major challenge in the form of pathogen-induced crop loss, with Blister Blight (BB) caused by Exobasidium vexans posing the greatest threat, leading to harvest losses of over 30%. This fungus attacks the tender tea shoots, resulting in a direct negative impact on the tea harvest. This paper presents a system to identify the suspicious tea leaves and BB disease at its early stages along with an assessment of severity, offering a potential solution to this critical issue. By utilizing real-time object detection, the system filters out non-tea leaves from the captured initial image of a segment of a tea plant. The identified tea leaves are then subjected to BB identification and severity assessment based on differing visual symptoms of the BB stages. This approach enables the system to accurately identify BB in the initial stage and severity stage, allowing for timely and targeted intervention to minimize crop losses. The YOLOv8 model has been able to correctly identify 98% of the objects it has detected as relevant (precision), and it has been able to correctly identify 96% of all the relevant objects present in the scene (recall). The Residual Network 50 (Resnet50) convolutional neural network (CNN) model was selected as the final model, achieving an accuracy of 89.90% during the training phase and an accuracy of 88.26% during the testing phase. © 2023 IEEE.

关键词： Convolutional neural networks

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Pipeline Representation Using Generic Parallel Algorithms in Heterogeneous Parallel Computing. A comparative analysis 36

Pipeline Representation Using Generic Parallel Algorithms in...

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36th European Modeling and Simulation Symposium, EMSS 2024, Held at the 21st International Multidisciplinary Modeling and Simulation Multiconference, I3M 2024

作者： Rossainz-López, Mario Sánchez-Rinza, Bárbara López-Sosa, Zuriel Capel-Tuñon, Manuel Faculty of Computer Science Autonomous University of Puebla Av. San Claudio and 14 Sur Street San Manuel PueblaC.P. 72570 Mexico Software Engineering Department College of Informatics and Telecommunications ETSIIT University of Granada Daniel Saucedo Aranda s/n Granada18071 Spain

ISBN: (纸本)9791281988026

Using Parallel Objects and Structured Parallel Programming, the parallel representation of the Communication Pattern between Processes called Pipeline is shown, whose implementation is carried out through different models of Generic Parallel Algorithms within Heterogeneous Parallel Computing (HPC): As a Parallel Design Pattern or PDP, as High-Level Parallel Composition or HLPC and as Flow Graph Interfaces or FGI through programming with message passing. An original and effective development methodology for new programmers in parallelism is proposed for each of them. In these three proposals for Generic Parallel Algorithms, the same problem-example is solved as a case study, and a comparative analysis of their models and designs is carried out, as well as the performance obtained in the execution of said proposals and demonstrate their usefulness, programmability, and performance. The objective of this work is to provide the programmer and/or novice user with different multicore programming approaches so that without much effort they can develop their programs according to a sequential programming style, obtaining automatically, easily and the counterpart parallelization of your code with the help of a specific programming environment like the one proposed. © 2024 The Authors.

关键词： Structured programming

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Design of Concurrent engineering Systems for Global Product Development Using Artificial Intelligence 10th

Design of Concurrent Engineering Systems for Global Product ...

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10th International Conference on Innovations in computer science and engineering, ICICSE 2022

作者： Karn, Arodh Lal Mehbodniya, Abolfazl Webber, Julian L. Jayagopal, Vellingiri Stalin David, D. Rangasamy, Rajasekar Sengan, Sudhakar Department of Financial and Actuarial Mathematics School of Mathematics and Physics Xi’an Jiaotong-Liverpool University Jiangsu Suzhou215123 China Kuwait City Kuwait Department of Software and System Engineering School of Information Technology and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Information Technology Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Tamil Nadu Chennai600072 India Department of Computer Science and Engineering GITAM School of Technology GITAM University Karnataka Bengaluru561203 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli627152 India

ISBN: (纸本)9789811974540

Competition and concurrent engineering (CE) businesses are growing. Faster product creation, quality improvement, manufacturing process adoption, and reduced client demand cost are vital for corporate success. Implement concurrent engineering to make high-quality goods. CE is a parallel, systematic method to design. The chief executive officer (CEO’s) approach impacted how many firms build products. You may use the fundamental ideas of concurrent engineering conversation and the failure mode and effects analysis (FMEA) tool. To provide a smooth and efficient CE environment, conduct the product development process from market research to detailed design. Concurrent engineering is a novel product development (NPD) approach. Concurrency and cross-functional integration separate CE from traditional product development. NPD continues through developing ideas, validation, proof-of-concept validation, Minimum Viable Product development, pre-launch, and post-launch. In other words, product discovery should continue throughout its life span. Each organization's growth operations operate simultaneously. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Product development

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A Novel Model-Driven Framework for Performance Testing of Service-Oriented Architecture

A Novel Model-Driven Framework for Performance Testing of Se...

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International Bhurban Conference on Applied sciences & Technology, IBCAST

作者： Ibrahim Khalil Farooque Azam Wasi Haider Butt Muhammad Waseem Anwar Yawar Rasheed Israr Ahmad Abdul Waheed Department of Computer and Software Engineering College of Electrical and Mechanical Engineering National University of Sciences and Technology (NUST) Islamabad Pakistan Department of Computer Science Women University Swabi Pakistan

ISBN: (数字)9798350308259

ISBN: (纸本)9798350308266

Nowadays the software development approach is changing from the complete applications development to obtaining off the shelf services. The organizations are acquiring services from the relevant software service providers as desired. This concept is rapidly growing due to its benefits such as risk avoidance and time efficiency. One of the problems to the client in the service oriented architecture is the endorsement of the expected service for the allowed time frame. Services that are to be obtained are supposed to be highly responsive. The focus of research is to measure the performance of the service that is to be added into the service oriented architecture. The adapted approach in the paper is Model-Driven. A Meta-Model is proposed that enables the testing for the performance of the service. Further the framework is supporting its tree editor and Sirius tool which are both distributable via eclipse bases plugin. In the proposed framework, performance with respect to response time of the service and the request processing time of the service is calculated, analyzed, and highlight the performance of the service to be obtained. For the performance test, test cases are generated through an algorithm using sequence and use case diagram. The framework is validated through a related case study in which performance testing is performed on a service being obtained.

关键词： Adaptation models Organizations software Service-oriented architecture Time factors Testing software development management

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A Review of Autonomous Glaucomatous Grading via OCT Imagery 2

A Review of Autonomous Glaucomatous Grading via OCT Imagery

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2nd International Conference on Digital Futures and Transformative Technologies, ICoDT2 2022

作者： Raja, Hina Hassan, Taimur Hassan, Bilal Seneviratne, Lakmal Dias, Jorge Werghi, Naoufel Nust Biomisa Lab Department of Computer and Software Engineering College of Eme Rawalpindi Pakistan Khalifa University C2PS and Kucars Department of Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates Khalifa University Kucars Department of Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates Khalifa University Kucars Department of Mechanical Engineering & Robotics Abu Dhabi United Arab Emirates

ISBN: (纸本)9781665498197

Glaucoma is a progressive and degenerative optic neuropathy that damages the optic nerve due to increased intraocular pressure and yields various visual field abnormalities. Any damage to the optic nerve (due to glaucoma) is irreversible because of the incapacity of the retinal nerve fibers to regenerate. However, the glaucomatous progression can be properly cured if it is diagnosed in the early stages. Furthermore, health authorities worldwide have prioritized the early detection and prevention of glaucomatous progression, and in this regard, presenting a comprehensive insight on the recent literature serves as a critical benchmark to both researchers and clinicians. Towards this end, this paper presents a detailed overview of the state-of-the-art clinical and technical methods related to glaucomatous detection, grading, and prognosis. The clinical work focuses on glaucoma diagnosis and progression tracking in different retinal regions such as the retinal layers, optic never head, and lamina cribrosa, while the technical works focus on producing lesion-aware screening and grading of glaucomatous pathologies via optical coherence tomography. © 2022 IEEE.

关键词： Grading

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A Facial Expression Recognition Method based on Residual Separable Convolutional Neural Network

Journal of Network Intelligence

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Journal of Network Intelligence 2022年第1期7卷 59-69页

作者： Xu, Xiaoyu Cui, Jianfeng Chen, Xuhui Chen, Chin-Ling College of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China College of Software Engineering Xiamen University of Technology Xiamen361024 China Fujian Key Laboratory of Internet of things application technology Xiamen361024 China Xiamen City University of Technology Xiamen361024 China School of Computer and Information Engineering Xiamen City University of Technology Xiamen361024 China School of Information Engineering Changchun Sci-Tech University Changchun130600 China Department of Computer Science and Information Engineering Chaoyang University of Technology Taichung41349 Taiwan

Facial expression recognition is a typical application of artificial intelligence. It has practical value and commercial prospects in application scenarios such as safe driving, medical assistance, online classrooms, and human-computer interaction. Traditional convolutional neural networks have low accuracy in facial expression recognition. To solve this problem, we propose a residual separable convolutional neural network, which combines depth-wise separable convolution with an improved residual network. When transmitting feature information, it fuses facial expression features through skip connection and utilizes global average pooling to reduce dimension, thus preventing gradient disappearance. We conduct experiments based on the data set FER2013. The results show that it can improve the accuracy of facial expression recognition, reduce model parameters and raise the utilization efficiency of the parameters effectively. © The Authors.

关键词： Convolution

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Empirical Study on Mispronunciation Detection for Tajweed Rules during Quran Recitation

Empirical Study on Mispronunciation Detection for Tajweed Ru...

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Computing and Informatics (ICCI), International Conference on

作者： Yousef S Alsahafi Muhammad Asad Department of Information technology Computing and Information technology at Khulis College University of Jeddah Jeddah Saudi Arabia Department of Computer and Software Engineering College of Electrical and Mechanical Engineering National University of Science and Technology Islamabad Pakistan

This paper presents an extensive empirical study aiming to identify the optimal combination of feature extraction techniques and machine learning algorithms, including deep learning, for automated mispronunciation detection during Quran recitation. Three feature extraction methods-Mel-Frequency Cepstral Coefficients (MFCC), Spectrogram, and Kaldi pitch - are systematically evaluated in conjunction with a diverse set of classifiers, including Gradient Boosting (GB), Support Vector Machines (SVM), Logistic Regression (LR), Random Forest (RF), Artificial Neural Network (ANN), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) networks. The study leverages the QDAT dataset, comprising 1500 audio samples from 150 readers, with metadata specifying age, gender, and three distinct rules for mispronunciation detection. Experimental results demonstrate that the integration of MFCC with deep learning classifiers consistently achieves the highest performance across multiple evaluation metrics. Model robustness is validated using receiver operator characteristic curves (ROC) and accuracy metrics.

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Application of Deep Learning Algorithm for Prediction of Flood Severity

Application of Deep Learning Algorithm for Prediction of Flo...

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Sustainable Computing and Smart Systems (ICSCSS), International Conference on

作者： C Jamunadevi N Naveen M Manimaran M Janani Department of Computer Technology PG Kongu Engineering College Erode Department of IT Excel Engineering College Namakkal School of Computing Science and Engineering VIT Bhopal University Bhopal Madhya Pradesh India M.Sc (Software Systems) Kongu Engineering College Erode

ISBN: (数字)9798350379990

ISBN: (纸本)9798350391558

Natural disasters like floods have caused immense harm to lives and resources, underscoring the need for accurate prediction methods to mitigate their impact. Meteorological flood indices such as the standardized precipitation index help identify flood occurrences and severity levels, enabling proactive measures. While past research primarily used statistical methods like Least Square-Support Vector Machine (LS-SVM), these often lack accuracy for long-term forecasts. Advancements in machine learning, particularly artificial neural networks and deep neural networks, offer more accurate predictions over extended periods. Long Short-Term Memory (LSTM) models and Convolutional Neural Networks (CNN) are used to predict flood indices, handling real-time nonlinear data effectively. Benchmarking trials have shown that an LSTM and CNN configuration produced the best predictions, with an accuracy, prediction, MSE, and RMSE of 97.93,0.89, 0.01, and 0.10 respectively.

关键词： Accuracy Weather forecasting Predictive models Surfaces Data models Vectors Floods

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WiFi-based Positioning System with k-means Clustering and Outlier Removal: Evidence from Multiple Datasets 23

WiFi-based Positioning System with k-means Clustering and Ou...

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12th International Symposium on Information and Communication Technology, SOICT 2023

作者： Pham-Hai, Dang Duong-Bao, Ninh He, Jing Nguyen Thi, Luong Lee, Seon-Woo Nguyen-Huu, Khanh Department of Electronics and Telecommunications Dalat University Dalat Viet Nam College of Computer Science and Electronic Engineering Hunan University Changsha China Faculty of Information Technology Dalat University Dalat Viet Nam Division of Software Hallym University Chuncheon Korea Republic of

ISBN: (纸本)9798400708916

Indoor positioning systems are receiving much attention due to their practical applications in our daily lives. WiFi Fingerprinting is the technique that is commonly used for indoor positioning due to its utilization of the WiFi infrastructure which is ubiquitously deployed in modern buildings. However, this technique suffers from the computational burden when determining the user's position in big areas that consist of lots of fingerprints and reference points. To reduce the search space as well as computing time, the clustering algorithm can be applied. In this paper, we evaluate the efficacy of k-means clustering in the WiFi Fingerprinting-based positioning system. Moreover, k-means clustering with outlier removal is applied in several public datasets, in which the WiFi data was collected in different environmental conditions, to show how much the clustering algorithm can help in reducing the computing time as well as improving the positioning accuracy. From the experimental results, compared to popular deterministic-based algorithms, the clustering algorithm can significantly reduce the computing time by up to 79.07%. Nevertheless, it cannot surpass the positioning accuracy of the compared algorithms. © 2023 ACM.

关键词： Indoor positioning systems

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