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Enhanced Pediatric Pneumonia Diagnosis with Chest X-Ray using Deep Attention Mechanism 11

Enhanced Pediatric Pneumonia Diagnosis with Chest X-Ray usin...

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11th IEEE/ACM international conference on Big data Computing, Applications and Technologies, BDCAT 2024

作者： Zaman, Muhammad Fatima, Tahseen Hameed, Sana Haider, Shahzeb Akhunzada, Adnan Azeem, Muhammad University of Lahore Faculty of Computer Science Sargodha Pakistan University of Saint Thomas School of Engineering Saint PaulMN United States University of Doha for Science and Technology Department of Data and Cybersecurity Doha24449 Qatar

ISBN: (纸本)9798350367300

Development of deep learning technologies has resulted in a major revolution in the field of medical diagnostics in recent years. the purpose of this research is to examine the usage of Convolutional Neural Networks (CNNs) in combination with an integrated attention mechanism known as CoATNet to improve the accuracy and responsiveness of paediatric pneumonia diagnosis using chest X-ray analysis. the major goal of this research project is to build and verify a powerful deep learning model capable of detecting mild pneumonia symptoms in children's chest X-rays. CoATNet's innovative attention approach allows it to provide more weight to areas deemed to be extremely important. this is in addition to its ability to recognise major patterns in images. Furthermore, the paper considers the issues posed by class imbalances in medical imaging datasets and undertakes an in-depth assessment of how effectively CoATNet works across a large dataset of paediatric chest X-rays. CoATNet's remarkable performance was shown by its ability to achieve a Micro Average of 98.2%, a Weighted Average of 98.7%, and an Accuracy of 99.7%. these measures show that the model can distinguish between instances of pneumonia and those do not have the illness, and that it is resilient when faced with class imbalances. F1 Score of 98.9% reflects a well-balanced trade-off between Precision 98.7% and Recall 98.2%. the integration of deep learning technologies with attention processes in CoATNet has resulted in a big step forward in the development of diagnostic models that are more visible and useful. © 2024 IEEE.

关键词： Pediatrics

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7th international conference on Innovative Computing, IC 2024

7th International Conference on Innovative Computing, IC 202...

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7th international conference on Innovative Computing, IC 2024

ISBN: (纸本)9789819741816

the proceedings contain 157 papers. the special focus in this conference is on Innovative Computing. the topics include: Proactive Auto Scaling Based on Marginal Request Change Analysis for Reducing Tail Latency in Kubernetes Cluster;Integration of Generative Adversarial Networks (GAN) and AI Drawing in Criminal Sketches—Applied in Crime Scene Investigation by Law Enforcement;implementation of a High Quality Cardiopulmonary Resuscitation Teaching Intelligence Training and Assessment System;Predicting Twitter Posts from Fake Accounts Using XGBoost Model;the Interplay Between Self-regulated learning and Programming learning Achievement;a Study on the Continued Viewing Intention of YouTuber Videos: Experiential Marketing, Social Capital, and Habit Perspectives;an Exploratory Study on the Design of Human-Computer Interaction Interfaces for Augmented Reality Integrated Museum Exhibitions;communication-Aware Optimization of Microservice Scheduling on Edge Computing;Design of Computer Model for Raw Material Supplier Selection Based on Entropy Weight TOPSIS Method;exploring How Cybersecurity Competitions Can Help Improve Students’ Practical Abilities in Cybersecurity;Adversarially Residual U2Net for COVID-19 Lung Infection Segmentation from CT Images;partnerships Analysis Based on Measurements of Common Research Interest;System Design of Operating Costs of intelligent Healthcare Services for the AHP Algorithm;a Multi-party Private Set Union Protocol Against Malicious Adversary;analysis of the Current Status of Research on Unmanned Collaborative Equipment Above and Below Water;data Mining Analysis of New Energy Vehicles Based on Cluster Analysis Technology;Two-Layer Minimum Variance FIR Filter-Based DPLL Design;fuzzy Proximity Two-Dimension Inductance Gesture Recognition System Analysis and Implement;proximity Capacitive Gesture Recognition for Recursive Neighbor Memory Neural Network;application of Genetic Algorithms in automated Mechanical Design.

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Introducing Task-Adaptive Loss to Multitask learning for Electronic Healthcare Prediction 11

Introducing Task-Adaptive Loss to Multitask Learning for Ele...

引用

11th IEEE international conference on Healthcare Informatics, ICHI 2023

作者： Tsien, Edward Wu, Dezhi Tong, Yan Lopez-De Fede, Ana Gareau, Sarah University of South Carolina College of Engineering and Computing ColumbiaSC United States University of South Carolina Institute for Families in Society ColumbiaSC United States

ISBN: (纸本)9798350302639

Multi-Task learning (MTL) is a machine learning paradigm that allows for a model to simultaneously train and predict multiple related tasks/targets using a shared core network and task-specific branching layers. the related nature of the tasks can be exploited to regularize the model and has been shown to increase performance in all tasks. Medical healthcare records (MHRs) taken on events such as pregnancy often track multiple related targets such as maternal morbidity factors and long-term health outcomes which serve as an excellent testbed for MTL models. Imbalance in tasks leads to MTL models being dominated by a few tasks as well as struggling to progress during training due to conflicting loss function parameters, resulting in generalized but unspecific models. this study introduces Task-Adaptive Loss weighting, a novel method to address imbalanced tasks and conflicting loss and demonstrates iton a prenatal MHR dataset to create a prediction tool for adverse maternal outcomes. © 2023 IEEE.

关键词： Deep learning

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Enhancing Efficiency of Multi-Label X-Ray Image Classification with Self-Supervised learning Based On Compact Swin Transformers 11th

Enhancing Efficiency of Multi-Label X-Ray Image Classificat...

引用

11th international conference on Future data and Security engineering, FDSE 2024

作者： Vo, Tri-thuc Do, thanh-Nghi College of Information Technology Can Tho University Cantho92000 Viet Nam Sorbonne University Pierre and Marie Curie University Paris 6 France

ISBN: (纸本)9789819604333

In this paper, we introduce an approach to improve performance of Multi-Label Classification of X-Ray images with Self-Supervised learning (MLCXR-SSL). the SwinT-Compact architecture is also proposed to reduce model complexity and increase computational efficiency. By leveraging contrastive learning, features/representations are extracted from a wealth of unlabeled data, thereby improving data efficiency and overcoming the challenges posed by the restricted labeling of medical data. Our contribution includes refining an architecture to effectively apply self-supervised learning (SSL) to multi-label classification problems. Additionally, we conduct extensive experiments to compare the fine-tuning of the linear classifier from the ImageNet pre-trained model with those from the unlabeled X-ray image pre-trained model. this comparison is performed on both the SwinT architecture and our proposed SwinT-Compact architecture, both based on the Swin Transformer, using the Chest X-ray 14 dataset. Results show a significant performance gain achieved by fine-tuning the unlabeled X-ray image pre-trained model compared to the ImageNet pre-trained model, especially notable in SwinT (AUC 0.809 vs. 0.77). Furthermore, our proposed architecture maintains multi-label classification performance comparable to the SwinT architecture while reducing model complexity and training time. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Self-supervised learning

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Calibration of Traffic Microsimulation Models Using the Results of intelligent Video Analysis of the Traffic 27

Calibration of Traffic Microsimulation Models Using the Resu...

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27th international Scientific conference on Transport Means 2023

作者： Sistuk, V. Monastyrskyi, Y. Maksymenko, I. Kryvyi Rih National University V.Matusevych St.11 Kryvyi Rih50027 Ukraine

ISBN: (纸本)9781713879534

Estimation of traffic state, road design, and traffic re-engineering at the sections of the road network requires data on traffic volumes, vehicle speeds, accelerations/decelerations, and other traffic parameters for the specific time horizon. However, traffic counting utilizing visual observations in the field conditions is raveled by the negative impact of the environment on traffic counter, working hours increasing, unpredictability, and unavailability of observation results verification. Overcoming these difficulties is in the realm of possibilities by using automated traffic data processing. automated traffic video analysis software with artificial intelligence (AI) technology is competing with traditional methods of field traffic data collection. In recent years, field traffic data collection methods based on intelligent video analysis of traffic are gained ground. Origin-destination matrixes and traffic dynamics measures for each vehicle can be obtained from traffic video footage post-process. that provides opportunity to develop a traffic microsimulation model more reliably and accurately. the paper presents the technique of traffic microsimulation model calibration by using parameters obtained because of traffic video analysis. © 2023 Kaunas University of Technology. All rights reserved.

关键词： Computer vision

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learning Optimization for Dispatch of Interregional Power Grid under Uncertain Environment 11

Learning Optimization for Dispatch of Interregional Power Gr...

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11th IEEE data Driven Control and learning Systems conference, DDCLS 2022

作者： Tang, Hao Zhang, Yan Li, Xiaoqing Lv, Kai School of Electrical Engineering and Automation Hefei University of Technology Hefei230009 China Earth observation R & D Center The 38th Research Institute of China Electronics Technology Group Corporation Hefei China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

this study investigates the dispatch problem in interregional power grids with uncertain renewable energy sources and loads. A knowledge-transfer-based hierarchical learning optimization method is designed to search for the optimal dispatch strategy. First, the model of various sources, loads, and tie-lines are established according to each components characteristics in the system. the dispatch process of an interregional power grid is correspondingly divided into tie-line dispatch in the upper layer and both generator units and flexible load dispatch in the lower layer. the dispatch optimization problems of both layers are formulated with a hierarchical Markov decision process model. Finally, a learning optimization method is adopted to obtain the dispatch strategy, in which a pre-learning technique is incorporated. A case study is performed to validate the effectiveness of the proposed method, and the simulation results show that the pre-learning technique improves the learning speed and reduces the cost of relearning. © 2022 IEEE.

关键词： Renewable energy resources

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Research on a New Flow Measurement for intelligent Injection Tools 11

Research on a New Flow Measurement for Intelligent Injection...

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11th Annual international conference on Material Science and Environmental engineering, MSEE 2023

作者： Wang, Min Deng, Gang Liu, Junli Yue, Qingfeng Sun, Yufei Daqing Oilfield Production Technology Institute Daqing Oilfield Limited Company Daqing163453 China Heilongjiang Provincial Key Laboratory of Oil and Gas Reservoir Stimulation Daqing163453 China Development Department of Daqing Oilfield Limited Company Daqing163453 China No.4 Oil Production Plant Daqing Oilfield Limited Company Daqing163453 China

intelligent separate injection technology can achieve continuous monitoring data of layered water Injection wells. So it can find out the inefficient water circulating formation timely. then the water injection schemes can be adjusted in real-time to ensure the high water injection pass rate. It can also increase the working efficiency of the workers' because of its automatic mode. But its prime cost is high, which limits its large-scale industrialization application in fine layered water injection wells. the key part is the intelligent water distributor in the intelligent separate injection technology. the cost of electromagnetic flow meter accounts for 38% in the intelligent water distributor. the paper intends to research on flow measurement without flow meter to reduce the high-cost. the pressure difference equation was established for flow rate and the opening ratio of the nozzle using the dimensionless analysis method. the empirical parameters of the equation were obtained through laboratory experiments. the pressure sensors are used to monitor the pressure of water injection wells while achieving the measurement of layered flow rate. the laboratory experiments results indicate that the calculated flow rate obtained by this method coincides well with the standard flow rate. the relative error is less than 12%. then, it provides a new flow measurement method for intelligent injection tools. the most significant feature is that the flow meter is no longer mandatory for intelligent water distributors. Due to the cheaper cost that it provides a technical support for the large-scale industrialization application of intelligent separate injection technology in fine layered water injection wells. © 2024 Institute of Physics Publishing. All rights reserved.

关键词： Difference equations

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Multi-objective Multigraph A∗ Search with learning Heuristics based on Node Metrics and Graph Embedding 11

Multi-objective Multigraph A∗ Search with Learning Heuristi...

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11th IEEE international conference on intelligent Systems, IS 2022

作者： Liu, Songwei Chen, Jun Weiszer, Michal University of London School of Engineering and Materials Science Queen Mary London United Kingdom

ISBN: (纸本)9781665456562

To solve general multi-objective multigraph shortest path problems, this paper proposes an algorithm (MOMGA*) that incorporates an online likely-admissible learning-based heuristic function to accelerate the solution-finding process. MOMGA∗ is an extended and generalised version of the airport multi-objective A∗ (AMOA*) algorithm that is tailored for a specific application problem. the online heuristic function is added and developed using artificial neural networks that estimate the costs between two nodes based on their metrics. To implement this metric-based prediction, a graph embedding technique is adopted to learn node feature representations. Results on a range of benchmark multi-objective multigraphs show that (i) in the absence of heuristic information, MOMGA∗ can deliver the same Pareto optimal solutions as AMOA∗ does, while requiring less computational time, and (ii) empowered by the likely-admissible learning- based heuristics, MOMGA∗ is able to provide a set of optimal and near-optimal solutions and strike a good balance between optimality and tractability. © 2022 IEEE.

关键词： Neural networks

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7th international conference on Innovative Computing, IC 2024

7th International Conference on Innovative Computing, IC 202...

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7th international conference on Innovative Computing, IC 2024

ISBN: (纸本)9789819741922

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Machine learning-Driven Optimization of Livestock Management: Classification of Cattle Behaviors for Enhanced Monitoring Efficiency 24

Machine Learning-Driven Optimization of Livestock Management...

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7th international conference on Machine learning and Machine Intelligence, MLMI 2024

作者： Zhao, Zhuqing Shehada, Halah Ha, Dong Dos Reis, Barbara White, Robin Shin, Sook Electrical and Computer Engineering Virginia Tech Blacksburg United States Department of CREC-White Sand Unit Mississippi State University Starkville United States School of Animal Sciences Virginia Tech Blacksburg United States

ISBN: (纸本)9798400717833

Monitoring cattle health in remote and expansive pastures poses significant challenges that necessitate automated, continuous, and real-time behavior monitoring. this paper investigates the effectiveness and reliability sensor-based cattle behavior classification for such monitoring, emphasizing the impact of intelligent feature selection in enhancing classification performance. To achieve this, we developed Wireless Sensor Nodes (WSN) affixed to individual cattle, enabling the capture of 3-axis acceleration data from five cows across varying seasons, spanning from summer to winter. Initially, we extracted a comprehensive set of 52 features, representing a broad spectrum of cow behaviors alongside statistical attributes. To enhance computational efficiency, we employed the Recursive Feature Elimination (RFE) method to distill 30 critical features by discarding redundant or less significant ones. Subsequently, these optimized features were utilized to train four machine learning (ML) models: Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), Random Forest (RF), and Histogram-based Gradient Boosted Decision Trees (HGBDT). Notably, the HGBDT model demonstrated superior performance, achieving remarkable F1-scores of 99.01% for'grazing', 98.74% for'ruminating', 89.62% for'lying', 84.06% for'standing', and 91.87% for'walking'. these findings underscore the potential of our approach to serve as a robust framework for precision livestock farming, offering valuable insights into enhancing cattle health monitoring in remote environments. © 2024 Copyright held by the owner/author(s).

关键词： Support vector machines

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