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7th International Conference on Emerging Applications of information Technology, EAIT 2022

作者： Chakraborty, Somenath Murali, Beddhu Department of Computer Science and Information Systems Leonard C. Nelson College of Engineering and Sciences West Virginia University Institute of Technology Beckley United States Computer Science and Engineering University of North Texas Denton United States

ISBN: (纸本)9789811951909

The COVID-19 which is caused by the severe acute respiratory syndrome coronavirus 2 or SARS-CoV-2, has taken a lot of human life and still continuing, and significantly disrupting the healthcare system. Due to challenges and controversies to testing for COVID-19, improved, alternative cost-effective, and machine learning methods are needed to detect the disease and related data analysis. For this purpose, machine learning (ML) approaches emerge as a strong forecasting method to detect a disease including COVID-19. Our proposed ensemble machine learning (EML) is a technique that leverages multiple deep learning models and then combines them to produce improved results. In this paper, we proposed an EML approach to detect COVID-19 using chest x-ray images. Radiographic images are readily available, which can be used as an effective tool compared to other expensive and time-consuming pathological tests, but not to replace pathological tests but rather give alternative extra confirmation and more detailed analysis to the medical fraternity. In conclusion, automatic computational machine learning models allow for rapid analysis of chest X-ray images and thus enable radiologists to filter potential candidates in a time-effective manner to detect COVID-19. Our proposed approach has very promising results with an average detection accuracy of 93.56% and a sensitivity of 91.24%, and an F1 score is 0.91. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Coronavirus

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An Isomerism Learning Model to Solve Time-Varying Problems Through Intelligent Collaboration

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IEEE/CAA Journal of Automatica Sinica 2023年第8期10卷 1772-1774页

作者： Zhihao Hao Guancheng Wang Bob Zhang Leyuan Fang Haisheng Li the Department of Computer and Information Science University of MacaoMacao 999078China the School of Data Science the Chinese University of Hong KongShenzhen 518172 Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen 518000 China Industrial Control Systems Cyber Emergency Response Team Beijing 100040China IEEE the College of Electrical and Information Engineering Hunan UniversityChangsha 410082 the Peng Cheng Laboratory Shenzhen 518000China the Beijing Key Laboratory of Big Data Technology for Food Safety Beijing Technology and Business UniversityBeijing 100048 the School of Computer Science and Engineering Beijing Technology and Business UniversityBeijing 100048China

Dear Editor,This letter deals with a solution for time-varying problems using an intelligent computational(IC)algorithm driven by a novel decentralized machine learning approach called isomerism *** order to meet the challenges of the model’s privacy and security brought by traditional centralized learning models,a private permissioned blockchain is utilized to decentralize the model in order to achieve an effective coordination,thereby ensuring the credibility of the overall model without exposing the specific parameters and solution process.

关键词： ism thereby letter

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Augmenting the Perceptual Experience of Being Faced Using Gaze Modeling during Online Video Viewing

Augmenting the Perceptual Experience of Being Faced Using Ga...

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IEEE International Conference on systems, Man and Cybernetics

作者： Yuto Nakajima Masakazu Hirokawa Modar Hassan Kenji Suzuki Systems and Information Engineering Graduate School of Science and Technology University of Tsukuba Japan Data Science Laboratories NEC Corporation Japan Institute of Systems and Information Engineering University of Tsukuba Japan

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Live Performances have been conducted online in recent years due to the influence of the COVID-19 pandemic. This generated a novel problem of losing eye contact with the performers, which deteriorates the viewing experience in the online setting. This study proposes a feedback system based on gaze modeling to augment the perceptual experience of being faced by the performer during an online live performance viewing. This system aims to improve the experience of viewing online live performances by recreating a perceptual experience of interaction between the performer and the audience. The proposed system consists of a gaze recognition unit that judges whether the performer is looking at the camera based on gaze modeling in the video, and a stimulus presentation unit that presents the judgement result to the audience through vibration stimulus. This paper describes the gaze recognition model, experiments to determine the parameters to be used in the model, and the evaluation of live performance viewing experiments using the proposed system.

关键词： Vibrations COVID-19 Analytical models Visualization Sensitivity Pandemics Cameras Cybernetics

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Parallelism or Fairness? How to Be Friendly for SSDs in Cloud Environments

Parallelism or Fairness? How to Be Friendly for SSDs in Clou...

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2024 IEEE International Conference on Cluster Computing, CLUSTER 2024

作者： Zhou, Yang Wang, Fang Shi, Zhan Feng, Dan Engineering Research Center of Data Storage Systems and Technology Ministry of Education of China Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System Wuhan China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798350358711

Modern SSDs achieve low latency and high throughput by utilizing multiple levels of SSD parallelism. Fairness is also a critical design consideration in cloud environments and has inspired great interest in recent years to study it. However, we find that excessive striving for SSD parallelism can inadvertently harm the SSD fairness due to the striped data layout. We observe from our experimental analysis that some common access patterns in cloud loads (e.g., high-intensity I/O) are not addressed by SSD parallelism, which can affect both SSD parallelism and fairness. In this paper, we analyze two address mapping policies with orthogonal characteristics from SSD parallelism and fairness perspectives. Based on extensive experiments and observations, we schedule a hybrid address mapping policy to achieve fairness of SSDs in cloud environments by distinguishing different I/O access modes and cold/hot data, while ensuring SSD performance. Experimental results on the latest block-level I/O traces show that our approach significantly improves performance and fairness by up to 77.3% and 62.8%, respectively, over the previous schemes with negligible cost. Meanwhile, it can also reduce the write amplification by 23.5% to 33.5%. © 2024 IEEE.

关键词： Cloud platforms

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Image-based Cutting Tool Health Diagnosis in Multi-machine Settings 6

Image-based Cutting Tool Health Diagnosis in Multi-machine S...

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6th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Mishra, Debasish Pattipati, Krishna R. Bollas, George M. Data Science and Information Systems Krea University 5655 Central Expressway Sri City517646 India Electrical and Computer Engineering University of Connecticut 371 Fairfield Way Storrs06269 United States Chemical and Biomolecular Engineering University of Connecticut 159 Discovery Drive Storrs06269 United States

Manufacturers are increasingly focused on evaluating the condition of cutting tools during machining processes to ensure high-quality production. This study introduces a method for accurately and cost-effectively diagnosing the health of cutting tools. The method employs image similarity indices-such as Bhattacharyya distance, Fourier transform, and Cosine similarity-to compute indicators of tool health from wavelet coefficient maps of cutting signals. These indicators are then used in an unsupervised machine learning (ML) algorithm, agglomerative clustering, to categorize the tool health state. To validate the method, run-to-failure tests of the tool were conducted on two different machines with varying specifications and machining parameters. The results demonstrate a strong association between the computed indicators and the tool health, regardless of the differences between the two machines and their cutting settings. This is due to the reliance of the method on the physics of machining processes to derive the health indicators. The ML model effectively categorizes the progressive health of cutting tools and provides easily interpretable results on the health of the tool due to the explainability of the indicators. © 2025 The Authors. Published by Elsevier B.V.

关键词： Clustering Machine Learning Precision machining Similarity Tool condition monitoring Unsupervised

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Geospatial Analysis of Extreme Temperature Impacts in Agricultural systems Using Machine Learning

Geospatial Analysis of Extreme Temperature Impacts in Agricu...

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IEEE International Symposium on systems engineering (ISSE)

作者： Rayshaun L. Wheeler John S. Miller Gia N. Smith James H. Lambert Cody A. Pennetti Garrick E. Louis Deandre A. Johnson Department of Civil & Environmental Engineering University of Virginia Charlottesville VA USA School of Data Science University of Virginia Charlottesville VA USA Department of Systems & Information Engineering University of Virginia Charlottesville VA USA Department of Engineering & Society University of Virginia Charlottesville VA USA

ISBN: (数字)9798350353723

ISBN: (纸本)9798350353730

Recent higher temperatures, observed compared to previous years, have led to more frequent and intense heat waves, adversely affecting agriculture and human health. This paper explores using Inverse Distance Weighting (IDW) and Kriging to develop GIS layers for a heat-based risk index. Recent increases in global mean temperatures have led to more frequent and intense heat waves, impacting food supply, vegetation, and human health. Optimal plant growth temperatures in temperate climates range from 20°C to 30°C, with nighttime temperatures also being critical. Exceeding these temperature ranges can reduce plant immunity, cause disease, and result in poor crop yields. For example, mango cultivars need a "safe" temperature range of 10°C to 12°C to avoid damage. The intensification of higher temperatures established July 2023 record the hottest days on Earth, highlighting the need for effective monitoring and management tools. IDW, a deterministic method, offers a straightforward and efficient approach by assuming closer points have more influence. Kriging, a probabilistic geostatistical technique, accounts for spatial autocorrelation and prediction uncertainty, making it suitable for complex datasets. The study compares these methods' performance in creating continuous surfaces representing heat-based risk. IDW is simple and efficient but less accurate in sparsely populated areas, while Kriging is more accurate and detailed but requires more computational resources and expertise. An automated workflow for data preprocessing, model training, and cross-validation ensures consistent results. The study recommends adopting IDW for initial assessments and transitioning to Kriging as expertise grows. Enhanced data collection on temperature and population density is also advised to improve GIS layer accuracy, aiding better decision-making and resource allocation to mitigate extreme heat effects on agriculture and public health.

关键词： Heating systems Support vector machines Training Temperature distribution Accuracy Computational modeling Decision making Linear regression Resource management Random forests Climate change

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Emergency Vehicle Detection Using Deep ConvNet2D and Computer Vision 2

Emergency Vehicle Detection Using Deep ConvNet2D and Compute...

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2nd International Conference on Emerging Trends in information Technology and engineering, ic-ETITE 2024

作者： Poongavanam, Elumalaivasan Prathik, A. Munirathinam, T. Kayalvizhi, V. Rajesh, R. Mahavishnu, C. Srm Institute of Science and Technology Department of Data Science and Business Systems Chennai Kattankulathur India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Sona College of Technology Department of Information Technology Salem India School of Computer Science and Engineering and Information Science Presidency University Bangalore India School of Computer Science and Engineering Vellore Institute of Technology Chennai India Shreenivasa Engineering College Department of Computer Science and Engineering Dharmapuri India

ISBN: (纸本)9798350328202

A surveillance system detects emergency vehicles stuck in traffic. This system helps manage traffic because the number of vehicles on the road has been increasing daily for years, causing congestion. This project implements Deep ConvNet2D (Convolutional Network 2D) and Computer Vision emergency vehicle recognition. We propose a CNN-based real-time image processing model for emergency vehicle detection. The signal control unit can be set to terminate the round robin sequence when an emergency vehicle is detected. A CNN trained on Indian ambulance images solves the problem. Tensor Flow, a Python library, was used for training. Our method detects and classifies emergency cars well. Existing systems use ANN algorithm, which is inaccurate and inefficient. The system uses Deep ConvNet2D Algorithm. The proposed real-time system is accurate. The proposed system loads and executes faster than the existing system. The system is efficient, scalable, and enhanced for complex use cases. © 2024 IEEE.

关键词： Real time systems

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A New Tree Structure for Local Diagnosis

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Journal of information science and engineering 2024年第2期40卷 231-244页

作者： Chen, Meirun Li, Xiao-Yan Lin, Cheng-Kuan Pai, Kung-Jui School of Mathematics and Statistics Xiamen University of Technology Xiamen361024 China College of Computer and Data Science Fuzhou University Fuzhou350108 China Department of Computer Science National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Undergraduate Degree Program of Systems Engineering and Technology National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Computer Science and Information Engineering Chung Cheng Institute of Technology National Defense University Taoyuan30010 Taiwan Department of Industrial Engineering and Management Ming Chi University of Technology New Taipei City243303 Taiwan

Diagnosability is an important parameter to measure the fault tolerance of a multiprocessor system. If we only care about the state of a node, instead of doing the global diagnosis, Hsu and Tan proposed the idea of local diagnosis. Chiang and Tan provided an extended star structure to diagnose a node under comparison model. In this work, we evaluate the local diagnosability better by proposing a tree structure around this node. We provide the corresponding algorithm to diagnose the node. Simulation results are presented for different failure probability of a node in the tree and different percentage of faulty nodes in the tree, showing the performance of our algorithm. © 2024 Institute of information science. All rights reserved.

关键词： comparison model diagnosis algorithm fault diagnosis local diagnosability tree

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Gengmm: Generalized Gaussian-Mixture-Based Domain Adaptation Model for Semantic Segmentation

Gengmm: Generalized Gaussian-Mixture-Based Domain Adaptation...

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IEEE International Conference on Image Processing

作者： Nazanin Moradinasab Hassan Jafarzadeh Donald E. Brown Department of Engineering Systems and Environment University of Virginia Charlottesville VA USA School of Data Science University of Virginia Charlottesville VA USA

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

Domain adaptive semantic segmentation is the task of generating precise and dense predictions for an unlabeled target domain using a model trained on a labeled source domain. While significant efforts have been devoted to improving unsupervised domain adaptation for this task, it is crucial to note that many models rely on a strong assumption that the source data is entirely and accurately labeled, while the target data is unlabeled. In real-world scenarios, however, we often encounter partially or noisy labeled data in source and target domains, referred to as Generalized Domain Adaptation (GDA). In such cases, we suggest leveraging weak or unlabeled data from both domains to narrow the gap between them, resulting in effective adaptation. We introduce the Generalized Gaussian-mixture-based (GenGMM) domain adaptation model, which harnesses the underlying data distribution in both domains to refine noisy weak and pseudo labels. The experiments demonstrate the effectiveness of our approach.

关键词： Bridges Adaptation models Semantic segmentation Image processing Predictive models data models Noise measurement

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An efficient memory data organization strategy for application-characteristic graph processing

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Frontiers of Computer science 2022年第1期16卷 191-193页

作者： Peng FANG Fang WANG Zhan SHI Dan FENG Qianxu YI Xianghao XU Yongxuan ZHANG Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage SystemEngineering Research Center of data storage systems and TechnologyMinistry of Education of ChinaSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan430074China

1 Introduction Graph processing has received significant attention for its ability to cope with large-scale and complex unstructured data in the ***,most of the graph processing applications exhibit an irregular memor... 详细信息

关键词： application processing pattern

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