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arXiv 2020年

作者： Fu, Jun Chen, Zhibo Chen, Xiaoming Li, Weiping CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China School of Computer Science and Engineering Beijing Technology and Business University China

In the tile-based 360-degree video streaming, predicting user’s future viewpoints and developing adaptive bitrate (ABR) algorithms are essential for optimizing user’s quality of experience (QoE). Traditional single-user based viewpoint prediction methods fail to achieve good performance in long-term prediction, and the recently proposed reinforcement learning (RL) based ABR schemes applied in traditional video streaming can not be directly applied in the tile-based 360-degree video streaming due to the exponential action space. Therefore, we propose a sequential reinforced 360-degree video streaming scheme with cross-user attentive network. Firstly, considering different users may have the similar viewing preference on the same video, we propose a cross-user attentive network (CUAN), boosting the performance of long-term viewpoint prediction by selectively utilizing cross-user information. Secondly, we propose a sequential RL-based (360SRL) ABR approach, transforming action space size of each decision step from exponential to linear via introducing a sequential decision structure. We evaluate the proposed CUAN and 360SRL using trace-driven experiments and experimental results demonstrate that CUAN and 360SRL outperform existing viewpoint prediction and ABR approaches with a noticeable margin. Copyright © 2020, The Authors. All rights reserved.

关键词： Forecasting

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An analysis of dropout students in education system of Kerala

An analysis of dropout students in education system of Keral...

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International Conference on Physics and Photonics Processes in Nano sciences 2019

作者： Manoj, Vishnu Sarika, S.G. Raji, P. Thomson, Lincy Dept of Computer Application Amrita School of Engineering Amrita VishwaVidyapeetham Amritapuri Campus Kollam Kerala India Dept of Mathamtics Amrita School of Arts and Science Amrita ViswaVidyapeetham Amritapuri Campus Kollam Kerala India

At a time when Kerala achieved hundred percentage primary education status, the state Education Department has reported a sharp increase in the dropout rates of Dalit students in the state. Educational experts criticize that the lack of coordination and poor realization of schemes by the government were the reasons for the rising number of dropouts from schools. The highest dropout rate was recorded in the border districts of Wayanad, Idukki and Kollam. The overall aim of this paper is to provide the scenario of drop outs students of scheduled cast (boys and girls). Also, we interpret the analysis of variance among the drop out students in different classes. © Published under licence by IOP Publishing Ltd.

关键词： Students

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Use of a wearable device to compare subjective and objective fatigue in lung cancer patients and cancer-free controls

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European Journal of Oncology Nursing 2024年 70卷 102587-102587页

作者： Chou, Ting-Ling Shih, Chi-Huang Chou, Pai-Chien Lai, Jun-Hung Huang, Tsai-Wei School of Nursing College of Nursing Taipei Medical University Taipei Taiwan Department of Computer Science and Information Engineering National Chin-Yi University of Technology Taichung Taiwan Department of Internal Medicine Taipei Medical University Hospital Taipei Taiwan Division of Thoracic Medicine Department of Internal Medicine School of Medicine Taipei Medical University Taipei Taiwan Division of Gastroenterology & Hepatology Department of Internal Medicine Erlin Christian Hospital Changhua Taiwan Cochrane Taiwan Taipei Medical University Taipei Taiwan Research Center in Nursing Clinical Application Wan Fang Hospital Taipei Medical University Taipei Taiwan Department of Nursing Wan Fang Hospital Taipei Medical University Taipei Taiwan

Purpose: The study evaluates the use of heart rate variability (HRV), a measure of autonomic nervous system (ANS) modulation via wearable smart bands, to objectively assess cancer-related fatigue (CRF) levels. It aims to enhance understanding of fatigue by distinguishing between LF/HF ratios and LF/HF disorder ratios through HRV and photoplethysmography (PPG), identifying them as potential biomarkers. Methods: Seventy-one lung cancer patients and 75 non-cancer controls wore smart bands for one week. Fatigue was assessed using Brief Fatigue Inventory, alongside sleep quality and daily interference. HRV parameters were analyzed to compare groups. Results: Cancer patients showed higher fatigue and interference levels than controls (64.8% vs. 54.7%). Those with mild fatigue had elevated LF/HF disorder ratios during sleep (40% vs. 20%, P = 0.01), similar to those with moderate to severe fatigue (50% vs. 20%, P = 0.01), indicating more significant autonomic dysregulation. Notably, mild fatigue patients had higher mean LF/HF ratios than controls (1.9 ± 1.34 vs. 1.2 ± 0.6, P = 0.01), underscoring the potential of disorder ratios in signaling fatigue severity. Conclusions: Utilizing wearable smart bands for HRV-based analysis is feasible for objectively assess CRF levels in cancer patients, especially during sleep. By distinguishing between LF/HF ratios and LF/HF disorder ratios, our findings suggest that wearable technology and detailed HRV analysis offer promising avenues for real-time fatigue monitoring. This approach has the potential to significantly improve cancer care by providing new methods for managing and intervening in CRF, particularly with a focus on autonomic dysregulation as a crucial factor. © 2024 Elsevier Ltd

关键词： Cancer-related fatigue Heart rate variability Photoplethysmography Smart band Wearable device

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A comparative study of large-scale cluster workload traces via multiview analysis 21

A comparative study of large-scale cluster workload traces v...

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21st IEEE International Conference on High Performance Computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on Data science and Systems, HPCC/SmartCity/DSS 2019

作者： Ruan, Li Xu, Xiangrong Xiao, Limin Yuan, Feng Li, Yin Dai, Dong State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing China Institute of Software Application Technology Guangzhou AND Chinese Academy of Sciences Guangzhou China Department of Computer Science University of North Carolina Charlotte CharlotteNC United States

ISBN: (纸本)9781728120584

Understanding the characteristics of workloads of the large-scale Clusters is the key to diagnose the system bottlenecks, making optimal configuration decisions, improving the system throughput and resource usage. Due to the diversity and multiview of the workload traces, featuring the good designs and bottlenecks by analyzing the workloads under realworld scenarios becomes increasingly challenging. This paper introduces a multiview based trace comparative analysis method by comparatively characterizing how the architecture, jobs, tasks, machines, and resources usage were managed among different platforms. A case study is performed which verified the effectiveness of our method by comparatively analyzing two most representative big traces: Google trace and Alibaba 2018 trace. Quantitative findings, together with the performance bottleneck inferences and suggestions are also presented. To the best of our knowledge, we are the first to perform such comparative empirical study on these two traces using a multiview based approach. Our multifaceted analyses and new findings not only reveal insights that we believe are useful for system designers, IT practitioners and users, but also can promote more researches on big trace data comparative analysis in large-scale clusters. © 2019 IEEE.

关键词： Trace analysis

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Heavy metals prediction in coastal marine sediments using hybridized machine learning models with metaheuristic optimization algorithm

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Chemosphere 2024年 352卷 141329页

作者： Yaseen, Zaher Mundher Melini Wan Mohtar, Wan Hanna Homod, Raad Z. Alawi, Omer A. Abba, Sani I. Oudah, Atheer Y. Togun, Hussein Goliatt, Leonardo Ul Hassan Kazmi, Syed Shabi Tao, Hai Civil and Environmental Engineering Department King Fahd University of Petroleum and Minerals Dhahran 31261 Saudi Arabia Interdisciplinary Research Center for Membranes and Water Security King Fahd University of Petroleum & Minerals (KFUPM) Dhahran Saudi Arabia Department of Civil Engineering Faculty of Engineering and Built Environment Universiti Kebangsaan Malaysia UKM Selangor Bangi 43600 Malaysia Environmental Management Centre Institute of Climate Change Universiti Kebangsaan Malaysia Selangor UKM Bangi 43600 Malaysia Department of Oil and Gas Engineering Basrah University for Oil and Gas Basra Iraq Department of Thermofluids School of Mechanical Engineering Universiti Teknologi Malaysia UTM Skudai Johor Bahru 81310 Malaysia Department of Computer Sciences College of Education for Pure Science University of Thi-Qar Nasiriyah 64001 Iraq Information and Communication Technology Research Group Scientific Research Center Al-Ayen University Nasiriyah 64001 Iraq Department of Mechanical Engineering College of Engineering University of Baghdad Baghdad Iraq Computational and Applied Mechanics Department Federal University of Juiz de Fora 36036-900 Brazil Guangdong Provincial Key Laboratory of Marine Disaster Prediction and Prevention and Guangdong Provincial Key Laboratory of Marine Biotechnology Shantou University Shantou 515063 China School of Computer and Information Qiannan Normal University for Nationalities Guizhou Duyun 558000 China Institute of Big Data Application and Artificial Intelligence Qiannan Normal University for Nationalities Guizhou Duyun 558000 China Faculty of Data Science and Information Technology INTI International University 71800 Malaysia

This study proposes different standalone models viz: Elman neural network (ENN), Boosted Tree algorithm (BTA), and f relevance vector machine (RVM) for modeling arsenic (As (mg/kg)) and zinc (Zn (mg/kg)) in marine sediments owing to anthropogenic activities. A heuristic algorithm based on the potential of RVM and a flower pollination algorithm (RVM-FPA) was developed to improve the prediction performance. Several evaluation indicators and graphical methods coupled with visualized cumulative probability function (CDF) were used to evaluate the accuracy of the models. Akaike (AIC) and Schwarz (SCI) information criteria based on Dickey-Fuller (ADF) and Philip Perron (PP) tests were introduced to check the reliability and stationarity of the data. The prediction performance in the verification phase indicated that RVM-M2 (PBAIS = -o.0465, MAE = 0.0335) and ENN-M2 (PBAIS = 0.0043, MAE = 0.0322) emerged as the best model for As (mg/kg) and Zn (mg/kg), respectively. In contrast with the standalone approaches, the simulated hybrid RVM-FPA proved merit and the most reliable, with a 5 % and 18 % predictive increase for As (mg/kg) and Zn (mg/kg), respectively. The study's findings validated the potential for estimating complex HMs through intelligent data-driven models and heuristic optimization. The study also generated valuable insights that can inform the decision-makers and stockholders for environmental management strategies. © 2024 Elsevier Ltd

关键词： Artificial intelligence Heavy metals Sensitivity analysis Soil contamination

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Hybridizing deep neural network for genes expression classification using histone modification

Hybridizing deep neural network for genes expression classif...

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3rd Smart Cities Symposium (SCS 2020)

作者： R. Nair A. Bhagat P. K. Shukla P. K. Dutta SoET Jagran Lakecity University Bhopal India Department of Computer Application MANIT Bhopal India University Institute of Technology Department of Computer Science & Engineering RGPV Bhopal M.P. India Amity University Kolkata Department of Electronics and Communication Engineering Amity School of Engineering and Technology Kolkata India

Genes expression datasets are considered to be high dimensional dataset. It is very much needed that before predicting the genes expression dimensionality reduction must be performed. Machine learning algorithms fail to analyze genes expression due to its high dimensionality behaviour. These algorithms have to perform feature selection or feature extraction before classification. To overcome this dimensionality reduction approach deep learning algorithms are implemented which has shown improved performance. This paper is also based on the approach of deep learning algorithm which is based on a classification approach using hybridization of a deep neural network named Hybrid Deep Neural Network (HDNN) in which Convolutional Neural Networks are combined with Recurrent Neural Networks to perform prediction. Parameters like accuracy, AUC, precision, and F1score have shown improved performance than other state of art algorithms with 85.5%, 0.84, 0.81, and 0.79 respectively.

关键词： image classification neural nets learning (artificial intelligence) recurrent neural nets feature extraction pattern classification genetics

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COVID-19 screening on chest X-ray images using deep learning based anomaly detection

arXiv

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arXiv 2020年

作者： Zhang, Jianpeng Xie, Yutong Li, Yi Shen, Chunhua Xia, Yong National Engineering Lab for Integrated Aerospace-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University China University of Adelaide Australia JF Healthcare Crop. China

Coronaviruses are important human and animal pathogens. To date the novel COVID-19 coronavirus is rapidly spreading worldwide and subsequently threatening health of billions of humans. Clinical studies have shown that most COVID-19 patients suffer from the lung infection. Although chest CT has been shown to be an effective imaging technique for lung-related disease diagnosis, chest X-ray is more widely available due to its faster imaging time and considerably lower cost than CT. Deep learning, one of the most successful AI techniques, is an effective means to assist radiologists to analyze the vast amount of chest X-ray images, which can be critical for efficient and reliable COVID-19 screening. In this work, we aim to develop a new deep anomaly detection model for fast, reliable screening. To evaluate the model performance, we have collected 100 chest X-ray images of 70 patients confirmed with COVID-19 from the Github repository1. To facilitate deep learning, more data are needed. Thus, we have also collected 1431 additional chest X-ray images confirmed as other pneumonia of 1008 patients from the public ChestX-ray14 dataset. Our initial experimental results show that the model developed here can reliably detect 96.00% COVID-19 cases (sensitivity being 96.00%) and 70.65% non-COVID-19 cases (specificity being 70.65%) when evaluated on 1531 X-ray images with two splits of the dataset, offering a promise of our proposed model for reliable COVID-19 screening of chest X-ray images. Copyright © 2020, The Authors. All rights reserved.

关键词： Coronavirus

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A Robust Attentional Framework for License Plate Recognition in the Wild

arXiv

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arXiv 2020年

作者： Zhang, Linjiang Wang, Peng Li, Hui Li, Zhen Shen, Chunhua Zhang, Yanning School of Computer Science Northwestern Polytechnical University Xi’an China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology China School of Computer Science University of Adelaide SA5005 Australia big data and AI technology department Minsheng Fintech Corp. LTD.

Recognizing car license plates in natural scene images is an important yet still challenging task in realistic applications. Many existing approaches perform well for license plates collected under constrained conditions, e.g., shooting in frontal and horizontal view-angles and under good lighting conditions. However, their performance drops significantly in an unconstrained environment that features rotation, distortion, occlusion, blurring, shading or extreme dark or bright conditions. In this work, we propose a robust framework for license plate recognition in the wild. It is composed of a tailored CycleGAN model for license plate image generation and an elaborate designed image-to-sequence network for plate recognition. On one hand, the CycleGAN based plate generation engine alleviates the exhausting human annotation work. Massive amount of training data can be obtained with a more balanced character distribution and various shooting conditions, which helps to boost the recognition accuracy to a large extent. On the other hand, the 2D attentional based license plate recognizer with an Xception-based CNN encoder is capable of recognizing license plates with different patterns under various scenarios accurately and robustly. Without using any heuristics rule or post-processing, our method achieves the state-of-the-art performance on four public datasets, which demonstrates the generality and robustness of our framework. Moreover, we released a new license plate dataset, named "CLPD", with 1200 images from all 31 provinces in mainland China. The dataset can be available from: https://***/wangpengnorman/CLPD_dataset. Copyright © 2020, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Natural image classification based on multi-parameter optimization in deep convolutional neural network

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International Journal of Performability engineering 2019年第9期15卷 2515-2521页

作者： Wang, Lei Zhang, Yanning Xi, Runping Ling, Lu School of Computer Northwestern Polytechnical University Xi'an710072 China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi'an710072 China Jiangxi Engineering Technology Research Center of Nuclear Geoscience Data Science and System East China University of Technology Nanchang330013 China

Traditional machine learning algorithms cannot adequately train the parameters of networks using massive data. A deep convolutional neural network based on multi-parameter optimization by the TensorFlow deep learning framework is designed in this paper. In order to improve the training speed and prevent over-fitting, we improve and optimize the multi-parameters, including the batch value, dropout, and momentum in the network structure. The experiment involves training and testing on the standard natural image data sets in cifar-10 and cifar-100. The experimental results show that the method achieve better classification accuracy in less time compared with other algorithms, such as Conv-KN, ImageNet-2010, SVM, LR, and Boosting. © 2019 Totem Publisher, Inc. All rights reserved.

关键词： Image classification

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Research on IETM Data Interactive Reading Technology

Research on IETM Data Interactive Reading Technology

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Chinese Automation Congress (CAC)

作者： Jiaju Wu Hongfu Zuo Zheng Cheng Yonghui Yang College of Civil Aviation Nanjing University of Aeronautics and Astronautic Nanjing China Institute of Computer Application China Academy of Engineering Physics Mianyang China School of Automation University of Electronic Science and Technology of China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

The interactive electronic technical manual is the digital, intelligent and interactive display of equipment technical information. It is a comprehensive application system with multiple functions such as auxiliary maintenance, auxiliary training, information inquiry and browsing. Based on the analysis of IETM data interactive reading requirements, this paper designs the IETM data interactive process, studies the IETM data interactive reading principle, designs the IETM data interactive reading function, and uses J2EE technology to implement the IETM data interactive reader.

关键词： Manuals Software Data models Maintenance engineering XML Springs Loading

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