Pedestrian Motion Path Detection Method Based on Deep Learning and Foreground Detection by Meiman Li, Wenfu Xie

“…For the surveillance video images captured by monocular camera, this paper proposes a method combining foreground detection and deep learning to detect moving pedestrians, making full use of the invariable background of video image. …”
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Scheduling framework based on reinforcement learning in online-offline colocated cloud environment by Ling MA, Qiliang FAN, Ting XU, Guanchen GUO, Shenglin ZHANG, Yongqian SUN, Yuzhi ZHANG

Subjects: “…reinforcement learning…”
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Transforming Cardiac Care: Machine Learning in Heart Condition Prediction Using Phonocardiograms by Sandra D’Souza, Niranjan Reddy S, Saikonda Krishna Tarun, Sohan P, aneesha acharya k

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Energy consumption forecasting for oil and coal in China based on hybrid deep learning by Jiao He, Yuhang Li, Xiaochuan Xu, Di Wu

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Deep Learning‐Assisted Label‐Free Parallel Cell Sorting with Digital Microfluidics by Zongliang Guo, Fenggang Li, Hang Li, Menglei Zhao, Haobing Liu, Haopu Wang, Hanqi Hu, Rongxin Fu, Yao Lu, Siyi Hu, Huikai Xie, Hanbin Ma, Shuailong Zhang

“…In this work, a novel label‐free cell sorting method is presented that integrates deep learning image recognition with microfluidic manipulation to differentiate cells based on morphology. …”
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Bebras: A Social Approach for Concept Based Learning of Informatics and Computational Thinking by Filiz Kalelioğlu, Dilek Doğan, Erinç Karataş, Yasemin Gülbahar

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Multilingualism in the Teaching and Learning of Accounting in Chris Hani West District, South Africa by Avela Ngqunguza, Zikhona Seleke

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Emotion Recognition from EEG Signals Using Advanced Transformations and Deep Learning by Jonathan Axel Cruz-Vazquez, Jesús Yaljá Montiel-Pérez, Rodolfo Romero-Herrera, Elsa Rubio-Espino

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Method based on contrastive incremental learning for fine-grained malicious traffic classification by Yifeng WANG, Yuanbo GUO, Qingli CHEN, Chen FANG, Renhao LIN, Yongliang ZHOU, Jiali MA

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Research on Gear Surface Damage Recognition Based on Small Sample Deep Learning by Wang Xiaopeng, Hua Hongpeng, Lu Changqing, Peng Kun, Zhong Yuan, Wu Biqiong

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Deep Learning Algorithm Analysis of Potato Disease Classification for System on Chip Implementation by John Adebisi, Sesham Srinu, Varqa Mitonga

“…This work uses a deep learning algorithm to investigate the classification of potato diseases as a case study which can be leveraged on by other agricultural products for reference.  …”
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Making virtual learning environment more intelligent: application of Markov decision process by Dalia Baziukaitė

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Editorial – Living, Caring, Learning: The nurse-patient relationship in bleeding disorders care by Smith Clive

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Benefits and Application of IoB in Educational Businesses: Smart, Sustainable, and Personalized Learning by ZAHRA SADEQI-ARANI, REZA VAHIDNIA, ESMAEIL MAZROUI NASRABADI

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A Survey on Review Spam Detection Methods using Deep Learning Approach by Mahmoud Aliarab, Kazim Fouladi-Ghaleh

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Modified Autoencoder Training and Scoring for Robust Unsupervised Anomaly Detection in Deep Learning by Nicholas Merrill, Azim Eskandarian

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Adaptive Traffic Signal Control Model on Intersections Based on Deep Reinforcement Learning by Duowei Li, Jianping Wu, Ming Xu, Ziheng Wang, Kezhen Hu

“…., “Simulation of Urban Mobility” (SUMO), using the technology of modern deep reinforcement learning. The model is proposed based on a deep Q-network algorithm that precisely represents the elements associated with the problem: agents, environments, and actions. …”
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An Update on the Controversies in Anemia Management in Chronic Kidney Disease: Lessons Learned and Lost by Geoffrey Teehan, Robert L. Benz

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A Deep Reinforcement Learning Approach to the Optimization of Data Center Task Scheduling by Haiying Che, Zixing Bai, Rong Zuo, Honglei Li

“…This study aimed at building a new scheduling model with deep reinforcement learning algorithm, which integrated the task scheduling with resource-utilization optimization. …”
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Data augmentation based multi-view contrastive learning graph anomaly detection by LI Yifan, LI Jiayin, LIN Xingpeng, DAI Yuanfei, XU Li

Subjects: “…multi-view contrastive learning…”
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