In recent years, China, the United States and other countries, Google and other high-tech companies have increased investment in artificial intelligence. Deep learning is one of the current artificial intelligence research's key areas. This paper analyzes and summarizes the latest progress and future research directions of deep learning. Firstly, three basic models of deep learning are outlined, including multilayer perceptrons, convolutional neural networks, and recurrent neural networks. On this basis, we further analyze the emerging new models of convolution neural networks and recurrent neural networks. This paper then summarizes deep learning's applications in many areas of artificial intelligence, including speech processing, computer vision, natural language processing and so on. Finally, this paper discusses the existing problems of deep learning and gives the corresponding possible solutions.
Bringing AI To Edge: From Deep Learning's Perspective
HP-NTU Digital Manufacturing Corporate Lab, Nanyang Technological University（南洋理工大学惠普-南大数字制造企业实验室）；School of Computer Science and Engineering, Nanyang Technological University（南洋理工大学计算机科学与工程学院）；Innovations and Experiences–Business Personal Systems, HP Inc.（惠普公司创新与体验-个人系统业务部）
Explainable Artificial Intelligence: a Systematic Review
School of Computer Science, College of Science and Health, Technological University Dublin（都柏林理工大学科学与健康学院计算机科学学院）；Technological University Dublin（都柏林理工大学）
Reviewing continual learning from the perspective of human-level intelligence
University of Science and Technology of China（中国科学技术大学）；Institute of Intelligent Machines, Chinese Academy of Sciences（中国科学院合肥智能机械研究所）；Mississippi State University（密西西比州立大学）；Sichuan University（四川大学）；Southwest Jiaotong University（西南交通大学）；Central South University（中南大学）
Explainable Artificial Intelligence and Multicollinearity : A Mini Review of Current Approaches
University of Leicester（莱斯特大学）；William Harvey Research Institute（威廉·哈维研究所）；Queen Mary University of London（伦敦玛丽女王大学）；Barts Health NHS Trust（巴特健康 NHS 信托）；St Bartholomew’s Hospital（圣巴塞洛缪医院）；University of Zakho（扎胡大学）
Recent Developments in Detection of Central Serous Retinopathy through Imaging and Artificial Intelligence Techniques A Review
Riphah International University（里法国际大学）；Prince Sultan University（苏丹王子大学）；Liverpool John Moores University（利物浦约翰摩尔大学）；Staffordshire University（斯塔福德郡大学）；Prince Sattam bin Abdulaziz University（萨塔姆·本·阿卜杜勒阿齐兹王子大学）
Explainable Artificial Intelligence Methods in Combating Pandemics: A Systematic Review
Wallace H. Coulter School of Biomedical Engineering, Georgia Institute of Technology（佐治亚理工学院华莱士·H·库尔特生物医学工程学院）；Emory University（埃默里大学）；Department of Electrical and Computer Engineering, Georgia Institute of Technology（佐治亚理工学院电气与计算机工程系）；School of Biology, Georgia Institute of Technology（佐治亚理工学院生物学院）；Department of Nuclear and Radiological Engineering, Georgia Institute of Technology（佐治亚理工学院核与放射工程系）
Artificial Intelligence-Assisted Energy and Thermal Comfort Control for Sustainable Buildings: An Extended Representation of the Systematic Review
Mohammed V University of Rabat（拉巴特穆罕默德五世大学）；MENTIS SA（MENTIS SA公司）；University of Nebraska at Kearney (UNK)（内布拉斯加大学凯尔尼分校）；Information Technology University（信息技术大学）；Alfaisal University（费萨尔国王大学）；Hamad Bin Khalifa University（哈马德·本·哈利法大学）；Western Michigan University（西密歇根大学）；Alakhawayn University in Ifrane（伊夫兰阿赫瓦因大学）；University of Houston（休斯顿大学）
不同的因素，如热舒适度，湿度，空气质量和噪音有显着的综合影响的可接受性和质量的活动进行的建筑物居住者谁花了他们的大部分时间在室内。在所引用的因素中，热舒适性有助于人类的福祉，因为它与人体的温度调节有关。因此，创造热舒适和节能的环境在建筑物的设计中非常重要，因此供暖，通风和空调系统也非常重要。最近的工作已经针对更先进的控制策略，主要是基于人工智能，具有模仿人类行为的能力。本文系统地综述了建筑物内的智能控制策略，并探讨了它们在室内环境中平衡热舒适性和节能优化的能力。方法.系统的文献综述使用ACM数字图书馆，Scopus，Google Scholar，IEEE Xplore（IEOL），Web of Science和Science Direct（SDOL）以及手动搜索的其他来源检查了同行评审的研究作品。包含以下字符串术语：热舒适性、舒适温度、首选温度、智能控制、高级控制、人工智能、计算智能、建筑、室内和建筑环境。入选标准为：英国，研究监测，主要是在建筑物中的人体热舒适性和能源效率同时基于控制策略，使用智能方法。使用系统性综述和荟萃分析指南的首选报告项目。最初，产生了1，077篇文章，最终有120篇符合纳入标准并进行了审查。
Evaluating the Effectiveness of Artificial Intelligence in Predicting Adverse Drug Reactions among Cancer Patients: A Systematic Review and Meta-Analysis
National Polytechnic School of Oran - Maurice Audin（奥兰国家理工学院 - 莫里斯·奥丹）
Artificial intelligence for partial differential equations in computational mechanics: A review
Department of Engineering Mechanics, Tsinghua University, Beijing 100084, China（清华大学工程力学系）；Simulation Technology, Institute of Photonics, Leibniz University Hannover, Hannover 30167, Germany（汉诺威莱布尼茨大学光子研究所仿真技术部）；Drilling Mechanical Department, CNPC Engineering Technology RD Company Limited, Beijing 102206, China（中石油工程科技研发有限公司钻探机械部）；School of Mechanical, Medical and Process Engineering, Queensland University of Technology, Brisbane, QLD 4000, Australia（昆士兰科技大学机械、医疗与加工工程学院）；ARC Industrial Transformation Training Centre—Joint Biomechanics, Queensland University of Technology, Brisbane, QLD 4000, Australia（昆士兰科技大学 ARC 工业转型培训中心—联合生物力学）
Interplay between Federated Learning and Explainable Artificial Intelligence: a Scoping Review
Simula Metropolitan Center for Digital Engineering（西米拉数字工程中心）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；University Hospital Bonn（波恩大学医院）；Norwegian University of Science and Technology（挪威科技大学）；Charité - Universitätsmedizin Berlin（柏林夏里特医学院）；Birmingham City University（伯明翰城市大学）；ETH Zurich（苏黎世联邦理工学院）；Technical University of Munich（慕尼黑工业大学）
