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  • Open Access

    ARTICLE

    Collision Observation-Based Optimization of Low-Power and Lossy IoT Network Using Reinforcement Learning

    Arslan Musaddiq1, Rashid Ali2, Jin-Ghoo Choi1, Byung-Seo Kim3,*, Sung-Won Kim1

    CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 799-814, 2021, DOI:10.32604/cmc.2021.014751 - 12 January 2021

    Abstract The Internet of Things (IoT) has numerous applications in every domain, e.g., smart cities to provide intelligent services to sustainable cities. The next-generation of IoT networks is expected to be densely deployed in a resource-constrained and lossy environment. The densely deployed nodes producing radically heterogeneous traffic pattern causes congestion and collision in the network. At the medium access control (MAC) layer, mitigating channel collision is still one of the main challenges of future IoT networks. Similarly, the standardized network layer uses a ranking mechanism based on hop-counts and expected transmission counts (ETX), which often does… More >

  • Open Access

    ARTICLE

    Cooperative Channel Assignment for VANETs Based on Dual Reinforcement Learning

    Xuting Duan1,2, Yuanhao Zhao1,2, Kunxian Zheng1,2,*, Daxin Tian1,2, Jianshan Zhou1,2,3, Jian Gao4

    CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 2127-2140, 2021, DOI:10.32604/cmc.2020.014484 - 26 November 2020

    Abstract Dynamic channel assignment (DCA) is significant for extending vehicular ad hoc network (VANET) capacity and mitigating congestion. However, the un-known global state information and the lack of centralized control make channel assignment performances a challenging task in a distributed vehicular direct communication scenario. In our preliminary field test for communication under V2X scenario, we find that the existing DCA technology cannot fully meet the communication performance requirements of VANET. In order to improve the communication performance, we firstly demonstrate the feasibility and potential of reinforcement learning (RL) method in joint channel selection decision and access fallback More >

  • Open Access

    ARTICLE

    A Novel Framework for Biomedical Text Mining

    Janyl Jumadinova1, Oliver Bonham-Carter1, Hanzhong Zheng1,2,*, Michael Camara1, Dejie Shi3

    Journal on Big Data, Vol.2, No.4, pp. 145-155, 2020, DOI:10.32604/jbd.2020.010090 - 24 December 2020

    Abstract Text mining has emerged as an effective method of handling and extracting useful information from the exponentially growing biomedical literature and biomedical databases. We developed a novel biomedical text mining model implemented by a multi-agent system and distributed computing mechanism. Our distributed system, TextMed, comprises of several software agents, where each agent uses a reinforcement learning method to update the sentiment of relevant text from a particular set of research articles related to specific keywords. TextMed can also operate on different physical machines to expedite its knowledge extraction by utilizing a clustering technique. We collected More >

  • Open Access

    ARTICLE

    The Influence of Two Natural Reinforcement Fibers on the Hygrothermal Properties of Earthen Plasters in Mogao Grottoes of China

    Wenbei Bi1, Zengfeng Yan1,*, Huan Zhao1, Lixin Sun2, Xudong Wang3,4, Zhengmo Zhang3

    Journal of Renewable Materials, Vol.8, No.12, pp. 1691-1710, 2020, DOI:10.32604/jrm.2020.012808 - 12 November 2020

    Abstract Murals in Mogao Grottoes consist of three parts: support layer, earthen plasters and paint layer. The earthen plasters play a key role in the preservation of murals. It is a mixture of Dengban soil, sand, and plant fiber. Two different natural fibers, hemp fiber and cotton fiber, were reinforced to earthen plasters in the same percentage to evaluate the influence on hygrothermal performance. The two types of earthen plasters were studied: one containing hemp fiber in the fine plaster (HFP) and the other containing cotton fiber in the fine plaster (CFP). Specific heat capacity, dry… More >

  • Open Access

    ARTICLE

    Millimeter-Wave Concurrent Beamforming: A Multi-Player Multi-Armed Bandit Approach

    Ehab Mahmoud Mohamed1, 2, *, Sherief Hashima3, 4, Kohei Hatano3, 5, Hani Kasban4, Mohamed Rihan6

    CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 1987-2007, 2020, DOI:10.32604/cmc.2020.011816 - 16 September 2020

    Abstract The communication in the Millimeter-wave (mmWave) band, i.e., 30~300 GHz, is characterized by short-range transmissions and the use of antenna beamforming (BF). Thus, multiple mmWave access points (APs) should be installed to fully cover a target environment with gigabits per second (Gbps) connectivity. However, inter-beam interference prevents maximizing the sum rates of the established concurrent links. In this paper, a reinforcement learning (RL) approach is proposed for enabling mmWave concurrent transmissions by finding out beam directions that maximize the long-term average sum rates of the concurrent links. Specifically, the problem is formulated as a multiplayer… More >

  • Open Access

    ARTICLE

    Efficient Virtual Resource Allocation in Mobile Edge Networks Based on Machine Learning

    Li Li1,*, Yifei Wei1, Lianping Zhang2, Xiaojun Wang3

    Journal of Cyber Security, Vol.2, No.3, pp. 141-150, 2020, DOI:10.32604/jcs.2020.010764 - 14 September 2020

    Abstract The rapid growth of Internet content, applications and services require more computing and storage capacity and higher bandwidth. Traditionally, internet services are provided from the cloud (i.e., from far away) and consumed on increasingly smart devices. Edge computing and caching provides these services from nearby smart devices. Blending both approaches should combine the power of cloud services and the responsiveness of edge networks. This paper investigates how to intelligently use the caching and computing capabilities of edge nodes/cloudlets through the use of artificial intelligence-based policies. We first analyze the scenarios of mobile edge networks with… More >

  • Open Access

    ARTICLE

    A Reinforcement Learning System for Fault Detection and Diagnosis in Mechatronic Systems

    Wanxin Zhang1,*, Jihong Zhu2

    CMES-Computer Modeling in Engineering & Sciences, Vol.124, No.3, pp. 1119-1130, 2020, DOI:10.32604/cmes.2020.010986 - 21 August 2020

    Abstract With the increasing demand for the automation of operations and processes in mechatronic systems, fault detection and diagnosis has become a major topic to guarantee the process performance. There exist numerous studies on the topic of applying artificial intelligence methods for fault detection and diagnosis. However, much of the focus has been given on the detection of faults. In terms of the diagnosis of faults, on one hand, assumptions are required, which restricts the diagnosis range. On the other hand, different faults with similar symptoms cannot be distinguished, especially when the model is not trained… More >

  • Open Access

    ARTICLE

    Auxiliary Diagnosis Based on the Knowledge Graph of TCM Syndrome

    Yonghong Xie1, 3, Liangyuan Hu1, 3, Xingxing Chen2, 3, Jim Feng4, Dezheng Zhang1, 3, *

    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 481-494, 2020, DOI:10.32604/cmc.2020.010297 - 23 July 2020

    Abstract As one of the most valuable assets in China, traditional medicine has a long history and contains pieces of knowledge. The diagnosis and treatment of Traditional Chinese Medicine (TCM) has benefited from the natural language processing technology. This paper proposes a knowledge-based syndrome reasoning method in computerassisted diagnosis. This method is based on the established knowledge graph of TCM and this paper introduces the reinforcement learning algorithm to mine the hidden relationship among the entities and obtain the reasoning path. According to this reasoning path, we could infer the path from the symptoms to the More >

  • Open Access

    ARTICLE

    A Novel Beam Search to Improve Neural Machine Translation for English-Chinese

    Xinyue Lin1, Jin Liu1, *, Jianming Zhang2, Se-Jung Lim3

    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 387-404, 2020, DOI:10.32604/cmc.2020.010984 - 23 July 2020

    Abstract Neural Machine Translation (NMT) is an end-to-end learning approach for automated translation, overcoming the weaknesses of conventional phrase-based translation systems. Although NMT based systems have gained their popularity in commercial translation applications, there is still plenty of room for improvement. Being the most popular search algorithm in NMT, beam search is vital to the translation result. However, traditional beam search can produce duplicate or missing translation due to its target sequence selection strategy. Aiming to alleviate this problem, this paper proposed neural machine translation improvements based on a novel beam search evaluation function. And we More >

  • Open Access

    ARTICLE

    Survey on the Application of Deep Reinforcement Learning in Image Processing

    Wei Fang1, 2, 3, ∗, Lin Pang1, Weinan Yi1

    Journal on Artificial Intelligence, Vol.2, No.1, pp. 39-58, 2020, DOI:10.32604/jai.2020.09789 - 15 July 2020

    Abstract In recent years, with the rapid development of human society, more and more complex tasks have emerged that require deep learning to automatically extract abstract feature representations from a large amount of data, and use reinforcement learning to learn the best strategy to complete the task. Through the combination of deep learning and reinforcement learning, end-to-end input and output can be achieved, and substantial breakthroughs have been made in many planning and decision-making systems with infinite states, such as games, in particular, AlphaGo, robotics, natural language processing, dialogue systems, machine translation, and computer vision. In More >

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