Janyl Jumadinova¹, Oliver Bonham-Carter¹, Hanzhong Zheng^1,2,*, Michael Camara¹, Dejie Shi³

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

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 the biomedical textual data from… More >

Xuting Duan^1,2, Yuanhao Zhao^1,2, Kunxian Zheng^1,2,*, Daxin Tian^1,2, Jianshan Zhou^1,2,3, Jian Gao⁴

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

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 adaptation design in this… More >

Wenbei Bi¹, Zengfeng Yan^1,*, Huan Zhao¹, Lixin Sun², Xudong Wang^3,4, Zhengmo Zhang³

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

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 thermal conductivity, water vapor permeability,… More >

Ehab Mahmoud Mohamed^{1, 2, *}, Sherief Hashima^{3, 4}, Kohei Hatano^{3, 5}, Hani Kasban⁴, Mohamed Rihan⁶

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

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 multiarmed bandit (MAB), where mmWave… More >

Li Li^1,*, Yifei Wei¹, Lianping Zhang², Xiaojun Wang³

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

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 edge computing and caching abilities,… More >

Wanxin Zhang^1,*, Jihong Zhu²

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

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 by plenty of data. In… More >

Jia‐Shing Sheu^a, I‐Chen Chen^b, Yi‐Syong Liao^a

Intelligent Automation & Soft Computing, Vol.25, No.2, pp. 395-404, 2019, DOI:10.31209/2019.100000101

Abstract This study proposed a household energy state monitoring system (HESMS) and a household energy load monitoring system (HELMS) for monitoring smart appliances. The HESMS applies reinforcement learning to receive changes in the external environment and the state of an electrical appliance, determines if the electrical appliance should be turned on, and controls the signals sent to the HELMS according to these decisions. The HELMS implements an ON/OFF control mechanism for household appliances according to the control signals and the power consumption state. The proposed systems are based on the wireless communication network and can monitor household appliances’ energy usage, control… More >

Yonghong Xie^{1, 3}, Liangyuan Hu^{1, 3}, Xingxing Chen^{2, 3}, Jim Feng⁴, Dezheng Zhang^{1, 3, *}

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

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 syndrome and get all possibilities… More >

Xinyue Lin¹, Jin Liu^{1, *}, Jianming Zhang², Se-Jung Lim³

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

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 use reinforcement learning to train… More >

Wei Fang^{1, 2, 3, ∗}, Lin Pang¹, Weinan Yi¹

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

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 this paper we have summarized… More >