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  • Open Access

    ARTICLE

    Multi-Versus Optimization with Deep Reinforcement Learning Enabled Affect Analysis on Arabic Corpus

    Mesfer Al Duhayyim1,*, Badriyya B. Al-onazi2, Jaber S. Alzahrani3, Hussain Alshahrani4, Mohamed Ahmed Elfaki4, Abdullah Mohamed5, Ishfaq Yaseen6, Gouse Pasha Mohammed6, Mohammed Rizwanullah6, Abu Sarwar Zamani6

    Computer Systems Science and Engineering, Vol.47, No.3, pp. 3049-3065, 2023, DOI:10.32604/csse.2023.033836

    Abstract Sentiment analysis (SA) of the Arabic language becomes important despite scarce annotated corpora and confined sources. Arabic affect Analysis has become an active research zone nowadays. But still, the Arabic language lags behind adequate language sources for enabling the SA tasks. Thus, Arabic still faces challenges in natural language processing (NLP) tasks because of its structure complexities, history, and distinct cultures. It has gained lesser effort than the other languages. This paper developed a Multi-versus Optimization with Deep Reinforcement Learning Enabled Affect Analysis (MVODRL-AA) on Arabic Corpus. The presented MVODRL-AA model majorly concentrates on identifying and classifying effects or emotions… More >

  • Open Access

    ARTICLE

    An Intelligent Algorithm for Solving Weapon-Target Assignment Problem: DDPG-DNPE Algorithm

    Tengda Li, Gang Wang, Qiang Fu*, Xiangke Guo, Minrui Zhao, Xiangyu Liu

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3499-3522, 2023, DOI:10.32604/cmc.2023.041253

    Abstract Aiming at the problems of traditional dynamic weapon-target assignment algorithms in command decision-making, such as large computational amount, slow solution speed, and low calculation accuracy, combined with deep reinforcement learning theory, an improved Deep Deterministic Policy Gradient algorithm with dual noise and prioritized experience replay is proposed, which uses a double noise mechanism to expand the search range of the action, and introduces a priority experience playback mechanism to effectively achieve data utilization. Finally, the algorithm is simulated and validated on the ground-to-air countermeasures digital battlefield. The results of the experiment show that, under the framework of the deep neural… More >

  • Open Access

    ARTICLE

    Multi-Agent Deep Reinforcement Learning for Efficient Computation Offloading in Mobile Edge Computing

    Tianzhe Jiao, Xiaoyue Feng, Chaopeng Guo, Dongqi Wang, Jie Song*

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3585-3603, 2023, DOI:10.32604/cmc.2023.040068

    Abstract Mobile-edge computing (MEC) is a promising technology for the fifth-generation (5G) and sixth-generation (6G) architectures, which provides resourceful computing capabilities for Internet of Things (IoT) devices, such as virtual reality, mobile devices, and smart cities. In general, these IoT applications always bring higher energy consumption than traditional applications, which are usually energy-constrained. To provide persistent energy, many references have studied the offloading problem to save energy consumption. However, the dynamic environment dramatically increases the optimization difficulty of the offloading decision. In this paper, we aim to minimize the energy consumption of the entire MEC system under the latency constraint by… More >

  • Open Access

    ARTICLE

    Dynamic Security SFC Branching Path Selection Using Deep Reinforcement Learning

    Shuangxing Deng, Man Li*, Huachun Zhou

    Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2919-2939, 2023, DOI:10.32604/iasc.2023.039985

    Abstract Security service function chaining (SFC) based on software-defined networking (SDN) and network function virtualization (NFV) technology allows traffic to be forwarded sequentially among different security service functions to achieve a combination of security functions. Security SFC can be deployed according to requirements, but the current SFC is not flexible enough and lacks an effective feedback mechanism. The SFC is not traffic aware and the changes of traffic may cause the previously deployed security SFC to be invalid. How to establish a closed-loop mechanism to enhance the adaptive capability of the security SFC to malicious traffic has become an important issue.… More >

  • Open Access

    ARTICLE

    Reactive Power Flow Convergence Adjustment Based on Deep Reinforcement Learning

    Wei Zhang1, Bin Ji2, Ping He1, Nanqin Wang1, Yuwei Wang1, Mengzhe Zhang2,*

    Energy Engineering, Vol.120, No.9, pp. 2177-2192, 2023, DOI:10.32604/ee.2023.026504

    Abstract Power flow calculation is the basis of power grid planning and many system analysis tasks require convergent power flow conditions. To address the unsolvable power flow problem caused by the reactive power imbalance, a method for adjusting reactive power flow convergence based on deep reinforcement learning is proposed. The deep reinforcement learning method takes switching parallel reactive compensation as the action space and sets the reward value based on the power flow convergence and reactive power adjustment. For the non-convergence power flow, the 500 kV nodes with reactive power compensation devices on the low-voltage side are converted into PV nodes… More >

  • Open Access

    ARTICLE

    Reliable Scheduling Method for Sensitive Power Business Based on Deep Reinforcement Learning

    Shen Guo*, Jiaying Lin, Shuaitao Bai, Jichuan Zhang, Peng Wang

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 1053-1066, 2023, DOI:10.32604/iasc.2023.038332

    Abstract The main function of the power communication business is to monitor, control and manage the power communication network to ensure normal and stable operation of the power communication network. Communication services related to dispatching data networks and the transmission of fault information or feeder automation have high requirements for delay. If processing time is prolonged, a power business cascade reaction may be triggered. In order to solve the above problems, this paper establishes an edge object-linked agent business deployment model for power communication network to unify the management of data collection, resource allocation and task scheduling within the system, realizes… More >

  • Open Access

    ARTICLE

    Feature Selection with Deep Reinforcement Learning for Intrusion Detection System

    S. Priya1,*, K. Pradeep Mohan Kumar2

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3339-3353, 2023, DOI:10.32604/csse.2023.030630

    Abstract An intrusion detection system (IDS) becomes an important tool for ensuring security in the network. In recent times, machine learning (ML) and deep learning (DL) models can be applied for the identification of intrusions over the network effectively. To resolve the security issues, this paper presents a new Binary Butterfly Optimization algorithm based on Feature Selection with DRL technique, called BBOFS-DRL for intrusion detection. The proposed BBOFSDRL model mainly accomplishes the recognition of intrusions in the network. To attain this, the BBOFS-DRL model initially designs the BBOFS algorithm based on the traditional butterfly optimization algorithm (BOA) to elect feature subsets.… More >

  • Open Access

    ARTICLE

    MAQMC: Multi-Agent Deep Q-Network for Multi-Zone Residential HVAC Control

    Zhengkai Ding1,2, Qiming Fu1,2,*, Jianping Chen2,3,4,*, You Lu1,2, Hongjie Wu1, Nengwei Fang4, Bin Xing4

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 2759-2785, 2023, DOI:10.32604/cmes.2023.026091

    Abstract The optimization of multi-zone residential heating, ventilation, and air conditioning (HVAC) control is not an easy task due to its complex dynamic thermal model and the uncertainty of occupant-driven cooling loads. Deep reinforcement learning (DRL) methods have recently been proposed to address the HVAC control problem. However, the application of single-agent DRL for multi-zone residential HVAC control may lead to non-convergence or slow convergence. In this paper, we propose MAQMC (Multi-Agent deep Q-network for multi-zone residential HVAC Control) to address this challenge with the goal of minimizing energy consumption while maintaining occupants’ thermal comfort. MAQMC is divided into MAQMC2 (MAQMC… More >

  • Open Access

    ARTICLE

    Implementation of Hybrid Deep Reinforcement Learning Technique for Speech Signal Classification

    R. Gayathri1,*, K. Sheela Sobana Rani2

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 43-56, 2023, DOI:10.32604/csse.2023.032491

    Abstract Classification of speech signals is a vital part of speech signal processing systems. With the advent of speech coding and synthesis, the classification of the speech signal is made accurate and faster. Conventional methods are considered inaccurate due to the uncertainty and diversity of speech signals in the case of real speech signal classification. In this paper, we use efficient speech signal classification using a series of neural network classifiers with reinforcement learning operations. Prior classification of speech signals, the study extracts the essential features from the speech signal using Cepstral Analysis. The features are extracted by converting the speech… More >

  • Open Access

    ARTICLE

    Optimizing Service Stipulation Uncertainty with Deep Reinforcement Learning for Internet Vehicle Systems

    Zulqar Nain1, B. Shahana2, Shehzad Ashraf Chaudhry3, P. Viswanathan4, M.S. Mekala1, Sung Won Kim1,*

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5705-5721, 2023, DOI:10.32604/cmc.2023.033194

    Abstract Fog computing brings computational services near the network edge to meet the latency constraints of cyber-physical System (CPS) applications. Edge devices enable limited computational capacity and energy availability that hamper end user performance. We designed a novel performance measurement index to gauge a device’s resource capacity. This examination addresses the offloading mechanism issues, where the end user (EU) offloads a part of its workload to a nearby edge server (ES). Sometimes, the ES further offloads the workload to another ES or cloud server to achieve reliable performance because of limited resources (such as storage and computation). The manuscript aims to… More >

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