Sepehr Soltani¹, Ehsan Ghafourian², Reza Salehi³, Diego Martín^3,*, Milad Vahidi⁴

Intelligent Automation & Soft Computing, Vol.39, No.1, pp. 93-108, 2024, DOI:10.32604/iasc.2024.042693 - 29 March 2024

Abstract For many years, researchers have explored power allocation (PA) algorithms driven by models in wireless networks where multiple-user communications with interference are present. Nowadays, data-driven machine learning methods have become quite popular in analyzing wireless communication systems, which among them deep reinforcement learning (DRL) has a significant role in solving optimization issues under certain constraints. To this purpose, in this paper, we investigate the PA problem in a -user multiple access channels (MAC), where transmitters (e.g., mobile users) aim to send an independent message to a common receiver (e.g., base station) through wireless channels. To… More >

Yan Li, Qiyuan Wang^*, Kaidi Jia

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2469-2489, 2024, DOI:10.32604/cmc.2024.047822 - 27 February 2024

Abstract Image description task is the intersection of computer vision and natural language processing, and it has important prospects, including helping computers understand images and obtaining information for the visually impaired. This study presents an innovative approach employing deep reinforcement learning to enhance the accuracy of natural language descriptions of images. Our method focuses on refining the reward function in deep reinforcement learning, facilitating the generation of precise descriptions by aligning visual and textual features more closely. Our approach comprises three key architectures. Firstly, it utilizes Residual Network 101 (ResNet-101) and Faster Region-based Convolutional Neural Network… More >

Mesfer Al Duhayyim^1,*, 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 - 09 November 2023

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… More >

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 - 08 October 2023

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… More >

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 - 08 October 2023

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… More >

Shuangxing Deng, Man Li^*, Huachun Zhou

Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2919-2939, 2023, DOI:10.32604/iasc.2023.039985 - 11 September 2023

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… More >

Wei Zhang¹, Bin Ji², Ping He¹, Nanqin Wang¹, Yuwei Wang¹, Mengzhe Zhang^2,*

Energy Engineering, Vol.120, No.9, pp. 2177-2192, 2023, DOI:10.32604/ee.2023.026504 - 03 August 2023

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 More >

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 - 29 April 2023

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… More >

S. Priya^1,*, K. Pradeep Mohan Kumar²

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3339-3353, 2023, DOI:10.32604/csse.2023.030630 - 03 April 2023

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 More >

Zhengkai Ding^1,2, Qiming Fu^1,2,*, Jianping Chen^2,3,4,*, You Lu^1,2, 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 - 09 March 2023

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… More >