Patikorn Anchuen, Peerapong Uthansakul^*, Monthippa Uthansakul, Settawit Poochaya

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1925-1943, 2022, DOI:10.32604/cmc.2022.022444

Abstract Smart electric motorcycle-sharing systems based on the digital platform are one of the public transportations that we use in daily lives when the sharing economy is considered. This transportation provides convenience for users with low-cost systems while it also promotes an environmental conservation. Normally, users rent the vehicle to travel from the origin station to another station near their destination with a one-way trip in which the demand of renting and returning at each station is different. This leads to unbalanced vehicle rental systems. To avoid the full or empty inventory, the electric motorcycle-sharing rebalancing with the fleet optimization is… More >

Jehangir Arshad¹, Ayesha Khan¹, Mariam Aftab¹, Mujtaba Hussain¹, Ateeq Ur Rehman², Shafiq Ahmad³, Adel M. Al-Shayea³, Muhammad Shafiq^4,*

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1153-1169, 2022, DOI:10.32604/cmc.2022.021917

Abstract Controlling feedback control systems in continuous action spaces has always been a challenging problem. Nevertheless, reinforcement learning is mainly an area of artificial intelligence (AI) because it has been used in process control for more than a decade. However, the existing algorithms are unable to provide satisfactory results. Therefore, this research uses a reinforcement learning (RL) algorithm to manage the control system. We propose an adaptive speed control of the motor system based on depth deterministic strategy gradient (DDPG). The actor-critic scenario using DDPG is implemented to build the RL agent. In addition, a framework has been created for traditional… More >

Mohammad Shoab, Abdullah Shawan Alotaibi^*

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5765-5781, 2022, DOI:10.32604/cmc.2022.021941

Abstract Deep Reinforcement Learning (DRL) is a class of Machine Learning (ML) that combines Deep Learning with Reinforcement Learning and provides a framework by which a system can learn from its previous actions in an environment to select its efforts in the future efficiently. DRL has been used in many application fields, including games, robots, networks, etc. for creating autonomous systems that improve themselves with experience. It is well acknowledged that DRL is well suited to solve optimization problems in distributed systems in general and network routing especially. Therefore, a novel query routing approach called Deep Reinforcement Learning based Route Selection… More >

Ao Xiong¹, Meng Chen^1,*, Shaoyong Guo¹, Yongjie Li², Yujing Zhao², Qinghai Ou³, Chuan Liu⁴, Siwen Xu⁵, Xiangang Liu⁶

Intelligent Automation & Soft Computing, Vol.31, No.3, pp. 1641-1654, 2022, DOI:10.32604/iasc.2022.018881

Abstract To solve the problem of energy consumption optimization of edge servers in the process of edge task unloading, we propose a task unloading algorithm based on reinforcement learning in this paper. The algorithm observes and analyzes the current environment state, selects the deployment location of edge tasks according to current states, and realizes the edge task unloading oriented to energy consumption optimization. To achieve the above goals, we first construct a network energy consumption model including servers’ energy consumption and link transmission energy consumption, which improves the accuracy of network energy consumption evaluation. Because of the complexity and variability of… More >

V. Praveena¹, A. Vijayaraj², P. Chinnasamy³, Ihsan Ali^4,*, Roobaea Alroobaea⁵, Saleh Yahya Alyahyan⁶, Muhammad Ahsan Raza⁷

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2639-2653, 2022, DOI:10.32604/cmc.2022.020066

Abstract In recent years, progressive developments have been observed in recent technologies and the production cost has been continuously decreasing. In such scenario, Internet of Things (IoT) network which is comprised of a set of Unmanned Aerial Vehicles (UAV), has received more attention from civilian to military applications. But network security poses a serious challenge to UAV networks whereas the intrusion detection system (IDS) is found to be an effective process to secure the UAV networks. Classical IDSs are not adequate to handle the latest computer networks that possess maximum bandwidth and data traffic. In order to improve the detection performance… More >

Wadhah Zeyad Tareq^*, Mehmet Fatih Amasyali

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2401-2414, 2022, DOI:10.32604/cmc.2022.020036

Abstract The main idea of reinforcement learning is evaluating the chosen action depending on the current reward. According to this concept, many algorithms achieved proper performance on classic Atari 2600 games. The main challenge is when the reward is sparse or missing. Such environments are complex exploration environments like Montezuma’s Revenge, Pitfall, and Private Eye games. Approaches built to deal with such challenges were very demanding. This work introduced a different reward system that enables the simple classical algorithm to learn fast and achieve high performance in hard exploration environments. Moreover, we added some simple enhancements to several hyperparameters, such as… More >

Abdullah Musaed Alkhiari¹, Shailendra Mishra^2,*, Mohammed AlShehri¹

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2149-2169, 2022, DOI:10.32604/cmc.2022.019562

Abstract Smart Grid is a power grid that improves flexibility, reliability, and efficiency through smart meters. Due to extensive data exchange over the Internet, the smart grid faces many security challenges that have led to data loss, data compromise, and high power consumption. Moreover, the lack of hardware protection and physical attacks reduce the overall performance of the smart grid network. We proposed the BLIDSE model (Blockchain-based secure quantum key distribution and Intrusion Detection System in Edge Enables Smart Grid Network) to address these issues. The proposed model includes five phases: The first phase is blockchain-based secure user authentication, where all… More >

Meteb M. Altaf^1,*, Ahmed Samir Roshdy², Hatoon S. AlSagri³

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3955-3967, 2022, DOI:10.32604/cmc.2022.019448

Abstract The overall healthcare system has been prioritized within development top lists worldwide. Since many national populations are aging, combined with the availability of sophisticated medical treatments, healthcare expenditures are rapidly growing. Blood banks are a major component of any healthcare system, which store and provide the blood products needed for organ transplants, emergency medical treatments, and routine surgeries. Timely delivery of blood products is vital, especially in emergency settings. Hence, blood delivery process parameters such as safety and speed have received attention in the literature, as well as other parameters such as delivery cost. In this paper, delivery time and… More >

K. M. Ameen Suhail¹, Syam Sankar¹, Ashok S. Kumar², Tsafack Nestor³, Naglaa F. Soliman^4,*, Abeer D. Algarni⁴, Walid El-Shafai⁵, Fathi E. Abd El-Samie^4,5

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 935-950, 2022, DOI:10.32604/cmc.2022.017069

Abstract Stock market is a place, where shares of different companies are traded. It is a collection of buyers’ and sellers’ stocks. In this digital era, analysis and prediction in the stock market have gained an essential role in shaping today's economy. Stock market analysis can be either fundamental or technical. Technical analysis can be performed either with technical indicators or through machine learning techniques. In this paper, we report a system that uses a Reinforcement Learning (RL) network and market sentiments to make decisions about stock market trading. The system uses sentiment analysis on daily market news to spot trends… More >

Ying Wang^*, Haoran Shi, Shibo Ren

CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.3, pp. 967-983, 2021, DOI:10.32604/cmes.2021.015792

Abstract The corrosion of reinforcement in the concrete will cause the effective cross-sectional area of reinforcement to be weakened and the performance of reinforcement to change and lead to the degradation of the bond behavior between reinforcement and concrete, which can seriously affect the mechanical properties of the structural elements. Therefore, it is of great practical significance to accurately simulate the corrosion morphology and the corrosion products of reinforcement. This paper improves the previous cellular automata models and establishes a new cellular automata model framework for simulating the random pitting corrosion process of reinforcement in concrete. This model defines the detailed… More >