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  • Open Access

    ARTICLE

    Deep Reinforcement Learning Based Unmanned Aerial Vehicle (UAV) Control Using 3D Hand Gestures

    Fawad Salam Khan1,4, Mohd Norzali Haji Mohd1,*, Saiful Azrin B. M. Zulkifli2, Ghulam E Mustafa Abro2, Suhail Kazi3, Dur Muhammad Soomro1

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5741-5759, 2022, DOI:10.32604/cmc.2022.024927

    Abstract The evident change in the design of the autopilot system produced massive help for the aviation industry and it required frequent upgrades. Reinforcement learning delivers appropriate outcomes when considering a continuous environment where the controlling Unmanned Aerial Vehicle (UAV) required maximum accuracy. In this paper, we designed a hybrid framework, which is based on Reinforcement Learning and Deep Learning where the traditional electronic flight controller is replaced by using 3D hand gestures. The algorithm is designed to take the input from 3D hand gestures and integrate with the Deep Deterministic Policy Gradient (DDPG) to receive the best reward and take… More >

  • Open Access

    ARTICLE

    Multi-Agent Deep Reinforcement Learning-Based Resource Allocation in HPC/AI Converged Cluster

    Jargalsaikhan Narantuya1,*, Jun-Sik Shin2, Sun Park2, JongWon Kim2

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4375-4395, 2022, DOI:10.32604/cmc.2022.023318

    Abstract As the complexity of deep learning (DL) networks and training data grows enormously, methods that scale with computation are becoming the future of artificial intelligence (AI) development. In this regard, the interplay between machine learning (ML) and high-performance computing (HPC) is an innovative paradigm to speed up the efficiency of AI research and development. However, building and operating an HPC/AI converged system require broad knowledge to leverage the latest computing, networking, and storage technologies. Moreover, an HPC-based AI computing environment needs an appropriate resource allocation and monitoring strategy to efficiently utilize the system resources. In this regard, we introduce a… More >

  • Open Access

    ARTICLE

    Deep Reinforcement Learning Enabled Smart City Recycling Waste Object Classification

    Mesfer Al Duhayyim1, Taiseer Abdalla Elfadil Eisa2, Fahd N. Al-Wesabi3,4, Abdelzahir Abdelmaboud5, Manar Ahmed Hamza6,*, Abu Sarwar Zamani6, Mohammed Rizwanullah6, Radwa Marzouk7,8

    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5699-5715, 2022, DOI:10.32604/cmc.2022.024431

    Abstract The Smart City concept revolves around gathering real time data from citizen, personal vehicle, public transports, building, and other urban infrastructures like power grid and waste disposal system. The understandings obtained from the data can assist municipal authorities handle assets and services effectually. At the same time, the massive increase in environmental pollution and degradation leads to ecological imbalance is a hot research topic. Besides, the progressive development of smart cities over the globe requires the design of intelligent waste management systems to properly categorize the waste depending upon the nature of biodegradability. Few of the commonly available wastes are… More >

  • Open Access

    ARTICLE

    Deep Reinforcement Learning-Based Long Short-Term Memory for Satellite IoT Channel Allocation

    S. Lakshmi Durga1, Ch. Rajeshwari1, Khalid Hamed Allehaibi2, Nishu Gupta3,*, Nasser Nammas Albaqami4, Isha Bharti5, Ahmad Hoirul Basori6

    Intelligent Automation & Soft Computing, Vol.33, No.1, pp. 1-19, 2022, DOI:10.32604/iasc.2022.022536

    Abstract In recent years, the demand for smart wireless communication technology has increased tremendously, and it urges to extend internet services globally with high reliability, less cost and minimal delay. In this connection, low earth orbit (LEO) satellites have played prominent role by reducing the terrestrial infrastructure facilities and providing global coverage all over the earth with the help of satellite internet of things (SIoT). LEO satellites provide wide coverage area to dynamically accessing network with limited resources. Presently, most resource allocation schemes are designed only for geostationary earth orbit (GEO) satellites. For LEO satellites, resource allocation is challenging due to… More >

  • Open Access

    ARTICLE

    Deep Reinforcement Learning for Addressing Disruptions in Traffic Light Control

    Faizan Rasheed1, Kok-Lim Alvin Yau2, Rafidah Md Noor3, Yung-Wey Chong4,*

    CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 2225-2247, 2022, DOI:10.32604/cmc.2022.022952

    Abstract This paper investigates the use of multi-agent deep Q-network (MADQN) to address the curse of dimensionality issue occurred in the traditional multi-agent reinforcement learning (MARL) approach. The proposed MADQN is applied to traffic light controllers at multiple intersections with busy traffic and traffic disruptions, particularly rainfall. MADQN is based on deep Q-network (DQN), which is an integration of the traditional reinforcement learning (RL) and the newly emerging deep learning (DL) approaches. MADQN enables traffic light controllers to learn, exchange knowledge with neighboring agents, and select optimal joint actions in a collaborative manner. A case study based on a real traffic… More >

  • Open Access

    ARTICLE

    Deep Reinforcement Learning Empowered Edge Collaborative Caching Scheme for Internet of Vehicles

    Xin Liu1, Siya Xu1, Chao Yang2, Zhili Wang1,*, Hao Zhang3, Jingye Chi1, Qinghan Li4

    Computer Systems Science and Engineering, Vol.42, No.1, pp. 271-287, 2022, DOI:10.32604/csse.2022.022103

    Abstract With the development of internet of vehicles, the traditional centralized content caching mode transmits content through the core network, which causes a large delay and cannot meet the demands for delay-sensitive services. To solve these problems, on basis of vehicle caching network, we propose an edge collaborative caching scheme. Road side unit (RSU) and mobile edge computing (MEC) are used to collect vehicle information, predict and cache popular content, thereby provide low-latency content delivery services. However, the storage capacity of a single RSU severely limits the edge caching performance and cannot handle intensive content requests at the same time. Through… More >

  • Open Access

    ARTICLE

    Fleet Optimization of Smart Electric Motorcycle System Using Deep Reinforcement Learning

    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 >

  • Open Access

    ARTICLE

    Optimal Deep Reinforcement Learning for Intrusion Detection in UAVs

    V. Praveena1, A. Vijayaraj2, P. Chinnasamy3, Ihsan Ali4,*, 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 >

  • Open Access

    ARTICLE

    A New Reward System Based on Human Demonstrations for Hard Exploration Games

    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 >

  • Open Access

    ARTICLE

    Blockchain-Based SQKD and IDS in Edge Enabled Smart Grid Network

    Abdullah Musaed Alkhiari1, Shailendra Mishra2,*, 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 >

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