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  • Open Access


    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


    Reinforcement Learning with an Ensemble of Binary Action Deep Q-Networks

    A. M. Hafiz1, M. Hassaballah2,3,*, Abdullah Alqahtani3, Shtwai Alsubai3, Mohamed Abdel Hameed4

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 2651-2666, 2023, DOI:10.32604/csse.2023.031720

    Abstract With the advent of Reinforcement Learning (RL) and its continuous progress, state-of-the-art RL systems have come up for many challenging and real-world tasks. Given the scope of this area, various techniques are found in the literature. One such notable technique, Multiple Deep Q-Network (DQN) based RL systems use multiple DQN-based-entities, which learn together and communicate with each other. The learning has to be distributed wisely among all entities in such a scheme and the inter-entity communication protocol has to be carefully designed. As more complex DQNs come to the fore, the overall complexity of these multi-entity systems has increased many… More >

  • Open Access


    Generation and Simulation of Basic Maneuver Action Library for 6-DOF Aircraft by Reinforcement Learning

    Jinlin Wang1, Jitao Teng3, Yang He1, Hongyu Yang1,*, Yulong Ji2,*, Zhikun Tang4, Ningwei Bai5

    Journal on Internet of Things, Vol.4, No.2, pp. 85-98, 2022, DOI:10.32604/jiot.2022.031043

    Abstract The development of modern air combat requires aircraft to have certain intelligent decision-making ability. In some of the existing solutions, the automatic control of aircraft is mostly composed of the upper mission decision and the lower control system. Although the underlying PID (Proportional Integral Derivative) based controller has a good performance in stable conditions, it lacks stability in complex environments. So, we need to design a new system for the problem of aircraft decision making. Studies have shown that the behavior of an aircraft can be viewed as a combination of several basic maneuvers. The establishment of aircraft basic motion… More >

  • Open Access


    Adaptive Cyber Defense Technique Based on Multiagent Reinforcement Learning Strategies

    Adel Alshamrani1,*, Abdullah Alshahrani2

    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2757-2771, 2023, DOI:10.32604/iasc.2023.032835

    Abstract The static nature of cyber defense systems gives attackers a sufficient amount of time to explore and further exploit the vulnerabilities of information technology systems. In this paper, we investigate a problem where multiagent systems sensing and acting in an environment contribute to adaptive cyber defense. We present a learning strategy that enables multiple agents to learn optimal policies using multiagent reinforcement learning (MARL). Our proposed approach is inspired by the multiarmed bandits (MAB) learning technique for multiple agents to cooperate in decision making or to work independently. We study a MAB approach in which defenders visit a system multiple… More >

  • Open Access


    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


    Double Deep Q-Network Method for Energy Efficiency and Throughput in a UAV-Assisted Terrestrial Network

    Mohamed Amine Ouamri1,2, Reem Alkanhel3,*, Daljeet Singh4, El-sayed M. El-kenaway5, Sherif S. M. Ghoneim6

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 73-92, 2023, DOI:10.32604/csse.2023.034461

    Abstract Increasing the coverage and capacity of cellular networks by deploying additional base stations is one of the fundamental objectives of fifth-generation (5G) networks. However, it leads to performance degradation and huge spectral consumption due to the massive densification of connected devices and simultaneous access demand. To meet these access conditions and improve Quality of Service, resource allocation (RA) should be carefully optimized. Traditionally, RA problems are nonconvex optimizations, which are performed using heuristic methods, such as genetic algorithm, particle swarm optimization, and simulated annealing. However, the application of these approaches remains computationally expensive and unattractive for dense cellular networks. Therefore,… More >

  • Open Access


    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


    B-Spline-Based Curve Fitting to Cam Pitch Curve Using Reinforcement Learning

    Zhiwei Lin1, Tianding Chen1,*, Yingtao Jiang2, Hui Wang1, Shuqin Lin1, Ming Zhu2

    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 2145-2164, 2023, DOI:10.32604/iasc.2023.035555

    Abstract Directly applying the B-spline interpolation function to process plate cams in a computer numerical control (CNC) system may produce verbose tool-path codes and unsmooth trajectories. This paper is devoted to addressing the problem of B-spline fitting for cam pitch curves. Considering that the B-spline curve needs to meet the motion law of the follower to approximate the pitch curve, we use the radial error to quantify the effects of the fitting B-spline curve and the pitch curve. The problem thus boils down to solving a difficult global optimization problem to find the numbers and positions of the control points or… More >

  • Open Access


    Reinforcement Learning to Improve QoS and Minimizing Delay in IoT

    Mahendrakumar Subramaniam1,*, V. Vedanarayanan2, Azath Mubarakali3, S. Sathiya Priya4

    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1603-1612, 2023, DOI:10.32604/iasc.2023.032396

    Abstract Machine Learning concepts have raised executions in all knowledge domains, including the Internet of Thing (IoT) and several business domains. Quality of Service (QoS) has become an important problem in IoT surrounding since there is a vast explosion of connecting sensors, information and usage. Sensor data gathering is an efficient solution to collect information from spatially disseminated IoT nodes. Reinforcement Learning Mechanism to improve the QoS (RLMQ) and use a Mobile Sink (MS) to minimize the delay in the wireless IoT s proposed in this paper. Here, we use machine learning concepts like Reinforcement Learning (RL) to improve the QoS… More >

  • Open Access


    DQN-Based Proactive Trajectory Planning of UAVs in Multi-Access Edge Computing

    Adil Khan1,*, Jinling Zhang1, Shabeer Ahmad1, Saifullah Memon2, Babar Hayat1, Ahsan Rafiq3

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 4685-4702, 2023, DOI:10.32604/cmc.2023.034892

    Abstract The main aim of future mobile networks is to provide secure, reliable, intelligent, and seamless connectivity. It also enables mobile network operators to ensure their customer’s a better quality of service (QoS). Nowadays, Unmanned Aerial Vehicles (UAVs) are a significant part of the mobile network due to their continuously growing use in various applications. For better coverage, cost-effective, and seamless service connectivity and provisioning, UAVs have emerged as the best choice for telco operators. UAVs can be used as flying base stations, edge servers, and relay nodes in mobile networks. On the other side, Multi-access Edge Computing (MEC) technology also… More >

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