Huixiang Xu^*

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2309-2335, 2024, DOI:10.32604/cmc.2024.046253 - 27 February 2024

Abstract The Internet of Things (IoT) has revolutionized how we interact with and gather data from our surrounding environment. IoT devices with various sensors and actuators generate vast amounts of data that can be harnessed to derive valuable insights. The rapid proliferation of Internet of Things (IoT) devices has ushered in an era of unprecedented data generation and connectivity. These IoT devices, equipped with many sensors and actuators, continuously produce vast volumes of data. However, the conventional approach of transmitting all this data to centralized cloud infrastructures for processing and analysis poses significant challenges. However, transmitting… More >

R. T. Pavendan^1,*, K. Sankar¹, K. A. Varun Kumar²

Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3331-3348, 2023, DOI:10.32604/iasc.2023.028029 - 17 August 2022

Abstract Attacks on the cyber space is getting exponential in recent times. Illegal penetrations and breaches are real threats to the individuals and organizations. Conventional security systems are good enough to detect the known threats but when it comes to Advanced Persistent Threats (APTs) they fails. These APTs are targeted, more sophisticated and very persistent and incorporates lot of evasive techniques to bypass the existing defenses. Hence, there is a need for an effective defense system that can achieve a complete reliance of security. To address the above-mentioned issues, this paper proposes a novel honeypot system More >

Nitin Mittal¹, Harbinder Singh¹, Vikas Mittal², Shubham Mahajan³, Amit Kant Pandit³, Mehedi Masud⁴, Mohammed Baz⁵, Mohamed Abouhawwash^6,7,*

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3821-3835, 2022, DOI:10.32604/cmc.2022.020592 - 27 September 2021

Abstract Cognitive Radio (CR) has been developed as an enabling technology that allows the unused or underused spectrum to be used dynamically to increase spectral efficiency. To improve the overall performance of the CR system it is extremely important to adapt or reconfigure the system parameters. The Decision Engine is a major module in the CR-based system that not only includes radio monitoring and cognition functions but also responsible for parameter adaptation. As meta-heuristic algorithms offer numerous advantages compared to traditional mathematical approaches, the performance of these algorithms is investigated in order to design an efficient… More >

Michal Prauzek^*, Tereza Paterova, Jaromir Konecny, Radek Martinek

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2601-2618, 2022, DOI:10.32604/cmc.2022.019705 - 27 September 2021

Abstract Nowadays, there is a significant need for maintenance free modern Internet of things (IoT) devices which can monitor an environment. IoT devices such as these are mobile embedded devices which provide data to the internet via Low Power Wide Area Network (LPWAN). LPWAN is a promising communications technology which allows machine to machine (M2M) communication and is suitable for small mobile embedded devices. The paper presents a novel data-driven self-learning (DDSL) controller algorithm which is dedicated to controlling small mobile maintenance-free embedded IoT devices. The DDSL algorithm is based on a modified Q-learning algorithm which… More >

Kyungsuk Jang¹, Gun Jin Yun^2,*

CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1091-1120, 2021, DOI:10.32604/cmc.2020.012911 - 26 November 2020

Abstract This paper first proposes a new self-learning data-driven methodology that can develop the failure criteria of unknown anisotropic ductile materials from the minimal number of experimental tests. Establishing failure criteria of anisotropic ductile materials requires time-consuming tests and manual data evaluation. The proposed method can overcome such practical challenges. The methodology is formalized by combining four ideas: 1) The deep learning neural network (DLNN)-based material constitutive model, 2) Self-learning inverse finite element (SELIFE) simulation, 3) Algorithmic identification of failure points from the self-learned stress-strain curves and 4) Derivation of the failure criteria through symbolic regression More >

Xia Li^1,2, Peifeng Niu^1,*, Jianping Liu², Qing Liu²

CMES-Computer Modeling in Engineering & Sciences, Vol.117, No.1, pp. 29-57, 2018, DOI:10.31614/cmes.2018.04020

Abstract An improved teaching-learning-based optimization (I-TLBO) algorithm is proposed to adjust the parameters of extreme learning machine with parallel layer perception (PELM), and a well-generalized I-TLBO-PELM model is obtained to build the model of NO_X emissions of a boiler. In the I-TLBO algorithm, there are four major highlights. Firstly, a quantum initialized population by using the qubits on Bloch sphere replaces a randomly initialized population. Secondly, two kinds of angles in Bloch sphere are generated by using cube chaos mapping. Thirdly, an adaptive control parameter is added into the teacher phase to speed up the convergent speed. More >