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  • Open Access

    ARTICLE

    Predicting Reliability and Remaining Useful Life of Rolling Bearings Based on Optimized Neural Networks

    Tiantian Liang*, Runze Wang, Xuxiu Zhang, Yingdong Wang, Jianxiong Yang

    Structural Durability & Health Monitoring, Vol.17, No.5, pp. 433-455, 2023, DOI:10.32604/sdhm.2023.029331 - 07 September 2023

    Abstract In this study, an optimized long short-term memory (LSTM) network is proposed to predict the reliability and remaining useful life (RUL) of rolling bearings based on an improved whale-optimized algorithm (IWOA). The multi-domain features are extracted to construct the feature dataset because the single-domain features are difficult to characterize the performance degeneration of the rolling bearing. To provide covariates for reliability assessment, a kernel principal component analysis is used to reduce the dimensionality of the features. A Weibull distribution proportional hazard model (WPHM) is used for the reliability assessment of rolling bearing, and a beluga… More > Graphic Abstract

    Predicting Reliability and Remaining Useful Life of Rolling Bearings Based on Optimized Neural Networks

  • Open Access

    ARTICLE

    Improving Recommendation for Effective Personalization in Context-Aware Data Using Novel Neural Network

    R. Sujatha1,*, T. Abirami2

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 1775-1787, 2023, DOI:10.32604/csse.2023.031552 - 09 February 2023

    Abstract The digital technologies that run based on users’ content provide a platform for users to help air their opinions on various aspects of a particular subject or product. The recommendation agents play a crucial role in personalizing the needs of individual users. Therefore, it is essential to improve the user experience. The recommender system focuses on recommending a set of items to a user to help the decision-making process and is prevalent across e-commerce and media websites. In Context-Aware Recommender Systems (CARS), several influential and contextual variables are identified to provide an effective recommendation. A… More >

  • Open Access

    ARTICLE

    Optimized Neural Network-Based Micro Strip Patch Antenna Design for Radar Application

    A. Yogeshwaran1,*, K. Umadevi2

    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1491-1503, 2023, DOI:10.32604/iasc.2023.026424 - 19 July 2022

    Abstract Microstrip antennas are low-profile antennas that are utilized in wireless communication systems. In recent years, communication engineers have been increasingly interested in it. Because of downsizing, novelty, and cost reduction, the number of wireless standards has expanded in recent years. Wideband technologies have evolved in addition to analog and digital services. Radars necessitate antenna subsystems that are low-profile and lightweight. Microstrip antennas have these qualities and are suited for radars as an alternative to the bulky and heavyweight reflector/slotted waveguide array antennas. A perforated corner single-line fed microstrip antenna is designed here. When compared to… More >

  • Open Access

    ARTICLE

    An Optimized Neural Network with Bat Algorithm for DNA Sequence Classification

    Muhammad Zubair Rehman1, Muhammad Aamir2,*, Nazri Mohd. Nawi3, Abdullah Khan4, Saima Anwar Lashari5, Siyab Khan4

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 493-511, 2022, DOI:10.32604/cmc.2022.021787 - 18 May 2022

    Abstract

    Recently, many researchers have used nature inspired metaheuristic algorithms due to their ability to perform optimally on complex problems. To solve problems in a simple way, in the recent era bat algorithm has become famous due to its high tendency towards convergence to the global optimum most of the time. But, still the standard bat with random walk has a problem of getting stuck in local minima. In order to solve this problem, this research proposed bat algorithm with levy flight random walk. Then, the proposed Bat with Levy flight algorithm is further hybridized with

    More >

  • Open Access

    ARTICLE

    Detection of Attackers in Cognitive Radio Network Using Optimized Neural Networks

    V. P. Ajay1,*, M. Nesasudha2

    Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 193-204, 2022, DOI:10.32604/iasc.2022.024839 - 15 April 2022

    Abstract Cognitive radio network (CRN) is a growing technology targeting more resourcefully exploiting the available spectrum for opportunistic network usage. By the concept of cognitive radio, the wastage of available spectrum reduced about 30% worldwide. The key operation of CRN is spectrum sensing. The sensing results about the spectrum are directly proportional to the performance of the network. In CRN, the final result about the available spectrum is decided by combing the local sensing results. The presence or participation of attackers in the network leads to false decisions and the performance of the network will be More >

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