Dazhi Wang^*, Pengyi Pan, Bowen Niu

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1535-1555, 2023, DOI:10.32604/cmc.2023.042286

Abstract The permanent magnet eddy current coupler (PMEC) solves the problem of flexible connection and speed regulation between the motor and the load and is widely used in electrical transmission systems. It provides torque to the load and generates heat and losses, reducing its energy transfer efficiency. This issue has become an obstacle for PMEC to develop toward a higher power. This paper aims to improve the overall performance of PMEC through multi-objective optimization methods. Firstly, a PMEC modeling method based on the Levenberg-Marquardt back propagation (LMBP) neural network is proposed, aiming at the characteristics of the complex input-output relationship and… More >

Zaihe Cheng¹, Yuwen Tao², Xiaoqing Gu³, Yizhang Jiang², Pengjiang Qian^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 1613-1633, 2023, DOI:10.32604/cmes.2023.027708

Abstract Through semi-supervised learning and knowledge inheritance, a novel Takagi-Sugeno-Kang (TSK) fuzzy system framework is proposed for epilepsy data classification in this study. The new method is based on the maximum mean discrepancy (MMD) method and TSK fuzzy system, as a basic model for the classification of epilepsy data. First, for medical data, the interpretability of TSK fuzzy systems can ensure that the prediction results are traceable and safe. Second, in view of the deviation in the data distribution between the real source domain and the target domain, MMD is used to measure the distance between dierent data distributions. The objective… More >

Tusongjiang Kari¹, Sun Guoliang², Lei Kesong¹, Ma Xiaojing^1,*, Wu Xian¹

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1437-1452, 2023, DOI:10.32604/iasc.2023.037012

Abstract Wind power volatility not only limits the large-scale grid connection but also poses many challenges to safe grid operation. Accurate wind power prediction can mitigate the adverse effects of wind power volatility on wind power grid connections. For the characteristics of wind power antecedent data and precedent data jointly to determine the prediction accuracy of the prediction model, the short-term prediction of wind power based on a combined neural network is proposed. First, the Bi-directional Long Short Term Memory (BiLSTM) network prediction model is constructed, and the bi-directional nature of the BiLSTM network is used to deeply mine the wind… More >

Aravind Athimoolam^1,*, Karthik Balasubramanian²

Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1331-1347, 2023, DOI:10.32604/iasc.2023.033465

Abstract This paper focuses on the development of a diagnostic tool for detecting insulated gate bipolar transistor power electronic switch flaws caused by both open and short circuit faults in multi-level inverter time-frequency output voltage specifications. High-resolution laboratory virtual instrument engineering workbench software testing tool with a sample rate data collection system, as well as specialized signal processing and soft computing technologies, are used in this proposed method. On a single-phase cascaded H-bridge multilevel inverter, simulation and experimental investigations of both open and short issues of the insulated gate bipolar transistor components are performed out. In all conceivable switch issues, the… More >

WENLI ZENG¹, FENG LING², KAINUO DANG³, QINGJIA CHI^3,*

BIOCELL, Vol.47, No.3, pp. 581-592, 2023, DOI:10.32604/biocell.2023.025957

Abstract Hepatocellular carcinoma (HCC) is associated with poor prognosis and fluctuations in immune status. Although studies have found that secreted phosphoprotein 1 (SPP1) is involved in HCC progression, its independent prognostic value and immune-mediated role remain unclear. Using The Cancer Genome Atlas and Gene Expression Omnibus data, we found that low expression of SPP1 is significantly associated with improved survival of HCC patients and that SPP1 expression is correlated with clinical characteristics. Univariate and multivariate Cox regression confirmed that SPP1 is an independent prognostic factor of HCC. Subsequently, we found that T cell CD4 memory-activated monocytes, M0 macrophages, and resting mast… More >

Xiangyan Tang^1,2, Dengfang Feng^2,*, KeQiu Li¹, Jingxin Liu², Jinyang Song³, Victor S. Sheng⁴

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2789-2802, 2023, DOI:10.32604/cmc.2023.031304

Abstract Data prediction can improve the science of decision-making by making predictions about what happens in daily life based on natural law trends. Back propagation (BP) neural network is a widely used prediction method. To reduce its probability of falling into local optimum and improve the prediction accuracy, we propose an improved BP neural network prediction method based on a multi-strategy sparrow search algorithm (MSSA). The weights and thresholds of the BP neural network are optimized using the sparrow search algorithm (SSA). Three strategies are designed to improve the SSA to enhance its optimization-seeking ability, leading to the MSSA-BP prediction model.… More >

Ammar Almomani^1,2,*, Mohammed Alweshah³, Waleed Alomoush⁴, Mohammad Alauthman⁵, Aseel Jabai², Anwar Abbass², Ghufran Hamad², Meral Abdalla², Brij B. Gupta^1,6,7

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3045-3062, 2023, DOI:10.32604/cmc.2023.030567

Abstract Voice classification is important in creating more intelligent systems that help with student exams, identifying criminals, and security systems. The main aim of the research is to develop a system able to predicate and classify gender, age, and accent. So, a new system called Classifying Voice Gender, Age, and Accent (CVGAA) is proposed. Backpropagation and bagging algorithms are designed to improve voice recognition systems that incorporate sensory voice features such as rhythm-based features used to train the device to distinguish between the two gender categories. It has high precision compared to other algorithms used in this problem, as the adaptive… More >

Hao Han, Wei Wang^*

CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 1353-1370, 2023, DOI:10.32604/cmes.2022.021494

Abstract Accurate prediction of ship motion is very important for ensuring marine safety, weapon control, and aircraft carrier landing, etc. Ship motion is a complex time-varying nonlinear process which is affected by many factors. Time series analysis method and many machine learning methods such as neural networks, support vector machines regression (SVR) have been widely used in ship motion predictions. However, these single models have certain limitations, so this paper adopts a multi-model prediction method. First, ensemble empirical mode decomposition (EEMD) is used to remove noise in ship motion data. Then the random forest (RF) prediction model optimized by genetic algorithm… More >

C. Edwin Singh^1,*, S. Maria Celestin Vigila²

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1737-1751, 2023, DOI:10.32604/iasc.2023.028022

Abstract Mobile ad-hoc networks (MANET) are garnering a lot of attention because of their potential to provide low-cost solutions to real-world communications. MANETs are more vulnerable to security threats. Changes in nodes, bandwidth limits, and centralized control and management are some of the characteristics. IDS (Intrusion Detection System) are the aid for detection, determination, and identification of illegal system activity such as use, copying, modification, and destruction of data. To address the identified issues, academics have begun to concentrate on building IDS-based machine learning algorithms. Deep learning is a type of machine learning that can produce exceptional outcomes. This study proposes… More >

S. Muthukumaran^1,*, P. Geetha², E. Ramaraj¹

Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 215-230, 2023, DOI:10.32604/iasc.2023.027449

Abstract Agriculture plays a vital role in the food production process that occupies nearly one-third of the total surface of the earth. Rice is propagated from the seeds of paddy and it is a stable food almost used by fifty percent of the total world population. The extensive growth of the human population alarms us to ensure food security and the country should take proper food steps to improve the yield of food grains. This paper concentrates on improving the yield of paddy by predicting the factors that influence the growth of paddy with the help of Evolutionary Computation Techniques. Most… More >