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. K. Morarji^1,*, N. Sathish Kumar²

Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 2875-2892, 2023, DOI:10.32604/iasc.2023.025776

Abstract One of the recent advancements in the electrical power systems is the smart-grid technology. For the effective functioning of the smart grid, the process like monitoring and controlling have to be given importance. In this paper, the Wireless Sensor Network (WSN) is utilized for tracking the power in smart grid applications. The smart grid is used to produce the electricity and it is connected with the sensor to transmit or receive the data. The data is transmitted quickly by using the Probabilistic Neural Network (PNN), which aids in identifying the shortest path of the nodes. While transmitting the data from… More >

R. Hemalatha^1,*, M. Ramasamy²

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1505-1525, 2023, DOI:10.32604/iasc.2023.026301

Abstract In recent day’s power distribution system is distress from acute power quality issues. In this work, for compensating Power Quality (PQ) disturbances a seven level cascaded H-bridge inverter is implemented in distribution static compensator which protects power quality problems in currents. Distribution Static Compensator (DSTATCOM) aid to enhances power factor and removes total harmonic distortion which is drawn from non-linear load. The D–Q reference theory based hysteresis current controller is employed to generate reference current for compensation of harmonics and reactive power, additionally Probabilistic Neural Network (PNN) classifier is used which easily separates exact harmonics. In the meantime fuzzy logic… More >

Andrea Lapucci^*†1, Gabriele Perrone^*†1, Antonello Di Paolo^‡§, Cristina Napoli^*†, Ida Landini^*†, Giandomenico Roviello^*†, Laura Calosi^¶, Antonio Giuseppe Naccarato^#, Alfredo Falcone^#, Daniele Bani^¶, Enrico Mini^*†§2, Stefania Nobili^*†§2,3

Oncology Research, Vol.28, No.6, pp. 631-644, 2020, DOI:10.3727/096504020X16056983169118

Abstract The benefit of adjuvant chemotherapy in the early stages of colorectal cancer (CRC) is still disappointing and the prediction of treatment outcome quite difficult. Recently, through a transcriptomic approach, we evidenced a role of PNN and KCNQ1OT1 gene expression in predicting response to fluoropyrimidine-based adjuvant chemotherapy in stage III CRC patients. Thus, the aim of this study was to validate in an independent cohort of stages II–III CRC patients our previous findings. PNN and KCNQ1OT1 mRNA expression levels were evaluated in 74 formalin-fixed paraffin-embedded tumor and matched normal mucosa samples obtained by stages II–III CRC patients treated with fluoropyrimidine-based adjuvant… More >

A. Alice Blessie^1,*, P. Ramesh²

Computer Systems Science and Engineering, Vol.44, No.2, pp. 1467-1484, 2023, DOI:10.32604/csse.2023.025399

Abstract The present progress of visual-based detection of the diseased area of a malady plays an essential part in the medical field. In that case, the image processing is performed to improve the image data, wherein it inhibits unintended distortion of image features or it enhances further processing in various applications and fields. This helps to show better results especially for diagnosing diseases. Of late the early prediction of cancer is necessary to prevent disease-causing problems. This work is proposed to identify lung cancer using lung computed tomography (CT) scan images. It helps to identify cancer cells’ affected areas. In the… More >

Mohammad Wedyan¹, Omar Elshaweesh², Enas Ramadan³, Ryan Alturki^4,*

Computer Systems Science and Engineering, Vol.43, No.3, pp. 1189-1206, 2022, DOI:10.32604/csse.2022.024210

Abstract Big data is a term that refers to a set of data that, due to its largeness or complexity, cannot be stored or processed with one of the usual tools or applications for data management, and it has become a prominent word in recent years for the massive development of technology. Almost immediately thereafter, the term “big data mining” emerged, i.e., mining from big data even as an emerging and interconnected field of research. Classification is an important stage in data mining since it helps people make better decisions in a variety of situations, including scientific endeavors, biomedical research, and… More >

Ivan Izonin^1,*, Roman Tkachenko¹, Michal Gregus², Zoia Duriagina^1,3, Nataliya Shakhovska¹

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5933-5947, 2022, DOI:10.32604/cmc.2022.022582

Abstract The advent of additive technologies has provided a significant breakthrough in the production of medical implants. It has reduced costs, increased productivity and accuracy of the implant manufacturing process. However, there are problems associated with assessing defects in the microstructure, mechanical and technological properties of alloys, both during their production by powder metallurgy and in the process of 3D printing. Thus traditional research methods of alloys properties demand considerable human, material, and time resources. At the same time, artificial intelligence tools create opportunities for intelligent evaluation of the conformity for the microstructure, phase composition, and properties of titanium powder’s alloys.… More >

Noha E. El-Attar^1,*, Sahar F. Sabbeh^1,2, Heba Fasihuddin², Wael A. Awad³

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 2249-2269, 2022, DOI:10.32604/cmc.2022.022673

Abstract The outbreak of Covid-19 has taken the lives of many patients so far. The symptoms of COVID-19 include muscle pains, loss of taste and smell, coughs, fever, and sore throat, which can lead to severe cases of breathing difficulties, organ failure, and death. Thus, the early detection of the virus is very crucial. COVID-19 can be detected using clinical tests, making us need to know the most important symptoms/features that can enhance the decision process. In this work, we propose a modified multilayer perceptron (MLP) with feature selection (MLPFS) to predict the positive COVID-19 cases based on symptoms and features… More >

Shui-Hua Wang¹, Muhammad Attique Khan², Yu-Dong Zhang^3,*

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3081-3097, 2022, DOI:10.32604/cmc.2022.019447

Abstract Aim Alcoholism is a disease that a patient becomes dependent or addicted to alcohol. This paper aims to design a novel artificial intelligence model that can recognize alcoholism more accurately. Methods We propose the VGG-Inspired stochastic pooling neural network (VISPNN) model based on three components: (i) a VGG-inspired mainstay network, (ii) the stochastic pooling technique, which aims to outperform traditional max pooling and average pooling, and (iii) an improved 20-way data augmentation (Gaussian noise, salt-and-pepper noise, speckle noise, Poisson noise, horizontal shear, vertical shear, rotation, Gamma correction, random translation, and scaling on both raw image and its horizontally mirrored image).… More >

Ming Wan¹, Jinfang Li¹, Jiangyuan Yao^{2, *}, Rongbing Wang^{1, 3}, Hao Luo¹

CMC-Computers, Materials & Continua, Vol.63, No.3, pp. 1415-1431, 2020, DOI:10.32604/cmc.2020.09692

Abstract In process industries, the characteristics of industrial activities focus on the integrality and continuity of production process, which can contribute to excavating the appropriate features for industrial anomaly detection. From this perspective, this paper proposes a novel state-based control feature extraction approach, which regards the finite control operations as different states. Furthermore, the procedure of state transition can adequately express the change of successive control operations, and the statistical information between different states can be used to calculate the feature values. Additionally, OCSVM (One Class Support Vector Machine) and BPNN (BP Neural Network), which are optimized by PSO (Particle Swarm… More >