Gang Yu^1,4,5, Jian Tang^2,*, Jian Zhang³, Zhonghui Wang⁶

Intelligent Automation & Soft Computing, Vol.29, No.1, pp. 273-290, 2021, DOI:10.32604/iasc.2021.016981

Abstract Kernel learning based on structure risk minimum can be employed to build a soft measuring model for analyzing small samples. However, it is difficult to select learning parameters, such as kernel parameter (KP) and regularization parameter (RP). In this paper, a soft measuring method is investigated to select learning parameters, which is based on adaptive multi-layer selective ensemble (AMLSEN) and least-square support vector machine (LSSVM). First, candidate kernels and RPs with K and R numbers are preset based on prior knowledge, and candidate sub-sub-models with K*R numbers are constructed through utilizing LSSVM. Second, the candidate sub-sub-models with same KPs and… More >

Imam Yuadi^1,*, Myrtati D. Artaria², Sakina³, A. Taufiq Asyhari⁴

CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3979-3995, 2021, DOI:10.32604/cmc.2021.015417

Abstract The size, shape, and physical characteristics of the human skull are distinct when considering individual humans. In physical anthropology, the accurate management of skull collections is crucial for storing and maintaining collections in a cost-effective manner. For example, labeling skulls inaccurately or attaching printed labels to skulls can affect the authenticity of collections. Given the multiple issues associated with the manual identification of skulls, we propose an automatic human skull classification approach that uses a support vector machine and different feature extraction methods such as gray-level co-occurrence matrix features, Gabor features, fractal features, discrete wavelet transforms, and combinations of features.… More >

Anshu Parashar^*, Nidhi Kalra, Jaskirat Singh, Raman Kumar Goyal

Intelligent Automation & Soft Computing, Vol.28, No.3, pp. 669-682, 2021, DOI:10.32604/iasc.2021.017478

Abstract Electrophysiological (EEG) signals provide good temporal resolution and can be effectively used to assess and diagnose children with Attention Deficit Hyperactivity Disorder (ADHD). This study aims to develop a machine learning model to classify children with ADHD and Healthy Controls. In this study, EEG signals captured under cognitive tasks were obtained from an open-access database of 60 children with ADHD and 60 Healthy Controls children of similar age. The regional contributions towards attaining higher accuracy are identified and further tested using three classifiers: AdaBoost, Random Forest and Support Vector Machine. The EEG data from 19 channels is taken as input… More >

Hongshuo Zhang¹, Bo Zhu^1,*, Kaimin Pang¹, Chunmei Chen¹, Yuwei Wan²

Intelligent Automation & Soft Computing, Vol.28, No.3, pp. 797-810, 2021, DOI:10.32604/iasc.2021.017232

Abstract Using machine learning method to recognize abnormal patterns covers the shortage of traditional control charts for autocorrelation processes, which violate the applicable conditions of the control chart, i.e., the independent identically distributed (IID) assumption. In this study, we propose a recognition model based on support vector machine (SVM) for the AR (1) type of autocorrelation process. For achieving a higher recognition performance, the cuckoo search algorithm (CS) is used to optimize the two hyper-parameters of SVM, namely the penalty parameter c and the radial basis kernel parameter g. By using Monte Carlo simulation methods, the data sets containing samples of… More >

Haibo Liu^1,*, Yujie Dong², Fuzhong Wang¹

Energy Engineering, Vol.118, No.3, pp. 679-689, 2021, DOI: 10.32604/EE.2021.014630

Abstract For the problems of nonlinearity, uncertainty and low prediction accuracy in the gas outburst prediction of coal mining face, the least squares support vector machine (LSSVM) is proposed to establish the prediction model. Firstly, considering the inertia coefficients as global parameters lacks the ability to improve the solution for the traditional particle swarm optimization (PSO), an improved PSO (IPSO) algorithm is introduced to adjust different inertia weights in updating the particle swarm and solve the fitness to stagnate. Secondly, the penalty factor and kernel function parameter of LSSVM are searched automatically, and the regression accuracy and generalization performance is enhanced… More >

Mohammed Farsi^*

Intelligent Automation & Soft Computing, Vol.28, No.1, pp. 83-92, 2021, DOI:10.32604/iasc.2021.015460

Abstract Microarray cancer data poses many challenges for machine-learning (ML) classification including noisy data, small sample size, high dimensionality, and imbalanced class labels. In this paper, we propose a framework to address these problems by properly utilizing feature-selection techniques. The most important features of the cancer datasets were extracted with Logistic Regression (LR), Chi-2, Random Forest (RF), and LightGBM. These extracted features served as input columns in an applied classification task. This framework’s main advantages are reducing time complexity and the number of irrelevant features for the dataset. For evaluation, the proposed method was compared to models using Support Vector Machine… More >

Gurparsad Singh Suri¹, Gurleen Kaur¹, Sara Moein^2,*

Intelligent Automation & Soft Computing, Vol.27, No.3, pp. 723-735, 2021, DOI:10.32604/iasc.2021.015049

Abstract Schizophrenia (SZ) is a mental heterogeneous psychiatric disorder with unknown cause. Neuroscientists postulate that it is related to brain networks. Recently, scientists applied machine learning (ML) and artificial intelligence for the detection, monitoring, and prognosis of a range of diseases, including SZ, because these techniques show a high performance in discovering an association between disease symptoms and disease. Regions of the brain have significant connections to the symptoms of SZ. ML has the power to detect these associations. ML interests researchers because of its ability to reduce the number of input features when the data are high dimensional. In this… More >

Sulaiman Khan¹, Shah Nazir¹, Habib Ullah Khan^2,*, Anwar Hussain¹

CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 2831-2844, 2021, DOI:10.32604/cmc.2021.015054

Abstract During the last two decades significant work has been reported in the field of cursive language’s recognition especially, in the Arabic, the Urdu and the Persian languages. The unavailability of such work in the Pashto language is because of: the absence of a standard database and of significant research work that ultimately acts as a big barrier for the research community. The slight change in the Pashto characters’ shape is an additional challenge for researchers. This paper presents an efficient OCR system for the handwritten Pashto characters based on multi-class enabled support vector machine using manifold feature extraction techniques. These… More >

Sunil Kr. Jha^1,*, Ivan Uzunov², Xiaorui Zhang¹

CMES-Computer Modeling in Engineering & Sciences, Vol.126, No.3, pp. 991-1009, 2021, DOI:10.32604/cmes.2021.011821

Abstract Ohrid trout (Salamo letnica) is an endemic species of fish found in Lake Ohrid in the Former Yugoslav Republic of Macedonia (FYROM). The growth of Ohrid trout was examined in a controlled environment for a certain period, thereafter released into the lake to grow their natural population. The external features of the fish were measured regularly during the cultivation period in the laboratory to monitor their growth. The data mining methods-based computational model can be used for fast, accurate, reliable, automatic, and improved growth monitoring procedures and classification of Ohrid trout. With this motivation, a combined approach of principal component… More >

Mohammed A. Aleid¹, Khaled A. Z. Alyamani², Mohieddine Rahmouni^2,3, Theyazn H. H. Aldhyani^2,*, Nizar Alsharif⁴, Mohammed Y. Alzahrani⁴

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2029-2047, 2021, DOI:10.32604/cmc.2021.014873

Abstract This article aims to assess health habits, safety behaviors, and anxiety factors in the community during the novel coronavirus disease (COVID-19) pandemic in Saudi Arabia based on primary data collected through a questionnaire with 320 respondents. In other words, this paper aims to provide empirical insights into the correlation and the correspondence between socio-demographic factors (gender, nationality, age, citizenship factors, income, and education), and psycho-behavioral effects on individuals in response to the emergence of this new pandemic. To focus on the interaction between these variables and their effects, we suggest different methods of analysis, comprising regression trees and support vector… More >