Omar M. El-Habbak¹, Abdelrahman M. Abdelalim¹, Nour H. Mohamed¹, Habiba M. Abd-Elaty¹, Mostafa A. Hammouda¹, Yasmeen Y. Mohamed¹, Mohanad A. Taifor¹, Ali W. Mohamed^2,3,*

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2953-2969, 2022, DOI:10.32604/cmc.2022.020109 - 27 September 2021

Abstract Parkinson’s disease (PD), one of whose symptoms is dysphonia, is a prevalent neurodegenerative disease. The use of outdated diagnosis techniques, which yield inaccurate and unreliable results, continues to represent an obstacle in early-stage detection and diagnosis for clinical professionals in the medical field. To solve this issue, the study proposes using machine learning and deep learning models to analyze processed speech signals of patients’ voice recordings. Datasets of these processed speech signals were obtained and experimented on by random forest and logistic regression classifiers. Results were highly successful, with 90% accuracy produced by the random More >

Ashit Kumar Dutta^*

Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 1007-1023, 2022, DOI:10.32604/iasc.2022.019778 - 22 September 2021

Abstract In recent days, Internet of Things (IoT) based image classification technique in the healthcare services is becoming a familiar concept that supports the process of detecting cancers with Computer Tomography (CT) images. Lung cancer is one of the perilous diseases that increases the mortality rate exponentially. IoT based image classifiers have the ability to detect cancer at an early stage and increases the life span of a patient. It supports oncologist to monitor and evaluate the health condition of a patient. Also, it can decipher cancer risk marker and act upon them. The process of… More >

Ziyan Yu¹, Cong Zhang^1,*, Naixue Xiong², Fang Chen¹

Computer Systems Science and Engineering, Vol.40, No.1, pp. 207-221, 2022, DOI:10.32604/csse.2022.018301 - 26 August 2021

Abstract As soil heavy metal pollution is increasing year by year, the risk assessment of soil heavy metal pollution is gradually gaining attention. Soil heavy metal datasets are usually imbalanced datasets in which most of the samples are safe samples that are not contaminated with heavy metals. Random Forest (RF) has strong generalization ability and is not easy to overfit. In this paper, we improve the Bagging algorithm and simple voting method of RF. A W-RF algorithm based on adaptive Bagging and weighted voting is proposed to improve the classification performance of RF on imbalanced datasets.… More >

Yue Jiang, Xinyu Zhang, Wohuan Jia, Li Xu^*

Journal on Artificial Intelligence, Vol.3, No.4, pp. 163-169, 2021, DOI:10.32604/jai.2021.027590 - 07 February 2022

Abstract As a new type of knowledge sharing platform, the community question answer website realizes the acquisition and sharing of knowledge, and is loved and sought after by the majority of users. But for multi-answer questions, answer quality assessment becomes a challenge. The answer selection in CQA (Community Question Answer) was proposed as a challenge task in the SemEval competition, which gave a data set and proposed two subtasks. Task-A is to give a question (including short title and extended description) and its answers, and divide each answer into absolutely relevant (good), potentially relevant (potential) and… More >

Fuyun Zhu, Guoqing Wu^*

Energy Engineering, Vol.118, No.6, pp. 1703-1712, 2021, DOI:10.32604/EE.2021.015602 - 10 September 2021

Abstract Accurate power load forecasting plays an important role in the power dispatching and security of grid. In this paper, a mathematical model for power load forecasting based on the random forest regression (RFR) was established. The input parameters of RFR model were determined by means of the grid search algorithm. The prediction results for this model were compared with those for several other common machine learning methods. It was found that the coefficient of determination (R²) of test set based on the RFR model was the highest, reaching 0.514 while the corresponding mean absolute error (MAE) More >

Mounita Ghosh¹, Md. Mohsin Sarker Raihan¹, M. Raihan², Laboni Akter¹, Anupam Kumar Bairagi³, Sultan S. Alshamrani⁴, Mehedi Masud^5,*

Intelligent Automation & Soft Computing, Vol.30, No.3, pp. 917-928, 2021, DOI:10.32604/iasc.2021.017989 - 20 August 2021

Abstract The liver is considered an essential organ in the human body. Liver disorders have risen globally at an unprecedented pace due to unhealthy lifestyles and excessive alcohol consumption. Chronic liver disease is one of the principal causes of death affecting large portions of the global population. An accumulation of liver-damaging factors deteriorates this condition. Obesity, an undiagnosed hepatitis infection, alcohol abuse, coughing or vomiting blood, kidney or hepatic failure, jaundice, liver encephalopathy, and many more disorders are responsible for it. Thus, immediate intervention is needed to diagnose the ailment before it is too late. Therefore,… More >

A. Sheryl Oliver¹, T. Jayasankar², K. R. Sekar^3,*, T. Kalavathi Devi⁴, R. Shalini⁵, S. Poojalaxmi⁵, N. G. Viswesh⁵

Intelligent Automation & Soft Computing, Vol.30, No.3, pp. 755-770, 2021, DOI:10.32604/iasc.2021.016242 - 20 August 2021

Abstract Lung cancer is a poorly understood disease. Smokers may develop lung cancer due to the inhalation of carcinogenic substances while smoking, but non-smokers may develop this disease as well. Lung cancer can spread to other parts of the body and this process is called metastasis. Because the lung cancer is difficult to identify in the initial stages. The objective of this work is to reduce the mortality rate of the disease by identifying it at an earlier stage based on the existing symptoms. Artificial intelligence plays active roles in tasks such as entropy extraction through… More >

Haiquan Li¹, Qinghui Zhang^2,*, Xiaoqian Chen³

CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.2, pp. 605-621, 2021, DOI:10.32604/cmes.2021.015747 - 22 July 2021

Abstract Flight load computations (FLC) are generally expensive and time-consuming. This paper studies deep learning (DL)-based surrogate models of FLC to provide a reliable basis for the strength design of aircraft structures. We mainly analyze the influence of Mach number, overload, angle of attack, elevator deflection, altitude, and other factors on the loads of key monitoring components, based on which input and output variables are set. The data used to train and validate the DL surrogate models are derived using aircraft flight load simulation results based on wind tunnel test data. According to the FLC features, More >

Weiping Luo^1,2, Dajun Yuan^1,2, Dalong Jin^1,2,*, Ping Lu^1,2, Jian Chen³

CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.1, pp. 109-127, 2021, DOI:10.32604/cmes.2021.015683 - 28 June 2021

Abstract The control of slurry pressure aiming to be consistent with the external water and earth pressure during shield tunnelling has great significance for face stability, especially in urban areas or underwater where the surrounding environment is very sensitive to the fluctuation of slurry pressure. In this study, an optimal control method for slurry pressure during shield tunnelling is developed, which is composed of an identifier and a controller. The established identifier based on the random forest (RF) can describe the complex non-linear relationship between slurry pressure and its influencing factors. The proposed controller based on More >

Shufang Song^1,*, Ruyang He¹, Zhaoyin Shi¹, Weiya Zhang²

CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.1, pp. 65-85, 2021, DOI:10.32604/cmes.2021.015378 - 28 June 2021

Abstract The variable importance measure (VIM) can be implemented to rank or select important variables, which can effectively reduce the variable dimension and shorten the computational time. Random forest (RF) is an ensemble learning method by constructing multiple decision trees. In order to improve the prediction accuracy of random forest, advanced random forest is presented by using Kriging models as the models of leaf nodes in all the decision trees. Referring to the Mean Decrease Accuracy (MDA) index based on Out-of-Bag (OOB) data, the single variable, group variables and correlated variables importance measures are proposed to More >