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An Effective Feature Generation and Selection Approach for Lymph Disease Recognition

Computer Modeling in Engineering & Sciences 2021年第11期129卷 567-594页

作者： sunil kr.jha Zulfiqar Ahmad School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China Institute of Hydrobiology Chinese Academy of SciencesWuhan430072China

Health care data mining is noteworthy in disease diagnosis and recognition *** exist several potentials to further improve the performance of machine learning based-classification methods in healthcare data *** selection of a substantial subset of features is one of the feasible approaches to achieve improved recognition results of classification methods in disease diagnosis *** the present study,a novel combined approach of feature generation using latent semantic analysis(LSA)and selection using ranker search(RAS)has been proposed to improve the performance of classification methods in lymph disease diagnosis *** performance of the proposed combined approach(LSA-RAS)for feature generation and selection is validated using three function-based and two tree-based classification *** performance of the LSA-RAS selected features is compared with the original attributes and other subsets of attributes and features chosen by nine different attributes and features selection approaches in the analysis of a most widely used benchmark and open access lymph disease *** LSA-RAS selected features improve the recognition accuracy of the classification methods significantly in the diagnosis prediction of the lymph *** tree-based classification methods have better recognition accuracy than the function-based classification *** best performance(recognition accuracy of 93.91%)is achieved for the logistic model tree(LMT)classification method using the feature subset generated by the proposed combined approach(LSA-RAS).

关键词： Disease data mining feature selection classification lymph diagnosis

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Soil Microbial Dynamics Modeling in Fluctuating Ecological Situations by Using Subtractive Clustering and Fuzzy Rule-Based Inference Systems

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Computer Modeling in Engineering & Sciences 2017年第4期113卷 443-459页

作者： sunil kr.jha Zulfiqar Ahmad School of Computer and Software Nanjing University of Information Science and TechnologyNanjing 210044China Department of Environmental Sciences University of CaliforniaRiversideCA 92521USA

Microbial population and enzyme activities are the significant indicators of soil *** microbial dynamics characterize microbial population and enzyme *** present study explores the development of efficient predictive modeling systems for the estimation of specific soil microbial dynamics,like rock phosphate solubilization,bacterial population,and ACC-deaminase *** specifically,optimized subtractive clustering(SC)and Wang and Mendel's(WM)fuzzy inference systems(FIS)have been implemented with the objective to achieve the best estimation accuracy of microbial *** measurements were performed using controlled pot experiment using minimal salt media with rock phosphate as sole carbon source inoculated with phosphate solubilizing microorganism in order to estimate rock phosphate solubilization potential of selected *** experimental parameters,including temperature,pH,and incubation period have been used as inputs SC-FIS and *** better performance of the SC-FIS has been observed as compared to the WM-FIS in the estimation of phosphate solubilization and bacterial population with the maximum value of the coefficient of determination(0.9988)2 R=in the estimation of previous microbial dynamics.

关键词： Phosphate solubilizing bacteria bacterial population ACC-deaminase activity subtractive clustering fuzzy rule-based prediction system

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A Combined Approach of Principal Component Analysis and Support Vector Machine for Early Development Phase Modeling of Ohrid Trout(Salmo Letnica)

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Computer Modeling in Engineering & Sciences 2021年第3期126卷 991-1009页

作者： sunil kr.jha Ivan Uzunov Xiaorui Zhang School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China Faculty of Computer Science and Engineering University of Information Science and TechnologyOhrid6000Republic of Macedonia

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 *** external features of the fish were measured regularly during the cultivation period in the laboratory to monitor their *** data mining methods-based computational model can be used for fast,accurate,reliable,automatic,and improved growth monitoring procedures and classification of Ohrid *** this motivation,a combined approach of principal component analysis(PCA)and support vectormachine(SVM)has been implemented for the visual discrimination and quantitative classification of Ohrid trout of the experimental and natural breeding and their growth *** PCA results in better discrimination of breeding categories of Ohrid trout at different development phases while the maximum classification accuracy of 98.33% was achieved using the combination of PCA and *** classification performance of the combination of PCA and SVM has been compared to combinations of PCA and other classification methods(multilayer perceptron,naive Bayes,randomcommittee,decision stump,random forest,and random tree).Besides,the classification accuracy of multilayer perceptron using the original features has been studied.

关键词： Salamo letnica growth phase modeling PCA SVM

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Median Filtering Detection Based on Quaternion Convolutional Neural Network

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Computers, Materials & Continua 2020年第10期65卷 929-943页

作者： Jinwei Wang Qiye Ni Yang Zhang Xiangyang Luo Yunqing Shi Jiangtao Zhai sunil kr.jha Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Nanjing University of Information Science&TechnologyNanjing210044China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001China Department of Computer and Software Nanjing University of Information Science&TechnologyNanjing210044China State Key Laboratory of Information Security Institute of Information EngineeringChinese Academy of SciencesBeijing100093China Department of Electrical Computer Engineering New Jersey Institute of TechnologyNew JerseyNJ07102USA

Median filtering is a nonlinear signal processing technique and has an advantage in the field of image ***,more attention has been paid to the forensics research of median *** this paper,a median filtering forensics method based on quaternion convolutional neural network(QCNN)is *** median filtering residuals(MFR)are used to preprocess the *** the output of MFR is expanded to four channels and used as the input of *** QCNN,quaternion convolution is designed that can better mix the information of different channels than traditional *** quaternion pooling layer is designed to evaluate the result of quaternion *** is proposed to features well combine the three-channel information of color image and fully extract forensics *** show that the proposed method has higher accuracy and shorter training time than the traditional convolutional neural network with the same convolution depth.

关键词： Median filtering forensics quaternion convolution layer quaternion pooling layer color image

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