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Liver Tumor Decision Support System on Human Magnetic Resonance Images:A Comparative Study

Computer Systems Science & Engineering 2023年第8期46卷 1653-1671页

作者： Hiam Alquran Yazan Al-Issa Mohammed Alslatie Isam Abu-Qasmieh Amin Alqudah Wan Azani Mustafa Yasmin Mohd Yacob Department of Biomedical Systems and Informatics Engineering Yarmouk University 556Irbid21163Jordan Department of Biomedical Engineering Jordan University of Science and TechnologyIrbid22110Jordan Department of Computer Engineering Yarmouk University 556Irbid21163Jordan The of Biomedical Technology King Hussein Medical CenterRoyal Jordanian Medical ServiceAmman11855Jordan Faculty of Electrical Engineering&Technology Campus Pauh PutraUniversiti Malaysia PerlisArau02600PerlisMalaysia Faculty of Electronic Engineering&Technology Campus Pauh PutraUniversiti Malaysia PerlisArau02600PerlisMalaysia Advanced Computing Centre of Excellence(CoE)Universiti Malaysia PerlisArau02600PerlisMalaysia

Liver cancer is the second leading cause of cancer death *** tumor detection may help identify suitable treatment and increase the survival *** imaging is a non-invasive tool that can help uncover abnormalities in human *** Resonance Imaging(MRI),in particular,uses magnetic fields and radio waves to differentiate internal human organs ***,the interpretation of medical images requires the subjective expertise of a radiologist and ***,building an automated diagnosis computer-based system can help specialists reduce incorrect *** paper proposes a hybrid automated system to compare the performance of 3D features and 2D features in classifying magnetic resonance liver tumor *** paper proposed two models;the first one employed the 3D features while the second exploited the 2D *** first system uses 3D texture attributes,3D shape features,and 3D graphical deep descriptors beside an ensemble classifier to differentiate between four 3D tumor *** top of that,the proposed method is applied to 2D slices for comparison *** proposed approach attained 100%accuracy in discriminating between all types of tumors,100%Area Under the Curve(AUC),100%sensitivity,and 100%specificity and precision as well in 3D liver *** the other hand,the performance is lower in 2D *** maximum accuracy reached 96.4%for two classes and 92.1%for four *** top-class performance of the proposed system can be attributed to the exploitation of various types of feature selection methods besides utilizing the ReliefF features selection technique to choose the most relevant features associated with different *** novelty of this work appeared in building a highly accurate system under specific circumstances without any processing for the images and human input,besides comparing the performance between 2D and 3D *** the future,the presented work can be extended to be

关键词： Liver tumors ensemble classifier 3D shape features 3D cooccurrence matrix ResNet101

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An ensemble Based Approach for Sentiment Classification in Asian Regional Language

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Computer Systems Science & Engineering 2023年第3期44卷 2457-2468页

作者： Mahesh B.Shelke Jeong Gon Lee Sovan Samanta Sachin N.Deshmukh G.Bhalke Daulappa Rahul B.Mannade Arun Kumar Sivaraman Department of Computer Science and Information Technology Dr.Babasaheb Ambedkar Marathwada UniversityAurangabadMaharashtra431004India Division of Applied Mathematics Wonkwang University460Iksan daeroIksan SiJeonbuk54538Korea Department of Mathematics Tamralipta MahavidyalayaTamlukWest Bengal721636India Department of Electronics and Telecommunication Engineering AISSMSCOEPuneMaharashtra411001India Department of Information Technology Govemment College of EngineeringAurangabadMaharashtra431005India School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India

In today’s digital world,millions of individuals are linked to one another via the Internet and social *** opens up new avenues for information exchange with *** analysis(SA)has gotten a lot of attention during the last *** analyse the challenges of Sentiment Analysis(SA)in one of the Asian regional languages known as Marathi in this study by providing a benchmark setup in which wefirst produced an annotated dataset composed of Marathi text acquired from microblogging websites such as *** also choose domain experts to manually annotate Marathi microblogging posts with positive,negative,and neutral *** addition,to show the efficient use of the annotated dataset,an ensemble-based model for sentiment analysis was *** contrast to others machine learning classifier,we achieved better performance in terms of accuracy for ensemble classifier with 10-fold cross-validation(cv),outcomes as 97.77%,f-score is 97.89%.

关键词： Sentiment analysis machine learning lexical resource ensemble classifier

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Software Reliability Prediction Using ensemble Learning on Selected Features in Imbalanced and Balanced Datasets: A Review

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Computer Systems Science & Engineering 2024年第6期48卷 1513-1536页

作者： Suneel Kumar Rath Madhusmita Sahu Shom Prasad Das Junali Jasmine Jena Chitralekha Jena Baseem Khan Ahmed Ali Pitshou Bokoro Department of Computer Science and Engineering C.V.Raman Global UniversityBhubaneswar752054India Department of Computer Science and Engineering Birla Global UniversityBhubaneswar751029India School of Computer Engineering KIIT(Deemed to be)UniversityBhubaneswar751024India School of Electrical Engineering KIIT(Deemed to be)UniversityBhubaneswar751024India Department of Electrical and Computer Engineering Hawassa UniversityHawassaP.O.Box 05Ethiopia Center for Renewable Energy and Microgrids Huanjiang LaboratoryZhejiang UniversityShaoxing311816China Department of Electrical and Electronic Engineering Technology University of JohannesburgJohannesburg2028South Africa

Redundancy,correlation,feature irrelevance,and missing samples are just a few problems that make it difficult to analyze software defect ***,it might be challenging to maintain an even distribution of data relating to both defective and non-defective *** latter software class’s data are predominately present in the dataset in the majority of experimental *** objective of this review study is to demonstrate the effectiveness of combining ensemble learning and feature selection in improving the performance of defect *** the successful feature selection approach,a novel variant of the ensemble learning technique is analyzed to address the challenges of feature redundancy and data imbalance,providing robustness in the classification *** overcome these problems and lessen their impact on the fault classification performance,authors carefully integrate effective feature selection with ensemble learning *** selection demonstrates that a significant area under the receiver operating curve(ROC)can be attributed to only a small subset of *** Greedy forward selection(GFS)technique outperformed Pearson’s correlation method when evaluating feature selection techniques on the *** learners,such as random forests(RF)and the proposed average probability ensemble(APE),demonstrate greater resistance to the impact of weak features when compared to weighted support vector machines(W-SVMs)and extreme learning machines(ELM).Furthermore,in the case of the NASA and Java datasets,the enhanced average probability ensemble model,which incorporates the Greedy forward selection technique with the average probability ensemble model,achieved remarkably high accuracy for the area under the *** approached a value of 1.0,indicating exceptional *** review emphasizes the importance of meticulously selecting attributes in a software dataset to accurately classify damaged *** addition,t

关键词： ensemble classifier hybrid classifier software reliability prediction

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