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| 1 | Bug Prioritization Using Average One Dependence Estimator显示文摘Automation software need to be continuously updated by addressing software bugs contained in their repositories.However,bugs have different levels of importance;hence,it is essential to prioritize bug reports based on their sever-ity and importance.Manually managing the deluge of incoming bug reports faces time and resource constraints from the development team and delays the resolu-tion of critical bugs.Therefore,bug report prioritization is vital.This study pro-poses a new model for bug prioritization based on average one dependence estimator;it prioritizes bug reports based on severity,which is determined by the number of attributes.The more the number of attributes,the more the severity.The proposed model is evaluated using precision,recall,F1-Score,accuracy,G-Measure,and Matthew’s correlation coefficient.Results of the proposed model are compared with those of the support vector machine(SVM)and Naive Bayes(NB)models.Eclipse and Mozilla datasetswere used as the sources of bug reports.The proposed model improved the bug repository management and out-performed the SVM and NB models.Additionally,the proposed model used a weaker attribute independence supposition than the former models,thereby improving prediction accuracy with minimal computational cost. | Kashif Saleem Rashid Naseem Khalil Khan Siraj Muhammad Ikram Syed Jaehyuk Choi | 2023 | Intelligent Automation & Soft Computing2023，，6： | 0 |
| 2 | Liver Ailment Prediction Using Random Forest Model显示文摘Today,liver disease,or any deterioration in one’s ability to survive,is extremely common all around the world.Previous research has indicated that liver disease is more frequent in younger people than in older ones.When the liver’s capability begins to deteriorate,life can be shortened to one or two days,and early prediction of such diseases is difficult.Using several machine learning(ML)approaches,researchers analyzed a variety of models for predicting liver disorders in their early stages.As a result,this research looks at using the Random Forest(RF)classifier to diagnose the liver disease early on.The dataset was picked from the University of California,Irvine repository.RF’s accomplishments are contrasted to those of Multi-Layer Perceptron(MLP),Average One Dependency Estimator(A1DE),Support Vector Machine(SVM),Credal Decision Tree(CDT),Composite Hypercube on Iterated Random Projection(CHIRP),K-nearest neighbor(KNN),Naïve Bayes(NB),J48-Decision Tree(J48),and Forest by Penalizing Attributes(Forest-PA).Some of the assessment measures used to evaluate each classifier include Root Relative Squared Error(RRSE),Root Mean Squared Error(RMSE),accuracy,recall,precision,specificity,Matthew’s Correlation Coefficient(MCC),F-measure,and G-measure.RF has an RRSE performance of 87.6766 and an RMSE performance of 0.4328,however,its percentage accuracy is 72.1739.The widely acknowledged result of this work can be used as a starting point for subsequent research.As a result,every claim that a new model,framework,or method enhances forecastingmay be benchmarked and demonstrated. | Fazal Muhammad Bilal Khan Rashid Naseem Abdullah A Asiri Hassan A Alshamrani Khalaf A Alshamrani Samar M Alqhtani Muhammad Irfan Khlood M Mehdar Hanan Talal Halawani | 2023 | Computers, Materials & Continua2023，，1： | 0 |
| 3 | Effective Classification of Synovial Sarcoma Cancer Using Structure Features and Support Vectors显示文摘In this research work,we proposed a medical image analysis framework with two separate releases whether or not Synovial Sarcoma(SS)is the cell structure for cancer.Within this framework the histopathology images are decomposed into a third-level sub-band using a two-dimensional Discrete Wavelet Transform.Subsequently,the structure features(SFs)such as PrincipalComponentsAnalysis(PCA),Independent ComponentsAnalysis(ICA)and Linear Discriminant Analysis(LDA)were extracted from this subband image representation with the distribution of wavelet coefficients.These SFs are used as inputs of the Support Vector Machine(SVM)classifier.Also,classification of PCA+SVM,ICA+SVM,and LDA+SVM with Radial Basis Function(RBF)kernel the efficiency of the process is differentiated and compared with the best classification results.Furthermore,data collected on the internet from various histopathological centres via the Internet of Things(IoT)are stored and shared on blockchain technology across a wide range of image distribution across secure data IoT devices.Due to this,the minimum and maximum values of the kernel parameter are adjusted and updated periodically for the purpose of industrial application in device calibration.Consequently,these resolutions are presented with an excellent example of a technique for training and testing the cancer cell structure prognosis methods in spindle shaped cell(SSC)histopathological imaging databases.The performance characteristics of cross-validation are evaluated with the help of the receiver operating characteristics(ROC)curve,and significant differences in classification performance between the techniques are analyzed.The combination of LDA+SVM technique has been proven to be essential for intelligent SS cancer detection in the future,and it offers excellent classification accuracy,sensitivity,specificity. | P.Arunachalam N.Janakiraman Junaid Rashid Jungeun Kim Sovan Samanta Usman Naseem Arun Kumar Sivaraman A.Balasundaram | 2022 | Computers, Materials & Continua2022，，8： | 0 |
| 4 | Heart Disease Diagnosis Using the Brute Force Algorithm and Machine Learning Techniques显示文摘Heart disease is one of the leading causes of death in the world today.Prediction of heart disease is a prominent topic in the clinical data processing.To increase patient survival rates,early diagnosis of heart disease is an important field of research in the medical field.There are many studies on the prediction of heart disease,but limited work is done on the selection of features.The selection of features is one of the best techniques for the diagnosis of heart diseases.In this research paper,we find optimal features using the brute-force algorithm,and machine learning techniques are used to improve the accuracy of heart disease prediction.For performance evaluation,accuracy,sensitivity,and specificity are used with split and cross-validation techniques.The results of the proposed technique are evaluated in three different heart disease datasets with a different number of records,and the proposed technique is found to have superior performance.The selection of optimized features generated by the brute force algorithm is used as input to machine learning algorithms such as Support Vector Machine(SVM),Random Forest(RF),K Nearest Neighbor(KNN),and Naive Bayes(NB).The proposed technique achieved 97%accuracy with Naive Bayes through split validation and 95%accuracy with Random Forest through cross-validation.Naive Bayes and Random Forest are found to outperform other classification approaches when accurately evaluated.The results of the proposed technique are compared with the results of the existing study,and the results of the proposed technique are found to be better than other state-of-the-artmethods.Therefore,our proposed approach plays an important role in the selection of important features and the automatic detection of heart disease. | Junaid Rashid Samina Kanwal Jungeun Kim Muhammad Wasif Nisar Usman Naseem Amir Hussain | 2022 | Computers, Materials & Continua2022，，8： | 0 |
| 5 | Immobilization of β-glucuronidase:biocatalysis of glycyrrhizin to 18β-glycyrrhetinic acid and in-silico lead finding显示文摘The non-covalent immobilization ofβ-glucuronidase enzyme obtained from Rhizopus oryzae was carried out by entrapment in natural fiber(papaya and coconut).The bioconversion capability of immobilized enzyme was analyzed based on conversion of glycyrrhizin to 18β-glycyrrhetinic acid under different conditions.The hydrolytic activity of theβ-glucuronidase enzyme was highly depended on the microbial source and matrix,in which enzyme was immobilized.R.oryzaeβ-glucuronidase immobilized in papaya fibers produced the highest GA content(13.170μg/mL)at 10 h of reaction.However R.oryzaeβ-glucuronidase immobilized in coconut fibers produced the highest GA content(21.425μg/mL)at 15 h of reaction.Online Molinspiration software was used to predict drug like molecular properties of the 18β-glycyrrhetinic acid,and software suggested that the compounds had potential of becoming the orally active molecules.Therefore,in silico studies were conducted on proposed 18β-glycyrrhetinic acid to select the best possible drug candidates based on drug properties and bioactivity score of the compounds. | Makhmur Ahmad Mohammad Rashid Babar Ali Shamshir Khan Naseem Akhtar Mohd Faiyaz Khan Bibhu Prasad Panda | 2020 | Journal of Chinese Pharmaceutical Sciences2020，29，5： | 0 |
| 6 | Improved binary similarity measures for software modularization显示文摘目的:各种各样的二元相似度测量在聚类方法中被用来确定数据中的相似实体的同类组。这些相似度测量大多数仅基于特征的存在或缺失。二元相似度测量在软件模块化中亦能与不同的聚类方法一起用于提高软件系统的可理解性与可管理性。每种相似度测量都有其优势与不足,分别能使聚类结果优化或恶化。创新点:本文强调了软件模块化中一些已有的著名的二元相似度测量的优势。此外,基于这些已有的相似度测量,新提出了几种改进的相似度测量。方法:首先,介绍了一些软件模块化中已有的著名的二元相似度测量的优势。接着,提出了几种改进的新的相似度测量。结合具体例子,说明这些新方法整合了JCJNM和RR这几种已有的二元相似度测量的优势。最后,通过实验比较新方法与已有方法的结果,验证所提新方法的有效性。结论:实验结果表明相较于已有的相似度测量,本文所提出的新的二元相似度测量结果的可信度更高。这些新方法能减少任意决策的数量,增加聚类过程中聚类的数量。尽管这些新方法仅基于数据的二元特征向量表达,它们能被用来测试任何编程语言编写的软件系统。 | Rashid NASEEM Mustafa Bin Mat DERIS Onaiza MAQBOOL Jing-peng LI Sara SHAHZAD Habib SHAH | 2017 | Frontiers of Information Technology & Electronic Engineering2017，18，8： | 0 |
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