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| 1 | Digital design of a power system stabilizer for a power system based on plant input mapping 显示文摘 | Shabib G | 2013 | Interna- tional Journal of Electrical Power & Energy Systems2013，，49： | 1 |
| 2 | Association of gastric metaplasia and duodenitis with Helicobacter pylori infection in children显示文摘 | SHABIB S CUTZ E DRUMM B | 1994 | Am J Clin Pathol1994，102，2： | 1 |
| 3 | Capsaicin as an inhibitor of the growth of the gastric pathogen Helicobacter pyori显示文摘 | Nicola L Jones Souheil Shabib Philip M Sheman | 1997 | FEMS Microbiology Letters1997，146，2： | 1 |
| 4 | Capsaicin asan inhibitor of the growth of the gastric pathogen Helicobacter py/or/显示文摘 | JONES N L SHABIB S SHERMAN P M | 1997 | FEMS Microbiology Letters1997，146，2： | 1 |
| 5 | Capsaicin as an inhibitor of the growth of the gastric pathogen Helicobacter pylori显示文摘 | Nicola L Jones Souheil Shabib Philip M Sherman | 1997 | FEMS Microbiology Letters1997，，2： | 1 |
| 6 | Capsaicin as an inhibitor of the growth of the gastric pathogen Helicobacter pyori显示文摘 | JONES N L SHABIB S SHEMAN P M | 1997 | FEMS Microbiology Letters1997，146，2： | 1 |
| 7 | Capsaicin as an inhibitor of the growth of the gastric pathogen Helicobacter pyori显示文摘 | Nicola L Jones Souheil Shabib Philip M Sheman | 1997 | FEMS Microbiology Letters1997，146，2： | 1 |
| 8 | Thermal Analysis of Shale Oil Using Thermogravimetry and Differential Scanning Calorimetry 显示文摘 | Khraisha Y H Shabib I M | 2002 | Energy Conversi on and Man agement2002，，43： | 1 |
| 9 | Spontaneous recovery of propylthiouracil-induced fulminant hepatic failure in an 8-year old child显示文摘 | Bin-Abbas BS Shabib SM A1-Dekhail WM | 2007 | Saudi Med J2007，28，5： | 1 |
| 10 | 99TcZ-human serum al- bumin scans in children with protein-losing enteropathy显示文摘 | Halaby I-I Bakheet SM Shabib S | 2000 | J NuclMed2000，41，： | 1 |
| 11 | Thermal analysis of shale oil using thermogravimetry and differential scanning calorimetry显示文摘 | Khrasiha Y H Shabib I M | 2002 | Energy Conversion and Management2002，43，2： | 1 |
| 12 | Does an intraabdominally placed LNG-IUS have an adverse effect on fertility: a case report显示文摘 | Doris N Shabib G Corbett S | 2014 | Con- traception2014，89，1： | 1 |
| 13 | Capsaicin as an inhibitor of the growth of the grastric pathogen Helicobacter Pylori显示文摘 | Jones NL shabib S Sherman PM | 1997 | FEMS Microbiol Lett1997，146，2： | 1 |
| 14 | Capsaicin as an inhibitor of the growth of the gastric pathogen Helicobacter py- Iori显示文摘 | JONES NL SHABIB S SHERMAN PM | 1997 | FEMS Microbiology Letters1997，146，2： | 1 |
| 15 | Joint Channel and Multi-User Detection Empowered with Machine Learning显示文摘The numbers of multimedia applications and their users increase with each passing day.Different multi-carrier systems have been developed along with varying techniques of space-time coding to address the demand of the future generation of network systems.In this article,a fuzzy logic empowered adaptive backpropagation neural network(FLeABPNN)algorithm is proposed for joint channel and multi-user detection(CMD).FLeABPNN has two stages.The first stage estimates the channel parameters,and the second performsmulti-user detection.The proposed approach capitalizes on a neuro-fuzzy hybrid systemthat combines the competencies of both fuzzy logic and neural networks.This study analyzes the results of using FLeABPNN based on a multiple-input andmultiple-output(MIMO)receiver with conventional partial oppositemutant particle swarmoptimization(POMPSO),total-OMPSO(TOMPSO),fuzzy logic empowered POMPSO(FL-POMPSO),and FL-TOMPSO-based MIMO receivers.The FLeABPNN-based receiver renders better results than other techniques in terms of minimum mean square error,minimum mean channel error,and bit error rate. | Mohammad Sh.Daoud Areej Fatima Waseem Ahmad Khan Muhammad Adnan Khan Sagheer Abbas Baha Ihnaini Munir Ahmad Muhammad Sheraz Javeid Shabib Aftab | 2022 | Computers, Materials & Continua2022，，1： | 0 |
| 16 | Data and Ensemble Machine Learning Fusion Based Intelligent Software Defect Prediction System显示文摘The software engineering field has long focused on creating high-quality software despite limited resources.Detecting defects before the testing stage of software development can enable quality assurance engineers to con-centrate on problematic modules rather than all the modules.This approach can enhance the quality of the final product while lowering development costs.Identifying defective modules early on can allow for early corrections and ensure the timely delivery of a high-quality product that satisfies customers and instills greater confidence in the development team.This process is known as software defect prediction,and it can improve end-product quality while reducing the cost of testing and maintenance.This study proposes a software defect prediction system that utilizes data fusion,feature selection,and ensemble machine learning fusion techniques.A novel filter-based metric selection technique is proposed in the framework to select the optimum features.A three-step nested approach is presented for predicting defective modules to achieve high accuracy.In the first step,three supervised machine learning techniques,including Decision Tree,Support Vector Machines,and Naïve Bayes,are used to detect faulty modules.The second step involves integrating the predictive accuracy of these classification techniques through three ensemble machine-learning methods:Bagging,Voting,and Stacking.Finally,in the third step,a fuzzy logic technique is employed to integrate the predictive accuracy of the ensemble machine learning techniques.The experiments are performed on a fused software defect dataset to ensure that the developed fused ensemble model can perform effectively on diverse datasets.Five NASA datasets are integrated to create the fused dataset:MW1,PC1,PC3,PC4,and CM1.According to the results,the proposed system exhibited superior performance to other advanced techniques for predicting software defects,achieving a remarkable accuracy rate of 92.08%. | Sagheer Abbas Shabib Aftab Muhammad Adnan Khan Taher MGhazal Hussam Al Hamadi Chan Yeob Yeun | 2023 | Computers, Materials & Continua2023，，6： | 0 |
| 17 | Data and Machine Learning Fusion Architecture for Cardiovascular Disease Prediction显示文摘Heart disease,which is also known as cardiovascular disease,includes various conditions that affect the heart and has been considered a major cause of death over the past decades.Accurate and timely detection of heart disease is the single key factor for appropriate investigation,treatment,and prescription of medication.Emerging technologies such as fog,cloud,and mobile computing provide substantial support for the diagnosis and prediction of fatal diseases such as diabetes,cancer,and cardiovascular disease.Cloud computing provides a cost-efficient infrastructure for data processing,storage,and retrieval,with much of the extant research recommending machine learning(ML)algorithms for generating models for sample data.ML is considered best suited to explore hidden patterns,which is ultimately helpful for analysis and prediction.Accordingly,this study combines cloud computing with ML,collecting datasets from different geographical areas and applying fusion techniques to maintain data accuracy and consistency for the ML algorithms.Our recommended model considered three ML techniques:Artificial Neural Network,Decision Tree,and Naïve Bayes.Real-time patient data were extracted using the fuzzy-based model stored in the cloud. | Munir Ahmad Majed Alfayad Shabib Aftab Muhammad Adnan Khan Areej Fatima Bilal Shoaib Mohammad Sh.Daoud Nouh Sabri Elmitwally | 2021 | Computers, Materials & Continua2021，，11： | 0 |
| 18 | Cloud-Based Diabetes Decision Support System Using Machine Learning Fusion显示文摘Diabetes mellitus,generally known as diabetes,is one of the most common diseases worldwide.It is a metabolic disease characterized by insulin deciency,or glucose(blood sugar)levels that exceed 200 mg/dL(11.1 ml/L)for prolonged periods,and may lead to death if left uncontrolled by medication or insulin injections.Diabetes is categorized into two main types—type 1 and type 2—both of which feature glucose levels above“normal,”dened as 140 mg/dL.Diabetes is triggered by malfunction of the pancreas,which releases insulin,a natural hormone responsible for controlling glucose levels in blood cells.Diagnosis and comprehensive analysis of this potentially fatal disease necessitate application of techniques with minimal rates of error.The primary purpose of this research study is to assess the potential role of machine learning in predicting a person’s risk of developing diabetes.Historically,research has supported the use of various machine algorithms,such as naïve Bayes,decision trees,and articial neural networks,for early diagnosis of diabetes.However,to achieve maximum accuracy and minimal error in diagnostic predictions,there remains an immense need for further research and innovation to improve the machine-learning tools and techniques available to healthcare professionals.Therefore,in this paper,we propose a novel cloud-based machine-learning fusion technique involving synthesis of three machine algorithms and use of fuzzy systems for collective generation of highly accurate nal decisions regarding early diagnosis of diabetes. | Shabib Aftab Saad Alanazi Munir Ahmad Muhammad Adnan Khan Areej Fatima Nouh Sabri Elmitwally | 2021 | Computers, Materials & Continua2021，，7： | 0 |
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