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Journal of Diabetes Mellitus 2014年第2期4卷 85-95页

作者： Muneer Alsayadi Yaser Al Jawfi Meriem Belarbi Zoubida Soualem-Mami Hafida Merzouk Daoudi Chaban Sari fatima sabri Meriem Ghalim Department of Food Science and Technology Faculty of agricultureIbb UniversityIbbYemen Laboratory of Natural Products Department of BiologyFaculty of Natural and Life SciencesEarth andUniverseUniversity of Abou-Bekr BelkaidTlemcenAlgeria Department of Food Safety Station of Agriculture ResearchesSana’aYemen Laboratory of Physiology Physiopathology and Biochemistry of NutritionDepartment of BiologyFaculty of Natural and Life SciencesEarth and UniverseUniversity of Abou-Bekr BelkaidTlemcenAlgeria

Diabetes mellitus is a predominant chronic disease which causes mortality of millions of people yearly. Its prevalence is on the rise worldwide. Water kefir is fermented food produced by a matrix of polysaccharides containing bacteria and yeasts, with therapeutic properties. Our study aimed to evaluate anti-hyperglycemic and anti-hyperlipidemic activities of water kefir on streptozotocin-induced diabetic Wistar rats. Adult Wistar rats were made diabetic by intraperitoneal injection of streptozotocin, and were given or not kefir in drinking water for 5 weeks. Body weight, glucose and lipid levels were measured. The results demonstrated evident improvement in body weight, glucose, and lipid profiles of treated rats comparing with diabetic or control rats. Water kefir is found to be less cost hypoglycemic and hypolipidimic treatment and less time consuming. Water kefir can potentially be useful food for diabetes to control glucose and lipid levels.

关键词： Component Formatting Style Styling Anti-Hyperglycemic Anti-Hyperlipidaemic Water Kefir Diabetes Wistar Rat

Cloud-Based Diabetes Decision Support System Using Machine Learning Fusion

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Computers, Materials & Continua 2021年第7期68卷 1341-1357页

作者： Shabib Aftab Saad Alanazi Munir Ahmad Muhammad Adnan Khan Areej fatima Nouh sabri Elmitwally School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Department of Computer Science Virtual University of PakistanLahore54000Pakistan College of Computer and Information Sciences Jouf UniversitySakaka72341Saudi Arabia Riphah School of Computing&Innovation Riphah International UniversityLahore CampusLahore54000Pakistan Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan Department of Computer Science Faculty of Computers and Articial IntelligenceCairo University12613Egypt

Diabetes mellitus,generally known as diabetes,is one of the most common diseases *** 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 *** is categorized into two main types—type 1 and type 2—both of which feature glucose levels above“normal,”dened as 140 mg/*** is triggered by malfunction of the pancreas,which releases insulin,a natural hormone responsible for controlling glucose levels in blood *** and comprehensive analysis of this potentially fatal disease necessitate application of techniques with minimal rates of *** primary purpose of this research study is to assess the potential role of machine learning in predicting a person’s risk of developing ***,research has supported the use of various machine algorithms,such as naïve Bayes,decision trees,and articial neural networks,for early diagnosis of ***,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 ***,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.

关键词： Machine learning fusion articial neural network decision trees naïve Bayes diabetes prediction

Modelling Intelligent Driving Behaviour Using Machine Learning

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Computers, Materials & Continua 2021年第9期68卷 3061-3077页

作者： Qura-Tul-Ain Khan Sagheer Abbas Muhammad Adnan Khan Areej fatima Saad Alanazi Nouh sabri Elmitwally School of Computer Science NCBA&ELahore54000Pakistan Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan Department of Computer Science College of Computer and Information SciencesJouf UniversitySkakaAljouf72341Saudi Arabia Department of Computer Science Faculty of Computers and Artificial IntelligenceCairo University12613Egypt

In vehicular systems,driving is considered to be the most complex task,involving many aspects of external sensory skills as well as cognitive *** skills include the estimation of distance and speed,time perception,visual and auditory perception,attention,the capability to drive safely and action-reaction *** intelligence works as an internal mechanism that manages and holds the overall driver’s intelligent *** cognitive capacities constitute the frontiers for generating adaptive behaviour for dynamic *** parameters for understanding intelligent behaviour are knowledge,reasoning,decision making,habit and cognitive *** intelligent behaviour reveals that many of these parameters operate simultaneously to enable drivers to react to current *** changes prompt the parameter values to change,a process which continues unless and until all processes are *** paper model intelligent behaviour by using a‘driver behaviour model’to obtain accurate intelligent driving behaviour *** model works on layering patterns in which hierarchy and coherence are maintained to transfer the data with accuracy from one module to *** patterns constitute the outcome of different modules that collaborate to generate appropriate *** this case,accurate patterns were acquired using ANN static and dynamic non-linear autoregressive approach was used and for further accuracy validation,time-series dynamic backpropagation artificial neural network,multilayer perceptron and random sub-space on real-world data were also applied.

关键词： Machine learning artificial neural network ann time series intelligent behaviour agent

Data and Machine Learning Fusion Architecture for Cardiovascular Disease Prediction

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Computers, Materials & Continua 2021年第11期69卷 2717-2731页

作者： Munir Ahmad Majed Alfayad Shabib Aftab Muhammad Adnan Khan Areej fatima Bilal Shoaib Mohammad Sh.Daoud Nouh sabri Elmitwally School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan College of Computer and Information Sciences Jouf UniversitySakaka72341Saudi Arabia Department of Computer Science Virtual University of PakistanLahore54000Pakistan Riphah School of Computing&Innovation Riphah International UniversityLahore CampusLahore54000Pakistan Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan Department of Computer Science Minhaj University LahoreLahore54000Pakistan College of Engineering Al Ain UniversityAbu Dhabi112612UAE Department of Computer Science Faculty of Computers and Artificial IntelligenceCairo University12613Egypt

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 *** and timely detection of heart disease is the single key factor for appropriate investigation,treatment,and prescription of *** 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 *** 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 *** is considered best suited to explore hidden patterns,which is ultimately helpful for analysis and ***,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 *** recommended model considered three ML techniques:Artificial Neural Network,Decision Tree,and Naïve ***-time patient data were extracted using the fuzzy-based model stored in the cloud.

关键词： Machine learning fusion cardiovascular disease data fusion fuzzy system disease prediction

Convolutional Neural Network Based Intelligent Handwritten Document Recognition

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Computers, Materials & Continua 2022年第3期70卷 4563-4581页

作者： Sagheer Abbas Yousef Alhwaiti Areej fatima Muhammad A.Khan Muhammad Adnan Khan Taher M.Ghazal Asma Kanwal Munir Ahmad Nouh sabri Elmitwally School of Computer Science National College of Business administration and EconomicsLahore54000Pakistan College of Computer and Information Sciences Jouf UniversitySakaka72341Saudi Arabia Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnam13557Korea Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan Malaysia(UKM)Bangi 43600SelangorMalaysia School of Information Technology Skyline University CollegeUniversity City Sharjah1797SharjahUAE Department of Computer Science GC UniversityLahorePakistan Department of Computer Science Faculty of Computers and Artificial IntelligenceCairo University12613Egypt

This paper presents a handwritten document recognition system based on the convolutional neural network *** today’s world,handwritten document recognition is rapidly attaining the attention of researchers due to its promising behavior as assisting technology for visually impaired *** technology is also helpful for the automatic data entry *** the proposed systemprepared a dataset of English language handwritten character *** proposed system has been trained for the large set of sample data and tested on the sample images of user-defined handwritten *** this research,multiple experiments get very worthy recognition *** proposed systemwill first performimage pre-processing stages to prepare data for training using a convolutional neural *** this processing,the input document is segmented using line,word and character *** proposed system get the accuracy during the character segmentation up to 86%.Then these segmented characters are sent to a convolutional neural network for their *** recognition and segmentation technique proposed in this paper is providing the most acceptable accurate results on a given *** proposed work approaches to the accuracy of the result during convolutional neural network training up to 93%,and for validation that accuracy slightly decreases with 90.42%.

关键词： Convolutional neural network segmentation skew cursive characters recognition