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Cloud-Based Diabetes Decision Support System Using Machine Learning Fusion

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

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Autonomous Parking-Lots Detection with Multi-Sensor Data Fusion Using Machine Deep Learning Techniques

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Computers, Materials & Continua 2021年第2期66卷 1595-1612页

作者： Kashif Iqbal Sagheer Abbas Muhammad Adnan Khan Atifa Ather Muhammad Saleem Khan areej fatima Gulzar Ahmad School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Department of Computer Sciences GC UniversityLahore54000Pakistan Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan Department of Computer Science Comsats University IslamabadLahore CampusLahore54000Pakistan

The rapid development and progress in deep machine-learning techniques have become a key factor in solving the future challenges of ***-based target detection and object classification have been improved due to the development of deep learning *** fusion in autonomous driving is a fact and a prerequisite task of data preprocessing from multi-sensors that provide a precise,well-engineered,and complete detection of objects,scene or *** target of the current study is to develop an in-vehicle information system to prevent or at least mitigate traffic issues related to parking detection and traffic congestion *** this study we examined to solve these problems described by(1)extracting region-of-interest in the images(2)vehicle detection based on instance segmentation,and(3)building deep learning model based on the key features obtained from input parking *** build a deep machine learning algorithm that enables collecting real video-camera feeds from vision sensors and predicting free parking *** augmentation techniques were performed using edge detection,cropping,refined by rotating,thresholding,resizing,or color augment to predict the region of bounding boxes.A deep convolutional neural network F-MTCNN model is proposed that simultaneously capable for compiling,training,validating and testing on parking video frames through *** results of proposed model employing on publicly available PK-Lot parking dataset and the optimized model achieved a relatively higher accuracy 97.6%than previous reported ***,this article presents mathematical and simulation results using state-of-the-art deep learning technologies for smart parking space *** results are verified using Python,TensorFlow,OpenCV computer simulation frameworks.

关键词： Smart parking-lot detection deep convolutional neural network data augmentation region-of-interest object detection

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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

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基于Cattaneo-Christov热通量模型的倾斜磁驱动Casson纳米流体在径向拉伸板上的流动

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Journal of Central South University 2023年第11期30卷 3721-3736页

作者： areej fatima Muhammad SAGHEER Shafqat HUSSAIN Capital University of Science&Technology Islamabad 46000Pakistan Technical University Dortmund 44135Germany

本文以二维倾斜磁驱动Casson纳米流体为对象,研究了其在驻点附近流过径向拉伸板时的传导、热辐射和放热/吸热现象。为了研究传热特性,采用了Cattaneo-Christov热通量模型。利用Buongiorno模型描述纳米流体流动问题方程,以探讨布朗运动... 详细信息

本文以二维倾斜磁驱动Casson纳米流体为对象,研究了其在驻点附近流过径向拉伸板时的传导、热辐射和放热/吸热现象。为了研究传热特性,采用了Cattaneo-Christov热通量模型。利用Buongiorno模型描述纳米流体流动问题方程,以探讨布朗运动、热泳、热滑移和质量滑移条件的影响。采用一组相似变换将研究模型的高阶非线性偏微分方程转化为常微分方程组。利用射击法的效率优势,对所提出的流动问题方程进行了数值管理。通过图表讨论了流动参数对流体速度、浓度和温度的影响。随着Casson参数的增大,流体速度加快,表面摩擦因数也增大;随着磁参数增大,流体的速度减慢。

关键词： Casson纳米流体径向拉伸板驻点磁流体动力学放热/吸热滑动条件

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IoMT-Based Smart Monitoring Hierarchical Fuzzy Inference System for Diagnosis of COVID-19

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Computers, Materials & Continua 2020年第12期65卷 2591-2605页

作者： Tahir Abbas Khan Sagheer Abbas Allah Ditta Muhammad Adnan Khan Hani Alquhayz areej fatima Muhammad Farhan Khan Department of Computer Science National College of Business Administration and EconomicsLahore54000Pakistan Department of Information Sciences Division of Science&TechnologyUniversity of EducationLahore54000Pakistan Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan Department of Computer Science and Information College of Science in ZulfiMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Forensic Sciences University of Health SciencesLahore54000Pakistan

The prediction of human diseases,particularly COVID-19,is an extremely challenging task not only for medical experts but also for the technologists supporting them in diagnosis and *** deal with the prediction and diagnosis of COVID-19,we propose an Internet of Medical Things-based Smart Monitoring Hierarchical Mamdani Fuzzy Inference System(IoMTSM-HMFIS).The proposed system determines the various factors like fever,cough,complete blood count,respiratory rate,Ct-chest,Erythrocyte sedimentation rate and C-reactive protein,family history,and antibody detection(lgG)that are directly involved in *** expert system has two input variables in layer 1,and seven input variables in layer *** layer 1,the initial identification for COVID-19 is considered,whereas in layer 2,the different factors involved are ***,advanced lab tests are conducted to identify the actual current status of the *** major focus of this study is to build an IoMT-based smart monitoring system that can be used by anyone exposed to COVID-19;the system would evaluate the user’s health condition and inform them if they need consultation with a specialist for ***-2019a tool is used to conduct the *** COVID-19 IoMTSM-HMFIS system has an overall accuracy of approximately 83%.Finally,to achieve improved performance,the analysis results of the system were shared with experts of the Lahore General Hospital,Lahore,Pakistan.

关键词： IoMT MERS-COV Ct-chest ESR/CRP ABD(lgG) Fuzzy logic HMFIS WHO

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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

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Joint Channel and Multi-User Detection Empowered with Machine Learning

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Computers, Materials & Continua 2022年第1期70卷 109-121页

作者： Mohammad Sh.Daoud areej fatima Waseem Ahmad Khan Muhammad Adnan Khan Sagheer Abbas Baha Ihnaini Munir Ahmad Muhammad Sheraz Javeid Shabib Aftab College of Engineering Al Ain UniversityAbu Dhabi112612UAE Department of Computer Science Lahore Garrison UniversityLahore54792Pakistan School of Computer Science National College of Business Administration and EconomicsLahore54000Pakistan Riphah School of Computing and Innovation Faculty of ComputingRiphah International UniversityLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of Software EngineeringGachon UniversitySeongnam13557South Korea Department of Computer Science College of Science and TechnologyWenzhou Kean University325060USA Department of Computer Science Hameeda Rasheed Institute of Science and TechnologyMultan66000Pakistan

The numbers of multimedia applications and their users increase with each passing *** multi-carrier systems have been developed along with varying techniques of space-time coding to address the demand of the future generation of network *** 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 *** first stage estimates the channel parameters,and the second performsmulti-user *** proposed approach capitalizes on a neuro-fuzzy hybrid systemthat combines the competencies of both fuzzy logic and neural *** 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 *** FLeABPNN-based receiver renders better results than other techniques in terms of minimum mean square error,minimum mean channel error,and bit error rate.

关键词： Channel and multi-user detection minimum mean square error multiple-input and multiple-output minimum mean channel error bit error rate

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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

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The Effect of the Menstrual Cycle on Cognitive Performance: Spatial Reasoning, Visual & Numerical Memory

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Journal of Behavioral and Brain Science 2024年第10期14卷 276-296页

作者： Anusha Asim Rifah Maryam Zahra Sultan areej Shahid fatima Yousaf Ishika Khandelwal Isra Allana Girl Up Emirates Girl Up United Nations Foundation Dubai United Arab Emirates

The menstrual cycle has been a topic of interest in relation to behavior and cognition for many years, with historical beliefs associating it with cognitive impairment. However, recent research has challenged these beliefs and suggested potential positive effects of the menstrual cycle on cognitive performance. Despite these emerging findings, there is still a lack of consensus regarding the impact of the menstrual cycle on cognition, particularly in domains such as spatial reasoning, visual memory, and numerical memory. Hence, this study aimed to explore the relationship between the menstrual cycle and cognitive performance in these specific domains. Previous studies have reported mixed findings, with some suggesting no significant association and others indicating potential differences across the menstrual cycle. To contribute to this body of knowledge, we explored the research question of whether the menstrual cycles have a significant effect on cognition, particularly in the domains of spatial reasoning, visual and numerical memory in a regionally diverse sample of menstruating females. A total of 30 menstruating females from mixed geographical backgrounds participated in the study, and a repeated measures design was used to assess their cognitive performance in two phases of the menstrual cycle: follicular and luteal. The results of the study revealed that while spatial reasoning was not significantly related to the menstrual cycle (p = 0.256), both visual and numerical memory had significant positive associations (p < 0.001) with the luteal phase. However, since the effect sizes were very small, the importance of this relationship might be commonly overestimated. Future studies could thus entail designs with larger sample sizes, including neuro-biological measures of menstrual stages, and consequently inform competent interventions and support systems.

关键词： Menstrual Health Menstrual Cycle Menstruation Mental Health Cognition Spatial Reasoning Visual Memory Numerical Memory

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Effect of Combined Therapy on Colistin Resistant <i>Pseudomonas aeruginosa</i>: An <i>in Vitro</i>Study

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Advances in Microbiology 2019年第10期9卷 877-892页

作者： Nuzhath fatima Amany Marwan Alkhansa Anwar Aymen Madhkhali Nahed Ali areej Gadi Amany Hasnany Razan Hassan Hamali Mousa Ali Ayyashi Yahya Hobani Department of Medical Laboratory Technology College of Applied Medical Sciences Jazan University Jazan KSA College of Applied Medical Sciences Jazan University Jazan KSA Prince Mohammed Bin Nasser Hospital Jazan KSA

Pseudomonas aeruginosa is known for its antibiotic resistance to the clinicians. The infections caused by this pathogen are hard to treat because of its highly versatile property to mutate and acquire drug resistance. Pseudomonas aeruginosa also possesses intrinsic property of resistance to certain antibiotics like tetracyclines. However;in a practice to overcome the problem of multi drug resistance, clinicians restored the use of some antibiotics that were previously been used to treat the Pseudomonal infections;but they were discontinued because of its toxic effects. Colistin is an example of one such antibiotic. Use of Colistin was barred for its neurotoxicity. However in recent clinical trials, Colistin was reintroduced to fight with this superbug. Sadly in recent years, Pseudomonas aeruginosa developed resistance to Colistin as well. Therefore combined therapy is an alternate and suitable treatment to overcome the infections caused by multidrug-resistant Pseudomonas aeruginosa. The present study is an in vitro study;in which we tested synergy between two antibiotics namely streptomycin and Colistin on 29 clinical isolates of P. aeruginosa collected from hospitals in Jazan city KSA. The combination of two drugs showed synergistic activity on 55.1% of tested strains, while 20.6% strains had partial synergy, whereas indifferent synergy was observed in 13.8% strains and the 6.8% of strains had additive synergy. In addition to this, the drugs when combined also showed antagonism on one strain (3.44%). The present study showed synergistic action on Colistin-resistant Pseudomonas aeruginosa to greater extent (55.1%) by the two tested drugs. Hence Colistin and streptomycin can be used as a suitable combination therapy (in vivo) to treat multidrug resistant P. aeruginosa infections.

关键词： “MDR”—Multidrug Resistant “MIC”—Minimum Inhibitory Concentration “FICI”—Fractional Inhibitory Concentration Index “MHB”—Muller Hinton Broth Pseudomonas aeruginosa

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