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Alzheimer’s Disease Diagnosis Based on a Semantic Rule-Based Modeling and Reasoning Approach

Computers, Materials & Continua 2021年第12期69卷 3531-3548页

作者： Nora Shoaip Amira Rezk shaker el-sappagh Tamer Abuhmed Sherif Barakat Mohammed elmogy Department of Information Systems Faculty of Computers and InformationMansoura UniversityEgypt Centro Singular de Investigaci’on en Tecnolox’ias Intelixentes(CiTIUS)Universidade de Santiago de Compostela Santiago de Compostela15782Spain Department of Information Systems Faculty of Computers and Artificial IntelligenceBenha UniversityBanha13518Egypt Department of Computer Science and Engineering College of ComputingSungkyunkwan UniversityRepublic of Korea Department of Information Technology Faculty of Computers and InformationMansoura UniversityMansoura35516Egypt

Alzheimer’s disease(AD)is a very complex disease that causes brain failure,then eventually,dementia *** is a global health problem.99%of clinical trials have failed to limit the progression of this *** risks and barriers to detecting AD are huge as pathological events begin decades before appearing clinical *** for AD are likely to be more helpful if the diagnosis is determined early before the final stage of neurological *** this regard,the need becomes more urgent for biomarker-based detection.A key issue in understanding AD is the need to solve complex and high-dimensional datasets and heterogeneous biomarkers,such as genetics,magnetic resonance imaging(MRI),cerebrospinal fluid(CSF),and cognitive *** an interpretable reasoning system and performing interoperability that achieves in terms of a semantic model is potentially very ***,our aim in this work is to propose an interpretable approach to detect AD based on Alzheimer’s disease diagnosis ontology(ADDO)and the expression of semantic web rule language(SWRL).This work implements an ontology-based application that exploits three different machine learning *** models are random forest(RF),JRip,and J48,which have been used along with the voting *** dataset was used for this *** proposed classifier’s result with the voting ensemble achieves a higher accuracy of 94.1%and precision of 94.3%.Our approach provides effective inference ***,it contributes to a real,accurate,and interpretable classifier model based on various AD biomarkers for inferring whether the subject is a normal cognitive(NC),significant memory concern(SMC),early mild cognitive impairment(EMCI),late mild cognitive impairment(LMCI),or AD.

关键词： Mild cognitive impairment Alzheimer’s disease knowledge based semantic web rule language reasoning system ADNI dataset machine learning techniques

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Early Detection of Alzheimer’s Disease Based on Laplacian Re-Decomposition and XGBoosting

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Computer Systems Science & Engineering 2023年第9期46卷 2773-2795页

作者： Hala Ahmed Hassan Soliman shaker el-sappagh Tamer Abuhmed Mohammed elmogy Information Technology Department Faculty of Computers and InformationMansoura UniversityMansoura35511Egypt Faculty of Computer Science and Engineering Galala UniversitySuez435611Egypt Faculty of Computers and Artificial Intelligence Benha UniversityBanha13518Egypt College of Computing and Informatics Sungkyunkwan UniversitySuwonKorea

The precise diagnosis of Alzheimer’s disease is critical for patient treatment,especially at the early stage,because awareness of the severity and progression risks lets patients take preventative actions before irreversible brain damage *** is possible to gain a holistic view of Alzheimer’s disease staging by combining multiple data modalities,known as image *** this paper,the study proposes the early detection of Alzheimer’s disease using different modalities of Alzheimer’s disease brain ***,the preprocessing was performed on the ***,the data augmentation techniques are used to handle ***,the skull is removed to lead to good *** the second phase,two fusion stages are used:pixel level(early fusion)and feature level(late fusion).We fused magnetic resonance imaging and positron emission tomography images using early fusion(Laplacian Re-Decomposition)and late fusion(Canonical Correlation Analysis).The proposed system used magnetic resonance imaging and positron emission tomography to take advantage of *** resonance imaging system’s primary benefits are providing images with excellent spatial resolution and structural information for specific *** emission tomography images can provide functional information and the metabolisms of particular *** characteristic helps clinicians detect diseases and tumor progression at an early ***,the feature extraction of fused images is extracted using a convolutional neural *** the case of late fusion,the features are extracted first and then ***,the proposed system performs XGB to classify Alzheimer’s *** system’s performance was evaluated using accuracy,specificity,and *** medical data were retrieved in the 2D format of 256×256 *** classifiers were optimized to achieve the final results:for the decision tree,the maximum depth of a tree was *** best number of trees for the random forest was 60;for

关键词： Alzheimer’s disease(AD) machine learning(ML) image fusion Laplacian Re-decomposition(LRD) XGBoosting

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Enhancing Task Assignment in Crowdsensing Systems Based on Sensing Intervals and Location

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Computers, Materials & Continua 2022年第6期71卷 5619-5638页

作者： Rasha Sleem Nagham Mekky shaker el-sappagh Louai Alarabi Noha AHikal Mohammed elmogy Faculty of Computers and Information Mansoura UniversityMansoura35516Egypt Faculty of Computers and Artificial Intelligence Benha UniversityBanha13518Egypt Faculty of Computer Science and Engineering Galala UniversitySuez435611Egypt College of Computer and Information Systems Umm Al-Qura UniversityMecca715Saudi Arabia

The popularity of mobile devices with sensors is captivating the attention of researchers to modern techniques,such as the internet of things(IoT)and mobile crowdsensing(MCS).The core concept behind MCS is to use the power of mobile sensors to accomplish a difficult task collaboratively,with each mobile user completing much simpler *** paper discusses the task assignment problem in mobile crowdsensing,which is dependent on sensing time and path planning with the constraints of participant travel distance budgets and sensing time *** goal is to minimize aggregate sensing time for mobile users,which reduces energy consumption to encourage more participants to engage in sensing activities and maximize total task *** paper introduces a two-phase task assignment framework called location time-based algorithm(LTBA).LTBA is a framework that enhances task assignment in MCS,whereas assigning tasks requires overlapping time intervals between tasks and mobile users’tasks and the location of tasks and mobile users’*** process of assigning the nearest task to the mobile user’s current path depends on the ant colony optimization algorithm(ACO)and Euclidean *** combines two algorithms:(1)greedy online allocation algorithm and(2)bio-inspired traveldistance-balance-based algorithm(B-DBA).The greedy algorithm was sensing time interval-based and worked on reducing the overall sensing time of the mobile user.B-DBA was location-based and worked on maximizing total task *** results demonstrate that the average task quality is 0.8158,0.7093,and 0.7733 for LTBA,B-DBA,and greedy,*** sensing time was reduced to 644,1782,and 685 time units for LTBA,B-DBA,and greedy,*** the algorithms improves task assignment in MCS for both total task quality and sensing *** results demonstrate that combining the two algorithms in LTBA is the best performance for total task quality and total sensing time,and the greed

关键词： Mobile crowdsensing online task assignment participatory sensing path planning sensing time intervals ant colony optimization

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Identification and Classification of Crowd Activities

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Computers, Materials & Continua 2022年第7期72卷 815-832页

作者： Manar elshahawy Ahmed O.Aseeri shaker el-sappagh Hassan Soliman Mohammed elmogy Mervat Abu-elkheir Department of Information Technology Faculty of Computers and InformationMansoura UniversityEgypt Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Information Systems Faculty of Computers and Artificial IntelligenceBenha UniversityEgypt Faculty of Computer Science and Engineering Galala University435611SuezEgypt Department of Computer Science Faculty of Media Engineering and TechnologyGerman UniversityEgypt

The identification and classification of collective people’s activities are gaining momentum as significant themes in machine learning,with many potential applications *** need for representation of collective human behavior is especially crucial in applications such as assessing security conditions and preventing crowd *** paper investigates the capability of deep neural network(DNN)algorithms to achieve our carefully engineered pipeline for crowd *** includes three principal stages that cover crowd analysis ***,individual’s detection is represented using the You Only Look Once(YOLO)model for human detection and Kalman filter for multiple human tracking;Second,the density map and crowd counting of a certain location are generated using bounding boxes from a human detector;and Finally,in order to classify normal or abnormal crowds,individual activities are identified with pose *** proposed system successfully achieves designing an effective collective representation of the crowd given the individuals in addition to introducing a significant change of crowd in terms of activities *** results onMOT20 and SDHA datasets demonstrate that the proposed system is robust and *** framework achieves an improved performance of recognition and detection peoplewith a mean average precision of 99.0%,a real-time speed of 0.6ms non-maximumsuppression(NMS)per image for the SDHAdataset,and 95.3%mean average precision for MOT20 with 1.5ms NMS per image.

关键词： Crowd analysis individual detection You Only Look Once(YOLO) multiple object tracking kalman filter pose estimation

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Overlapping Shadow Rendering for Outdoor Augmented Reality

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Computers, Materials & Continua 2021年第5期67卷 1915-1932页

作者： Naira elazab shaker el-sappagh Ahmed Atwan Hassan Soliman Mohammed elmogy Louai Alarabi Nagham Mekky Faculty of Computers and Information Mansoura UniversityMansoura35516Egypt Universidade de Santiago de Compostela Santiago de Compostela15705Spain Faculty of Computers and Articial Intelligence Benha UniversityBenha13512Egypt Faculty of Computer Sciences and Information Technology Northern Border UniversityArarSaudi Arabia College of Computer and Information Systems Umm Al-Qura UniversityMeccaSaudi Arabiagham Mekky

Realism rendering methods of outdoor augmented reality(AR)is an interesting *** items in outdoor AR need advanced impacts like shadows,sunshine,and relations between unreal items.A few realistic rendering approaches were built to overcome this *** of these approaches are not dealt with real-time ***,the issue remains an active research topic,especially in outdoor *** paper introduces a new approach to accomplish reality real-time outdoor rendering by considering the relation between items in AR regarding shadows in any place during *** proposed method includes three principal stages that cover various outdoor AR rendering ***,real shadow recognition was generated considering the sun’s location and the intensity of the *** second step involves real shadow ***,we introduced a shadow production algorithm technique and shades through its impacts on unreal items in the *** selected approach’s target is providing a fast shadow recognition technique without affecting the system’s *** achieved an average accuracy of 95.1%and an area under the curve(AUC)of 92.5%.The outputs demonstrated that the proposed approach had enhanced the reality of outside AR *** results of the proposed method outperformed other state-of-the-art rendering shadow techniques’outcomes.

关键词： Augmented reality outdoor rendering virtual shadow shadow overlapping hybrid shadow map

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Semantic Pneumonia Segmentation and Classification for Covid-19 Using Deep Learning Network

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Computers, Materials & Continua 2022年第10期73卷 1141-1158页

作者： M.M.Lotfy Hazem M.el-Bakry M.M.elgayar shaker el-sappagh G.Abdallah M.I A.A.Soliman Kyung Sup Kwak Department of Mathematics Faculty of ScienceArish UniversityArish45511Egypt Department of Information Systems Faculty of Computers and InformationMansoura UniversityMansoura35511Egypt Department of Information Technology Faculty of Computers and InformationMansoura UniversityMansoura35511Egypt Centro Singular de Investigación en Tecnoloxías Intelixentes(CiTIUS) Universidade de Santiago de CompostelaSantiago de Compostela15705Spain Information Systems Department Faculty of Computers and Artificial IntelligenceBenha UniversityBenha13512Egypt Department of Information and Communication Engineering Inha UniversityIncheon22212Korea

Early detection of the Covid-19 disease is essential due to its higher rate of infection affecting tens of millions of people,and its high number of deaths also by 7%.For that purpose,a proposed model of several stages was *** first stage is optimizing the images using dynamic adaptive histogram equalization,performing a semantic segmentation using DeepLabv3Plus,then augmenting the data by flipping it horizontally,rotating it,then flipping it *** second stage builds a custom convolutional neural network model using several pre-trained ***,the model compares the pre-trained data to the new output,while repeatedly trimming the best-performing models to reduce complexity and improve memory *** experiments were done using different techniques and ***,the proposed model achieved an average accuracy of 99.6%and an area under the curve of 0.996 in the Covid-19 *** paper will discuss how to train a customized intelligent convolutional neural network using various parameters on a set of chest X-rays with an accuracy of 99.6%.

关键词： SARS-COV2 COVID-19 pneumonia deep learning network semantic segmentation smart classification

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Timing and Classication of Patellofemoral Osteoarthritis Patients Using Fast Large Margin Classifier

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Computers, Materials & Continua 2021年第4期 393-409页

作者： Mai Ramadan Ibraheem Jilan Adel Alaa eldin Balbaa shaker el-sappagh Tamer Abuhmed Mohammed elmogy Faculty of Computers and Information Kafrelsheikh UniversityEgypt Faculty of Physical Therapy Kafrelsheikh UniversityEgypt Faculty of Physical Therapy Nahda UniversityNUBEgypt Universidade de Santiago de Compostela Santiago de CompostelaSpain College of Computing Sungkyunkwan UniversitySeoul06351Korea Faculty of Computers and Information Mansoura UniversityMansoura35516Egy

:Surface electromyogram(sEMG)processing and classication can assist neurophysiological standardization and evaluation and provide habitational *** timing of muscle activation is critical in determining various medical conditions when looking at sEMG *** muscle activation timing allows identication of muscle locations and feature validation for precise *** work aims to develop a predictive model to investigate and interpret Patellofemoral(PF)osteoarthritis based on features extracted from the sEMG signal using pattern *** this end,sEMG signals were acquired from ve core muscles over about 200 reads from healthy adult patients while they were going ***,offset,and time duration for the Transversus Abdominus(TrA),Vastus Medialis Obliquus(VMO),Gluteus Medius(GM),Vastus Lateralis(VL),and Multidus Muscles(ML)were acquired to construct a classication *** proposed classication model investigates function mapping from real-time space to a PF osteoarthritis discriminative feature *** activation feature space of muscle timing is used to train several large margin classiers to modulate muscle activations and account for such activation *** fast large margin classier achieved higher performance and faster convergence than support vector machines(SVMs)and other state-of-the-art *** proposed sEMG classication framework achieved an average accuracy of 98.8%after 7 s training time,improving other classication techniques in previous literature.

关键词： literature.Keywords:Muscle activation onset time LS-SVM surface electromyogram patellofemoral osteoarthritis the timing of core muscles

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Swift XRT Analysis of Type II-P Supernova SN 2008ij

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International Journal of Astronomy and Astrophysics 2024年第3期14卷 220-229页

作者： SH. M. Shehata Ahmed M. Fouad R. M. Samir A. A. shaker National Research Institute of Astronomy and Geophysics (NRIAG) Helwan Egypt

This study investigates the X-ray properties and evolution of Type II supernovae (SNe II) observed by Swift, examining variations among supernovae and exploring their X-ray characteristics over time. We present the first X-ray study of the Type IIp supernova SN 2008ij using data from the Swift X-ray mission. This investigation focuses on its spectral properties, identifying an X-ray flux of 1.20 (+0.11, −0.10) × 10−13 erg/cm2/s and a plasma temperature of 4.76 (+1.22, −0.83) keV. Our study marks an advancement in understanding SN 2008ij, providing crucial results into its X-ray emission characteristics. These results lay the groundwork for future studies of Type IIp supernovae, offering a foundation for exploring their evolutionary and physical processes.

关键词： X-Ray Core Collapse Supernova Swift (XRT) Observations

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A Two-Tier Framework Based on GoogLeNet and YOLOv3 Models for Tumor Detection in MRI

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Computers, Materials & Continua 2022年第7期72卷 73-92页

作者： Farman Ali Sadia Khan Arbab Waseem Abbas Babar Shah Tariq Hussain Dongho Song shaker EI-sappagh Jaiteg Singh Department of Software Sejong UniversitySeoul05006Korea Institute of Computer Sciences&Information Technology The University of AgriculturePeshawar25130Pakistan College of Technological Innovation Zayed UniversityDubai19282UAE Department of Software Korea Aerospace UniversitySeoul10540Korea Faculty of Computer Science and Engineering Galala UniversitySuez435611Egypt Information Systems Department Faculty of Computers and Artificial IntelligenceBenha UniversityBanha13518Egypt Chitkara University Institute of Engineering&Technology Chitkara UniversityRajpura140401India

Medical Image Analysis(MIA)is one of the active research areas in computer vision,where brain tumor detection is the most investigated domain among researchers due to its deadly *** tumor detection in magnetic resonance imaging(MRI)assists radiologists for better analysis about the exact size and location of the ***,the existing systems may not efficiently classify the human brain tumors with significantly higher *** addition,smart and easily implementable approaches are unavailable in 2D and 3D medical images,which is the main problem in detecting the *** this paper,we investigate various deep learning models for the detection and localization of the tumor in MRI.A novel twotier framework is proposed where the first tire classifies normal and tumor MRI followed by tumor regions localization in the second ***,in this paper,we introduce a well-annotated dataset comprised of tumor and normal *** experimental results demonstrate the effectiveness of the proposed framework by achieving 97%accuracy using GoogLeNet on the proposed dataset for classification and 83%for localization tasks after finetuning the pre-trained you only look once(YOLO)v3 model.

关键词： Tumor localization MRI Image classification GoogLeNet YOLOv3

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Prevalence of Mastitis and Associated Risk Factors in Lactating Camels (Camelus Dromedarius) on Dairy Farms in Benadir Region, Somalia

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Open Journal of Veterinary Medicine 2024年第6期14卷 111-123页

作者： Shafii Abdullahi Mohamed Mahad Mohamud Abdi Abdiaziz Idiris Mohamud Yonis Abukar Mohamed Ahmed Ali Omar Abdirahman Barre Chandra shaker Chouhan Md. Amimul Ehsan Faculty of Veterinary Science Benadir University Mogadishu Somalia Faculty of Veterinary Medicine and Animal Husbandry Somali National University Mogadishu Somalia Somali Animal Welfare Society (SAWS) Mogadishu Somalia Camel Research Center Mogadishu Somalia Department of Medicine Faculty of Veterinary Science Bangladesh Agricultural University Mymensingh Bangladesh

The study took a comprehensive approach to understanding mastitis in lactating camels, a disease that significantly impacts milk quantity and quality. This complex and multifactorial disease poses a significant challenge to dairy camel farming, particularly in Somalia. The studys objective was to determine the prevalence of mastitis and the risk factors associated with it among lactating camels on dairy farms in Benadir, Somalia. To achieve this, a cross-sectional study was conducted from May to September 2022 in the Benadir region of Somalia. A total of 96 lactating camels underwent examination using the California Mastitis Test (CMT) to identify clinical and subclinical mastitis cases. Additionally, a questionnaire survey was conducted among 20 farm employees/owners to gather information on hygiene practices, parity, lactation stage, tick infestation, and the presence of udder/teat lesions. Data regarding parity and lactation stage were obtained through owner interviews. The data collected on specifically designed forms were recorded and entered into a Microsoft Excel spreadsheet for analysis using SPSS version 20 statistical software, ensuring a comprehensive and reliable analysis of the data. The studys findings revealed a significant prevalence of camel mastitis, with 34.4% (33/96) of lactating camels affected and clinical and subclinical cases constituting 5.2% and 29.2%, respectively. Quarter-level prevalence was 46.3%, with clinical and subclinical mastitis at 4.7% and 41.6%, respectively. The study also identified significant associations (P els during early lactation compared to those in late lactation and younger camels. These findings underscore the crucial role of inadequate hygienic conditions on camel farms and udder tick infestations in driving the elevated prevalence of mastitis, highlighting the need for improved manage

关键词： Mastitis Camel Prevalence Risk Factors Somalia

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