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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

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

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

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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

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

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

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Computers, Materials & Continua 2021年第4期67卷 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

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

Efficient Computer Aided Diagnosis System for Hepatic Tumors Using Computed Tomography Scans

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Computers, Materials & Continua 2022年第6期71卷 4871-4894页

作者： Yasmeen Al-Saeed Wael A.Gab-Allah Hassan Soliman Maysoon F.Abulkhair Wafaa M.Shalash mohammed elmogy Faculty of Computers and Information Mansoura UniversityMansoura35516Egypt Faculty of Computers&Artificial Intelligence South Valley UniversityHurghada84511Egypt Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia Faculty of Computers and Artificial Intelligence Benha UniversityBanha13511Egypt

One of the leading causes of mortality worldwide is liver *** earlier the detection of hepatic tumors,the lower the mortality *** paper introduces a computer-aided diagnosis system to extract hepatic tumors from computed tomography scans and classify them into malignant or benign *** hepatic tumors from computed tomography scans is considered a challenging task due to the fuzziness in the liver pixel range,intensity values overlap between the liver and neighboring organs,high noise from computed tomography scanner,and large variance in tumors *** proposed method consists of three main stages;liver segmentation using Fast Generalized Fuzzy C-Means,tumor segmentation using dynamic thresholding,and the tumor’s classification into malignant/benign using support vector machines *** performance of the proposed system was evaluated using three liver benchmark datasets,which are MICCAI-Sliver07,LiTS17,and *** proposed computer adided diagnosis system achieved an average accuracy of 96.75%,sensetivity of 96.38%,specificity of 95.20%and Dice similarity coefficient of 95.13%.

关键词： Liver tumor hepatic tumors diagnosis CT scans analysis liver segmentation tumor segmentation features extraction tumors classification FGFCM CAD system