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Ontology-Based Crime News Semantic Retrieval System

Computers, Materials & Continua 2023年第10期77卷 601-614页

作者： fiaz majeed Afzaal Ahmad Muhammad Awais Hassan Muhammad Shafiq Jin-Ghoo Choi Habib Hamam Department of Information Technology University of GujratGujrat50700Pakistan Department of Computer Science University of Engineering&TechnologyLahore54890Pakistan Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea Faculty of Engineering Uni de MonctonMonctonNB E1A3E9Canada International Institute of Technology and Management Commune d’AkandaBPLibreville1989Gabon School of Electrical Engineering Department of Electrical and Electronic Engineering ScienceUniversity of JohannesburgJohannesburg2006South Africa Spectrum of Knowledge Production&Skills Development Sfax3027Tunisia

Every day,the media reports tons of crimes that are considered by a large number of users and accumulate on a regular *** news exists on the Internet in unstructured formats such as books,websites,documents,and *** such homogeneous data,it is very challenging to extract relevant information which is a time-consuming and critical task for the public and law enforcement ***-based Information Retrieval(IR)systems rely on statistics to retrieve results,making it difficult to obtain relevant *** are unable to understandthe user’s query and thus facewordmismatchesdue to context changes andthe inevitable semanticsof a given ***,such datasets need to be organized in a structured configuration,with the goal of efficiently manipulating the data while respecting the semantics of the *** ontological semantic IR systemis needed that can find the right investigative information and find important clues to solve criminal *** semantic system retrieves information in view of the similarity of the semantics among indexed data and user *** this paper,we develop anontology-based semantic IRsystemthat leverages the latest semantic technologies including resource description framework(RDF),semantic protocol and RDF query language(SPARQL),semantic web rule language(SWRL),and web ontology language(OWL).We have conducted two *** the first experiment,we implemented a keyword-based textual IR systemusing Apache *** the second experiment,we implemented a semantic systemthat uses ontology to store the data and retrieve precise results with high accuracy using SPARQL *** keyword-based system has filtered results with 51%accuracy,while the semantic system has filtered results with 95%accuracy,leading to significant improvements in the field and opening up new horizons for researchers.

关键词： Web 3.0 crime ontology semantic web knowledge representation

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COVIDX: Computer-aided diagnosis of COVID-19 and its severity prediction with raw digital chest X-ray scans

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Quantitative Biology 2022年第2期10卷 208-220页

作者： Wajid Arshad Abbasi Syed Ali Abbas Saiqa Andleeb Maryum Bibi fiaz majeed Abdul Jaleel Muhammad Naveed Akhtar Computational Biology and Data Analysis Lab Department of Computer Sciences&Information TechnologyKing Abdullah CampusUniversity of Azad Jammu&KashmirMuzaffarabadAJ&K 13100Pakistan Biotechnology Lab Department of ZoologyKing Abdullah CampusUniversity of Azad Jammu&KashmirMuzaffarabadAJ&K 13100Pakistan Department of Software Engineering University of GujratGujrat 50700Pakistan Department of Computer Science (RCET)UETLahore 54000Pakistan Computational and Internet Services Division Pakistan Institute of Engineering and Applied Sciences(PIEAS)Islamabad 44000Pakistan

Background: Coronavirus disease (COVID-19) is a contagious infection caused by severe acute respiratory syndrome coronavirus-2 (SARS-COV-2) and it has infected and killed millions of people across the ***: In the absence or inadequate provision of therapeutic treatments of COVID-19 and the limited convenience of diagnostic techniques, there is a necessity for some alternate spontaneous screening systems that can easily be used by the physicians to rapidly recognize and isolate the infected patients to circumvent onward surge. A chest X-ray (CXR) image can effortlessly be used as a substitute modality to diagnose the ***: In this study, we present an automatic COVID-19 diagnostic and severity prediction system (COVIDX) that uses deep feature maps of CXR images along with classical machine learning algorithms to identify COVID-19 and forecast its severity. The proposed system uses a three-phase classification approach (healthy vs unhealthy, COVID-19 vs pneumonia, and COVID-19 severity) using different conventional supervised classification ***: We evaluated COVIDX through 10-fold cross-validation, by using an external validation dataset, and also in a real setting by involving an experienced radiologist. In all the adopted evaluation settings, COVIDX showed strong generalization power and outperforms all the prevailing state-of-the-art methods designed for this ***: Our proposed method (COVIDX), with vivid performance in COVID-19 diagnosis and its severity prediction, can be used as an aiding tool for clinical physicians and radiologists in the diagnosis and follow-up studies of COVID-19 infected ***: We made COVIDX easily accessible through a cloud-based webserver and python code available at https://***/view/wajidarshad/software and https://***/wajidarshad/covidx, respectively.

关键词： coronavirus COVID-19 radiology machine learning chest X-ray contagious infection

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Noisy ECG Signal Data Transformation to Augment Classification Accuracy

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Computers, Materials & Continua 2022年第5期71卷 2191-2207页

作者： Iqra Afzal fiaz majeed Muhammad Usman Ali Shahzada Khurram Akber Abid Gardezi Shafiq Ahmad Saad Aladyan Almetwally M.Mostafa Muhammad Shafiq Department of Information Technology University of GujratGujrat50700Pakistan Department of Computer Science University of GujratGujrat50700Pakistan Faculty of Computing The Islamia University of BahawalpurBahawalpur63100Pakistan Department of Computer Science COMSATS University IslamabadIslamabadPakistan Industrial Engineering Department College of EngineeringKing Saud UniversityP.O.Box 800Riyadh 11421Saudi Arabia Department of Information Systems College of Computer and Information SciencesKing Saud UniversityP.O.Box 800Riyadh11421Saudi Arabia Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea

In this era of electronic health,healthcare data is very important because it contains information about human *** addition,the Internet of Things(IoT)revolution has redefined modern healthcare systems and management by providing continuous *** this case,the data related to the heart is more important and requires proper *** the analysis of heart data,Electrocardiogram(ECG)is *** this work,machine learning techniques,such as adaptive boosting(AdaBoost)is used for detecting normal sinus rhythm,atrial fibrillation(AF),and noise in ECG signals to improve the classification *** proposed model uses ECG signals as input and provides results in the form of the presence or absence of disease AF,and classifies other signals as normal,other,or *** article derives different features from the signal using Maximal Information Coefficient(MIC)and minimum Redundancy Maximum Relevance(mRMR)technique,and then classifies them based on their *** the ECG contains some kind of noise and irregular data streams so the purpose of this study is to remove artifacts from the ECG signal by deploying the method of Second-Order-Section(SOS)(filter)and correctly classify *** features were extracted to improve the detection of ECG *** with existing methods,this work gives promising results and can help improve the classification accuracy of the ECG signals.

关键词： ECG atrial fibrillation adaptive boosting heart rate variability

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A Lightweight Approach for Skin Lesion Detection Through Optimal Features Fusion

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Computers, Materials & Continua 2022年第1期70卷 1617-1630页

作者： Khadija Manzoor fiaz majeed Ansar Siddique Talha Meraj Hafiz Tayyab Rauf Mohammed A.El-Meligy Mohamed Sharaf Abd Elatty E.Abd Elgawad Department of Information Technology University of GujratGujrat50700Pakistan Department of Software Engineering University of GujratGujrat50700Pakistan Department of Computer Science COMSATS University Islamabad-Wah CampusWah Cantt47040Pakistan Centre for Smart Systems AI and CybersecurityStaffordshire UniversityStoke-on-TrentUK Advanced Manufacturing Institute King Saud UniversityRiyadh11421Saudi Arabia Department of Industrial Engineering College of EngineeringKing Saud UniversityRiyadh11421Saudi Arabia

Skin diseases effectively influence all parts of *** and accurate detection of skin cancer is necessary to avoid significant *** manual detection of skin diseases by dermatologists leads to misclassification due to the same intensity and color ***,an automated system to identify these skin diseases is *** studies on skin disease classification using different techniques have been ***,previous techniques failed to identify multi-class skin disease images due to their similar *** the proposed study,a computer-aided framework for automatic skin disease detection is *** the proposed research,we collected and normalized the datasets from two databases(ISIC archive,Mendeley)based on six Basal Cell Carcinoma(BCC),Actinic Keratosis(AK),Seborrheic Keratosis(SK),Nevus(N),Squamous Cell Carcinoma(SCC),and Melanoma(M)common skin ***,segmentation is performed using deep Convolutional Neural Networks(CNN).Furthermore,three types of features are extracted from segmented skin lesions:ABCD rule,GLCM,and in-depth *** transfer learning is used for deep feature extraction,while a support vector machine(SVM)is used for *** results show that SVM outperformed other studies in terms of accuracy,as AK disease achieved 100%accuracy,BCC 92.7%,M 95.1%,N 97.8%,SK 93.1%,SCC 91.4%with a global accuracy of 95.4%.

关键词： AlexNet CNN GLCM SVMs skin disease

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Ensemble Modeling for the Classification of Birth Data

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Intelligent Automation & Soft Computing 2024年第4期39卷 765-781页

作者： fiaz majeed Abdul Razzaq Ahmad Shakir Maqbool Ahmad Shahzada Khurram Muhammad Qaiser Saleem Muhammad Shafiq Jin-Ghoo Choi Habib Hamam Osama E.Sheta Department of Information Technology University of GujratGujrat50700Pakistan School of Digital Convergence Business University of Central PunjabRawalpindi46000Pakistan Faculty of Computing The Islamia University of BahawalpurBahawalpur63100Pakistan College of Computer Science and Information Technology Al Baha UniversityAl Baha1988Saudi Arabia School of Computer Science and Engineering Yeungnam UniversityGyeongsan38541Republic of Korea Faculty of Engineering Uni de MonctonMonctonNB E1A3E9Canada International Institute of Technology and Management Commune d’AkandaLibreville1989Gabon Department of Electrical and Electronic Engineering Science School of Electrical EngineeringUniversity of JohannesburgJohannesburg2006South Africa Spectrum of Knowledge Production&Skills Development Sfax3027Tunisia College of Computer Science and Engineering University of Ha’ilHa’il55476Saudi Arabia College of Science Zagazig UniversityZagazig44511Egypt

Machine learning(ML)and data mining are used in various fields such as data analysis,prediction,image processing and especially in *** in the past decade have focused on applying ML and data mining to generate conclusions from historical data in order to improve healthcare systems by making predictions about the *** ML algorithms,researchers have developed applications for decision support,analyzed clinical aspects,extracted informative information from historical data,predicted the outcomes and categorized diseases which help physicians make better *** is observed that there is a huge difference between women depending on the region and their social *** to these differences,scholars have been encouraged to conduct studies at a local level in order to better understand those factors that affect maternal health and the expected *** this study,the ensemble modeling technique is applied to classify birth outcomes based on either cesarean section(C-Section)or normal delivery.A voting ensemble model for the classification of a birth dataset was made by using a Random Forest(RF),Gradient Boosting Classifier,Extra Trees Classifier and Bagging Classifier as base *** is observed that the voting ensemble modal of proposed classifiers provides the best accuracy,i.e.,94.78%,as compared to the individual *** algorithms are more accurate due to ensemble models,which reduce variance and classification *** is reported that when a suitable classification model has been developed for birth classification,decision support systems can be created to enable clinicians to gain in-depth insights into the patterns in the *** such a system will not only allow health organizations to improve maternal health assessment processes,but also open doors for interdisciplinary research in two different fields in the region.

关键词： Birth data classification ensemble model machine learning

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Glinus lotoides ethanolic extract alleviates LPS-induced anxiety and depression-like behavior by modulating antioxidant and inflammatory biomarkers in rats

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Asian Pacific Journal of Tropical Biomedicine 2022年第2期12卷 78-86页

作者： Ambreen Mehmood Awan Wafa majeed Faraza Javed Bilal Aslam Asra Iftikhar Hafiza Arooj Kanwal Sobia fiaz Institute of Physiology and Pharmacology University of AgricultureFaisalabadPakistan Department of Pharmacology Faculty of Pharmacythe Islamia University of BahawalpurBahawalpurPakistan Department of Pharmacy University of FaisalabadFaisalabadPakistan Department of Pharmacy University of AgricultureFaisalabadPakistan

Objective:To explore the protective role of Glinus lotoides ethanolic extract in a depression model through modulating oxidant/antioxidant enzyme system and inflammatory ***:Phytochemical constituents of Glinus lotoides ethanolic extract were evaluated qualitatively and quantitatively along with *** were divided into six *** normal control and the intoxicated groups received normal saline,and the standard group received imipramine,while the remaining groups received 100,300,and 500 mg/kg Glinus lotoides ethanolic *** groups received treatments for 14 ***(LPS)were then administered i.p.(0.83 mg/kg)to all groups except the normal control *** 24 h,anxiety and depression-like behaviors were evaluated by performing behavioral analysis(open field,tail suspension,forced swim,sucrose preference test),and determining total oxidant status,total antioxidant capacity,catalase,and biochemical parameters[malondialdehyde,glutathione,superoxide dismutase,tumor necrosis factor(TNF)-alpha and interleukin(IL)-6].Results:Phytochemical studies confirmed the presence of phenols and flavonoids and HPLC analysis showed the presence of gallic acid,quercetin,chlorogenic,and caffeic *** oxidant status was significantly decreased,while total antioxidant capacity was significantly increased in the Glinus lotoides ethanolic extract treated ***,Glinus lotoides ethanolic extract diminished malondialdehyde,IL-6,and TNF-alpha levels,while increasing superoxide dismutase,catalase,and glutathione ***:Glinus lotoides ethanolic crude extract shows significant antidepressant activity by modulating oxidative and biochemical parameters that supports its folkloric use in traditional systems of medicine.

关键词： Glinus lotoides Biochemical markers Depression Traditional medicine Nervous system Anxiety Antioxidant Inflammation

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