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Identification of Anomaly Scenes in Videos Using Graph Neural Networks

Computers, Materials & Continua 2023年第3期74卷 5417-5430页

作者： Khalid Masood Mahmoud M.Al-Sakhnini Waqas Nawaz Tauqeer Faiz Abdul Salam Mohammad hamza Kashif Department of Software Engineering Lahore Garrison UniversityLahore54000Pakistan School of Business Skyline University CollegeSharjahUAE Al-Madinah International University–Faculty of Computer and Information Technology Kuala LumpurMalaysia College of Computer and Information Systems Islamic University of MadinahMadinahKingdom of Saudi Arabia School of IT Skyline University CollegeSharjahUAE Faculty of Engineering Computing and Science(FECS)-Swinburne University of TechnologySarawak Department of General Education Skyline University CollegeUniversity City of SharjahUAE Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan

Generally,conventional methods for anomaly detection rely on clustering,proximity,or *** themassive growth in surveillance videos,outliers or anomalies find ingenious ways to obscure themselves in the network and make conventional techniques *** research explores the structure of Graph neural networks(GNNs)that generalize deep learning frameworks to graph-structured *** node in the graph structure is labeled and anomalies,represented by unlabeled nodes,are predicted by performing random walks on the node-based graph *** to their strong learning abilities,GNNs gained popularity in various domains such as natural language processing,social network analytics and *** detection is a challenging task in computer vision but the proposed algorithm using GNNs efficiently performs the identification of *** Graph-based deep learning networks are designed to predict unknown objects and *** our case,they detect unusual objects in the form of malicious *** edges between nodes represent a relationship of nodes among each *** case of anomaly,such as the bike rider in Pedestrians data,the rider node has a negative value for the edge and it is identified as an *** encoding and decoding layers are crucial for determining how statistical measurements affect anomaly identification and for correcting the graph path to the best possible *** show that the proposed framework is a step ahead of the traditional approaches in detecting unusual activities,which shows a huge potential in automatically monitoring surveillance *** autonomous monitoring of CCTV,crime control and damage or destruction by a group of people or crowd can be identified and alarms may be triggered in unusual activities in streets or public *** suggested GNN model improves accuracy by 4%for the Pedestrian 2 dataset and 12%for the Pedestrian 1 dataset compared to a few state-of the-art techniq

关键词： Graph neural network deep learning anomaly detection auto encoders

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Trends in incidence and management of cancer of the ampulla of Vater

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World Journal of Gastroenterology 2014年第29期20卷 10144-10150页

作者： Florian Rostain Samia hamza Antoine Drouillard Jean Faivre Anne-Marie Bouvier C?me Lepage Registre Bourguignon des Cancers Digestifs INSERM U866CHU de DijonFaculté de Médecine21079 Dijon CedexFrance

AIM: To provide trends in incidence, management and survival of cancer of the ampulla of Vater in a well-defined French population.

关键词： Cancer of the ampulla of Vater Incidence Survival Treatment Epidemiology

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Experimental and first-principles investigation of crystal structure of powder metallurgy Al-1.1Sc and Al-2Sc alloys

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Transactions of Nonferrous Metals Society of China 2013年第10期23卷 3020-3026页

作者： hamza Yasar OCAK Ercan UGUN Rahmi NAL Department of Physics Faculty of Arts and Sciences Dumlupinar University Mechanical Engineering Department Engineering Faculty Gazi University

Al-1.1%Sc and A1-2%Sc （mass fraction） alloys were prepared using gas atomized alloy particles. Samples from consolidated alloys were analyzed by XRD for the determination of lattice parameters. Using these lattice parameters, the mechanical properties of the alloys were calculated theoretically with WIEN2k and EMTO programs, respectively. The elasticity moduli of the experimentally produced alloys were compared with the theoretical calculation results. The calculated mechanical properties of A13Sc phase and A1-Sc alloys were discussed to determine the optimum Sc content of Al-Sc alloys. It may be concluded that the Sc content should be the maximum about 1.0%, much more Sc addition could not improve the mechanical properties of the alloys.

关键词： A1-Sc alloy lattice constant elastic properties Ab initio calculations first principles

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An Efficient Mechanism for Product Data Extraction from E-Commerce Websites

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Computers, Materials & Continua 2020年第12期65卷 2639-2663页

作者： Malik Javed Akhtar Zahur Ahmad Rashid Amin Sultan H.Almotiri Mohammed A.Al Ghamdi hamza Aldabbas University of Engineering and Technology TaxilaPakistan Computer Science Department Umm Al-Qura UniversityMakkah CitySaudi Arabia Al-Balqa Applied University Al-SaltJordan

A large amount of data is present on the web which can be used for useful purposes like a product recommendation,price comparison and demand forecasting for a particular *** are designed for human understanding and not for ***,to make data machine-readable,it requires techniques to grab data from web *** have addressed the problem using two approaches,i.e.,knowledge engineering and machine *** of the art knowledge engineering approaches use the structure of documents,visual cues,clustering of attributes of data records and text processing techniques to identify data records on a web *** learning approaches use annotated pages to learn *** rules are used to extract data from unseen web *** structure of web documents is continuously ***,new techniques are needed to handle the emerging requirements of web data *** this paper,we have presented a novel,simple and efficient technique to extract data from web pages using visual styles and structure of *** proposed technique detects Rich Data Region(RDR)using query and correlative words of the *** is then divided into data records using style *** elements are removed using a Common Tag Sequence(CTS)and formatting *** system is implemented using JAVA and runs on the dataset of real-world working *** effectiveness of results is evaluated using precision,recall,and F-measure and compared with five existing systems.A comparison of the proposed technique to existing systems has shown encouraging results.

关键词： Document object model rich data region common tag sequence web data extraction deep web mining

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Nonlocal Integral Boundary Value Problem of Bagley-Torvik Type Fractional Differential Equations and Inclusions

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Journal of Mathematical Research with Applications 2019年第4期39卷 383-394页

作者： Lizhen CHEN Badawi hamza Eibadawi IBRAHIM Gang LI School of Applied Mathematics Shanxi University of Finance and Economics School of Mathematical Sciences Yangzhou University

In this article,we consider the Bagley-Torvik type fractional differential equation;D;l（t）-a;D;l（t） = g(t, l（t）) and differential inclusion;D;l（t）-a;D;l（t） ∈ G(t, l（t）),t ∈（0, 1） subjecting to l（0） = l;,and■ ds where 1 0, a, λ′are given constants. By using Leray-Schauder degree theory and fixed point theorems, we prove the existence of solutions. Our results extend the existence theorems for the classical Bagley-Torvik equation and some related models.

关键词： fractional differential equations and inclusions integral boundary conditions LeraySchauder theory

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Artificial Intelligence Based Clustering with Routing Protocol for Internet of Vehicles

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Computers, Materials & Continua 2022年第3期70卷 5835-5853页

作者： Manar Ahmed hamza Haya Mesfer Alshahrani Fahd NAl-Wesabi Mesfer Al Duhayyim Anwer Mustafa Hilal Hany Mahgoub Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlkharjSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversitySaudi Arabia Department of Computer Science King Khalid UniversityMuhayel AseerSaudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

With recent advances made in Internet of Vehicles(IoV)and Cloud Computing(CC),the Intelligent Transportation Systems(ITS)find it advantageous in terms of improvement in quality and interactivity of urban transportation service,mitigation of costs incurred,reduction in resource utilization,and improvement in traffic management *** trafficrelated problems in future smart cities can be sorted out with the incorporation of IoV in *** communication enables the collection and distribution of real-time essential data regarding road network *** this scenario,energy-efficient and reliable intercommunication routes are essential among vehicular nodes in sustainable urban *** this motivation,the current research article presents a new Artificial Intelligence-based Energy Efficient Clustering with Routing(AI-EECR)Protocol for IoV in urban *** proposed AI-EECR protocol operates under three stages namely,network initialization,Cluster Head(CH)selection,and routing *** presented AI-EECR protocol determines the CHs from vehicles with the help of Quantum Chemical Reaction Optimization(QCRO)*** a fitness function with the help of vehicle speed,trust level,and energy level of the *** order to make appropriate routing decisions,a set of relay nodeswas selected usingGroup Teaching Optimization Algorithm(GTOA).The performance of the presented AI-EECR model,in terms of energy efficiency,was validated against different aspects and a brief comparative analysis was *** experimental outcomes established that AI-EECR model outperformed the existing methods under different measures.

关键词： Vehicular communication internet of vehicles energy efficient smart transportation smart city urban computing

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Intelligent Deep Learning Based Automated Fish Detection Model for UWSN

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Computers, Materials & Continua 2022年第3期70卷 5871-5887页

作者： Mesfer Al Duhayyim Haya Mesfer Alshahrani Fahd NAl-Wesabi Mohammed Alamgeer Anwer Mustafa Hilal Manar Ahmed hamza Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversitySaudi Arabia Department of Computer Science King Khalid UniversityMuhayel AseerSaudi Arabia&Faculty of Computer and ITSana’a UniversitySana’aYemen Department of Information Systems King Khalid UniversityMuhayel AseerSaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

An exponential growth in advanced technologies has resulted in the exploration of Ocean *** has paved the way for new opportunities that can address questions relevant to diversity,uniqueness,and difficulty of marine *** Wireless Sensor Networks(UWSNs)are widely used to leverage such opportunities while these networks include a set of vehicles and sensors to monitor the environmental *** this scenario,it is fascinating to design an automated fish detection technique with the help of underwater videos and computer vision techniques so as to estimate and monitor fish biomass in water *** models have been developed earlier for fish ***,they lack robustness to accommodate considerable differences in scenes owing to poor luminosity,fish orientation,structure of seabed,aquatic plantmovement in the background and distinctive shapes and texture of fishes from different *** this motivation,the current research article introduces an Intelligent Deep Learning based Automated Fish Detection model for UWSN,named IDLAFD-UWSN *** presented IDLAFD-UWSN model aims at automatic detection of fishes from underwater videos,particularly in blurred and crowded ***-UWSN model makes use of Mask Region Convolutional Neural Network(Mask RCNN)with Capsule Network as a baseline model for fish ***,in order to train Mask RCNN,background subtraction process using GaussianMixtureModel(GMM)model is *** model makes use of motion details of fishes in video which consequently integrates the outcome with actual image for the generation of fish-dependent candidate ***,Wavelet Kernel Extreme Learning Machine(WKELM)model is utilized as a classifier *** performance of the proposed IDLAFD-UWSN model was tested against benchmark underwater video dataset and the experimental results achieved by IDLAFD-UWSN model were promising in comparison with other state-of-the-art methods under differe

关键词： Aquaculture background subtraction deep learning fish detection marine surveillance underwater sensor networks

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Hunger Search Optimization with Hybrid Deep Learning Enabled Phishing Detection and Classification Model

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Computers, Materials & Continua 2022年第12期73卷 6425-6441页

作者： Hadil Shaiba Jaber S.Alzahrani Majdy M.Eltahir Radwa Marzouk Heba Mohsen Manar Ahmed hamza Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Information Systems College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Phishing is one of the simplest ways in cybercrime to hack the reliable data of users such as passwords,account identifiers,bank details,*** general,these kinds of cyberattacks are made at users through phone calls,emails,or instant *** anti-phishing techniques,currently under use,aremainly based on source code features that need to scrape the webpage *** third party services,these techniques check the classification procedure of phishing Uniform Resource Locators(URLs).Even thoughMachine Learning(ML)techniques have been lately utilized in the identification of phishing,they still need to undergo feature engineering since the techniques are not well-versed in identifying phishing *** tremendous growth and evolution of Deep Learning(DL)techniques paved the way for increasing the accuracy of classification *** this background,the current research article presents a Hunger Search Optimization with Hybrid Deep Learning enabled Phishing Detection and Classification(HSOHDL-PDC)*** presented HSOHDL-PDC model focuses on effective recognition and classification of phishing based on website *** addition,SOHDL-PDC model uses character-level embedding instead of word-level embedding since the URLs generally utilize words with no ***,a hybrid Convolutional Neural Network-Long Short Term Memory(HCNN-LSTM)technique is also applied for identification and classification of *** hyperparameters involved in HCNN-LSTM model are optimized with the help of HSO algorithm which in turn produced improved *** performance of the proposed HSOHDL-PDC model was validated using different datasets and the outcomes confirmed the supremacy of the proposed model over other recent approaches.

关键词： Uniform resource locators phishing cyberattacks machine learning deep learning hyperparameter optimization

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Early Detection of Autism in Children Using Transfer Learning

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Intelligent Automation & Soft Computing 2023年第4期36卷 11-22页

作者： Taher M.Ghazal Sundus Munir Sagheer Abbas Atifa Athar hamza Alrababah Muhammad Adnan Khan School of Information Technology Skyline University CollegeSharjah1797UAE Network and Communication Technology Lab Center for Cyber SecurityFaculty of Information Science and Technology Universiti Kebangsaan Malaysia43600Malaysia School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Lahore Garrison University Lahore54000Pakistan Department of Computer Science COMSATS University Islamabad–Lahore CampusLahore54000Pakistan Department of Software Pattern Recognition and Machine Learning LabGachon UniversitySeongnamGyeonggido13120Korea

Autism spectrum disorder(ASD)is a challenging and complex neurodevelopment syndrome that affects the child’s language,speech,social skills,communication skills,and logical thinking *** early detection of ASD is essential for delivering effective,timely *** facial features such as a lack of eye contact,showing uncommon hand or body movements,bab-bling or talking in an unusual tone,and not using common gestures could be used to detect and classify ASD at an early *** study aimed to develop a deep transfer learning model to facilitate the early detection of ASD based on facial fea-tures.A dataset of facial images of autistic and non-autistic children was collected from the Kaggle data repository and was used to develop the transfer learning AlexNet(ASDDTLA)*** model achieved a detection accuracy of 87.7%and performed better than other established ASD detection ***,this model could facilitate the early detection of ASD in clinical practice.

关键词： Autism spectrum disorder convolutional neural network loss rate transfer learning AlexNet deep learning

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Diabetic patients and Ramadan fasting:how they live it?

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Asian Pacific Journal of Tropical Medicine 2008年第4期1卷 61-66页

作者： hamza Mohammad Abdulghani Shaffi Ahmed Shaikh Norah Abdullah Al-Rowais Eiad Abdulmohsin Alfaris Dept.of Family & Community Medicine College of MedicineKing Saud UniversityRiyadhKingdom of Saudi Arabia

Objective:To quantify the prevalence of fasting during Ramadan month among diabetic patients and to observe the factors which were deferring between fasting and non-fasting diabetic ***:A cross-sectional study was carried out at primary health care *** diabetic patients were interviewed with a structured data form by trained research ***’age,gender,marital status,weight and height,diabetes type,medication,compliance,follow up,self monitoring and any complication faced during the fasting were *** risk factors including smoking habit,hypertension,dyslipidemia and pre-existing cardiovascular diseases were also ***:Out of 920 diabetic patients,95%were suffering from DM type *** patients who fasted the whole month of Ramadan were 84%.The proportion of type 1 and type 2 was 71%and 85%*** 63%of DM type 2 patients were treated with oral hypoglycemic agents(OHAs) alone and 2.4%with combination of OHAs and *** 42%of DM type 1 and 36%of DM type 2 patients has visited their physicians prior to their fast for *** significant differing factors which make non-fasting in diabetic patients,were medication(insulin),complications,hyperglycemia,retinopathy, neuropathy,nephropathy and vascular ***:This study has identified the factors in diabetic patients,were being compliant with medication,oral treatments and family support which lead to more likely to fast in Ramadan *** which reduce fasting by these patients were acute and chronic complications,time since diagnosis and insulin *** would be acceptable for patients who are compliant with their diet and medication.

关键词： Diabetes mellitus Fasting,Ramadan Hypoglycemic agents Insulin Saudi Arabia

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