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Automatic Finding of Brain-Tumour Group Using CNN Segmentation and Moth-Flame-algorithm,Selected Deep and Handcrafted Features

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Computers, Materials & Continua 2024年第5期79卷 2585-2608页

作者： imad saud al naimi Syed alwee aljunid Syed Junid Muhammad lmran Ahmad K.Suresh Manic Faculty of Electronic Engineering Technology University Malaysia Perils(UniMAP)Pauh Putra CampusArauPerlis02600Malaysia Department of Electrical and Communication Engineering National University of Science and TechnologyAL HailAL Seeb130Sultanate of Oman Department of Research and Innovation Saveetha School of EngineeringSaveetha Institute of Medical and Technical Sciences(SIMATS)ChennaiTamiNadu602105India

Augmentation of abnormal cells in the brain causes brain tumor(BT),and early screening and treatmentwill reduce its harshness in ***’s clinical level screening is usually performed with Magnetic Resonance Imaging(MRI)due to its multi-modality *** overall aims of the study is to introduce,test and verify an advanced image processing technique with algorithms to automatically extract tumour sections from brain MRI scans,facilitating improved *** research intends to devise a reliable framework for detecting the BT region in the twodimensional(2D)MRI slice,and identifying its class with improved *** methodology for the devised framework comprises the phases of:(i)Collection and resizing of images,(ii)Implementation and Segmentation of Convolutional Neural Network(CNN),(iii)Deep feature extraction,(iv)Handcrafted feature extraction,(v)Moth-Flame-algorithm(MFA)supported feature reduction,and(vi)Performance *** study utilized clinical-grade brain MRI of BRATS and TCIA datasets for the *** framework segments detected the glioma(low/high grade)and glioblastoma class *** work helped to get a segmentation accuracy of over 98%with VGG-UNet and a classification accuracy of over 98%with the VGG16 *** study has confirmed that the implemented framework is very efficient in detecting the BT in MRI slices with/without the skull section.

关键词： Brain tumour VGG-UNet VGG16 moth-flame-algorithm classification

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Deep Learning Approach for Hand Gesture Recognition:Applications in Deaf Communication and Healthcare

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Computers, Materials & Continua 2024年第1期78卷 127-144页

作者： Khursheed Aurangzeb Khalid Javeed Musaed alhussein imad Rida Syed Irtaza Haider Anubha Parashar Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Kingdom of Saudi Arabia Department of Computer Engineering College of Computing and InformaticsUniversity of SharjahSharjah27272United Arab Emirates Laboratory Biomechanics and Bioengineering University of Technology of CompiegneCompiegne60200France Department of Computer Science and Engineering Manipal University JaipurJaipur303007India

Hand gestures have been used as a significant mode of communication since the advent of human *** facilitating human-computer interaction(HCI),hand gesture recognition(HGRoc)technology is crucial for seamless and error-free *** technology is pivotal in healthcare and communication for the deaf *** significant advancements in computer vision-based gesture recognition for language understanding,two considerable challenges persist in this field:(a)limited and common gestures are considered,(b)processing multiple channels of information across a network takes huge computational time during discriminative feature ***,a novel hand vision-based convolutional neural network(CNN)model named(HVCNNM)offers several benefits,notably enhanced accuracy,robustness to variations,real-time performance,reduced channels,and ***,these models can be optimized for real-time performance,learn from large amounts of data,and are scalable to handle complex recognition tasks for efficient human-computer *** proposed model was evaluated on two challenging datasets,namely the Massey University Dataset(MUD)and the American Sign Language(ASL)alphabet Dataset(ASLAD).On the MUD and ASLAD datasets,HVCNNM achieved a score of 99.23% and 99.00%,*** results demonstrate the effectiveness of CNN as a promising HGRoc *** findings suggest that the proposed model have potential roles in applications such as sign language recognition,human-computer interaction,and robotics.

关键词： Computer vision deep learning gait recognition sign language recognition machine learning

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A Machine Learning-Based Attack Detection and Prevention System in Vehicular Named Data Networking

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Computers, Materials & Continua 2023年第11期77卷 1445-1465页

作者： Arif Hussain Magsi ali Ghulam Saifullah Memon Khalid Javeed Musaed alhussein imad Rida State Key Laboratory of Networking and Switching Technology Beijing University of Post and TelecommunicationBeijing100876China Information Technology Center Sindh Agriculture UniversityTandojam70050Pakistan Department of Computer Engineering College of Computing and InformaticsUniversity of SharjahSharjah27272United Arab Emirate Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Laboratory Biomechanics and Bioengineering University of Technology of CompiegneCompiegne60200France

Named Data Networking(NDN)is gaining a significant attention in Vehicular Ad-hoc Networks(VANET)due to its in-network content caching,name-based routing,and mobility-supporting ***,existing NDN faces three significant challenges,including security,privacy,and *** particular,security attacks,such as Content Poisoning Attacks(CPA),can jeopardize legitimate vehicles with malicious *** instance,attacker host vehicles can serve consumers with invalid information,which has dire consequences,including road *** such a situation,trust in the content-providing vehicles brings a new *** the other hand,ensuring privacy and preventing unauthorized access in vehicular(VNDN)is another ***,NDN’s pull-based content retrieval mechanism is inefficient for delivering emergency messages in *** this connection,our contribution is *** existing rule-based reputation evaluation,we propose a Machine Learning(ML)-based reputation evaluation mechanism that identifies CPA attackers and legitimate *** on ML evaluation results,vehicles accept or discard served ***,we exploit a decentralized blockchain system to ensure vehicles’privacy by maintaining their information in a secure digital ***,we improve the default routing mechanism of VNDN from pull to a push-based content dissemination using Publish-Subscribe(Pub-Sub)*** implemented and evaluated our ML-based classification model on a publicly accessible BurST-Asutralian dataset for Misbehavior Detection(BurST-ADMA).We used five(05)hybrid ML classifiers,including Logistic Regression,Decision Tree,K-Nearest Neighbors,Random Forest,and Gaussian Naive *** qualitative results indicate that Random Forest has achieved the highest average accuracy rate of 100%.Our proposed research offers the most accurate solution to detect CPA in VNDN for safe,secure,and reliable vehicle communication.

关键词： Named data networking vehicular networks reputation caching machine-learning

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The Impact of a Structured Clinical Pathway on the Application of Management Standards in Patients with Diabetic Ketoacidosis and Its Acceptability by Medical Residents

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Journal of Diabetes Mellitus 2014年第4期4卷 264-272页

作者： imad S. Hassan Azzam D. al-Otaibi Meteb M. al-Bugami Salih Bin Salih Yousef al Saleh Salman Abdulaziz Department of Medicine & King saud bin Abdulaziz University for Health Sciences Riyadh KSA Department of Medicine King Abdulaziz Medical City Riyadh KSA Nephrology Dalhousie University Nova Scotia Canada Division of Endocrinology & King saud bin Abdulaziz University for Health Sciences Riyadh KSA Internal Medicine & Critical Care Rashid Hospital Dubai UAE

Diabetic Ketoacidosis (DKA) is a serious and potentially a fatal complication of diabetes mellitus. Tools to guarantee proper, evidence-based, guideline implementation are of paramount importance and an essential element for quality patient care. Clinical pathways represent one such tool that clearly promotes the implementation of guidelines and research evidence into clinical practice. The aims of this study were to measure quantitatively and qualitatively the impact of a specially structured Resident-friendly, DKA clinical pathway on the application of evidence-based management standards and its acceptability by the treating resident physicians. A retrospective chart review of patients who were admitted prior to and after the launching of the clinical pathway and a questionnaire assessment of resident’s acceptance of the pathway format were undertaken. Eighty one episodes of DKA in a total of 58 patients fulfilled the criteria for inclusion in the study. Thirty seven admissions were on the pathway (45.7%) and 44 were not (54.3%). Documentation of severity indices of patients who were admitted under the pathway were significantly improved with a trend for a shorter hospital stay. The duration of intravenous insulin therapy, intensive care unit consultation and diabetes educator involvement in patient care were not different between the two groups. Residents found the pathway user-friendly, educationally very valuable, reduced their workload and had a positive effect on their DKA management skills.?Conclusions: Use of specially structured, resident-friendly pathway led to significant improvement in documentation of DKA severity indices and empowered our residents with evidence-based knowledge and skills to deal with this serious diabetic complication.

关键词： Diabetic Ketoacidosis Clinical Pathway Trainees Quality

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Accuracy of urea breath test in Helicobacter pylori infection: Meta-analysis

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World Journal of Gastroenterology 2015年第4期21卷 1305-1314页

作者： Mazen Ferwana imad Abdulmajeed ali alhajiahmed Wedad Madani Belal Firwana Rim Hasan Osama altayar Paul J Limburg Mohammad Hassan Murad Bandar Knawy National and Gulf Center for Evidence-Based Health Practice King Saud Bin Abdulaziz University for Health SciencesRiyadh 11426Saudi Arabia Department of Medicine University of Missouri ColumbiaMissouriMO 65211United States Knowledge and Evaluation Research Unit Mayo ClinicRochesterMN 65212United States Knowledge and Evaluation Research Unit Mayo ClinicRochesterMN 55905United States Department of Internal Medicine Allegheny General HospitalPittsburgPA 55905United States Division of Gastroenterology Mayo ClinicRochesterMN 55905United States Division of Preventive Medicine Mayo ClinicRochesterMN 55905United States

AIM: To quantitatively summarize and appraise the available evidence of urea breath test(UBT) use to diagnose Helicobacter pylori(H. pylori) infection in patients with dyspepsia and provide pooled diagnostic accuracy ***: We searched MEDLINE, EMBASE, Cochrane library and other databases for studies addressing the value of UBT in the diagnosis of H. pylori infection. We included cross-sectional studies that evaluated the diagnostic accuracy of UBT in adult patients with dyspeptic symptoms. Risk of bias was assessed using QUADAS(Quality Assessment of Diagnostic Accuracy Studies)-2 tool. Diagnostic accuracy measures were pooled using the random-effects model. Subgroup analysis was conducted by UBT type(13C vs 14C) and by measurement technique(Infrared spectrometry vs Isotope Ratio Mass Spectrometry).RESULTS: Out of 1380 studies identified, only 23 met the eligibility criteria. Fourteen studies(61%) evaluated 13 C UBT and 9 studies(39%) evaluated 14 C UBT. There was significant variation in the type of reference standard tests used across *** sensitivity was 0.96(95%CI: 0.95-0.97) andpooled specificity was 0.93(95%CI: 0.91-0.94). Likelihood ratio for a positive test was 12 and for a negative test was 0.05 with an area under thecurve of 0.985. Meta-analyses were associated with a significant statistical heterogeneity that remained unexplained after subgroup analysis. The included studies had a moderate risk of ***: UBT has high diagnostic accuracy for detecting H. pylori infection in patients with dyspepsia. The reliability of diagnostic meta-analytic estimates however is limited by significant heterogeneity.

关键词： Helicobacter pylori Dyspepsia Breath tests Urea/an

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