检索结果-南通市图书馆

Real-Time Multi-Class Infection Classification for Respiratory Diseases

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2022年第11期73卷 4157-4177页

作者： Ahmed El.Shafee Walid El-Shafai abdulaziz alarifi Mohammed Amoon Aman Singh Moustafa H.Aly Department of Electrical Engineering Faculty of EngineeringAhram Canadian UniversityGizaEgypt Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Computer Science Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Higher Polytechnic School Universidad Europea del AtlánticoSantander39011Spain Electronics and Communications Engineering Department College of Engineering and TechnologyArab Academy for ScienceTechnology and Maritime TransportAlexandria1029Egypt

Real-time disease prediction has emerged as the main focus of study in the field of computerized *** disease identification framework can assist medical practitioners in diagnosing disease in a way that is reliable,consistent,and timely,successfully lowering mortality rates,particularly during endemics and *** prevent this pandemic’s rapid and widespread,it is vital to quickly identify,confine,and treat affected *** need for auxiliary computer-aided diagnostic(CAD)systems has *** recent studies have indicated that radiological pictures contained critical information regarding the COVID-19 *** advanced convolutional neural network(CNN)architectures in conjunction with radiological imaging makes it possible to provide rapid,accurate,and extremely useful susceptible *** research work proposes a methodology for real-time detection of COVID-19 infections caused by the Corona *** purpose of this study is to offer a two-way COVID-19(2WCD)diagnosis prediction deep learning system that is built on Transfer Learning Methodologies(TLM)and features customized fine-tuning on top of fully connected layered pre-trained CNN architectures.2WCD has applied modifications to pre-trained models for better *** is designed and implemented to improve the generalization ability of the classifier for binary and multi-class *** with the ability to differentiate COVID-19 and No-Patient in the binary class model and COVID-19,No-Patient,and Pneumonia in the multi-class model,our framework is augmented with a critical add-on for visually demonstrating infection in any tested radiological image by highlighting the affected region in the patient’s lung in a recognizable color *** proposed system is shown to be extremely robust and reliable for real-time COVID-19 diagnostic *** can also be used to forecast other lung-related *** the system can assist medical practitioner

关键词： COVID-19 real-time computerized disease prediction intelligent disease identification framework CAD systems X-rays CT-scans CNN real-time detection of COVID-19 infections

Mental health status among COVID-19 patients survivors of critical illness in Saudi Arabia:A 6-month follow-up questionnaire study

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

World Journal of Clinical Cases 2024年第15期12卷 2560-2567页

作者： Abdullah M Alhammad Namareq F Aldardeer abdulaziz Alqahtani Mohammad H Aljawadi Bushra Alnefaie Raghad Alonazi Mansour Almuqbil Abdulrahman Alsaadon Rakan M Alqahtani Rashid Alballaa Bader Alshehri Mohammed I alarifi Fahad D Alosaimi Department of Clinical Pharmacy College of PharmacyKing Saud UniversityRiyadh 12372Saudi Arabia Department of Pharmacy Services King Faisal Specialist Hospital and Research CentreJeddah 21499Saudi Arabia Corporate Department of Pharmacy Services King Saud University Medical CityRiyadh 12372Saudi Arabia College of Pharmacy King Saud UniversityRiyadh 12372Saudi Arabia Department of Critical Care Medicine College of MedicineKing Saud UniversityRiyadh 12372Saudi Arabia Department of Psychiatry College of MedicineKing Saud UniversityRiyadh 12372Saudi Arabia

BACKGROUND Psychological assessment after intensive care unit(ICU)discharge is increasingly used to assess patients'cognitive and psychological ***,few studies have examined those who recovered from coronavirus disease 2019(COVID-19).There is a paucity of data from the Middle East assessing the post-ICU discharge mental health status of patients who had *** To evaluate anxiety and depression among patients who had severe *** This is a prospective single-center follow-up questionnaire-based study of adults who were admitted to the ICU or under ICU consultation for>24 h for *** patients were contacted via *** patient’s anxiety and depression six months after ICU discharge were assessed using the Hospital Anxiety and Depression Scale(HADS).The primary outcome was the mean HADS *** secondary outcomes were risk factors of anxiety and/or *** Patients who were admitted to the ICU because of COVID-19 were screened(n=518).Of these,48 completed the *** mean age was 56.3±17.2 *** patients(62.5%)were *** main comorbidities were endocrine(n=24,50%)and cardiovascular(n=21,43.8%)*** mean overall HADS score for anxiety and depression at 6 months post-ICU discharge was 11.4(SD±8.5).A HADS score of>7 for anxiety and depression was detected in 15 patients(30%)and 18 patients(36%),*** from the multivariable ordered logistic regression demonstrated that vasopressor use was associated with the development of anxiety and depression[odds ratio(OR)39.06,95% confidence interval:1.309-1165.8;P<0.05].CONCLUSION Six months after ICU discharge,30% of patients who had COVID-19 demonstrated a HADS score that confirmed anxiety and *** compare the psychological status of patients following an ICU admission(with vs without COVID-19),further studies are warranted.

关键词： COVID-19 Critically ill Anxiety Depression HADS Saudi Arabia

A Lightweight Intrusion Detection System Using Convolutional Neural Network and Long Short-Term Memory in Fog Computing

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2024年第9期80卷 4703-4728页

作者： Hawazen Alzahrani Tarek Sheltami abdulaziz Barnawi Muhammad Imam Ansar Yaser Computer Engineering Department Interdisciplinary Research Center of Smart Mobility and LogisticsKing Fahd University of Petroleum and MineralsDhahran31261Saudi Arabia Computer Engineering Department Interdisciplinary Research Center for Intelligent Secure SystemsKing Fahd University of Petroleum and MineralsDhahran31261Saudi Arabia Transportation Research Institute(IMOB) Hasselt UniversityHasselt3500Belgium

The Internet of Things(IoT)links various devices to digital services and significantly improves the quality of our ***,as IoT connectivity is growing rapidly,so do the risks of network vulnerabilities and *** interesting Intrusion Detection Systems(IDSs)are presented based on machine learning(ML)techniques to overcome this *** the resource limitations of fog computing environments,a lightweight IDS is *** paper introduces a hybrid deep learning(DL)method that combines convolutional neural networks(CNN)and long short-term memory(LSTM)to build an energy-aware,anomaly-based *** test this system on a recent dataset,focusing on reducing overhead while maintaining high accuracy and a low false alarm *** compare CICIoT2023,KDD-99 and NSL-KDD datasets to evaluate the performance of the proposed IDS model based on key metrics,including latency,energy consumption,false alarm rate and detection rate *** findings show an accuracy rate over 92%and a false alarm rate below 0.38%.These results demonstrate that our system provides strong security without excessive resource *** practicality of deploying IDS with limited resources is demonstrated by the successful implementation of IDS functionality on a Raspberry Pi acting as a Fog *** proposed lightweight model,with a maximum power consumption of 6.12 W,demonstrates its potential to operate effectively on energy-limited devices such as low-power fog nodes or edge *** prioritize energy efficiency whilemaintaining high accuracy,distinguishing our scheme fromexisting *** experiments demonstrate a significant reduction in false positives,ensuring accurate identification of genuine security threats while minimizing unnecessary alerts.

关键词： Intrusion detection fog computing CNN LSTM energy consumption

Static Analysis Techniques for Fixing Software Defects in MPI-Based Parallel Programs

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2024年第5期79卷 3139-3173页

作者： Norah Abdullah Al-Johany Sanaa Abdullah Sharaf Fathy Elbouraey Eassa Reem abdulaziz Alnanih Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Software Engineering and Distributed System Research Group King Abdulaziz UniversityJeddah21589Saudi Arabia

The Message Passing Interface (MPI) is a widely accepted standard for parallel computing on distributed ***, MPI implementations can contain defects that impact the reliability and performance of parallelapplications. Detecting and correcting these defects is crucial, yet there is a lack of published models specificallydesigned for correctingMPI defects. To address this, we propose a model for detecting and correcting MPI defects(DC_MPI), which aims to detect and correct defects in various types of MPI communication, including blockingpoint-to-point (BPTP), nonblocking point-to-point (NBPTP), and collective communication (CC). The defectsaddressed by the DC_MPI model include illegal MPI calls, deadlocks (DL), race conditions (RC), and messagemismatches (MM). To assess the effectiveness of the DC_MPI model, we performed experiments on a datasetconsisting of 40 MPI codes. The results indicate that the model achieved a detection rate of 37 out of 40 codes,resulting in an overall detection accuracy of 92.5%. Additionally, the execution duration of the DC_MPI modelranged from 0.81 to 1.36 s. These findings show that the DC_MPI model is useful in detecting and correctingdefects in MPI implementations, thereby enhancing the reliability and performance of parallel applications. TheDC_MPImodel fills an important research gap and provides a valuable tool for improving the quality ofMPI-basedparallel computing systems.

关键词： High-performance computing parallel computing software engineering software defect message passing interface deadlock

Congenital absence of the lateral meniscus:A case report

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

World Journal of Orthopedics 2024年第5期15卷 477-482页

作者： Hamad Ali Alkhunayfir abdulaziz Ali AlQahtani Abdulrahman Jalwi Korkoman Department of Orthopedic Surgery Prince Muhammed bin Abdulaziz HospitalRiyadh 61422Saudi Arabia Department of Orthopedic Surgery Prince Sultan Military Medical CityRiyadh 12233Saudi Arabia Department of Orthopedic Surgery University of BishaBisha 67714Saudi Arabia

BACKGROUND Congenital absence of the menisci is a rare anatomical variation characterized by the absence or underdevelopment of one or both menisci in the knee *** menisci are crucial in load distribution,joint stability,and shock *** the clinical presentation,diagnosis,and management of this condition is important for optimal patient *** SUMMARY A 27-year-old male with a long-standing history of knee pain underwent diagnostic arthroscopy,revealing a congenital absence of the *** patient's clinical findings,imaging results,surgical procedures,and pertinent images are *** case presents a unique aspect with the congenital absence of the meniscus,contributing valuable insights to the literature on rare anatomical *** This case of congenital absence of the menisci highlights the diagnostic challenges posed by rare *** diagnostic arthroscopy played a crucial role in identifying the absence of the meniscus and providing an explanation for the patient's persistent knee *** case underscores the importance of individualized treatment approaches,including physical therapy,for optimal management of rare meniscal *** research is warranted to explore effective management strategies for the aforementioned cases and to expand our knowledge of these rare conditions.

关键词： Lateral meniscus Arthroscopy Knee joint Menisci reformation Magnetic resonance imaging Case report

Prediction of Geopolymer Concrete Compressive Strength Using Convolutional Neural Networks

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computer Modeling in Engineering & Sciences 2024年第5期139卷 1455-1486页

作者： Kolli Ramujee Pooja Sadula Golla Madhu Sandeep Kautish abdulaziz S.Almazyad Guojiang Xiong Ali Wagdy Mohamed Department of Civil Engineering VNR Vignana Jyothi Institute of Engineering and TechnologyHyderabadTelangana500090India Department of Information Technology VNR Vignana Jyothi Institute of Engineering and TechnologyHyderabadTelangana500090India Department of Computer Science LBEF Campus(Asia Pacific University of Technology&InnovationMalaysia)Kathmandu44600Nepal Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical EngineeringGuizhou UniversityGuiyang550025China Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmmanJordan

Geopolymer concrete emerges as a promising avenue for sustainable development and offers an effective solution to environmental *** attributes as a non-toxic,low-carbon,and economical substitute for conventional cement concrete,coupled with its elevated compressive strength and reduced shrinkage properties,position it as a pivotal material for diverse applications spanning from architectural structures to transportation *** this context,this study sets out the task of using machine learning(ML)algorithms to increase the accuracy and interpretability of predicting the compressive strength of geopolymer concrete in the civil engineering *** achieve this goal,a new approach using convolutional neural networks(CNNs)has been *** study focuses on creating a comprehensive dataset consisting of compositional and strength parameters of 162 geopolymer concrete mixes,all containing Class F fly *** selection of optimal input parameters is guided by two distinct *** first criterion leverages insights garnered from previous research on the influence of individual features on compressive *** second criterion scrutinizes the impact of these features within the model’s predictive *** to enhancing the CNN model’s performance is the meticulous determination of the optimal *** a systematic trial-and-error process,the study ascertains the ideal number of epochs for data division and the optimal value of k for k-fold cross-validation—a technique vital to the model’s *** model’s predictive prowess is rigorously assessed via a suite of performance metrics and comprehensive score ***,the model’s adaptability is gauged by integrating a secondary dataset into its predictive framework,facilitating a comparative evaluation against conventional prediction *** unravel the intricacies of the CNN model’s learning trajectory,a loss plot is deployed to elucidate its learning rat

关键词： Class F fly ash compressive strength geopolymer concrete prediction deep learning convolutional neural network

Improving Prediction of Chronic Kidney Disease Using KNN Imputed SMOTE Features and TrioNet Model

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computer Modeling in Engineering & Sciences 2024年第6期139卷 3513-3534页

作者： Nazik Alturki abdulaziz Altamimi Muhammad Umer Oumaima Saidani Amal Alshardan Shtwai Alsubai Marwan Omar Imran Ashraf Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department College of Computer Science and Engineering University of Hafr Al-BatinHafarAl-Batin39524Saudi Arabia Department of Computer Science&Information Technology The Islamia University of BahawalpurP.O.Box 63100BahawalpurPakistan Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityP.O.Box 151Al-Kharj11942Saudi Arabia Information Technology and Management Illinois Institute of TechnologyChicagoIL 60616-3793USA Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea

Chronic kidney disease(CKD)is a major health concern today,requiring early and accurate *** learning has emerged as a powerful tool for disease detection,and medical professionals are increasingly using ML classifier algorithms to identify CKD *** study explores the application of advanced machine learning techniques on a CKD dataset obtained from the University of California,UC Irvine Machine Learning *** research introduces TrioNet,an ensemble model combining extreme gradient boosting,random forest,and extra tree classifier,which excels in providing highly accurate predictions for ***,K nearest neighbor(KNN)imputer is utilized to deal withmissing values while synthetic minority oversampling(SMOTE)is used for class-imbalance *** ascertain the efficacy of the proposed model,a comprehensive comparative analysis is conducted with various machine learning *** proposed TrioNet using KNN imputer and SMOTE outperformed other models with 98.97%accuracy for *** in-depth analysis demonstrates the model’s capabilities and underscores its potential as a valuable tool in the diagnosis of CKD.

关键词： Precisionmedicine chronic kidney disease detection SMOTE missing values healthcare KNNimputer ensemble learning

Migration of varicocele coil leading to ureteral obstruction and hydronephrosis:A case report

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

World Journal of Clinical Cases 2024年第16期12卷 2856-2861页

作者： abdulaziz Alamri Department of Surgery College of MedicineKing Khalid UniversityAbhaAlfaraa 61421Saudi Arabia

BACKGROUND Varicocele embolization,a minimally invasive treatment for symptomatic varicoceles,carries a rare risk of complications like ureteral obstruction and *** case report documents such a case to raise awareness of these potential complications and showcase minimally invasive surgical management as a successful *** SUMMARY A 35-year-old male presented with flank pain and hematuria following varicocele *** confirmed left ureteral obstruction and *** ureterolysis successfully removed the embolization coil and repaired the ureter,resolving the patient's ***-up at six months and two years showed sustained *** Minimally invasive surgery offers an effective treatment option for rare complications like ureteral obstruction arising from varicocele embolization.

关键词： Varicocele embolization Ureteral obstruction Hydronephrosis Iatrogenic ureteral injury Laparoscopic ureterolysis Minimally invasive surgery Case report

BHJO: A Novel Hybrid Metaheuristic Algorithm Combining the Beluga Whale, Honey Badger, and Jellyfish Search Optimizers for Solving Engineering Design Problems

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computer Modeling in Engineering & Sciences 2024年第10期141卷 219-265页

作者： Farouq Zitouni Saad Harous abdulaziz S.Almazyad Ali Wagdy Mohamed Guojiang Xiong Fatima Zohra Khechiba Khadidja Â Kherchouche Department of Computer Science and Information Technology Kasdi Merbah UniversityP.O.Box 30000OuarglaAlgeria Department of Computer Science College of Computing and InformaticsUniversity of SharjahP.O.Box 27272SharjahUnited Arab Emirates Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11937Jordan Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical EngineeringGuizhou UniversityGuiyang550025China

Hybridizing metaheuristic algorithms involves synergistically combining different optimization techniques to effectively address complex and challenging optimization *** approach aims to leverage the strengths of multiple algorithms,enhancing solution quality,convergence speed,and robustness,thereby offering a more versatile and efficient means of solving intricate real-world optimization *** this paper,we introduce a hybrid algorithm that amalgamates three distinct metaheuristics:the Beluga Whale Optimization(BWO),the Honey Badger Algorithm(HBA),and the Jellyfish Search(JS)*** proposed hybrid algorithm will be referred to as *** this fusion,the BHJO algorithm aims to leverage the strengths of each *** this hybridization,we thoroughly examined the exploration and exploitation capabilities of the BWO,HBA,and JS metaheuristics,as well as their ability to strike a balance between exploration and *** meticulous analysis allowed us to identify the pros and cons of each algorithm,enabling us to combine them in a novel hybrid approach that capitalizes on their respective strengths for enhanced optimization *** addition,the BHJO algorithm incorporates Opposition-Based Learning(OBL)to harness the advantages offered by this technique,leveraging its diverse exploration,accelerated convergence,and improved solution quality to enhance the overall performance and effectiveness of the hybrid ***,the performance of the BHJO algorithm was evaluated across a range of both unconstrained and constrained optimization problems,providing a comprehensive assessment of its efficacy and applicability in diverse problem ***,the BHJO algorithm was subjected to a comparative analysis with several renowned algorithms,where mean and standard deviation values were utilized as evaluation *** rigorous comparison aimed to assess the performance of the BHJOalgorithmabout its counterparts,shedd

关键词： Global optimization hybridization of metaheuristics beluga whale optimization honey badger algorithm jellyfish search optimizer chaotic maps opposition-based learning