检索结果-内蒙古大学图书馆

Correction: Modeling of concrete-filled PVC tube columns confined with CFRP strips under uniaxial eccentric compression: machine learning and finite element approaches

引用

Journal of Big Data 2025年第1期12卷 1-2页

作者： Wang, Yan Elmeligy, Mohamed A. Isleem, Haytham F. Hamed, Asmaa Y. Qader, Diyar N. Sharaf, Mohamed Jangir, Pradeep Arpita Tejani, Ghanshyam G. School of Mechanical and Electrical Engineering Guilin University of Electronic Technology Guilin China Advanced Manufacturing Institute King Saud University Riyadh Saudi Arabia Jadara University Research Center Jadara University Irbid Jordan Department of Computer Science University of York York UK Construction and Building Department The Higher Institute of Engineering and Technology Luxor Luxor Egypt Civil Engineering Department University of Kirkuk Kirkuk Iraq Industrial Engineering Department College of Engineering King Saud University Riyadh Saudi Arabia University Centre for Research and Development Chandigarh University Gharuan Mohali India Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan Department of CSE Graphic Era Hill University Dehradun India Graphic Era Deemed Department of CSE To Be University Dehradun India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Industrial Engineering and Management Yuan Ze University Taoyuan Taiwan Applied Science Research Center Applied Science Private University Amman Jordan

来源：评论

学校读者我要写书评

暂无评论

Blockchain Assisted Intrusion Detection System Using Differential Flower Pollination Model

引用

computers, Materials & Continua 2022年第12期73卷 4695-4711页

作者： Mohammed altaf Ahmed Sara A althubiti Dronamraju Nageswara Rao ELaxmi Lydia Woong Cho Gyanendra Prasad Joshi Sung Won Kim Department of Computer Engineering College of Computer Engineering&SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Computer Science and Engineering Chitkara University Institute of Engineering and TechnologyChitkara UniversityPunjab140401India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Software Convergence Daegu Catholic UniversityGyeongsan38430Korea Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Information and Communication Engineering Yeungnam UniversityGyeongsan-siGyeongbuk-do38541Korea

Cyberattacks are developing gradually sophisticated,requiring effective intrusion detection systems(IDSs)for monitoring computer resources and creating reports on anomalous or suspicious *** the popularity of Internet of Things(IoT)technology,the security of IoT networks is developing a vital *** of the huge number and varied kinds of IoT devices,it can be challenging task for protecting the IoT framework utilizing a typical *** typical IDSs have their restrictions once executed to IoT networks because of resource constraints and ***,this paper presents a new Blockchain Assisted Intrusion Detection System using Differential Flower Pollination with Deep Learning(BAIDS-DFPDL)model in IoT *** presented BAIDS-DFPDLmodelmainly focuses on the identification and classification of intrusions in the IoT *** accomplish this,the presented BAIDS-DFPDL model follows blockchain(BC)technology for effective and secure data transmission among the ***,the presented BAIDSDFPDLmodel designs Differential Flower Pollination based feature selection(DFPFS)technique to elect ***,sailfish optimization(SFO)with Restricted Boltzmann Machine(RBM)model is applied for effectual recognition of *** simulation results on benchmark dataset exhibit the enhanced performance of the BAIDS-DFPDL model over other models on the recognition of intrusions.

关键词： Internet of things feature selection intrusion detection blockchain security deep learning

来源：评论

学校读者我要写书评

暂无评论

Drug repurposing targeting COVID-19 3CL protease using molecular docking and machine learning regression approaches

引用

Scientific Reports 2025年第1期15卷 1-19页

作者： Imra Aqeel Abdul Majid Abdullah albanyan Hassan Wasfi Biomedical Information Research Lab Department of Computer & Information Sciences Pakistan Institute of Engineering & Applied Sciences Nilore Islamabad 45650 Pakistan College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Al-Kharj Saudi Arabia Department of Information Technology Faculty of Computing and Information Technology in Rabigh King Abdulaziz University Rabigh 21911 Saudi Arabia

The COVID-19 pandemic has initiated a global health emergency, with an exigent need for an effective cure. Progressively, drug repurposing is emerging as a promising solution for saving time, cost, and labor. However, the number of drug candidates that have been identified for the treatment of COVID-19 is still insufficient, so more effective and thorough drug exploration strategies are required. In this study, we joined the molecular docking with machine learning approaches to find some prospective therapeutic candidates for COVID-19 treatment. We screened the 5903 approved drugs for their inhibition by targeting the replicating enzyme3CLproof SARS-CoV-2. Molecular docking is used to calculate the binding affinities of these drugs towards3CLpro. We employed several machine learning approaches for QSAR modeling to explore some potential drugs with high binding affinities. Our outcomes demonstrated that the Decision Tree Regression (DTR) model, with the best scores of R² and RMSE, is the most suitable model to explore the potential drugs. We shortlisted six favorable drugs with their respective Zinc IDs(3873365,85432544,203757351,85536956,8214470,and 261494640)within the range of -15 kcal/mol to -13 kcal/mol. We further examined the physiochemical and pharmacokinetic properties of these most potent drugs. Our study provides an efficient framework to explore the potential drugs against COVID-19 and establishes the impending combination of molecular docking with machine learning approaches to accelerate the identification of potential therapeutic candidates. Our verdicts contribute to the larger goal of finding effective cures for COVID-19, which is an acute global health challenge. The outcomes of our study provide valuable insights into potential therapeutic candidates for COVID-19 treatment.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Economical Requirements Elicitation Techniques During COVID-19: A Systematic Literature Review

引用

computers, Materials & Continua 2021年第5期67卷 2665-2680页

作者： Tauqeer ul Amin Basit Shahzad Fazal-e-Amin Muhammad Shoaib Department of Software Engineering National University of Modern LanguagesIslamabadPakistan Department of Software Engineering College of Computer and Information SciencesKing Saud UniversityRiyadhSaudi Arabia

Requirements elicitation is a fundamental phase of software development in which an analyst discovers the needs of different stakeholders and transforms them into *** phase is cost-and time-intensive,and a project may fail if there are excessive costs and schedule ***-19 has affected the software industry by reducing interactions between developers and *** a lack of interaction is a key reason for the failure of software *** can also fail when customers do not know precisely what they ***,selecting the unsuitable elicitation technique can also cause project *** present study,therefore,aimed to identify which requirements elicitation technique is the most cost-effective for large-scale projects when time to market is a critical issue or when the customer is not *** that end,we conducted a systematic literature review on requirements elicitation *** primary studies identified introspection as the best technique,followed by survey and *** finding suggests that introspection should be the first choice of elicitation technique,especially when the customer is not available or the project has strict time and cost ***,introspection should also be used as the starting point in the elicitation process of a large-scale project,and all known requirements should be elicited using this technique.

关键词： COVID-19 software engineering requirements engineering requirements elicitation introspection project failure SLR on elicitation techniques large-scale projects economical elicitation techniques costeffective time constraints

来源：评论

学校读者我要写书评

暂无评论

Automated Test Case Generation from Requirements: A Systematic Literature Review

引用

computers, Materials & Continua 2021年第5期67卷 1819-1833页

作者： Ahmad Mustafa Wan M.N.Wan-Kadir Noraini Ibrahim Muhammad Arif Shah Muhammad Younas Atif Khan Mahdi Zareei Faisal alanazi Department of Software Engineering School of ComputingUniversiti Teknologi Malaysia Johor BahruJohor81310Malaysia Department of Computer Science Government College UniversityFaisalabad38000Pakistan Department of Software Engineering Pak-Austria Fachhochschule Institute of Applied Sciences and TechnologyHaripur22620Pakistan Department of Computer Science Islamia College PeshawarPeshawarPakistan Tecnologico de Monterrey School of Engineering and SciencesZapopan45201Mexico Department of Electrical Engineering Prince Sattam Bin Abdulaziz UniversityAl Kharj11942Saudi Arabia

software testing is an important and cost intensive activity in software *** major contribution in cost is due to test case ***-based testing is an approach in which test cases are derivative from requirements without considering the implementation’s internal ***-based testing includes functional and nonfunctional *** objective of this study is to explore the approaches that generate test cases from requirements.A systematic literature review based on two research questions and extensive quality assessment criteria includes *** study identies 30 primary studies from 410 studies spanned from 2000 to *** review’s nding shows that 53%of journal papers,42%of conference papers,and 5%of book chapters’address requirementsbased *** of the studies use UML,activity,and use case diagrams for test case generation from *** of the signicant lessons learned is that most software testing errors are traced back to errors in natural language requirements.A substantial amount of work focuses on UML diagrams for test case generations,which cannot capture all the system’s developed ***,there is a lack of UML-based models that can generate test cases from natural language requirements by rening them in *** criteria indicate how efciently the testing has been performed 12.37%of studies use requirements coverage,20%of studies cover path coverage,and 17%study basic coverage.

关键词： Test case generation functional testing techniques requirementsbased test case generation system testing natural language requirement requirements tractability coverage criteria

来源：评论

学校读者我要写书评

暂无评论

Reinforcement Learning-Based Integrated Risk Aware Dynamic Treatment Strategy for Consumer-Centric Next-Gen Healthcare

引用

IEEE Transactions on Consumer Electronics 2025年

作者： Nimma, Divya Rao, Pinapati Lakshmana Ramesh, Janjhyam Venkata Naga Dahan, Fadl Reddy, Desidi Narsimha Selvakumar, Venkatachalam Ilkhamova, Yodgorkhon Jangir, Pradeep UMMC United States Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Guntur Vaddeswaram522302 India Graphic Era Hill University Department of CSE Dehradun248002 India Graphic Era Deemed To Be University Department of CSE Uttarakhand Dehradun248002 India Prince Sattam Bin Abdulaziz University Department of Management Information Systems College of Business Administration Hawtat Bani Tamim Al-Kharj11942 Saudi Arabia Soniks consulting LLC Soniks consulting Data Consultant Telangana E park blvd India Bhavan’s Vivekananda College of Science Humanities and Commerce Department of Maths and Statistics Telangana Hyderabad India Tashkent State University of Economics Department of Digital Economy Tashkent Uzbekistan Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Biosciences Chennai602 105 India Al-Ahliyya Amman University Hourani Center for Applied Scientific Research Amman19328 Jordan

Reinforcement learning (RL) has gained prominence in healthcare due to its ability to optimize treatment strategies without relying on predefined mathematical models. However, existing approaches face critical challenges: (1) the optimality of learned strategies is assessed without considering treatment risks, potentially leading to unsafe recommendations;(2) distribution shift issues cause learned strategies to diverge from physician decisions;and (3) past observational data and treatment history are often overlooked, leading to suboptimal state representations. To address these limitations, we propose a Dynamic Treatment Strategy Generation Model that integrates Dead Ends with an Offline Supervised Actor-Critic approach (DOSAC-DTR). Our model incorporates Dead Ends into the Actor-Critic framework to evaluate risks associated with recommended treatments. Additionally, physician oversight is embedded to mitigate distribution shift and align the learned strategy with expert decisions while maximizing expected outcomes. To enhance state representation, we employ an LSTM-based encoder-decoder model to capture essential patient history, ensuring robust decision-making. Experimental results on real-world datasets (MIMIC-III) demonstrate that DOSAC-DTR significantly reduces mortality rates (Sepsis: 3.51%, Ventilation: 13.74%) and improves treatment alignment with physicians (Jaccard similarity: 0.362, 0.126) compared to baseline models. These findings underscore the potential of reinforcement learning in personalized healthcare, improving both treatment efficacy and patient safety. © 1975-2011 IEEE.

关键词： Risk assessment

来源：评论

学校读者我要写书评

暂无评论

Transfontanelle Thermoacoustic Imaging of Intraventricular Brain Hemorrhages in Live Sheep

引用

IEEE Transactions on Medical Imaging 2025年 PP卷 PP页

作者： Islam, Md Tarikul Benavides, Juliana Prakash, Ravi Zafar, Mohsin McGuire, Laura Charbel, Fady Siegel, Amanda P. Erricolo, Danilo Lin, James Gelovani, Juri G. Avanaki, Kamran University of Illinois Chicago Richard and Loan Hill Department of Biomedical Engineering United States Neurosurgery Clinic University of Illinois Chicago United States University of Illinois Health Department of Neurosurgery United States University of Illinois Chicago Department of Electrical and Computer Engineering United States United Arab Emirates University College of Medicine and Health Sciences Al Ain United Arab Emirates Wayne State University Department of Biomedical Engineering DetroitMI48201 United States University of Illinois Chicago Richard and Loan Hill Department of Biomedical Engineering Departments of Dermatology and Pediatrics ChicagoIL60607 United States

Preterm neonates are vulnerable to periventricular-intraventricular hemorrhage since the periventricular germinal matrix blood vessels are still immature and fragile until around 36 weeks. While cranial ultrasound imaging (cUS) is the standard technique for diagnosing brain injury in neonates, it has limited accuracy in detecting early-stage hemorrhages due to poor sensitivity and specificity. Transfontanelle thermoacoustic imaging (TTAI) combines the advantages of high contrast from electromagnetic absorption with high-resolution from ultrasound imaging and represents a potential alternative to overcome the limitations of conventional cUS systems. We developed a TTAI system and evaluated its performance in a large animal model of intraventricular hemorrhage (IVH) in vivo. Our system demonstrated the ability to detect hemorrhages as small as 0.1 mL, which is better than the previously reported limit of detection for either ultrasound or photoacoustic imaging in the same animal model. These results indicate that TTAI is a novel imaging modality with high potential for accurate detection of IVH in neonates. © 1982-2012 IEEE.

关键词： Ultrasonography

来源：评论

学校读者我要写书评

暂无评论

Retraction notice to "Y2O3 reinforced B2O3–SiO2–BaO–Na2O glass system: A characterization study through physical, optical and gamma / neutron shields characteristics" [Opt. Mater. 152C (2024)/115512] (Optical Materials (2024) 152, (S0925346724006955), (10.1016/***.2024.115512))

引用

Optical Materials 2025年 165卷

作者： alsafi, Khalid Zakaly, Hesham M.H. aloraini, Dalal Abdullah al-Saleh, Wafa M. almutairi, Haifa M. Issa, Shams A.M. Shaaban, Kh S. Department of Radiology Medical Physics Unit Faculty of Medicine King Abdulaziz University Jeddah Saudi Arabia Medical Physics Unit Diagnostic Imaging Department King Abdulaziz University Hospital Jeddah Saudi Arabia Physics Department Faculty of Science Al-Azhar University Assiut Branch Egypt Istinye University Faculty of Engineering and Natural Sciences Computer Engineering Department Istanbul34396 Turkey Institute of Physics and Technology Ural Federal University Yekaterinburg620002 Russia Department of Physics College of Science Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia College of Science and Health Professions King Saud Bin Abdulaziz University for Health Sciences Al-Ahsa P.O. Box 6664 Al-Ahsa Hofuf31982 Saudi Arabia King Abdullah International Medical Research Center Al-Ahsa Hofuf Saudi Arabia Department of Physics Faculty of Sciences Umm AL-Qura University Mecca24382 Saudi Arabia Physics Department University of Tabuk Tabuk47512 Saudi Arabia Department of Chemistry Faculty of Science Al - Azhar University P.O. 71524 Assiut Egypt

This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://***/locate/withdrawalpolicy). Upon request of the Editor-in-Chief seven articles have been retracted, among which is this article: "through physical, optical and gamma/neutron shields characteristics,Vol.152,115512,June 2024". The XRD traces of selected samples, including across different publications reporting different materials, is exact to the level of the instrument noise. This exactness is not physically possible and indicates that the same data was used for different samples. Therefore, the EiC no longer has confidence in the authenticity of the data. In accordance with the journal's publishing policy, these articles are retracted. The authors disagree with the retraction and dispute the grounds for it. © 2025

关键词：

来源：评论

学校读者我要写书评

暂无评论

Fog-assisted healthcare framework for smart hospital environment

引用

Personal and Ubiquitous Computing 2024年第3-4期28卷 599-613页

作者： Ahanger, Tariq Ahamed aldaej, Abdulaziz alharbi, Yousef Department of Management Information Systems College of Business Administration Prince Sattam Bin Abdulaziz University Al-Kharj11942 Saudi Arabia College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia Department of Biomedical Technology College of Applied Medical Sciences Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia

The technological revolution brought by the Internet of Things (IoT) has mostly relied on cloud computing. However, to satisfy the demands of time-sensitive services in the medical industry, fog computing, a novel computational platform based on the cloud computing paradigm, has shown to be a useful tool by extending cloud resources to the network’s edge. The current paper examines the role of the fog paradigm in the domain of healthcare decision-making, focusing on its primary advantages in terms of latency, network utilization, and power consumption. A fog-computing-based health assessment framework is developed in the current paper. Moreover, based on effective performance parameters, the performance is evaluated and depicted. The results show that the presented strategy can reduce network congestion of the communication network by analyzing information at the local node. Moreover, increased security on health information can be maintained at local fog node, and enhanced data protection from unauthorized access can be acquired. Fog computing offers greater insights into the health condition of patients with enhanced accuracy, precision, reliability, and stability. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Fog computing

来源：评论

学校读者我要写书评

暂无评论

Fruit Leaf Diseases Classification: A Hierarchical Deep Learning Framework

引用

computers, Materials & Continua 2023年第4期75卷 1179-1194页

作者： Samra Rehman Muhammad Attique Khan Majed alhaisoni Ammar Armghan Fayadh alenezi Abdullah alqahtani Khean Vesal Yunyoung Nam Department of Computer Science HITEC UniversityTaxila47080Pakistan Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Electrical Engineering College of EngineeringJouf UniversitySakakah72311Saudi Arabia College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAl-Kharj16242Saudi Arabia Department of ICT Convergence Soonchunhyang UniversityAsan31538Korea

Manual inspection of fruit diseases is a time-consuming and costly because it is based on naked-eye *** authors present computer vision techniques for detecting and classifying fruit leaf *** of computer vision techniques are preprocessing original images for visualization of infected regions,feature extraction from raw or segmented images,feature fusion,feature selection,and *** following are the major challenges identified by researchers in the literature:(i)lowcontrast infected regions extract irrelevant and redundant information,which misleads classification accuracy;(ii)irrelevant and redundant information may increase computational time and reduce the designed model’s *** paper proposed a framework for fruit leaf disease classification based on deep hierarchical learning and best feature *** the proposed framework,contrast is first improved using a hybrid approach,and then data augmentation is used to solve the problem of an imbalanced *** next step is to use a pre-trained deep model named Darknet53 and fine-tune ***,deep transfer learning-based training is carried out,and features are extracted using an activation function on the average pooling ***,an improved butterfly optimization algorithm is proposed,which selects the best features for classification using machine learning *** experiment was carried out on augmented and original fruit datasets,yielding a maximum accuracy of 99.6%for apple diseases,99.6%for grapes,99.9%for peach diseases,and 100%for cherry *** overall average achieved accuracy is 99.7%,higher than previous techniques.

关键词： Fruit diseases data augmentation contrast enhancement deep learning improved butterfly optimization

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：