检索结果-内蒙古大学图书馆

Optimizing Medical Image Analysis: A Performance Evaluation of YOLO-Based Segmentation Models

International Journal of Advanced Computer Science and Applications 2025年第4期16卷 1167-1174页

作者： Alanazi, Haifa Department of Information Systems-Faculty of Computing and Information Technology Northern Border University Saudi Arabia

Instance segmentation is a critical component of medical image analysis, enabling tasks such as tissue and organ delineation, and disease detection. This paper provides a detailed comparative analysis of two fine-tuned one-stage object detection models, YOLOv11-seg and YOLOv9-seg, tailored for instance segmentation in medical imaging. Leveraging transfer learning, both models were initialized with pretrained weights and sub sequently fine-tuned on the NuInsSeg dataset, which comprises over 30,000 manually segmented nuclei across 665 image patches from various human and mouse organs. This approach facilitated faster convergence and improved generalization, particularly given the limited size and high complexity of the medical dataset. The models were evaluated against key performance metrics. The experimental results reveal that YOLOv11n-seg outperforms YOLOv9c-seg with a precision of 0.87, recall of 0.84, and mAP50 of 0.89, indicating superior segmentation quality and more accurate delineation of nuclei contours. This study highlights the robust performance and efficiency of YOLOv11n seg, demonstrating its superiority in medical image segmentation tasks, with notable advantages in both accuracy and real-time processing capabilities. © (2025), (Science and information Organization). All Rights Reserved.

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

Pervasive Attentive Neural Network for Intelligent Image Classification Based on N-CDE’s

引用

Computers, Materials & Continua 2024年第4期79卷 1137-1156页

作者： Anas W.Abulfaraj Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityRabigh21911Saudi Arabia

The utilization of visual attention enhances the performance of image classification *** attentionbased models have demonstrated notable performance,but many of these models exhibit reduced accuracy when confronted with inter-class and intra-class similarities and ***-Controlled Differential Equations(N-CDE’s)and Neural Ordinary Differential Equations(NODE’s)are extensively utilized within this ***’s possesses the capacity to effectively illustrate both inter-class and intra-class similarities and differences with enhanced *** this end,an attentive neural network has been proposed to generate attention maps,which uses two different types of N-CDE’s,one for adopting hidden layers and the other to generate attention *** distinct attention techniques are implemented including time-wise attention,also referred to as bottom N-CDE’s;and element-wise attention,called topN-CDE’***,a trainingmethodology is proposed to guarantee that the training problem is sufficiently *** classification tasks including fine-grained visual classification andmulti-label classification,are utilized to evaluate the *** proposedmethodology is employed on five publicly available datasets,including CUB-200-2011,ImageNet-1K,PASCAL VOC 2007,PASCAL VOC 2012,and MS *** obtained visualizations have demonstrated that N-CDE’s are better appropriate for attention-based activities in comparison to conventional NODE’s.

关键词： Differential equations neural-controlled DE image classification attention maps N-CDE’s

来源：评论

学校读者我要写书评

暂无评论

Clustering Analysis of Physicians’ Performance Evaluation: A Comparison of Feature Selection Strategies to Support Medical Decision-Making

引用

International Journal of Advanced Computer Science and Applications 2025年第4期16卷 700-707页

作者： Ghazzawi, Amani Mustafa Almagrabi, Alaa Omran Namankani, Hanaa Mohammed Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah. Saudi Arabia Department of Management Information Systems Faculty of Business Administration Taif University Taif Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia

Evaluating physicians’ performance is one of the fundamental pillars of improving the quality of healthcare in medical institutions, as it contributes to measuring their ability to provide appropriate treatment, interact effectively with patients, and work within healthcare teams. This study aims to explore the impact of attribute selection on the accuracy of physician clustering using the K-Means algorithm, to improve physician performance assessment. Three datasets containing professional, medical, and administrative attributes were analyzed, such as age, nationality, job title, years of experience, number of operations, and evaluations from various entities. The optimal number of clusters was determined using the Elbow and Silhouette Score methods. The results showed that the original feature set and Lasso features performed best at k = 3, with a clear distinction between clusters. The "three-star" cluster performed well at k = 2 but lost some fine details. It was also shown that attribute selection directly affects the number and accuracy of clusters resulting from clustering, allowing for a clearer classification of physician categories. The study recommends using either original features or Lasso features to achieve more effective clustering, which supports improved recruitment, training, and management decision-making processes in healthcare organizations. © (2025), (Science and information Organization). All rights reserved.

关键词： Risk perception

来源：评论

学校读者我要写书评

暂无评论

Dawwen: An Arabic Mental Health Mobile App Based on Natural Language Processing

International Journal of Interactive Mobile Technologies

引用

International Journal of Interactive Mobile Technologies 2025年第4期19卷 108-131页

作者： Wali, Arwa Almagrabi, Hana El-Feky, Sarah Jokhdar, Maryah Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia

People are increasingly concerned about their mental health wellness. Scientific studies suggest that online counselling for anxiety and depression is just as effective as in-person treatment. Additionally, journaling interventions have shown promise for individuals dealing with mental and psychological issues. In recent years, a growing number of mobile applications have been developed to improve people’s mental wellness and emotional commu-nication. However, many of these applications are not available in Arabic but are available only in English or the native languages of their users, while other applications have feature limitations. This study presents a prototype of an interactive mental health mobile application, called Dawwen, to assist Arab individuals in freely expressing their feelings through online journaling, receiving recommendations for practices and activities, and locating nearby therapy centers based on their geolocation. Dawwen is augmented with an easy-to-use interface, a natural language processing (NLP) technique for analyzing the user input, and integrated features. The system is implemented using Android Studio and various cloud-based tools, making it adaptable for the integration of more advanced artificial intelligence (AI) technologies in the future. The System Usability Scale (SUS) scored an average of 88.25%, indicat-ing high user satisfaction with the app’s functionality and ease of use. The study highlights Dawwen’s effectiveness in improving mental health support for Arabic-speaking users, filling a critical gap in existing digital mental health resources. © 2025 by the authors of this article. Published under CC-BY.

关键词： Electronic health record

来源：评论

学校读者我要写书评

暂无评论

Enhancing Ulcerative Colitis Diagnosis:A Multi-Level Classification Approach with Deep Learning

引用

Computer Modeling in Engineering & Sciences 2024年第7期140卷 1129-1142页

作者： Hasan J.Alyamani Department of Information Systems Faculty of Computing and Information Technology in RabighKing Abdulaziz UniversityJeddah21589Saudi Arabia

The evaluation of disease severity through endoscopy is pivotal in managing patients with ulcerative colitis,a condition with significant clinical ***,endoscopic assessment is susceptible to inherent variations,both within and between observers,compromising the reliability of individual *** study addresses this challenge by harnessing deep learning to develop a robust model capable of discerning discrete levels of endoscopic disease *** initiate this endeavor,a multi-faceted approach is embarked *** dataset is meticulously preprocessed,enhancing the quality and discriminative features of the images through contrast limited adaptive histogram equalization(CLAHE).A diverse array of data augmentation techniques,encompassing various geometric transformations,is leveraged to fortify the dataset’s diversity and facilitate effective feature extraction.A fundamental aspect of the approach involves the strategic incorporation of transfer learning principles,harnessing a modified ResNet-50 *** augmentation,informed by domain expertise,contributed significantly to enhancing the model’s classification *** outcome of this research endeavor yielded a highly promising model,demonstrating an accuracy rate of 86.85%,coupled with a recall rate of 82.11%and a precision rate of 89.23%.

关键词： Ulcerative colitis deep learning CLAHE transfer learning geometric transformations

来源：评论

学校读者我要写书评

暂无评论

Feature-Limited Prediction on the UCI Heart Disease Dataset

引用

Computers, Materials & Continua 2023年第3期74卷 5871-5883页

作者： Khadijah Mohammad Alfadli Alaa Omran Almagrabi Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia

Heart diseases are the undisputed leading causes of death ***,the conventional approach of relying solely on the patient’s medical history is not enough to reliably diagnose heart *** potentially indicative factors exist,such as abnormal pulse rate,high blood pressure,diabetes,high cholesterol,*** analyzing these health signals’interactions is challenging and requires years of medical training and ***,this work aims to harness machine learning techniques that have proved helpful for data-driven applications in the rise of the artificial intelligence *** specifically,this paper builds a hybrid model as a tool for data mining algorithms like feature *** goal is to determine the most critical factors that play a role in discriminating patients with heart illnesses from healthy *** contribution in this field is to provide the patients with accurate and timely tentative results to help prevent further complications and heart attacks using minimum *** developed model achieves 84.24%accuracy,89.22%Recall,and 83.49%Precision using only a subset of the features.

关键词： Machine learning feature selection heart disease

来源：评论

学校读者我要写书评

暂无评论

A Deep CNN-LSTM-Based Feature Extraction for Cyber-Physical System Monitoring

引用

Computers, Materials & Continua 2023年第8期76卷 2079-2093页

作者： Alaa Omran Almagrabi Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia

A potential concept that could be effective for multiple applications is a“cyber-physical system”(CPS).The Internet of Things(IoT)has evolved as a research area,presenting new challenges in obtaining valuable data through environmental *** existing work solely focuses on classifying the audio system of CPS without utilizing feature *** study employs a deep learning method,CNN-LSTM,and two-way feature extraction to classify audio systems within *** primary objective of this system,which is built upon a convolutional neural network(CNN)with Long Short Term Memory(LSTM),is to analyze the vocalization patterns of two different species of *** has been demonstrated that CNNs,when combined with mel-spectrograms for sound analysis,are suitable for classifying ambient ***,the data is augmented and ***,the mel spectrogram features are extracted through two-way feature ***,Principal Component Analysis(PCA)is utilized for dimensionality reduction,followed by Transfer learning for audio feature ***,the classification is performed using the CNN-LSTM *** methodology can potentially be employed for categorizing various biological acoustic objects and analyzing biodiversity indexes in natural environments,resulting in high classification *** study highlights that this CNNLSTM approach enables cost-effective and resource-efficient monitoring of large natural *** dissemination of updated CNN-LSTM models across distant IoT nodes is facilitated flexibly and dynamically through the utilization of CPS.

关键词： Cyber-physical system internet of things feature extraction,classification CNN principal component analysis mel spectrograms monitoring deep learning

来源：评论

学校读者我要写书评

暂无评论

Priority Detector and Classifier Techniques Based on ML for the IoMT

引用

Computers, Materials & Continua 2023年第8期76卷 1853-1870页

作者： Rayan A.Alsemmeari Mohamed Yehia Dahab Badraddin Alturki Abdulaziz A.Alsulami Department of Information Technology Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia

Emerging telemedicine trends,such as the Internet of Medical Things(IoMT),facilitate regular and efficient interactions between medical devices and computing *** importance of IoMT comes from the need to continuously monitor patients’health conditions in real-time during normal daily activities,which is realized with the help of various wearable devices and *** major health problem is workplace stress,which can lead to cardiovascular disease or psychiatric ***,real-time monitoring of employees’stress in the workplace is *** levels and the source of stress could be detected early in the fog layer so that the negative consequences can be mitigated ***,overwhelming the fog layer with extensive data will increase the load on fog nodes,leading to computational *** study aims to reduce fog computation by proposing machine learning(ML)models with two *** first phase of theMLmodel assesses the priority of the situation based on the stress *** the second phase,a classifier determines the cause of stress,which was either interruptions or time pressure while completing a *** approach reduced the computation cost for the fog node,as only high-priority records were transferred to the ***-priority records were forwarded to the *** MLapproaches were compared in terms of accuracy and prediction speed:Knearest neighbors(KNN),a support vector machine(SVM),a bagged tree(BT),and an artificial neural network(ANN).In our experiments,ANN performed best in both phases because it scored an F1 score of 99.97% and had the highest prediction speed compared with KNN,SVM,and BT.

关键词： Machine learning priority detector Internet of Medical Things IoMT fog computing cloud computing

来源：评论

学校读者我要写书评

暂无评论

A Proposed Feature Selection Particle Swarm Optimization Adaptation for Intelligent Logistics--A Supply Chain Backlog Elimination Framework

引用

Computers, Materials & Continua 2024年第6期79卷 4081-4105页

作者： Yasser Hachaichi Ayman E.Khedr Amira M.Idrees College of Computing and Information Technology at Khulais Department of Information SystemsUniversity of JeddahJeddah21959Saudi Arabia Faculty of Computers and Information Technology Future University in EgyptCairo11835Egypt

The diversity of data sources resulted in seeking effective manipulation and *** challenge that arises from the increasing dimensionality has a negative effect on the computation performance,efficiency,and stability of *** of the most successful optimization algorithms is Particle Swarm Optimization(PSO)which has proved its effectiveness in exploring the highest influencing features in the search space based on its fast convergence and the ability to utilize a small set of parameters in the search *** research proposes an effective enhancement of PSO that tackles the challenge of randomness search which directly enhances PSO *** the other hand,this research proposes a generic intelligent framework for early prediction of orders delay and eliminate orders backlogs which could be considered as an efficient potential solution for raising the supply chain *** proposed adapted algorithm has been applied to a supply chain dataset which minimized the features set from twenty-one features to ten significant *** confirm the proposed algorithm results,the updated data has been examined by eight of the well-known classification algorithms which reached a minimum accuracy percentage equal to 94.3%for random forest and a maximum of 99.0 for Naïve ***,the proposed algorithm adaptation has been compared with other proposed adaptations of PSO from the literature over different *** proposed PSO adaptation reached a higher accuracy compared with the literature ranging from 97.8 to 99.36 which also proved the advancement of the current research.

关键词： Optimization particle swarm optimization algorithm feature selection logistics supply chain management backlogs

来源：评论

学校读者我要写书评

暂无评论

Deep learning in crowd counting:A survey

引用

CAAI Transactions on Intelligence Technology 2024年第5期9卷 1043-1077页

作者： Lijia Deng Qinghua Zhou Shuihua Wang Juan Manuel Górriz Yudong Zhang School of Computing and Mathematical Sciences University of LeicesterLeicesterUK Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Signal Theory Networking and CommunicationsUniversity of GranadaGranadaSpain

Counting high-density objects quickly and accurately is a popular area of *** counting has significant social and economic value and is a major focus in artificial *** many advancements in this field,many of them are not widely known,especially in terms of research *** authors proposed a three-tier standardised dataset taxonomy(TSDT).The Taxonomy divides datasets into small-scale,large-scale and hyper-scale,according to different application *** theory can help researchers make more efficient use of datasets and improve the performance of AI algorithms in specific ***,the authors proposed a new evaluation index for the clarity of the dataset:average pixel occupied by each object(APO).This new evaluation index is more suitable for evaluating the clarity of the dataset in the object counting task than the image ***,the authors classified the crowd counting methods from a data-driven perspective:multi-scale networks,single-column networks,multi-column networks,multi-task networks,attention networks and weak-supervised networks and introduced the classic crowd counting methods of each *** authors classified the existing 36 datasets according to the theory of three-tier standardised dataset taxonomy and discussed and evaluated these *** authors evaluated the performance of more than 100 methods in the past five years on different levels of popular ***,progress in research on small-scale datasets has slowed *** are few new datasets and algorithms on small-scale *** studies focused on large or hyper-scale datasets appear to be reaching a saturation *** combined use of multiple approaches began to be a major research *** authors discussed the theoretical and practical challenges of crowd counting from the perspective of data,algorithms and computing *** field of crowd counting is moving towards combining multiple methods and requ

关键词： artificial intelligence computer vision image analysis image processing

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：