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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

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

International Journal of Interactive Mobile Technologies

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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

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

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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

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Exploring Content-Based Catalogs for Enhanced Discovery Services in Data Spaces 27

Exploring Content-Based Catalogs for Enhanced Discovery Serv...

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27th International Workshop on Design, Optimization, Languages and Analytical Processing of Big Data, DOLAP 2025

作者： Morejón, Adriana Berenguer, Alberto de Espona, Lucía Tomás, David Mazón, Jose-Norberto University Institute for Computing Research Department of Software and Computing Systems University of Alicante Spain

In the realm of data sharing, effective data discovery is critical for fostering collaboration and innovation across organizations. Data spaces merge the interoperability and secure sharing features of data ecosystems with the transactional and economic aspects of data markets, enabling seamless data exchange among data providers and consumers while preserving data sovereignty. Central to this paradigm is the data catalog, an entity that handles metadata and provides it for data discovery services. However, traditional data catalogs rely heavily on metadata related to high-level representation of data, as well as the overall features of datasets (e.g., dataset name, license or keywords, following standards such as DCAT). This limitation hampers data discovery, as these metadata alone may not effectively describe datasets to fully convey the relevance of datasets. To address this challenge, this paper proposes content-based catalogs to enhance data discovery within data spaces. Our approach for content-based catalogs incorporates three key assets for data consumers to discover relevant datasets and evaluate their utility before accessing them: (i) high-quality structural metadata for datasets;(ii) representative data samples from the dataset;and (iii) a discovery service for searching relevant datasets. Our novel content-based catalog reduces the risk of unintended exposure while still showcasing the value of data, thus preserving the interests of both data providers and consumers within data spaces. © 2025 Copyright for this paper by its authors.

关键词： Data assimilation

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To Write or Not to Write as a Machine? That's the Question

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IEEE Transactions on Big Data 2025年第03期11卷 1042-1053页

作者： Sepulveda-Torres, Robiert Martinez-Murillo, Ivan Saquete, Estela Lloret, Elena Palomar, Manuel Department of Software and Computing Systems University of Alicante Carretera de San Vicente s/n Alicante Spain

Considering the potential of tools such as ChatGPT or Gemini to generate texts in a similar way to a human would do, having reliable detectors of AI -AI-generated content (AIGC)- is vital to combat the misuse and the surrounding negative consequences of those tools. Most research on AIGC detection has focused on the English language, often overlooking other languages that also have tools capable of generating human-like texts, such is the case of the Spanish language. This paper proposes a novel multilingual and multi-task approach for detecting machine vs. human-generated text. The first task classifies whether a text is written by a machine or by a human, which is the research objective of this paper. The second task consists in detect the language of the text. To evaluate the results of our approach, this study has framed the scope of the AuTexTification shared task and also we have collected a different dataset in Spanish. The experiments carried out in Spanish and English show that our approach is very competitive concerning the state of the art, as well as it can generalize better, thus being able to detect an AI-generated text in multiple domains. © 2015 IEEE.

关键词： Adversarial machine learning

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Artificial Intelligence Applied to Air Quality in Smart Cities: A Bibliometric Analysis

Artificial Intelligence Applied to Air Quality in Smart Citi...

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International Conference on Communication and Applied Technologies, ICOMTA 2024

作者： Cáceres-Tello, Jesús Galán-Hernández, José Javier Department of Information Systems and Computing Complutense University Madrid28040 Spain

ISBN: (纸本)9789819604258

Artificial intelligence (AI) has proven to be a fundamental tool in various fields, and its application in managing air quality in Smart Cities is no exception. AI has facilitated the automated collection and analysis of environmental data, enabling precise and real-time monitoring of air quality in urban environments. Furthermore, it has enabled the prediction of air pollution levels, identification of pollutant sources, and implementation of effective corrective measures to improve air quality and protect citizens’ health. This bibliometric study, covering the period from 2019 to 2023 and employing tools such as VOSViewer for data visualization, reveals emerging trends in research on AI applied to air quality management in Smart Cities. Areas of interest identified include "AI Models for Environmental Monitoring," "AI systems for Air Quality Prediction," "AI Applications in Pollution Mitigation," and "AI Technologies for Environmental Management Decision-Making". This bibliometric analysis highlights the growing importance of AI in seeking innovative solutions to address air quality challenges in urban environments, thereby contributing to the creation of healthier and more sustainable cities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Environmental monitoring

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LoCo: Low-Bit Communication Adaptor for Large-Scale Model Training

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年第6期47卷 4285-4298页

作者： Xie, Xingyu Lin, Zhijie Toh, Kim-Chuan Zhou, Pan National University of Singapore Department of Mathematics Singapore Singapore Management University School of Computing and Information Systems Singapore Sea Ai Lab Singapore

To efficiently train large-scale models, low-bit gradient communication compresses full-precision gradients on local GPU nodes into low-precision ones for higher gradient synchronization efficiency among GPU nodes. However, it often degrades training quality due to compression information loss. To address this, we propose the Low-bit Communication Adaptor (LoCo), which compensates gradients on local GPU nodes before compression, ensuring efficient synchronization without compromising training quality. Specifically, LoCo designs a moving average of historical compensation errors to stably estimate concurrent compression error and then adopts it to compensate for the concurrent gradient compression, yielding a less lossless compression. This mechanism allows it to be compatible with general optimizers like Adam and sharding strategies like FSDP. Theoretical analysis shows that integrating LoCo into full-precision optimizers like Adam and SGD does not impair their convergence speed on non-convex problems. Experimental results show that across large-scale model training frameworks like Megatron-LM and PyTorch's FSDP, LoCo significantly improves communication efficiency, e.g., improving Adam's training speed by 14% to 40% without performance degradation on large language models like LLAMAs and MoEs. © 1979-2012 IEEE.

关键词： Synchronization

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Is facial beauty in the eyes? A multi-method approach to interpreting facial beauty prediction in machine learning models

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Discover Artificial Intelligence 2025年第1期5卷 1-18页

作者： Ibrahim, Ahmed Aman Ugail, Noah Hassan Ugail, Hassan Centre for Visual Computing and Intelligent Systems University of Bradford Bradford United Kingdom Department of Mathematical Sciences University of Bath Bath United Kingdom

Despite advances in facial beauty prediction, how specific facial regions contribute to perceptions of attractiveness remains largely unexplored, highlighting a critical interpretability gap in this domain. This study addresses the interpretability gap in facial beauty prediction (FBP) models by introducing a novel framework that combines global and local interpretability methods. We introduce Region Attribution, a technique that aggregates XRAI (eXplanation with Ranked Area Integrals) saliency maps across predefined facial regions to quantify their relative importance in individual predictions. Two global approaches complement this local interpretability: permutation feature importance, which systematically explores individual facial regions across the dataset to measure performance degradation, and individual feature prediction, where separate CNN models are trained on isolated facial regions to assess their independent predictive power. Using the SCUT-FBP5500 and MEBeauty datasets, we train convolutional neural networks on both full faces and individual facial features. While our findings reveal slight variations in feature rankings across the three methods, they consistently identify the eyes and nose regions as crucial determinants in facial beauty prediction. Thus, this study demonstrates the value of a multi-method approach in understanding the complex interplay of facial features in beauty prediction machine learning models. © The Author(s) 2025.

关键词： Prediction models

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Inverse kinematics solution for a six-degree-of-freedom upper limb rehabilitation robot using deep learning models

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Neural computing and Applications 2025年 1-19页

作者： Shah, Muhammad Faizan Khan, Naveed Ahmad Jamwal, Prashant K. Chetty, Girija Goecke, Roland Hussain, Shahid School of Information Technology & Systems University of Canberra Canberra Australia Department of Electrical and Computer Engineering Nazarbayev University Astana Kazakhstan School of Systems and Computing University of New South Wales Canberra Australia

The inverse kinematics problem in serially manipulated upper limb rehabilitation robots implies the usage of the end-effector position to obtain the joint rotation angles. In contrast to the forward kinematics, there are no systematic approaches for solving the inverse kinematics problem. Furthermore, for some morphology of the upper limb rehabilitation robots, the inverse kinematics problem is particularly challenging to solve. Conventional methods to solve the inverse kinematics problem reported in the literature are computationally expensive. In the present work, we propose a deep learning-based model to acquire the joint angles for a given end-effector position. The proposed approach exhibits high efficacy in determining the joint angles for various target positions and can accurately predict the end-effector positions once trained, improving the ability of the upper limb rehabilitation robot to adapt to varying patient needs. Due to its improved capability and effectiveness to track positions, the proposed algorithm lays the foundation for the development of efficient controllers in future. © The Author(s) 2025.

关键词： Inverse kinematics

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Aspect-based sentiment analysis in MOOCs: a systematic literature review introducing the MASC-MEF framework

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Journal of King Saud University - Computer and Information Sciences 2025年第1期37卷 1-33页

作者： Awadh, Wed Akeel Sulaiman, Rosnafisah Bte Mahmoud, Moamin A. Institute of Informatics and Computing in Energy University Tenaga Nasional Kajang Malaysia Department of Computer Information Systems University of Basrah Basrah Iraq Institute of Informatics and Computing in Energy Department of Informatics Universiti Tenaga Nasional Jalan IKRAM UNITEN Selangor Kajang Malaysia Institute of Informatics and Computing in Energy Department of Computing College of Computing and Informatics Universiti Tenaga Nasional Selangor Kajang Malaysia

Aspect-based sentiment analysis (ABSA) in massive open online courses (MOOCs) offers a powerful tool to understand student experiences and improve the quality of online education. As MOOCs grow in popularity, there is an increasing need to analyze student feedback at a granular level, focusing on specific course aspects rather than just overall sentiment. Despite ABSA’s potential in MOOCs, research in this specific domain remains limited. Thus, this review of the literature seeks to fill this gap by systematically examining ABSA within the realm of MOOCs. We utilized a PRISMA framework to guide the search process. Consequently, this review covered 57 studies conducted between 2019 and 2024, retrieved from eight electronic research databases. The studies included in this analysis were examined on the basis of their datasets, data preprocessing, ABSA tasks in MOOCs, evaluation aspects in online courses, fusion approach in ABSA, techniques of word representations, and evaluation criteria utilized to evaluate the proposed models. Additionally, this review introduces the multiaspect sentiment classification using multiembedding and fusion (MASC-MEF) framework, a novel approach that uniquely incorporates multiword embeddings and multifeature extraction along with advanced classification techniques. Results shed light on several critical issues and various limitations, pointing towards future directions for ABSA research within the realm of MOOCs. This review underscores significant contributions to improving ABSA in educational technology, with a focus on course effectiveness and advancements in online learning, guiding researchers towards innovative techniques to tackle this challenge in the future. © The Author(s) 2025.

关键词： Aspect-based sentiment analysis Massive open online courses Multiaspect sentiment classification Online education technology Systematic literature review

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