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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3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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Triple Sensors Integrated Deep Learning-Based Network for Detection of Defects in Medicines

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IEEE Sensors Journal 2025年第10期25卷 18171-18179页

作者： Ganesh, Karthikeyan Prakash, M. SRM Institute of Science and Technology School of Computing Department of Research Scholar Data Science and Business Systems Tamil Nadu Kattankulathur603203 India SRM Institute of Science and Technology Data Science and Business Systems School of Computing Tamil Nadu Kattankulathur603203 India

The accurate medicine dispensing is extremely important in all hospitals, as medical errors can cause serious health issues, underscoring the need for improved protocols. Numerous research has been conducted on developing detection techniques for unpackaged pills or drugs with similar packages. However, the existing frameworks face issues, such as dispensing errors and accurate defect prediction. To overcome these challenges, a novel Tri-SID Medi technique has been proposed for detecting the defects in medicines and delivering defect-free medicines using tri sensors, such as ultrasonic sensor, weight sensor, and global positioning system (GPS). Initially, the collected drug images from the hospital/pharmacy are preprocessed using the adaptive unsharp mask-guided filter to enhance the image quality and reduce noise artifacts. Then, the preprocessed images are fed into APM-YOLO to detect the images as defect or nondefect. The nondefect medicines are given into the first similarity group (SG), in which the squeeze and excitation-based bidirectional long short-term memory (SE-BiLSTM) framework is trained for grouping the medicinal products. Finally, the defect-free group medicines are directly delivery to the customers in the delivery phase by using tri sensors and global system for mobile communications (GSMs). The proposed Tri-SID Medi method achieves 99.03% accuracy, which is 9.11%, 2.45%, and 5.07% better than the existing techniques, such as YOLO, deep convolutional neural network (DCNN), and bidirectional encoder representations from transformer (BERT). The experimental results of the proposed techniques highlight the potential for real-time drug defect detection and similar drug groupings with diverse packaging, which can reduce medical errors and are key to ensure patient safety. © 2001-2012 IEEE.

关键词： Adaptive filters

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Digital Twin for Autonomous Guided Vehicles Based on Integrated Sensing and Communications

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 8413-8418页

作者： Bui, Van-Phuc De Sant Ana, Pedro Maia Gherekhloo, Soheil Pandey, Shashi Raj Popovski, Petar Aalborg University Department of Electronic Systems Denmark Corporate Research Denmark Robert Bosch GmbH Cross-Domain Computing Solutions Germany

This paper presents a Digital Twin (DT) framework for the remote control of an Autonomous Guided Vehicle (AGV) within a Network Control System (NCS). The AGV is monitored and controlled using Integrated Sensing and Communications (ISAC). In order to meet the real-time requirements, the DT computes the control signals and dynamically allocates resources for sensing and communication. A Reinforcement Learning (RL) algorithm is derived to learn and provide suitable actions while adjusting for the uncertainty in the AGV's position. We present closed-form expressions for the achievable communication rate and the Cramer-Rao bound (CRB) to determine the required number of Orthogonal Frequency-Division Multiplexing (OFDM) subcarriers, meeting the needs of both sensing and communication. The proposed algorithm is validated through a millimeter-Wave (mmWave) simulation, demonstrating significant improvements in both control precision and communication efficiency. © 1967-2012 IEEE.

关键词： Cramer-Rao bounds

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Beyond Functionality: Automating Algorithm Design Evaluation in Introductory Programming Courses 17

Beyond Functionality: Automating Algorithm Design Evaluation...

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17th International Conference on Computer Supported Education, CSEDU 2025

作者： Oliveira, Caio Silva, Leandra Brunet, João Department of Systems and Computing Federal University of Campina Grande Campina Grande Brazil

ISBN: (纸本)9789897587467

Introductory programming courses often enroll large numbers of students, making individualized feedback challenging. Automated grading tools are widely used, yet they typically focus on functional correctness rather than the structural aspects of algorithms-such as the use of appropriate functions or data structures. This limitation can lead students to create solutions that, while correct in functionality, contain structural flaws that might hinder their learning. To address this gap, we applied the concept of design tests to automatically detect structural issues in students' algorithms. Our tool, PyDesignWizard, provides an API leveraging Python's Abstract Syntax Tree (AST) to streamline the creation of design tests. We quantitatively evaluated this approach on 1, 714 student programs from an introductory course at the Federal University of Campina Grande, achieving 98.6% accuracy, a 0.99 true negative rate, 77% precision, and 84% recall. A qualitative evaluation was also conducted through interviews with 16 programming educators using the Think Aloud Protocol. The results indicated a high level of understanding and positive feedback: 15 educators grasped the concept quickly, and 8 highlighted the educational benefits of identifying design issues. In summary, our approach empowers educators to write structural tests for automated feedback, enhancing the quality of grading in programming education. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Students

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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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