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Iraqi Journal for Computer Science and Mathematics 2025年第2期6卷 1-11页

作者： Najjar, Fallah H. Kadhim, Karrar A. Mohamed, Farhan Rahim, Mohd Shafry Mohd Abdullah, Asniyani Nur Haidar Department of Emergent Computing Faculty of Computing Universiti Teknologi Malaysia Johor Bahru Malaysia Department of Computer Systems Techniques Technical Institute of Najaf Al-Furat Al-Awsat Technical University Najaf54001 Iraq Computer Techniques Engineering Department Faculty of Information Technology Imam Ja’afar Al-Sadiq University Baghdad Iraq Faculty of Information & Communication Technology Universiti Teknikal Malaysia Melaka Hang Tuah Jaya Melaka Durian Tunggal76100 Malaysia

In this paper, a new lightweight U-Net deep learning-based neural network designed for the segmentation of skin lesions is proposed. Segmentation of skin lesions is the most critical step in computer-aided dermatology diagnosis for the early detection of melanoma and other diseases. However, we address the difficulty related to the precise definition of the lesion margins with an eye on the computation cost. We have demonstrated the state-of-the-art performance of DeepSkinSeg in most metrics on dermoscopic images using the PH2 and Human Against Machine (HAM10000) datasets. The metrics of the DeepSkinSeg model were robustness measured as the Intersection over Union (IoU) at 91.49, Dice coefficient at 95.56, precision at 97.97, sensitivity at 96.84, and accuracy at 96.71 for the PH2 dataset. Other standard generalization capabilities for the HAM10000 dataset could be an IoU of 92.97, a Dice coefficient of 96.36, precision at 97.64, sensitivity at 95.10, and an accuracy of 94.59. DeepSkinSeg has a very efficient inference because the model itself is lightweight, proving to be very helpful for real-time dermatological analysis. This work further advanced the computer-aided diagnosis in the task of skin lesion classification, guaranteeing even more promising clinical applications. © 2025 The Author(s).

关键词： Computer aided diagnosis

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Integrating Advanced Healthcare AI into Higher Education of Smart Cities: Skin Cancer Classification with Custom Vision Transformers 2nd

Integrating Advanced Healthcare AI into Higher Education of ...

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2nd International Conference on Sustainability: Developments and Innovations, ICSDI 2024

作者： Usman, Syed Muhammad Shah, Syed Nehal Hassan Dicheva, Nevena Ur Rehman, Ikram Zaib, Samia Department of Computer Science Bahria School of Engineering and Applied Sciences Bahria University Islamabad44000 Pakistan Faculty of Computing and Artificial Intelligence Air University Islamabad44000 Pakistan School of Computing and Engineering University of West London London United Kingdom

ISBN: (纸本)9789819783441

In today’s smart cities, it’s essential to combine advanced healthcare with education. Our research introduces a groundbreaking method for detecting skin cancer, using a new type of artificial intelligence called a Vision Transformer. This model analyzes a wide range of skin images from different people and accurately identifies skin cancer, especially melanoma and benign *** beyond traditional techniques like convolutional neural networks, our approach uses advanced attentionmechanisms to improve accuracy by focusing on key areas in the skin images. The model has been thoroughly tested with specific criteria to ensure it can reliably detect skin cancer while reducing errors. This research is particularly important as skin cancer rates are increasing worldwide, and early, accurate detection is vital for saving lives. Our study focuses on providing assistance to healthcare practitioners for skin disease diagnostics as well as to assist in providing training and education to healthcare researchers. The proposed method outperforms in terms of ROC curve analysis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Lung cancer

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The Cost of Open-Access Publishing in an engineering Academic Community: A Study of Zagreb faculty of Electrical engineering and computing

The Cost of Open-Access Publishing in an Engineering Academi...

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Proceedings of the International Convention MIPRO

作者： M. Tucaković B. Marijanović J. Petrak Faculty of Electrical Engineering and Computing University of Zagreb FER Central Library Zagreb Croatia University of Zagreb School of Medicine/Central Medical Library Zagreb Croatia

Since the early 1990s, open access (OA) scholarly publishing has become widespread in the international scientific community. Although there are numerous benefits of OA publishing, researchers experience a number of disadvantages related to the transition to the OA model. Article processing charges (APCs) paid by authors/institutions have become a key mechanism for funding full OA (the gold OA model). This paper addresses the issue of OA publication costs and attempts to find out what financial resources have been used by researchers at the University of Zagreb, faculty of Electrical engineering and computing for publishing in gold OA journals over the last five years. The published OA articles indexed in the Web of Science Core Collection for the period from 2016 to 2020 were analysed. A quantitative analysis of Journal Citation Reports metrics for 163 journals was conducted to identify a relationship between APCs and journal rankings. In addition, a detailed study of gold OA articles was carried out by examining the websites of 31 publishers and identifying APCs for 64 journals. Funding and collaboration models were extracted for 174 gold OA articles as they provide insight into the extent of national funding in relation to financing based on international collaboration. Finally, a calculation of invested funds over the last five years is presented.

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Position Paper: Enhancing the Learning and Mastery of Academic Writing in the Serbian Language Through an AI Tool with Adaptive Scaffolding 17

Position Paper: Enhancing the Learning and Mastery of Academ...

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17th International Conference on Computer Supported Education, CSEDU 2025

作者： Francišković, Teodor Sakal Gajić, Dušan Luburić, Nikola Slivka, Jelena Department of Computing and Control Engineering Faculty of Technical Sciences University of Novi Sad Trg Dositeja Obradovića 6 Novi Sad Serbia

ISBN: (纸本)9789897587467

Academic writing is a significant challenge for many learners striving for proficiency. Adaptive scaffolding techniques and AI tools in education have proven effective in addressing this challenge and supporting learners in improving their academic writing skills when used correctly. This position paper proposes combining adaptive scaffolding techniques with AI tools in a public university's final year academic writing course to enhance the learning experience and mastery of academic writing skills in Serbian. The proposed plan outlines the course structure and details how the AI-driven adaptive scaffolding techniques will be integrated to support the learning experience, focusing on summative and formative feedback from the AI tool. The proposed plan is a work in progress. It will be implemented in the next iteration of the course for evaluation, taking into account potential counter-arguments and their impact on the tool's development and the student's learning experience and outcomes. This study will analyse our plan's effectiveness in enhancing the learning experience and outcomes. The expected outcome is to assist students in their learning while contributing to the development of AI in education and the Serbian language. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Students

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A novel context-sensitive attitude entropy-based multiclass segmentation method for brain MR images using enhanced flow directional algorithm

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Multimedia Tools and Applications 2025年第15期84卷 14759-14803页

作者： Naik, Manoj Kumar Jena, Bibekananda Panda, Rutuparna Wunnava, Aneesh Abraham, Ajith Faculty of Engineering and Technology Siksha O Anusandhan Odisha Bhubaneswar751030 India Department of Electronics and Communication Engineering Anil Neerukonda Institute of Technology & Sciences Andhra Pradesh Sangivalasa Visakhapatnam531162 India Department of Electronics and Telecommunication Engineering Veer Surendra Sai University of Technology Odisha Burla768018 India Faculty of Computing and Mathematical Sciences Bennett University Greater Noida201310 India

Computer vision techniques, aided by artificial intelligence, in medical image analysis, are currently encouraged for precise and speedy diagnosis of brain disorders. The brain MR image shows an intensity variation, which attracts multiclass segmentation for a meaningful representation. The existing entropy-based methods use the image histogram. Nevertheless, the 1D histogram-based technique has a low complexity. However, it lacks the contextual information. Further, these entropy-based methods suffer low accuracy when the gray value distribution is non-uniform. To facilitate a reliable and efficient thresholding process, a novel multiclass segmentation objective function is investigated along with an efficient optimizer to obtain the optimal threshold values. To bridge this research gap, we explore a context-sensitive attitude entropy (CSAE) based multiclass segmentation objective function, an energy curve is used together with the human decision-making ability. Context-sensitive attitude entropy (CSAE), in contrast to classical entropy, which considers each pixel separately, is a measure of uncertainty or randomness in an image considering the spatial relationships and context between adjacent pixels. This can be achieved by calculating the attitude entropy from the energy curve instead of the image’s histogram. To maximize the CSAE, we suggest a new enhanced flow directional algorithm (EFDA), which improves the flow directional algorithm (FDA) by adding randomization to both the velocity and the non-performer agents. The experimental effort begins with the EFDA performance validation utilising 23 well-known classical and 10 CEC-C06 2019 benchmark functions, which produce better results than cutting-edge optimizers. The EFDA’s supremacy over other optimizers is ensured by Friedman’s mean rank and Wilcoxon’s mean rank test. The suggested CSAE-EFDA method is tested using T2-weighted brain images from Harvard Medical School’s AANLIB database and Brain MR images for t

关键词： Decision making

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Multi-lingual news stack based ensemble classification for deep news feed analysis

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Neural computing and Applications 2025年 1-12页

作者： Sakthivel, Rakesh Kumar Nagasubramanian, Gayathri Sankayya, Muthuramalingam Kadry, Seifedine Rocha, Alvaro Karnataka Bengaluru India Department of Information Technology Thiagarajar College of Engineering Madurai India Faculty of Applied Computing and Technology Noroff University College Kristiansand Norway Alvaro Rocha ISEG University of Lisbon Lisbon Portugal

Analyzing and extracting effective words of news feeds are critical tasks. News making sources may feed their news articles in different manners based on their own perspective. In this situation, finding likelihood samples from the vast news feeds is an essential task. Moreover, acquiring optimal word deviations, news varieties, sentiment metrics, and multi-lingual analysis are major tasks of this proposed system. To achieve this massive news feed analysis task, the proposed system uses specially designed multi-classifier units and news stack manipulation procedures. The proposed system integrates different classifiers and dynamic news stacks to handle datasets of multi-lingual attributes. The results show that the proposed method performs with better accuracy rate than existing techniques. This gives 10 to 15% higher accuracy rate than existing works. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Data accuracy

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AFuNet: an attention-based fusion network to classify texts in a resource-constrained language

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Neural computing and Applications 2025年第9期37卷 6725-6748页

作者： Hossain, Md. Rajib Hoque, Mohammed Moshiul Dewan, M. Ali Akber Hoque, Enamul Siddique, Nazmul Department of Computer Science & Engineering Chittagong University of Engineering & Technology Chittagong4349 Bangladesh School of Computing and Information Systems Faculty of Science and Technology Athabasca University AthabascaABT9S 3A3 Canada School of Information Technology York University TorontoONM3J 1P3 Canada School of Computing Engineering and Intelligent Systems Ulster University Coleraine United Kingdom

In the era of widespread Internet use and extensive social media interaction, the digital realm is accumulating vast amounts of unstructured text data. This unstructured data often contain undesirable information, necessitating time-consuming manual classification efforts. An intelligent text classification system capable of automatically categorizing digitized texts based on semantic meaning is crucial. However, this task is particularly challenging for low-resource languages like Bengali due to a shortage of annotated corpora, issues with out-of-vocabulary words, lack of domain-specific hyperparameter tuning, limited ability to extract generalized text features, and class imbalances within the corpus. AFuNet: an attention-based fusion network to classify texts in a resource-constrained language. AFuNet undergoes a comprehensive four-phase experimental process, including baseline model evaluation and hyperparameter tuning, late fusion and model selection, attention-based early fusion and model identification, and an ablation study with impact analysis. Fine-tuned based on five Bengali text classification corpora, AFuNet achieves impressive accuracies: 96.60 ± 0.2 (BTCC11), 85.37 ± 0.2 (OSBC), 97.35 ± 0.2 (BARD), 93.74 ± 0.2 (IndicNLP), and 96.51 ± 0.2 (ProthomAlo). In comparison with previous state-of-the-art models on these corpora, AFuNet demonstrates significant accuracy improvements ranging from 0.54% to 4.49%, showcasing its effectiveness in advancing text classification capabilities for the Bengali language. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Semantics

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Enhancing student performance prediction: the role of class imbalance handling in machine learning models

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Discover computing 2025年第1期28卷 1-34页

作者： Althaqafi, Turki Saleem, Farrukh AL-Ghamdi, Abdullah AL-Malaise Computer Science Department School of Engineering Computing and Design Dar Al-Hekma University Jeddah 22246 Saudi Arabia School of Built Environment Engineering and Computing Leeds Beckett University Leeds LS6 3QR United Kingdom Information Systems Department Faculty of Computing and Information Technology King Abdulaziz University Jeddah 21589 Saudi Arabia

The Learning Management System (LMS) is an essential tool for educational institutions that facilitates content delivery, assessments, lecture delivery, and collaboration to enhance the learning experience. This study explores the role of LMS in creating an effective learning environment to improve students’ academic performance. To achieve the main objective of this study, we utilized a dataset [xAPI-Edu-Data] comprising multiple factors, such as academic, psychological, and cognitive engagement. Various machine learning techniques are employed to assess the impact of engagement activities on students’ performance. Initially, a class imbalance issue identified in the dataset and addressed using SMOTE technique. In addition, other resampling strategies applied to compare the effectiveness of proposed work. The model performance evaluated and compared using different evaluation metrics before and after data enrichment. In addition, hyperparameter optimization is conducted using a grid search approach to enhance models’ accuracy. The performance of individual models such as support vector machine (0.81), logistic regression (0.80), and decision tree (0.75) enhanced using the enriched dataset. The integration of multiple base learners into an ensemble model, with random forest as the stacking learner, achieved a weighted precision of 0.83, improving from 0.60 with the original dataset. The implementation of the stacking approach with enriched dataset has identified a better result and improved accuracy by 23%. The key contribution of this study includes identifying the effectiveness of data enrichment in improving prediction accuracy. Moreover, the research highlights the role of student engagement and behavior in measuring academic performance. The proposed model can identify the factors behind low performance, allowing further actions to be taken. Based on the prediction, the educators can work on the associated factors that could be low engagement, participation, or

关键词： Academic performance Learning management system Machine learning Predictive modeling Student engagement

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Electrical Load Classification Using Combinative Current-Power Characteristics in Time Domain and Machine-Learning-Based NILM Techniques 3rd

Electrical Load Classification Using Combinative Current-Pow...

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3rd International Conference on Advances in Information and Communication Technology, ICTA 2024

作者： Nguyen, Thanh Cong Van Pham, Nam Truong, Hai Nam Nguyen, Ngoc Son Cao, Song Toan Nguyen, Huy Tinh Dang, Quoc-Viet Ware, Jonathan Andrew Nguyen, Ngoc An VNU University of Engineering and Technology Hanoi Viet Nam Samueli School of Engineering University of California Irvine United States Faculty of Computing Engineering and Science University of South Wales Wales United Kingdom

ISBN: (纸本)9783031809422

Non-intrusive load monitoring (NILM) is a large research topic dedicated to the detection and management of electrical loads, drawing international interest in the context of a growing global concern on sustainability in the energy sector. However, most current public datasets offer inadequate and non-unified sets of measured features, electrical devices and measurement methods, causing difficulties in method comparison and the application of research in new environments. In addition, previous studies faced challenges in classifying appliances with small and similar operational power or with large disparities in operational power. In this study, we utilized a combination of current-power characteristics in the time domain (e.g. RMS current (Irms), real power (P), power factor (pf), reactive power (Q), apparent power (S)) to monitor electrical appliances using their steady states. The combinative current-power characteristics were used to train machine learning models that can efficiently distinguish between appliances while addressing the posed challenges. Our dataset comprises 128 device combinations sampled at steady-state from 07 common electrical appliances in Vietnam, collected at a high sampling rate of 600 Hz, resulting in 73 million data points. By leveraging the current-power signatures in the time domain, we achieved a classification accuracy of approximately 99.1%, capable of differentiating between combinations containing devices with small and similar operational power and those with substantial disparities. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： appliance classification load disaggregation load monitoring machine learning Non-intrusive Load Monitoring (NILM)

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YOLOv8n-CGW: A novel approach to multi-oriented vehicle detection in intelligent transportation systems

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Multimedia Tools and Applications 2025年第7期84卷 3809-3840页

作者： Berwo, Michael Abebe Fang, Yong Sarwar, Nadeem Mahmood, Jabar Aljohani, Mansourah Elhosseini, Mostafa School of Information and Engineering Chang’an University Shaanxi Xi’an710064 China Department of Computer Science Bahria University Lahore Campus Punjab Lahore54600 Pakistan Department of Information Technology Faculty of Computing and Information Technology University of Sialkot Punjab Sialkot51040 Pakistan College of Computer Science and Engineering Taibah University Yanbu46421 Saudi Arabia Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura35516 Egypt

In the context of Intelligent Transportation Systems (ITS), the role of vehicle detection and classification is indispensable for streamlining transportation management, refining traffic control, and conducting in-depth accident analyses. However, the intricate task of accurately detecting multi-oriented vehicles in diverse scenarios remains a challenge, even with the advancements in ITS. Factors such as vehicle morphology, design, road conditions, structural details, and climatic variables provide a significant part in the detection. To address this, our study introduces an advanced YOLOv8n model, specifically the YOLOv8n-CGW, which is built upon pre-trained deep learning frameworks. This model is meticulously tailored to enhance the detection capabilities for multi-oriented vehicles. Comprehensive evaluations on esteemed datasets like MtV, MERGED, Custom dataset, DAWN, WEDGE, and UA-DETRAC underscore the superiority of our approach over existing models. Notably, our YOLOv8n-CGW model achieves a remarkable mAP of 83.3% and boasts a swift inference time of 0.0019s, setting a new benchmark in the domain. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep neural networks

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