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An innovative approach to classify meniscus tears by reducing vision transformers features with elasticnet approach

Journal of Supercomputing 2025年第4期81卷 1-29页

作者： Genç, Hasan Koç, Canan Yüzgeç Özdemir, Esra Özyurt, Fatih Elazig Fethi Sekin City Hospital Elazig Turkey Faculty of Engineer Software Engineering Firat University Elazig Turkey Faculty of Engineering and Architecture Computer Engineering Bingöl University Elazig Turkey

Meniscal tears, a prevalent orthopedic condition caused by abrupt knee movements, excessive load, or injury, require an accurate diagnosis for effective treatment. This study investigates the vision transformer (ViT) models' efficacy in automated classification of meniscus pathologies. It also explores how feature reduction using the ElasticNet method can improve classification accuracy and computational efficiency. The study utilized MRI scans from a dataset comprising 5000 images collected from clinical cases. Initially, classification was performed using EfficientNet and SqueezeNet architectures. Subsequently, feature extraction was conducted using ViT models, generating a feature set of 1000 dimensions. ElasticNet was employed to reduce features before reclassification using support vector machines (SVM). Model performance was evaluated based on accuracy, precision, sensitivity, and specificity. The ViT_base_32 model achieved a classification accuracy of 99.9% with a processing time of 1.2 s. Feature reduction via ElasticNet significantly enhanced classification performance while maintaining high precision, sensitivity, and specificity. These improvements demonstrate the effectiveness of combining ViT models with ElasticNet to diagnose meniscal tears. The findings highlight the potential of vision transformer models, in conjunction with ElasticNet, to provide rapid and highly accurate diagnostic assistance for meniscal injuries. This methodology shows promise for application to other medical diagnostic domains, offering valuable advancements in healthcare technology. © The Author(s) 2025.

关键词： Medical imaging

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Application of Artificial Intelligence Methods for Generating Mathematical Problems in Primary Education: Analysis of Effectiveness and Prospects for Automated Assessment 7

Application of Artificial Intelligence Methods for Generatin...

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7th International Conference on Machine Learning and Natural Language Processing, MLNLP 2024

作者： Salavatovich, Nazipov Rustam Yurievna, Avksentieva Elena ITMO University Faculty of Software Engineering and Computer System Saint Petersburg Russia ITMO University Faculty of Software Engineering and Computer System Saint-Petersburg Russia

ISBN: (纸本)9798350354973

The present study aims to evaluate the effectiveness of using modern neural network models (ChatGPT 4o, Claude 3.5 Sonnet, GigaChat 3.0, YandexGPT 3) for generating mathematical problems for elementary school students. The research conducts a comparative analysis of the quality of generated problems, assesses the possibility of their direct use without expert review, and develops recommendations for automating the quality control process. The methodology includes the generation of various types of mathematical problems, their expert evaluation according to developed criteria, and statistical analysis of the results. The study identifies the strengths and weaknesses of each model, determines the types of problems in which the models demonstrate the best results, and suggests directions for further improvement of AI systems in the educational context. © 2024 IEEE.

关键词： Neural network models

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PoolKANNeXt: A new pooling-based Kolmogorov Arnold convolutional neural network

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Alexandria engineering Journal 2025年 128卷 144-152页

作者： Lv, Fangxing Wei, Qing Huang, Yuwen Tuncer, Turker Dogan, Sengul Özyurt, Fatih School of Computer Heze University Heze274015 China Department of Digital Forensics Engineering Technology Faculty Firat University Elazig Turkey Department of Software Engineering Engineering Faculty Firat University Elazig Turkey

The Kolmogorov–Arnold Network (KAN) is a new-generation neural network. It provides an alternative to multilayer perceptrons (MLPs). PoolFormer showed that pooling alone can mix features efficiently. We propose PoolKANNeXt, a CNN that merges the KAN structure with pooling-based feature mixing. This design targets high accuracy with a low number of the learnable parameters. We evaluated PoolKANNeXt on six image datasets, five biomedical and one general (CIFAR-10). The model comprises four stages. In the stem stage, a ConvNeXt-style patchify block converts each 224 × 224 × 3 image into a 56 × 56 × 96 tensor. In the main stage, average pooling first mixes local features;then two parallel 3 × 3 convolutions—one followed by GELU and the other by Swish—extract complementary representations, and a 1 × 1 convolution scales the combined output and adds it back to the input via a residual connection. In the downsampling stage, strided 3 × 3 convolutions halve spatial dimensions and double the channel count. In the output stage, global average pooling produces a feature vector that feeds into a softmax classifier. PoolKANNeXt achieved over 90 % accuracy on all datasets and reached 99.43 % on CIFAR-10, ranking among the top five models on that benchmark. PoolKANNeXt offers a lightweight yet powerful architecture. Its innovative combination of pooling and dual-activation KAN blocks yields strong performance across diverse tasks. The design is scalable and adaptable to larger or more complex datasets. © 2025 The Authors

关键词： Convolutional neural networks

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COVID-19 Prediction Model Empowered with Fused Computational Intelligence Technique

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IEIE Transactions on Smart Processing and Computing 2025年第1期14卷 109-117页

作者： Khan, Muhammad Adnan Naseer, Iftikhar Ali, Muhammad Nadeem Kim, Byung-Seo Department of Software Faculty of Artificial Intelligence & Software Gachon University Seongnam13120 Korea Republic of Faculty of Computer Science & Information Technology The Superior University Lahore54600 Pakistan Department of Software and Communications Engineering Hongik University Sejong Campus Korea Republic of

The novel Coronavirus (COVID-19) spread rapidly around the world and caused overwhelming effects on the health and economy of the world. It first appeared in Wuhan city of China and was declared a pandemic by the World Health Organization (WHO). Many researchers, as well as experts in clinical and artificial intelligence experts, are working together to control the rapid spread of COVID-19 with early detection. This study focused on intelligent prediction for coronavirus using computational intelligence approaches (IPC-FCIA) like convolutional neural networks, support vector machines, and fuzzy logic techniques. The proposed IPC-FCIA model is based on two sections namely the training section and the validation section. Features fusion and decision-level fusion are used in this study to enhance the performance of the recommended IPC-FCIA model. The proposed model predicts the early detection of COVID-19 in two types COVID-negative and COVID-positive. The benchmark results of the model show an accuracy of 97.66 % on decision-level fusion in the detection of COVID-19. The proposed model can be helpful for medical experts as well as COVID-19-affected patients. © 2025 Institute of Electronics Engineers of Korea. All rights reserved.

关键词： COVID-19

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Efficiency in Multi-Channel Data Transmission Systems with Priority Service Under High Load

Efficiency in Multi-Channel Data Transmission Systems with P...

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2024 International Russian Automation Conference, RusAutoCon 2024

作者： Lygdenov, Sodnom Itmo University Faculty of Software Engineering and Computer Engineering Saint Petersburg Russia

The subject of research. Analysis of the influence of priority service in multi-channel data transmission systems characterized by limited storage capacity and high load, taking into account the non-stationary characteristics of the intensity of packet arrivals and service time. Method. The functional characteristics of the multi-channel system were calculated and analyzed using simulation methods and mathematical statistics. Main results. A simulation model of a multi-channel system with priority service has been developed, designed to evaluate the functional characteristics of the system under high load. Experiments have been conducted to study the impact of priority service on delays and packet losses of heterogeneous traffic. Practical significance. The results obtained can be used in the design of multi-threaded data transmission systems operating under conditions of high heterogeneous load. © 2024 IEEE.

关键词： data transmission networks limited-capacity storage devices multi-channel systems network efficiency priority servicing of packets queuing systems simulation experiments

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Priority Service in Data Transmission Networks with Non-Stationary Flows

Priority Service in Data Transmission Networks with Non-Stat...

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2024 International Russian Smart Industry Conference, SmartIndustryCon 2024

作者： Lygdenov, Sodnom ITMO University Faculty of Software Engineering and Computer Engineering Saint Petersburg Russia

ISBN: (纸本)9798350395044

The subject of research. Analysis of the impact of priority data transmission in high-load multi-channel systems represented as a mass service system with request prioritization under different variations in the distribution of random variables of packet arrival intensity in the network and service time in the channels. Method. The study of the influence of priorities on the efficiency of data transmission in multi-channel systems was carried out using simulation and mathematical statistics methods. Main results. The method of numerical calculation of time parameters based on the simulation model of multi-channel data transmission system with heterogeneous load is proposed. Simulation experiments evaluating the impact of priority data transmission at different coefficients of variation of packet intensity and service time in the channels on the time characteristics of the multi-channel system are conducted. Practical significance. The obtained results can be applied to the design of multi-channel computer systems of high load data transmission, providing the optimal use of priorities to improve the efficiency of data transmission. © 2024 IEEE.

关键词： Random variables

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Nash Equilibria in Reverse Temporal Voronoi Games

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Journal of Graph Algorithms and Applications 2024年第1期28卷 301-312页

作者： Pawlowski, Simeon Froese, Vincent Technische Universität Berlin Faculty IV Institute of Software Engineering and Theoretical Computer Science Algorithmics and Computational Complexity Germany

We study Voronoi games on temporal graphs as introduced by Boehmer et al. (IJCAI ’21) where two players each select a vertex in a temporal graph with the goal of reaching the other vertices earlier than the other player. In this work, we consider the reverse temporal Voronoi game, that is, a player wants to maximize the number of vertices reaching her earlier than the other player. Since temporal distances in temporal graphs are not symmetric in general, this yields a different game. We investigate the difference between the two games with respect to the existence of Nash equilibria in various temporal graph classes including temporal trees, cycles, grids, cliques and split graphs. Our extensive results show that the two games indeed behave quite differently depending on the considered temporal graph class. © 2024, Brown University. All rights reserved.

关键词： Nash equilibrium

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Application of VGIS Digital Twin Platform for Enhanced Oilfield Disaster Management and Environmental Protection

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Informatica (Slovenia) 2024年第14期48卷 27-42页

作者： Alabbasi, Awab Aljabery, Maalim A. Bölük, Pınar Sarısaray Faculty of Engineering and Natural Sciences Electrical and Computer Engineering Dept. Bahcesehir University Istanbul34353 Turkey Faculty of Computer Science and Information Technology Computer Science Dept. University of Basrah Basrah61004 Iraq Faculty of Engineering and Natural Sciences Software Engineering Dept. Bahçeşehir University Istanbul34353 Turkey

VGIS (Virtual Geographic Information System) Platform is a unified oilfield operations management platform based on MaaS (Management as a Service) that integrates advanced technologies such as AIoT (Artificial Intelligence & Internet of Things), GIS (Geographic Information System), Digital Twin, and AR (Augmented Reality)/ VR (Virtual Reality). The VGIS platform connects, collects, integrates, analyses, and acts on disparate data sources to provide the oilfield with a centralized view to help make informed decisions fast, bring operational visibility across all assets to improve safety and operational efficiency, reduce costs and improve margins, provide comprehensive analytics and decision support, increase business agility, and reduce management complexity. This is a case study of the application of the VGIS platform Majnoon oilfield, Basrah, Iraq. VGIS digital transformation, production process, and KPIs (Key performance indicators) are highly managed taking oilfield management to a higher level globally, in this study, the objective is to address and resolve the issue of high costs associated with using cloud services by utilising a local Data Center (a large group of networked computer servers typically used by organizations for the remote storage, processing, or distribution of large amounts of data). This approach involves directly uploading 3D maps to ensure faster service on the intranet, eliminating the need to pay for cloud *** aims providing a platform for researching and implementing monitoring and controlling processes in a more convenient and remote manner. The vGIS platform has emerged as a crucial instrument in the realm of disaster response and crisis management. This technology enables responders to rapidly evaluate, analyse, and respond to crucial information in real-time. This research examined the application of vGIS technology in disaster response and analyse the potential advantages and obstacles associated with its utilisation. © 2

关键词： Information management

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Fingerprint Fraud Explainability Using Grad-Cam for Forensic Procedures 23

Fingerprint Fraud Explainability Using Grad-Cam for Forensic...

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23rd International Conference on New Trends in Intelligent software Methodologies, Tools and Techniques, SoMeT 2024

作者： Manai, Elyes Mejri, Mohamed Fattahi, Jaouhar Department of Computer & Software Engineering Faculty of Science and Engineering Laval University Quebec Canada

ISBN: (纸本)9781643685380

This paper investigates the application of GradCAM, an explainable AI (XAI) technique, to enhance the transparency and precision of fingerprint authentication systems in forensics, particularly in detecting fingerprint mutilation-a common method used to evade biometric security measures. Employing the SOCOfing dataset, which contains both unaltered and synthetically altered fingerprint images, we apply GradCAM to visualize and understand the decision-making process of a convolutional neural network (CNN) model trained to recognize and classify these alterations. Our study not only demonstrates the model's effectiveness in identifying different types of fingerprint modifications but also identifies areas where the model's performance can be enhanced. Through detailed visual analysis, we uncover the model's focus points and assess its reliability across various alteration types and difficulty levels. The insights gained underline the potential of XAI in improving the robustness and reliability of biometric verification systems, paving the way for more secure and equitable AI applications in high-stakes environments. © 2024 IOS Press. All rights reserved.

关键词： Convolutional neural networks

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A Trustworthy Analysis Approach for Chatbots on Health Data: ChatGPT-4 Example 29

A Trustworthy Analysis Approach for Chatbots on Health Data:...

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29th International Conference on Information Technology, IT 2025

作者： Arslanoǧlu, Kübra Karaköse, Mehmet The University of Firat The Faculty of Engineering The Department of Software Engineering Elazig Turkey The University of Firat The Faculty of Engineering The Department of Computer Engineering Elazig Turkey

ISBN: (纸本)9798331517649

Considering the increasing and widespread use of chatbots, it is of great importance to provide methods and tools to address ethical concerns and to make users aware of various aspects of a chatbot, including non-functional features. The Generative Pre-Trained Transformer (GPT) is a state-of-the-art Natural Language Generation (NLG) model, enhanced by the latest version of GPT-4. This study evaluates the trustworthiness of the responses of three different chatbot models, BlenderBot with Hugging Face, ChatGPT-3.5 and ChatGPT-4, to 100 randomly selected medical questions. The accuracy and semantic similarity of the answers were measured by BLEU, ROUGE-1 and Cosine Similarity metrics, and response times were also recorded as an important performance factor. The results showed that GPT-4 exhibited superior performance, thus being able to produce more accurate and contextually reliable responses. However, the significantly longer response time of GPT-4 emerged as a disadvantage that may affect real-time utilisation. These findings provide an important reference for the effective use of GPT-4 in the context of health chatbots. The study contributes to the literature on improving the effectiveness of chatbots in healthcare by drawing attention to the importance of balancing speed, accuracy and trustworthiness, as well as the fact that the answers obtained are drawn with Application Programming Interface (API). © 2025 IEEE.

关键词： Electronic health record

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