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2024 Cognitive Models and Artificial Intelligence Conference, AICCONF 2024

作者： Anarova, Shahzoda Jabbarov, Jamoliddin Uralova, Iroda Tashkent University of Information Technologies Department of Convergence of digital technologies Computer Engineering Faculty Tashkent Uzbekistan Department of Computer science and technology Intelligent systems and computer technologies Faculty Samarkand State University Samarkand Uzbekistan

ISBN: (纸本)9798400716928

This article is devoted to the determination of the fractal size of the damaged part of the human brain on the basis of images obtained from MRI (magnetic resonance imaging). There are various mathematical methods for determining the size of fractal structures, this article uses two variants of the cell counting method and describes the analysis of errors in determining the fractal size of the damaged part of the human brain. The article shows that the damaged part of the human brain has a fractal structure and that it has a fractal dimension. Fractal measurement has also been used to determine the size of the damaged part of the human brain, unlike common methods. In determining the fractal scale, cases of changes in human body organs were not taken into account. In the article, the determination of the size of the damaged part of the human brain is studied on the basis of mathematical formulas. Two different variants of the special cell counting method were used to calculate the fractal measurements, as well as the corresponding number of experimental results. In addition, these measurements were used to assess the extent to which patients suffered brain damage and to provide information on the practical application of these fractal measurements. © 2024 ACM.

关键词： Magnetic resonance imaging

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Multi-Class Vehicle Detection and Classification with YOLO11 on UAV-Captured Aerial Imagery 7

Multi-Class Vehicle Detection and Classification with YOLO11...

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7th IEEE International Conference on Actual Problems of Unmanned Aerial Vehicles Development, APUAVD 2024

作者： Bakirci, Murat Dmytrovych, Petro Bayraktar, Irem Anatoliyovych, Oleh Tarsus University Unmanned/Intelligent Systems Lab Faculty of Aeronautics and Astronautics Mersin Turkey Ivan Franko National University of Lviv Faculty of Electronics and Computer Technologies Lviv Ukraine

ISBN: (纸本)9798331534141

Aerial imaging and object detection using unmanned aerial vehicle (UAV) systems pose unique challenges, including varying altitudes, dynamic backgrounds, and changes in lighting and weather conditions. These factors complicate the detection process, demanding robust and adaptive algorithms. Furthermore, the need for real-time processing in UAV applications imposes stringent requirements on computational efficiency and resource management. This study presents a comparative analysis of the cutting-edge object detection algorithm YOLO11, specifically tailored for vehicle detection in UAV-captured traffic images. Using a custom dataset derived from UAV aerial imaging, the algorithm was trained and evaluated to assess both its performance in terms of speed and accuracy, and the results were compared with YOLOv10. Experimental findings indicate that while YOLOv10 achieves slightly faster inference speeds, YOLO11 offers marginally better detection accuracy. © 2024 IEEE.

关键词： Adaptive algorithms

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Challenges and Advances in UAV-Based Vehicle Detection Using YOLOv9 and YOLOv10 7

Challenges and Advances in UAV-Based Vehicle Detection Using...

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7th IEEE International Conference on Actual Problems of Unmanned Aerial Vehicles Development, APUAVD 2024

ISBN: (纸本)9798331534141

Aerial imaging and object detection with unmanned aerial vehicle (UAV) systems present unique challenges, including varying altitudes, dynamic backgrounds, and changes in lighting and weather conditions. These factors complicate the detection process, demanding robust and adaptive algorithms. Furthermore, the need for real-time processing in UAV applications imposes stringent requirements on computational efficiency and resource management. This study presents a comparative analysis of two cutting-edge object detection algorithms, YOLOv9 and YOLOv10, specifically tailored for vehicle detection in UAVcaptured traffic images. Leveraging a custom dataset derived from UAV aerial imaging, both algorithms were trained and evaluated to assess their performance in terms of speed and accuracy. The experimental results reveal that while YOLOv9 demonstrates a marginally superior inference speed, YOLOv10 excels slightly in detection accuracy. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Automatic extraction of lightweight and efficient neural network architecture of heavy convolutional architectures to predict microsatellite instability from hematoxylin and eosin histology in gastric cancer

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Neural Computing and Applications 2024年第25期36卷 15295-15321页

作者： Rostami, Habib Ashkpour, Maryam Behzadi-Khormouji, Hamed Mokhtari, Maral Khayati, Armin Keshavarz, Ahmad Talatian Azad, Saeed Tabesh, Yahya Computer Engineering Department Faculty of Intelligent Systems Engineering and Data Science Persian Gulf University Bushehr75168 Iran Artificial Intelligence and Intelligent Healthcare Lab Artificial Intelligence and Data Mining Research Group Faculty of Intelligent Systems Engineering and Data Science ICT Research Institute Persian Gulf University Bushehr75168 Iran Faculty of New Science and Technologies University of Tehran Tehran Iran Department of Computer Science Imec-IDLab University of Antwerp Sint-Pietersvliet 7 Antwerp2000 Belgium Shahid Faghihi Hospital Shiraz University of Medical Sciences Shiraz Iran Department of Computer Science Engineering and IT School of Electrical and Computer Engineering Shiraz University Shiraz Iran Electrical Engineering Department Faculty of Intelligent Systems Engineering and Data Science Persian Gulf University Bushehr75168 Iran IoT and Signal Processing Research Group Faculty of Intelligent Systems Engineering and Data Science ICT Research Institute Persian Gulf University Bushehr75168 Iran Department of Mathematical Sciences Sharif University of Technology Tehran Iran

Cancers have emerged as a significant concern due to their impact on public health and society. The examination and interpretation of tissue sections stained with Hematoxylin and Eosin (H&E) play a crucial role in disease assessment, particularly in cases like gastric cancer. Microsatellite instability (MSI) is suggested to contribute to the carcinogenesis of specific gastrointestinal tumors. However, due to the nonspecific morphology observed in H&E-stained tissue sections, MSI determination often requires costly evaluations through various molecular studies and immunohistochemistry methods in specialized molecular pathology laboratories. Despite the high cost, international guidelines recommend MSI testing for gastrointestinal cancers. Thus, there is a pressing need for a new diagnostic modality with lower costs and widespread applicability for MSI detection. This study aims to detect MSI directly from H&E histology slides in gastric cancer, providing a cost-effective alternative. The performance of well-known deep convolutional neural networks (DCNNs) and a proposed architecture are compared. Medical image datasets are typically smaller than benchmark datasets like ImageNet, necessitating the use of off-the-shelf DCNN architectures developed for large datasets through techniques such as transfer learning. Designing an architecture proportional to a custom dataset can be tedious and may not yield desirable results. In this work, we propose an automatic method to extract a lightweight and efficient architecture from a given heavy architecture (e.g., well-known off-the-shelf DCNNs) proportional to a specific dataset. To predict MSI instability, we extracted the MicroNet architecture from the Xception network using the proposed method and compared its performance with other well-known architectures. The models were trained using tiles extracted from whole-slide images, and two evaluation strategies, tile-based and whole-slide image (WSI)-based, were employed and comp

关键词： Network architecture

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A variable structure model of neurons’ application in computer vision

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Multimedia Tools and Applications 2025年 1-11页

作者： Sridi, Alaeddine Attia, Mouna Bouallegue, Kais Electronics and Micro-Electronics Laboratory Faculty of Sciences of Monastir University of Monastir Monastir Tunisia The Higher Institute of Computer Science and Communication Technologies of Hammam Sousse University of Sousse Sousse Tunisia Laboratory of Computer Science Parallelism and Intelligent Communicating Systems Faculty of Sciences of Tunisia University of Tunis ELManar Tunis Tunisia Department of Electrical Engineering Higher Institute of Applied Sciences and Technology of Sousse University of Sousse Sousse Tunisia

The neuron doctrine defines the neuron as the basic unit of the nervous system, which drives the dynamic behavior of our organs. This has led to neurons becoming the focus of modern neuroscience research and to the rise of neurocomputing, in which we try to digitally simulate human behavior. The goal being to better understand the human nervous system and gain more control over neuron-related diseases, helping doctors establish faster and more reliable medical diagnoses. In this context, we aim to simulate eye diseases and vision problems by convolving input images using a new class of neural networks based on a variable structure model of neurons presented in [1]. In this work, we focus on recreating the common convolution filters as a first step. This is the first time in neurocomputing literature that dendritic neurons have been used for dynamic convolution kernels generation and eye diseases simulation. We start with a presentation of our upgrades to the model’s flexibility and accuracy. Then, we propose a novel kernel generator algorithm to convolve the input images. Finally, we show its promising results in creating the outline, pixelization, and night convolution kernels, as well as control some metrics, including brightness and data retention. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Neurons

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On the Approximation Algorithms for Solving Generalized Bernoulli Differential Equations Using Piecewise Constant Argument Method

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International Journal of Applied and Computational Mathematics 2025年第2期11卷 1-12页

作者： Muminov, Mukhiddin I. Jumaev, Zafar Z. Faculty of Mathematics Samarkand State University University Boulevard 15 Samarkand 140104 Uzbekistan V.I Romanovskiy Institute of Mathematics Uzbekistan Academy of Sciences Tashkent Uzbekistan Faculty of Intelligent Systems and Computer Technologies Samarkand State University University Boulevard 15 Samarkand 140104 Uzbekistan

The study examines a generalized Bernoulli differential equation with piecewise constant arguments, which includes two terms with powers of the unknown. It presents an efficient approach to determining an approximate solution to the equation. A differential equation with piecewise constant arguments corresponding to the considered initial value problem, which depends on a positive integer N, is introduced. It is proven that this equation has a unique piecewise-smooth solution, which serves as an approximate solution to the initial value problem for large N. Numerical results are provided through examples, demonstrating the efficiency and high accuracy of the proposed method. © The Author(s), under exclusive licence to Springer Nature India Private Limited 2025.

关键词： Absolute error Approximate solution Generalized Bernoulli differential equation Piecewise constant argument Variable coefficients

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Hybrid stock analysis model for financial market forecasting 18

Hybrid stock analysis model for financial market forecasting

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18th IEEE International Conference on computer Science and Information technologies, CSIT 2023

作者： Korablyov, Mykola Fomichov, Oleksandr Antonov, Danylo Dykyi, Stanislav Ivanisenko, Ihor Lutskyy, Sergey Kharkiv National University of Radio Electronics Department of Computer Intelligent Technologies and Systems Kharkiv Ukraine Kharkiv National University of Radio Electronics Department of Electronic Computer Devises Kharkiv Ukraine University of Jyväskylä Faculty of Information Technology Jyväskylä Finland

ISBN: (纸本)9798350360462

Various approaches are used to analyze stocks for the purpose of forecasting the financial market. Because stocks exist in a large and interconnected market, traditional methods based on time series information for a single stock do not take into account the relationships between other stocks. Taking into account the relationships between stocks can improve the effectiveness of stock price forecasting. The paper proposes a hybrid stock analysis model that uses a combination of various intelligent technologies: recurrent neural networks (RNN), artificial immune systems (AIS), and graphical neural networks (GNN). Time series in the form of daily sales volumes and stock prices are fed to the inputs of the RNN to obtain stock price characteristics. These characteristics are fed to the input of the clustering model to obtain information about the relationship between stocks in the form of a graph with selected clusters of stocks. The GNN inputs are a graph whose nodes display the characteristics of a stock exchange time series, and the arcs show the connectivity between them. The outputs of GNN are stock returns. Using this model allows you to more effectively predict the financial market and make more informed decisions in order to obtain high profits with low risks. © 2023 IEEE.

关键词： Forecasting

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A novel myocarditis detection combining deep reinforcement learning and an improved differential evolution algorithm

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CAAI Transactions on Intelligence Technology 2024年第6期9卷 1347-1360页

作者： Jing Yang Touseef Sadiq Jiale Xiong Muhammad Awais Uzair Aslam Bhatti Roohallah Alizadehsani Juan Manuel Gorriz Department of Computer System and Technology Faculty of Computer Science and Information TechnologyUniversiti MalayaKuala LumpurMalaysia Centre for Artificial Intelligence Research(CAIR) Department of Information and Communication TechnologyUniversity of AgderGrimstadNorway Department of Creative Technologies Air UniversityIslamabadPakistan School of Information and Communication Engineering Hainan UniversityHaikouHainanChina Institute for Intelligent Systems Research and Innovation(IISRI) Deakin UniversityWaurn PondsVictoriaAustralia Data Science and Computational Intelligence Institute University of GranadaGranadaSpain

Myocarditis is a serious cardiovascular ailment that can lead to severe consequences if not promptly *** is triggered by viral infections and presents symptoms such as chest pain and heart *** detection is crucial for successful treatment,and cardiac magnetic resonance imaging(CMR)is a valuable tool for identifying this ***,the detection of myocarditis using CMR images can be challenging due to low contrast,variable noise,and the presence of multiple high CMR slices per *** overcome these challenges,the approach proposed incorporates advanced techniques such as convolutional neural networks(CNNs),an improved differential evolution(DE)algorithm for pre-training,and a reinforcement learning(RL)-based model for *** this method presented a significant challenge due to the imbalanced classification of the Z-Alizadeh Sani myocarditis dataset from Omid Hospital in *** address this,the training process is framed as a sequential decision-making process,where the agent receives higher rewards/penalties for correctly/incorrectly classifying the minority/majority ***,the authors suggest an enhanced DE algorithm to initiate the backpropagation(BP)process,overcoming the initialisation sensitivity issue of gradient-based methods like back-propagation during the training *** effectiveness of the proposed model in diagnosing myocarditis is demonstrated through experimental results based on standard performance ***,this method shows promise in expediting the triage of CMR images for automatic screening,facilitating early detection and successful treatment of myocarditis.

关键词： classification differential evolution myocarditis reinforcement learning

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Multi-Class Vehicle Detection and Classification with YOLO11 on UAV-Captured Aerial Imagery

Multi-Class Vehicle Detection and Classification with YOLO11...

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IEEE International Conference on Actual Problems of Unmanned Air Vehicles Developments Proceedings (APUAVD)

作者： Murat Bakirci Petro Dmytrovych Irem Bayraktar Oleh Anatoliyovych Unmanned/Intelligent Systems Lab Faculty of Aeronautics and Astronautics Tarsus University Mersin Turkey Faculty of Electronics and Computer Technologies Ivan Franko National University of Lviv Lviv Ukraine

ISBN: (数字)9798331534141

ISBN: (纸本)9798331534158

关键词： YOLO Accuracy Vehicle detection Imaging Autonomous aerial vehicles Real-time systems Inference algorithms Classification algorithms Automobiles Vehicle dynamics

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Challenges and Advances in UAV-Based Vehicle Detection Using YOLOv9 and YOLOv10

Challenges and Advances in UAV-Based Vehicle Detection Using...

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IEEE International Conference on Actual Problems of Unmanned Air Vehicles Developments Proceedings (APUAVD)

ISBN: (数字)9798331534141

ISBN: (纸本)9798331534158

关键词： YOLO Accuracy Vehicle detection Imaging Autonomous aerial vehicles Real-time systems Inference algorithms Automobiles Resource management Vehicle dynamics

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