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Intelligent Systems Conference

作者： Park, Jiyoon Branksome Hall Asia Seogwipo South Korea

ISBN: (纸本)9783031664304;9783031664311

Recently, due to the rapid development of generative AI technologies, the use of AI-generated images has increased significantly, making the distinction between real and fake images crucial. Generative images may be used in various ways such as data training and fast image generation, but a potential for misuse, such as in deep fake or spreading false information, still exists. This study explores a novel model using the architecture of Swin-Transformer to distinguish between fake and real images generated based on CNN (Convolutional neural networks) and GAN (Generative Adversarial networks). The Swin-Transformer, a successor model of Vision in Transformer (ViT), applies the structure of the Transformer, which has shown outstanding performance in natural language processing, to the field of images and demonstrates excellent pixel-level segmentation performance. Real and fake images require detailed pixel-level analysis, in which the Swin-Transformer exhibits higher accuracy. Improving the performance of distinguishing between real and fake images is expected to set limits on indiscreet image generation, bringing further effects such as preventing the indiscriminate use of AI images through program-based discrimination/legal sanctions.

关键词： artificial intelligence Convolution neural network Generative adversarial network Real and fake

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Traditional and deep-learning-based denoising methods for medical images

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MULTIMEDIA TOOLS AND applications 2023年第17期83卷 52061页

作者： El-Shafai, Walid El-Nabi, Samy Abd Ali, Anas M. El-Rabaie, El-Sayed M. Abd El-Samie, Fathi E. Prince Sultan Univ Secur Engn Lab Comp Sci Dept Riyadh 11586 Saudi Arabia Menoufia Univ Fac Elect Engn Dept Elect & Elect Commun Engn Menoufia 32952 Egypt King Salman Int Univ KSIU Fac Comp Sci & Engn Dept Artificial Intelligence Engn South Sinai 46511 Egypt Prince Sultan Univ Robot & Internet Things Lab Riyadh 12435 Saudi Arabia Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Technol POB 84428 Riyadh 11671 Saudi Arabia

Visual information is extremely important in today's world. Visual information transmitted in the form of digital images has become a critical mode of communication. As a result, digital image processing plays a critical role in advancing the image-related applications. Especially, in the medical field, the image processing stage is one of the important stages that need great accuracy to diagnose and determine the type of the disease. Its objective is to overcome the noise problems in medical images and preserve information and edges in images. Medical images can be enhanced by removing noise through the use of traditional and Deep Learning (DL) methods. DL methods depending on Convolutional neural networks (CNNs) have achieved great results in the processing stage for noise reduction in medical images. The DL is a promising and effective solution for estimating real noise and extracting representative features from images. This paper presents a review of image denoising methods for medical images, considering noise sources, and types of noise. The concepts of noise reduction (denoising) for various methods are presented. In addition, a comparative study is presented to clarify the advantages and disadvantages of each method. Finally, some possible trends for future work are introduced.

关键词： image denoising Medical images Deep Learning (DL) Spatial filters Wavelet transform Autoencoder

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Point-to-Spike Residual Learning for Energy-Efficient 3D Point Cloud Classification 38

Point-to-Spike Residual Learning for Energy-Efficient 3D Poi...

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38th AAAI Conference on artificial Intelligence (AAAI) / 36th Conference on Innovative applications of artificial Intelligence / 14th Symposium on Educational Advances in artificial Intelligence

作者： Wu, Qiaoyun Zhang, Quanxiao Tan, Chunyu Zhou, Yun Sun, Changyin Anhui Univ Sch Artificial Intelligence Hefei Peoples R China Minist Educ Engn Res Ctr Autonomous Unmanned Syst Technol Hefei Peoples R China Anhui Prov Engn Res Ctr Unmanned Syst & Intellige Hefei Peoples R China Hefei Comprehens Natl Sci Ctr Inst Artificial Intelligence Hefei Peoples R China

ISBN: (纸本)1577358872

Spiking neural networks (SNNs) have revolutionized neural learning and are making remarkable strides in image analysis and robot control tasks with ultra-low power consumption advantages. Inspired by this success, we investigate the application of spiking neural networks to 3D point cloud processing. We present a point-to-spike residual learning network for point cloud classification, which operates on points with binary spikes rather than floating-point numbers. Specifically, we first design a spatial-aware kernel point spiking neuron to relate spiking generation to point position in 3D space. On this basis, we then design a 3D spiking residual block for effective feature learning based on spike sequences. By stacking the 3D spiking residual blocks, we build the point-to-spike residual classification network, which achieves low computation cost and low accuracy loss on two benchmark datasets, ModelNet40 and ScanObjectNN. Moreover, the classifier strikes a good balance between classification accuracy and biological characteristics, allowing us to explore the deployment of 3D processing to neuromorphic chips for developing energyefficient 3D robotic perception systems.

关键词： Energy efficiency

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Learning Truly Monotone Operators with applications to Nonlinear Inverse Problems

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SIAM JOURNAL ON IMAGING SCIENCES 2025年第1期18卷 735-764页

作者： Belkouchi, Younes Pesquet, Jean-Christophe Repetti, Audrey Talbot, Hugues Univ Paris Saclay CentraleSupelec Inria F-91190 Gif Sur Yvette France Heriot Watt Univ Sch Math & Comp Sci Edinburgh EH14 4AS Scotland Heriot Watt Univ Sch Engn & Phys Sci Edinburgh EH14 4AS Scotland Bayes Ctr Maxwell Inst Math Sci Edinburgh EH9 3FD Scotland

This article introduces a novel approach to learning monotone neural networks (NNs) through a newly defined penalization loss. The proposed method is particularly effective in solving classes of variational problems, specifically monotone inclusion problems, commonly encountered in image processing tasks. The forward-backward-forward (FBF) algorithm is employed to address these problems, offering a solution even when the Lipschitz constant of the NN is unknown. Notably, the FBF algorithm provides convergence guarantees under the condition that the learned operator is monotone. Building on plug-and-play methodologies, our objective is to apply these newly learned operators to solving nonlinear inverse problems. To achieve this, we initially formulate the problem as a variational inclusion problem. Subsequently, we train a monotone NN to approximate an operator that may not inherently be monotone. Leveraging the FBF algorithm, we then show simulation examples where the nonlinear inverse problem is successfully solved.

关键词： monotone operator optimization inverse problem deep learning forward-backward-forward plug and play (PnP)

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Input parameterized physics informed neural networks for de noising, super-resolution, and imaging artifact mitigation in time resolved three dimensional phase-contrast magnetic resonance imaging

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ENGINEERING applications OF artificial INTELLIGENCE 2025年 150卷

作者： Kalajahi, Amin Pashaei Csala, Hunor Bin Mamun, Zayeed Yadav, Sangeeta Amili, Omid Arzani, Amirhossein D'Souza, Roshan M. Univ Wisconsin Milwaukee Dept Mech Engn Milwaukee WI 53211 USA Univ Utah Dept Mech Engn 1495 E 100 S Salt Lake City UT 84112 USA Univ Utah Sci Comp & Imaging Inst 72 S Cent Campus Dr Ste 3750 Salt Lake City UT 84112 USA Univ Toledo Mech Ind & Mfg Engn Dept 2801 West Bancroft St Toldeo OH 43606 USA

Motivation: Hemodynamic analysis is crucial for diagnosing and predicting cardiovascular diseases. However, methods relying on fluid flow simulations or blood flow imaging are complex, time-consuming, and require specialized expertise, limiting their clinical use. Goal: This research aims to automate the enhancement of blood flow images, providing clinicians with a fast, accurate tool for hemodynamic analysis without requiring advanced expertise. Objectives: A software tool based on physics-constrained neural networks was developed to enable clinicians to easily select and process regions of interest (ROIs) in time-resolved three-dimensional phase contrast magnetic resonance imaging (4D-Flow MRI) blood flow images for quick, accurate analysis. Methods: The Input Parameterized Physics-Informed neural Network (IP-PINN) was introduced to improve the spatio-temporal resolution of 4D-Flow MRI. IP-PINN mitigates noise, velocity aliasing, and phase errors. A convolutional neural network processes ROI data into latent vectors, which are then used to predict velocity, pressure, and spin density via a multi-layer perceptron. The method is trained with synthetic blood flow data using an innovative loss function that addresses noise and artifacts. Results: IP-PINN successfully enhanced image resolution, reducing noise and artifacts when tested on synthetic 4D-Flow MRI data derived from blood flow simulations of intracranial aneurysms. For data with 20 decibels (dB) signal-to-noise ratio, results closely matched the ground truth with less than 5.5% relative error. processing took under two minutes. The method also has the potential to reduce data acquisition time by 25%. Conclusions: IP-PINN could significantly enhance the clinical use of 4D-Flow MRI for personalized hemodynamic analysis in cardiovascular diseases.

关键词： Phase contrast magnetic resonance imaging Super-resolution Generalizability of physics-informed neural nets

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image processing for Smart Agriculture applications Using Cloud-Fog Computing

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SENSORS 2024年第18期24卷 5965页

作者： Markovic, Dusan Stamenkovic, Zoran Dordevic, Borislav Randic, Sinisa Univ Kragujevac Fac Agron Cacak Cara Dusana 34 Cacak 32102 Serbia Univ Potsdam Inst Comp Sci An Der Bahn 2 D-14476 Potsdam Germany IHP Leibniz Inst Innovat Mikroelekt Technol Pk 25 D-15236 Frankfurt Germany Inst Mihailo Pupin Volgina 15 Belgrade 11060 Serbia Univ Kragujevac Fac Tech Sci Cacak Svetog Save 65 Cacak 32102 Serbia

The widespread use of IoT devices has led to the generation of a huge amount of data and driven the need for analytical solutions in many areas of human activities, such as the field of smart agriculture. Continuous monitoring of crop growth stages enables timely interventions, such as control of weeds and plant diseases, as well as pest control, ensuring optimal development. Decision-making systems in smart agriculture involve image analysis with the potential to increase productivity, efficiency and sustainability. By applying Convolutional neural networks (CNNs), state recognition and classification can be performed based on images from specific locations. Thus, we have developed a solution for early problem detection and resource management optimization. The main concept of the proposed solution relies on a direct connection between Cloud and Edge devices, which is achieved through Fog computing. The goal of our work is creation of a deep learning model for image classification that can be optimized and adapted for implementation on devices with limited hardware resources at the level of Fog computing. This could increase the importance of image processing in the reduction of agricultural operating costs and manual labor. As a result of the off-load data processing at Edge and Fog devices, the system responsiveness can be improved, the costs associated with data transmission and storage can be reduced, and the overall system reliability and security can be increased. The proposed solution can choose classification algorithms to find a trade-off between size and accuracy of the model optimized for devices with limited hardware resources. After testing our model for tomato disease classification compiled for execution on FPGA, it was found that the decrease in test accuracy is as small as 0.83% (from 96.29% to 95.46%).

关键词： image classification cloud-fog computing deep learning agriculture application

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Swin transformer and ResNet based deep networks for low-light image enhancement

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MULTIMEDIA TOOLS AND applications 2024年第9期83卷 26621-26642页

作者： Xu, Lintao Hu, Changhui Zhang, Bo Wu, Fei Cai, Ziyun Nanjing Univ Posts & Telecommun Coll Automat Wenyuan Rd Nanjing 210023 Jiangsu Peoples R China Nanjing Univ Posts & Telecommun Coll Artificial Intelligence Wenyuan Rd Nanjing 210023 Jiangsu Peoples R China

Low-light image enhancement is a long-term low-level vision problem, which aims to improve the visual quality of images captured in low illumination environment. Convolutional neural network (CNN) is the foundation of the majority of low-light image enhancement algorithms now. The limitations of CNN receptive field lead to the inability to establish long-range context interaction. In recent years, Transformer has received increasing attention in computer vision due to its global attention. In this paper, we design the Swin Transformer and ResNet-based Generative Adversarial Network (STRN) for low-light image enhancement by combining the advantages of ResNet and the Swin Transformer. The STRN consists of a U-shaped generator and multiscale discriminators. The generator is composed of a shallow feature extraction, a deep feature extraction, and an image reconstruction module. To calculate the global and local attention, we alternately use Swin Transformer blocks and ResNet in the deep feature processing module. The self perceptual loss and the spatial consistency loss are employed to constrain the random paired training of STRN. The experimental results on benchmark datasets and real-world low-light images demonstrate that the proposed STRN achieves state-of-the-art performance on low-light image enhancement tasks in terms of visual quality and evaluation metrics.

关键词： Low-light image enhancement Generative adversarial network Swin transformer Random paired learning

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Modeling the Detection and Classification of Tomato Leaf Diseases Using a Robust Deep Learning Framework

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TRAITEMENT DU SIGNAL 2024年第4期41卷 1667-1678页

作者： Gupta, Manish Yadav, Dharmveer Khan, Safdar Sardar Kumawat, Ashish Kumar Chourasia, Ankita Rane, Pinky Ujlayan, Anshul GLA Univ Dept Elect & Commun Engn Mathura 281406 India St Xaviers Coll Jaipur Dept Comp Sci Jaipur 302029 India Engn Med Caps Univ Dept Comp Sci Indore 453331 India Graphnexti Chief Data Scientist & Mentor Dept Greater Noida 201310 India

Tomatoes areAa noteworthy horticultural crop that has considerable importance in a diverse range of culinary traditions. At now, the primary concern for food security is in the realm of plant diseases. Researchers are actively working towards developing a streamlined approach to detect and diagnose illnesses in their early stages, with the ultimate goal of enhancing the agricultural industry. Currently, computer scientists and engineers are actively engaged in the fast development of a diverse range of tools and methodologies, with a special focus on the field of artificial intelligence. The advancement of cutting-edge machine learning applications for artificial intelligence relies on the establishment of original methods and frameworks. In contrast to the single-layer topologies of more traditional neural network learning methods, "deep learning" makes use of networks with many processing layers. In this research, a DL model is developed to detect & diagnose plant diseases by analyzing healthy & unhealthy plant image samples using deep learning techniques. The dataset contains 43,823 images of plants, including healthy plants and unhealthy plants. For this model implementation, follow some methodology processes like data preprocessing, image segmentation, data balancing, data splitting classification and detection, and assess the model's efficacy. The study uses a Fine-Tuned EfficientNetB7 approach with an impressive Mean Average Accuracy of 99.31%. AThe proposed technique demonstrates efficacy in early detection and has the potential for further improvement in terms of performance, hence facilitating the development of a real-world automated system for detecting plant diseases in agricultural settings.

关键词： plant diseases image processing machine learning deep learning intelligence

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Integrating deep learning for accurate gastrointestinal cancer classification: a comprehensive analysis of MSI and MSS patterns using histopathology data

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neural Computing and applications 2024年第34期36卷 21273-21305页

作者： Wafa, Abeer A. Essa, Reham M. Abohany, Amr A. Abdelkader, Hanan E. Faculty of Computers and Artificial Intelligence Helwan University Helwan Egypt Department of Information System Higher Institute for Management and Information Technology Kafr El-Sheikh Egypt Faculty of Computers and Information Kafrelsheikh University Kafr El-Sheikh Egypt Faculty of Specific Education Mansoura University Mansoura Egypt

Early detection of microsatellite instability (MSI) and microsatellite stability (MSS) is crucial in the fight against gastrointestinal (GI) cancer. MSI is a sign of genetic instability often associated with DNA repair mechanism deficiencies, which can cause (GI) cancers. On the other hand, MSS signifies genomic stability in microsatellite regions. Differentiating between these two states is pivotal in clinical decision-making as it provides prognostic and predictive information and treatment strategies. Rapid identification of MSI and MSS enables oncologists to tailor therapies more accurately, potentially saving patients from unnecessary treatments and guiding them toward regimens with the highest likelihood of success. Detecting these microsatellite status markers at an initial stage can improve patient outcomes and quality of life in GI cancer management. Our research paper introduces a cutting-edge method for detecting early GI cancer using deep learning (DL). Our goal is to identify the optimal model for GI cancer detection that surpasses previous works. Our proposed model comprises four stages: data acquisition, image processing, feature extraction, and classification. We use histopathology images from the Cancer Genome Atlas (TCGA) and Kaggle website with some modifications for data acquisition. In the image processing stage, we apply various operations such as color transformation, resizing, normalization, and labeling to prepare the input image for enrollment in our DL models. We present five different DL models, including convolutional neural networks (CNNs), a hybrid of CNNs-simple RNN (recurrent neural network), a hybrid of CNNs with long short-term memory (LSTM) (CNNs-LSTM), a hybrid of CNNs with gated recurrent unit (GRU) (CNNs-GRU), and a hybrid of CNNs-SimpleRNN-LSTM-GRU. Our empirical results demonstrate that CNNs-SimpleRNN-LSTM-GRU outperforms other models in accuracy, specificity, recall, precision, AUC, and F1, achieving an accuracy of 99.90%. O

关键词： Diseases

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Design of Adaptive Control System Driven by artificial Intelligence and Its Application in Industry 4.0 2

Design of Adaptive Control System Driven by Artificial Intel...

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2nd IEEE International Conference on image processing and Computer applications, ICIPCA 2024

作者： Wen, Xiaoqian Business School Belarusian State University Minsk Belarus

ISBN: (纸本)9798350360240

In view of the difficulty of effective fusion of multi-source sensing data used in industrial production, a method based on multi-source sensing data fusion was proposed. A multi-sensor network model for real-time monitoring in industrial field is established. The key techniques of multi-dimensional data extraction, self-fuzzy inference, multi-dimensional group estimation and multi-dimensional grouping mechanism are studied by four-level data analysis method. This improves the control accuracy and effectiveness of the multidimensional system. This scheme is based on I2C bus structure and uses single chip microcomputer to control multiple sensor nodes, which improves the software reconfiguration ability and robustness, and reduces the maintenance and updating cost of the system. This project intends to establish an adaptive control model by combining FLMS and BP neural network. According to the system error, the weight coefficients of the two kinds of neural networks are determined. In the initial stage, the convergence rate of the neural networks is improved first, and then the nonlinear BP network is used as the main assistant to improve the control effect. © 2024 IEEE.

关键词： Microcomputers

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