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A deep learning framework for solving fractional partial differential equations

PHYSICA SCRIPTA 2025年第4期100卷 046012-046012页

作者： Ali, Amina Senu, Norazak Ahmadian, Ali Wahi, Nadihah Univ Putra Malaysia Dept Math & Stat UPM 43400 Serdang Malaysia Univ Sulaimani Coll Educ Dept Math Sulaymaniyah Iraq Univ Putra Malaysia Inst Math Res UPM 43400 Serdang Malaysia Jadara Univ Jadara Univ Res Ctr Jadara Jordan Istanbul Okan Univ Fac Engn & Nat Sci Istanbul Turkiye

This research focuses on the study and solution of fractional partial differential equations (FPDEs), a critical area in mathematical analysis. FPDEs pose significant challenges due to their complexity, often requiring extensive computational resources to solve. Given the scarcity of exact solutions, numerical methods have been a primary approach for tackling FPDEs. However, these methods often yield substantial but limited results. The ongoing quest for more effective solutions has led researchers to explore new methodologies. Recent advancements in deep learning (DL), particularly in deep neural networks (DNNs), offer promising tools for solving FPDEs due to their exceptional function-approximation capabilities, demonstrated in diverse applications such as image classification and natural language processing. This research addresses the challenges of solving FPDEs by proposing a novel deep feedforward neural network (DFNN) framework. The method integrates the Laplace transform for memory-efficient Caputo derivative approximations and demonstrates superior accuracy across various examples. The results highlight the framework's versatility and computational efficiency, establishing it as a powerful tool for solving FPDEs.

关键词： laplace transform method fractional partial differential equations artificial neural networks gradient descent deep neural network

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Performance Analysis of Deep CNN, YOLO, and LeNet for Handwritten Digit Classification 1

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International Conference on artificial Intelligence: Theory and applications (AITA)

作者： Sarmah, Jibok Saini, Madan Lal Kumar, Ankush Chasta, Vidhan Chandigarh Univ Dept CSE Apex Inst Technol Mohali Punjab India

ISBN: (数字)9789819984794

ISBN: (纸本)9789819984787;9789819984794

The fundamental applications of handwritten digit classification are in the fields of optical reorganization of digits, bank check processing, recognizing zip codes on mail for postal, processing bank check amounts, and numeric entries in forms filled up by hand. For processing these types of tasks, different kinds of learning algorithms are used. A comparative study on performance analysis was done for convolutional neural network, LeNet-5, and YOLOv7. Publically available MNIST, DIDA, and MNIST MIX handwritten digit dataset were used in experimental work. The objective of this study is to find the best algorithm which can give an acceptable accuracy. To implement the model, this paper uses a deep neural network CNN and its architecture LeNet-5 and YOLOv7 which have become a potent tool for image categorization problems in recent years. This paper demonstrates the efficacy of deep learning approaches for effective and precise digit recognition, which can be expanded to numerous real-world applications needing accurate and dependable recognition of digits written on paper. This research has achieved the highest accuracy of 99.38% for LeNet-5.

关键词： Handwritten digit recognition Convolutional neural networks LeNet-5 YOLOv7 MNIST

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SoN: Selective Optimal Network for smartphone-based indoor localization in real-time

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EXPERT SYSTEMS WITH applications 2025年 272卷

作者： Lee, Kyungsu Lee, Haeyun Hwang, Jae Youn Jeonbuk Natl Univ Dept Comp Sci & Artificial Intelligence Jeonju 54896 South Korea Korea Univ Technol & Educ Sch Comp Sci & Engn Cheonan 31253 South Korea Daegu Gyeongbuk Inst Sci & Technol DGIST Dept Elect Engn & Comp Sci Interdisciplinary Studies Artificial Intelligence Daegu 42988 South Korea

Deep learning-based scene recognition algorithms have been developed for real-time application in indoor localization systems. However, owing to the slow calculation time resulting from the deep structure of convolutional neural networks, deep learning-based algorithms have limitations in the usage of real-time applications, despite their high accuracy in classification tasks. To significantly reduce the computation time of these algorithms and slightly improve their accuracy, we thus propose a path-selective deep learning network, denoted as Selective Optimal Network (SoN). The SoN selectively uses the depth-variable networks depending on anew indicator, denoted as the classification-complexity of a source image. The SoN reduces the prediction time by selecting optimal depth for the baseline networks corresponding to the input samples. The network was evaluated using two public datasets and two custom datasets for indoor localization and scene classification, respectively. The experimental results indicated that, compared to other deep learning models, the SoN exhibited improved accuracy and enhanced the processing speed by up to 78.59%. Additionally, the SoN was applied to a smartphone-based indoor positioning system in real-time. The results indicated that the SoN shows excellent performance for rapid and accurate classification in real-time applications of indoor localization systems.

关键词： Deep learning Semi-supervised learning Efficient learning Scene recognition Indoor localization

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MLDC: multi-lung disease classification using quantum classifier and artificial neural networks

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neural COMPUTING & applications 2024年第7期36卷 3803-3816页

作者： Arora, Riya Rao, G. V. Eswara Banerjea, Shashwati Rajitha, B. Motilal Nehru Natl Inst Technol Dept Comp Sci & Engn Allahabad 211004 UP India

Lung diseases are one of the most common diseases around the world. The risk of these diseases are more in under-developed and developing countries, where millions of people are battling with poverty and living in polluted air. Chest X-Ray images are helpful screening tool for lung disease detection. However, disease diagnosis requires expert medical professionals. Furthermore, in developing and under-developed nations, the doctor-to-patient ratio is comparatively poor. Deep learning algorithms have recently demonstrated promise in the analysis of medical images and the discovery of patterns. In this current work, we have proposed a model MLDC (Multi-Lung Disease Classification) to detect common lung diseases. It introduces a MLDC feature extraction model with two different new classifiers, considering ANN (an artificial neural network) and QC (a quantum classifier). In this proposed model, tests are performed on the LDD (Lung Disease Dataset), which includes COVID-19, pneumonia, tuberculosis, and a healthy person's lung from chest X-ray images. Our proposed model achieves an accuracy of 95.6% for MLDC-ANN and 97.5% for MLDC-QC at a lower computational cost.

关键词： COVID-19 Pneumonia Tuberculosis image processing Deep learning Machine learning Quantum classifier

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End-to-end image Classification in Linear Hybrid Cellular Automata

End-to-end Image Classification in Linear Hybrid Cellular Au...

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International Joint Conference on neural networks (IJCNN)

作者： Sawahashi, Naoki Principe, Jose C. Univ Florida Elect & Comp Engn Gainesville FL 32610 USA

ISBN: (纸本)9798350359329;9798350359312

Cellular automata have ideal properties for scalable and efficient computing, but a lack of a training method limits their real-world applications. First, we propose to partition the cellular lattice into three regions: input, output, and processing. Second, we propose a novel synthesis method to train a linear hybrid cellular automaton. Third, we show image classification on the MNIST dataset using only logic operations. By mapping local states over the globally linear lattice, the proposed model achieved above 90% test accuracy in binary image classification. Our method does not require any pre or post-processors to perform computation over the lattice. Hence, the lattice maintains its massive parallelism and locality of computation, ideal for ultra-low power processing in machine learning.

关键词： cellular automata image classification parallel processing neuromorphic computing machine learning

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Pixel-level clustering network for unsupervised image segmentation

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ENGINEERING applications OF artificial INTELLIGENCE 2024年第PartB期127卷

作者： Hoang, Cuong Manh Kang, Byeongkeun Seoul Natl Univ Sci & Technol Dept Elect Engn 232 Gongneung Ro Seoul 01811 South Korea

While image segmentation is crucial in various computer vision applications, such as autonomous driving, grasping, and robot navigation, annotating all objects at the pixel-level for training is nearly impossible. There-fore, the study of unsupervised image segmentation methods is essential. In this paper, we present a pixel-level clustering framework for segmenting images into regions without using ground truth annotations. The proposed framework includes feature embedding modules with an attention mechanism, a feature statistics computing module, image reconstruction, and superpixel segmentation to achieve accurate unsupervised segmentation. Additionally, we propose a training strategy that utilizes intra-consistency within each superpixel, inter-similarity/dissimilarity between neighboring superpixels, and structural similarity between images. To avoid potential over-segmentation caused by superpixel-based losses, we also propose a post-processing method. Furthermore, we present an extension of the proposed method for unsupervised semantic segmentation. We conducted experiments on three publicly available datasets (Berkeley segmentation dataset, PASCAL VOC 2012 dataset, and COCO-Stuff dataset) to demonstrate the effectiveness of the proposed framework. The experimental results show that the proposed framework outperforms previous state-of-the-art methods.

关键词： Unsupervised image segmentation Convolutional neural networks Clustering Unsupervised semantic segmentation

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CFENet: Cost-effective underwater image enhancement network via cascaded feature extraction

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ENGINEERING applications OF artificial INTELLIGENCE 2024年第PartF期133卷

作者： Ji, Xun Wang, Xu Hao, Li-Ying Cai, Cheng-Tao Dalian Maritime Univ Sch Marine Elect Engn Dalian 116026 Peoples R China Harbin Engn Univ Coll Intelligent Syst Sci & Engn Harbin 150001 Peoples R China Heilongjiang Prov Key Lab Environm Intelligent Per Harbin 150001 Peoples R China Harbin Engn Univ Key Lab Intelligent Technol & Applicat Marine Equi Minist Educ Harbin 150001 Peoples R China

Due to the inevitable light absorption and scattering, underwater images always suffer from severe quality degradation, leading to significant performance decline for various maritime engineering-related applications. In recent years, deep neural networks (DNNs) have been proven to achieve high-quality enhancement of underwater images, which aims to extract and learn abstract features to exhibit superior performance. However, existing solutions often stack multilayer processing units to enrich features, which not only significantly burdens computational load but also encounters difficulties in fully utilizing and interacting between features at different depths. To address the above problems, this paper presents a cost-effective underwater image enhancement network via cascaded feature extraction, termed CFENet. Specifically, we develop a cost-effective cascaded structure for sufficient feature extraction and multiscale establishment of pixel-level long-range dependencies. In addition, we construct a dual-branch structure for effective feature fusion, so that the detailed texture and semantic information in the image can be simultaneously enhanced. Extensive experiments reveal the superiority of our proposed CFENet in both underwater image enhancement effects and computational complexity. Sufficient ablation study is conducted to demonstrate the effectiveness of each component in the network.

关键词： Underwater image enhancement Deep learning Deep neural networks Contextual information Feature extraction Feature fusion

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Automatic inverse design of second-order differential metasurfaces based on reinforcement learning

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ENGINEERING applications OF artificial INTELLIGENCE 2025年 153卷

作者： Li, Zhaohui Yang, Shuo Liu, Delian Xian Univ Sci & Technol Coll Commun & Informat Technol Xian 710054 Peoples R China Xian Inst Appl Opt Xian 710065 Peoples R China Xidian Univ Sch Optoelect Engn Xian 710071 Peoples R China

Second-order differential metasurfaces, as two-dimensional artificial nanostructures, hold great potential for various applications in optical computing, image processing, microscopy imaging, etc. They have the capability to perform second-order mathematical operations on incident optical fields. In general, the design of second-order differential optical processing metasurfaces needs to consider various constraints, including incident optical fields, material properties, and application requirements. This task is challenging and time-consuming, typically requiring manual intervention. Therefore, a reinforcement learning-based artificial intelligence algorithm is proposed for the automatic design of second-order differential metasurfaces. It can rapidly find the desired result by leveraging the powerful self-learning ability of reinforcement learning. This paper first introduces the basic principles of designing second-order differential optical processing metasurfaces. Then, it presents a new automatic design method for second-order differential metasurfaces based on reinforcement learning. Finally, a design of a second-order differential metasurface is presented. Simulation and experimental results validate the effectiveness of the proposed method. The proposed method establishes a basic framework for applying reinforcement learning to the automatic design of photonic devices and outlines the various constraint factors that need to be coupled and considered during the design process. A surrogate model based on neural networks is presented to fit the transmission calculated by finite difference time domain software. A new reward formula is given to sufficiently consider the transmission efficiency of whole devices. The proposed method effectively leverages the self-learning capability of reinforcement learning, circumventing challenges related to manual intervention, computational complexity, and time-consuming tasks in conventional methods.

关键词： Second-order differential metasurface Automatic inverse design artificial intelligence Reinforcement learning Multi-layer perceptron

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Diagnosing the spores of tomato fungal diseases using microscopic image processing and machine learning

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MULTIMEDIA TOOLS AND applications 2024年第26期83卷 67283-67301页

作者： Javidan, Seyed Mohamad Banakar, Ahmad Vakilian, Keyvan Asefpour Ampatzidis, Yiannis Rahnama, Kamran Tarbiat Modares Univ Dept Biosyst Engn Tehran Iran Gorgan Univ Agr Sci & Nat Resources Dept Biosyst Engn Gorgan Iran Univ Florida Southwest Florida Res & Educ Ctr Agr & Biol Engn Dept 2685 FL-29 Immokalee FL 34142 USA Gorgan Univ Agr Sci & Nat Resources Fac Plant Prod Dept Plant Protect Gorgan Iran

Accurate diagnosis of plant diseases by the assessment of pathogen presence to reduce disease-related production loss is one of the most fundamental issues for farmers and specialists. This will improve product quality, increase productivity, reduce the use of fungicides, and reduce the final cost of agricultural production. Today, new technologies such as image processing, artificial intelligence, and deep learning have provided reliable solutions in various fields of precision agriculture and smart farm management. In this research, microscopic image processing and machine learning have been used to identify the spores of four common tomato fungal diseases. A dataset including 100 microscopic images of spores for each disease was developed, followed by the extraction of the texture, color, and shape features from the images. The classification results using random forest revealed an accuracy higher than 98%. Besides, as a reliable feature selection algorithm, the butterfly optimization algorithm (BOA) was used to detect the effective image features to identify and classify diseases. This algorithm recognized image textural features as the most effective features in the diagnosis and classification of disease spores. Considering only the eight most effective features selected with BOA resulted in an accuracy of 95% in disease detection. To further investigate the performance of the proposed method, its accuracy was compared with the accuracies of convolutional neural networks and EfficientNet as two reliable deep learning algorithms. Not only the prediction accuracy of these methods was not favorable (65 and 83.55%, respectively), they were very time-consuming. According to the findings, the proposed framework has high reliability in disease diagnosis and can help in the management of tomato fungal diseases.

关键词： artificial intelligence Butterfly optimization algorithm Disease diagnosis Microscopic image processing Morphological features Tomato disease spores

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Correction of Banding Errors in Satellite images With Generative Adversarial networks (GAN)

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IEEE ACCESS 2023年 11卷 51960-51970页

作者： Paola, Zarate L. Jesus, Lopez S. Christian, Arroyo H. Sonia, Rincon U. Colombian AF Res Ctr Aerosp Technol CITAE Cali CO Colombia Univ Autonoma Occidente Sch Engn Cali CO760001 CO Colombia

This research proposes an innovative method for correcting banding errors in satellite images based on Generative Adversarial networks (GAN). Small satellites are frequently launched into space to obtain images that can be used in scientific or military research, commercial activities, and urban planning, among other applications. However, its small cameras are more susceptible to radiometric, geometric errors, and other distortions caused by atmospheric interference. The proposed method was compared to the conventional correction technique using experimental data, showing the similar performance (92.64% and 90.05% accuracy, respectively). These experimental results suggest that generative models utilizing artificial Intelligence (AI) techniques, specifically Deep Learning, are getting closer to achieving automatic correction close to conventional methods. Advantages of the GAN models include automating the task of correcting banding in satellite images, reducing the required time, and facilitating the processing without requiring prior technical knowledge in handling Geographic Information Systems (GIS). Potentially, this technique could represent a valuable tool for satellite image processing, improving the accuracy of the results and making the process more efficient. The research is particularly relevant to the field of remote sensing and can have practical applications in various industries.

关键词： Satellite broadcasting Generative adversarial networks Generators Training Radiometry Remote sensing image coding artificial neural network deep learning generative adversarial network satellite images radiometric error banding

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