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A Synergy Between machine learning and Formal Concept Analysis for Crowd Detection

IEEE ACCESS 2025年 13卷 36804-36823页

作者： Al-Oraiqat, Anas M. Drieiev, Oleksandr Almatarneh, Sattam Injadat, Mohammadnoor Al-Oraiqat, Karim A. Drieieva, Hanna Hasan, Yassin M. Y. Zarqa Univ Fac Informat Technol Dept Comp Sci Zarqa 13110 Jordan Cent Ukrainian Natl Tech Univ Dept Cybersecur & Software UA-25000 Kropyvnytskyi Ukraine Zarqa Univ Fac Informat Technol Dept Data Sci & Artificial Intelligence Zarqa 13110 Jordan Assiut Univ Elect Engn Dept Asyut 71515 Egypt Egypt Japan Univ Sci & Technol CSIT Alexandria 21934 Egypt

To enhance public safety, crowd detection and prevention systems have essentially become a natural means to manage diverse crowded areas, such as urban settings, transportation hubs, and event venues. Recent systems take advantage of the synergy between machine learning, data mining, and image processing to extract/analyze features from crowded zones and recognize patterns and anomalies from the crowd behavior. Additionally, image processing tools play a key role in real-time monitoring by analyzing video feeds to detect crowd density, flow direction, and identify potential risks like overcrowding or emergencies. However, most existing solutions focus on the detection phase and often overlook integrated error handling and robust decision-making frameworks to ensure accurate and actionable crowd prevention. Aiming to solve these issues, we take advantage of the prediction capabilities of machine learning models and the analysis and clustering strengths of Formal Concept Analysis (FCA) chosen for its strong mathematical foundation and superior clustering capabilities compared to traditional methods, as highlighted in recent works such as K-means or hierarchical clustering. We used the first technique to extract useful knowledge from areas' produced images while mitigating potential error accumulation through modular error-checking mechanisms. A neural network is used to mark human bodies, determine the position of walking individuals, and predict crowd levels. Such information is, thereafter, inputted to the FCA-based decision system to ensure an explicit representation and modelling of crowd data, thanks to lattice structures. These latter's hierarchical view helped us identify the crowded areas and manage them as clustered zones, based on their common crowd information. We also define bottom-up parsing algorithms to recommend the suitable crowd prevention plan w.r.t. the crowd level. Experiments have successfully proved the ability of FCA to exclude low-crowd zones,

关键词： Cameras Prevention and mitigation machine learning Vectors Real-time systems Predictive models neural networks Formal concept analysis Feature extraction Distortion Crowd detection feature extraction crowd decision fuzzy FCA neural networks clustering

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Application of deep reinforcement learning in various image processing tasks: a survey

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EVOLVING systems 2025年第1期16卷 1-20页

作者： Tadesse, Daniel Moges Kebede, Samuel Rahimeto Debele, Taye Girma Waldamichae, Fraol Gelana Ethiopian Artificial Intelligence Inst Addis Ababa 40782 Ethiopia Addis Ababa Sci & Technol Univ Coll Elect & Mech Engn Addis Ababa 120611 Ethiopia Debreberhan Univ Coll Engn Debreberhan 222 Ethiopia

A subset of machine learning algorithm called Deep Reinforcement learning (DRL) enables computers or agents to learn behavior by taking actions in a given environment through trial and error while observing the rewards. In this learning paradigm, the agent is given a set of actions to chose and is then rewarded or punished depending on the results of those actions. The agent gradually develops the ability to make the best decisions by maximizing its rewards. DRL blends the learning ability of deep neural networks into the decision making capability of reinforcement learning (RL) frameworks in order to seeks and identify the most favorable set of actions. This survey paper studies DRL applications for diverse image processing tasks. It starts by providing an overview of the latest model-free and model-based RL and DRL algorithms. Then, it looks at how DRL is being used for various image processing tasks including image segmentation and classification, object detection, image registration, image denoising, image restoration, and landmark detection. Lastly, the paper discusses the potential uses and challenges of DRL in the proposed area by addressing the research questions. Survey results have showed that DRL is a promising approach for image processing and that it has the potential to solve complex image processing tasks.

关键词： Deep reinforcement learning Deep neural networks image processing

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Smart machine Vision for Universal Spatial-Mode Reconstruction

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IEEE TRANSACTIONS ON neural networks AND learning systems 2025年 PP卷 PP页

作者： Huerta-Morales, Jose D. You, Chenglong Magana-Loaiza, Omar S. Dong, Shi-Hai Leon-Montiel, Roberto de J. Quiroz-Juarez, Mario A. Univ Nacl Autonoma Mexico Inst Ciencias Nucl Mexico City 04510 Mexico Louisiana State Univ Dept Phys & Astron Quantum Photon Lab Baton Rouge LA 70803 USA Huzhou Univ Res Ctr Quantum Phys Huzhou 310003 Peoples R China UPALM Inst Politecn Nacl Lab Cienciasde Informac Cuant CIC Mexico City 07700 Mexico Univ Nacl Autonoma Mexico Ctr Fis Aplicada & Tecnol Avanzada Queretaro 76230 Mexico

Structured light beams, in particular, those carrying orbital angular momentum (OAM), have gained a lot of attention due to their potential for enlarging the transmission capabilities of communication systems. However, the use of OAM-carrying light in communications faces two major problems, namely distortions introduced during propagation in disordered media, such as the atmosphere or optical fibers, and the large divergence that high-order OAM modes experience. While the use of nonorthogonal modes may offer a way to circumvent the divergence of high-order OAM fields, artificial intelligence (AI) algorithms have shown promise for solving the mode-distortion issue. Unfortunately, current AI-based algorithms make use of large-amount data-handling protocols that generally lead to large processing time and high power consumption. Here, we show that a low-power, low-cost image sensor can act as an artificial neural network that simultaneously detects and reconstructs distorted OAM-carrying beams. We demonstrate the capabilities of our device by reconstructing (with a 95% efficiency) individual Vortex, Laguerre-Gaussian (LG), and Bessel modes, as well as hybrid (nonorthogonal) coherent superpositions of such modes. Our work provides a potentially useful basis for the development of low-power-consumption, light-based communication devices.

关键词： Accuracy Laser beams image reconstruction Optical distortion Optical fiber networks Optical beams Convolutional neural networks Optical fibers Deep learning Crosstalk Artificial intelligence (AI) neural networks smart machine-vision device spatial-mode reconstruction

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A robotic fish processing line enhanced by machine learning

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AQUACULTURAL ENGINEERING 2025年 108卷

作者： Mainali, Sangam Li, Cheryl Univ New Haven Mech & Automat Lab West Haven CT 06516 USA

This paper presents the design of a comprehensive automatic fish processing line utilizing machine learning algorithms. The processing line encompasses several essential steps, including fish identification by type, fish sorting by size, fish orientation based on shape, and fish cutting at the optimal chopping points. The primary objective of this design is not just automation but also maximizing economic benefits by preserving the maximum amount of fish meat during the cutting process, achieved through the application of machine learning algorithms. To accomplish these goals, we employ a combination of transfer learning and convolutional neural networks to establish criteria for actions across all stages of automatic fish processing. At the heart of the processing station is a conveyor belt equipped with numerous sensors and lenses. Positioned along this conveyor belt are two robotic arms, responsible for precise positioning and cutting operations, all guided by the machine learning algorithms. To provide a visual representation of these design concepts, we have created a 3D SolidWorks model.

关键词： Food automation Fish processing Robotic arm machine learning image processing

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Key Technologies for machine Vision for Picking Robots:Review and Benchmarking

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machine Intelligence Research 2025年第1期22卷 2-16页

作者： Xu Xiao Yiming Jiang Yaonan Wang College of Electrical and Information Engineering Hunan UniversityChangsha 410082China National Engineering Research Center for Robot Vision Perception and Control Technology Hunan UniversityChangsha 410082China

The increase in precision agriculture has promoted the development of picking robot technology,and the visual recognition system at its core is crucial for improving the level of agricultural *** paper reviews the progress of visual recognition tech-nology for picking robots,including image capture technology,target detection algorithms,spatial positioning strategies and scene *** article begins with a description of the basic structure and function of the vision system of the picking robot and em-phasizes the importance of achieving high-efficiency and high-accuracy recognition in the natural agricultural ***-sequently,various image processing techniques and vision algorithms,including color image analysis,three-dimensional depth percep-tion,and automatic object recognition technology that integrates machine learning and deep learning algorithms,were *** the same time,the paper also highlights the challenges of existing technologies in dynamic lighting,occlusion problems,fruit maturity di-versity,and real-time processing *** paper further discusses multisensor information fusion technology and discusses methods for combining visual recognition with a robot control system to improve the accuracy and working rate of *** the same time,this paper also introduces innovative research,such as the application of convolutional neural networks(CNNs)for accurate fruit detection and the development of event-based vision systems to improve the response speed of the *** the end of this paper,the future development of visual recognition technology for picking robots is predicted,and new research trends are proposed,including the refinement of algorithms,hardware innovation,and the adaptability of technology to different agricultural *** purpose of this paper is to provide a comprehensive analysis of visual recognition technology for researchers and practitioners in the field of agricul-tural rob

关键词： Picking robot visual system perception technology image processing machine learning deep learning.

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Neuromorphic devices assisted by machine learning algorithms

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INTERNATIONAL JOURNAL OF EXTREME MANUFACTURING 2025年第4期7卷 042007-042007页

作者： Huo, Ziwei Sun, Qijun Yu, Jinran Wei, Yichen Wang, Yifei Cho, Jeong Ho Wang, Zhong Lin Chinese Acad Sci Beijing Inst Nanoenergy & Nanosyst Beijing 101400 Peoples R China Univ Chinese Acad Sci Sch Nanosci & Engn Beijing 100049 Peoples R China Guangxi Univ Ctr Nanoenergy Res Sch Phys Sci & Technol Nanning 530004 Peoples R China Shandong Zhongke Naneng Energy Technol Co Ltd Dongying Peoples R China Yonsei Univ Dept Chem & Biomol Engn Seoul 03722 South Korea Georgia Inst Technol Atlanta GA 30332 USA

Neuromorphic computing extends beyond sequential processing modalities and outperforms traditional von Neumann architectures in implementing more complicated tasks, e.g., pattern processing, image recognition, and decision making. It features parallel interconnected neural networks, high fault tolerance, robustness, autonomous learning capability, and ultralow energy dissipation. The algorithms of artificial neural network (ANN) have also been widely used because of their facile self-organization and self-learning capabilities, which mimic those of the human brain. To some extent, ANN reflects several basic functions of the human brain and can be efficiently integrated into neuromorphic devices to perform neuromorphic computations. This review highlights recent advances in neuromorphic devices assisted by machine learning algorithms. First, the basic structure of simple neuron models inspired by biological neurons and the information processing in simple neural networks are particularly discussed. Second, the fabrication and research progress of neuromorphic devices are presented regarding to materials and structures. Furthermore, the fabrication of neuromorphic devices, including stand-alone neuromorphic devices, neuromorphic device arrays, and integrated neuromorphic systems, is discussed and demonstrated with reference to some respective studies. The applications of neuromorphic devices assisted by machine learning algorithms in different fields are categorized and investigated. Finally, perspectives, suggestions, and potential solutions to the current challenges of neuromorphic devices are provided. The review discusses the basic structure of simple neuron models inspired by biological neurons and how they process information in simple neural networks, laying the foundation for neuromorphic device *** progress in the fabrication of neuromorphic devices is highlighted, focusing on advancements in materials, structures, and the development of st

关键词： neuromorphic devices machine learning algorithms artificial synapses memristors field-effect transistors

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Classification and reconstruction for single-pixel imaging with classical and quantum neural networks

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SIGNAL image AND VIDEO processing 2025年第4期19卷 1-11页

作者： Manko, Sofya Frolovtsev, Dmitry Lomonosov Moscow State Univ Dept Gen Phys & Wave Proc Moscow Russia

Single-pixel cameras are an effective solution for imaging beyond the visible spectrum, where traditional CMOS/CCD cameras face challenges. When combined with machine learning, they can analyze images quickly enough for practical applications. Solving the problem of high-dimensional single-pixel visualization can potentially be accelerated via quantum machine learning, thereby expanding the range of practical problems. In this work, we simulated a single-pixel imaging experiment using Hadamard basis patterns, where images from the MNIST handwritten digit dataset and FashionMNIST items of clothing dataset were used as objects. There were selected 64 measurements with maximum variance (6% of the number of pixels in the image). We created algorithms for classifying and reconstructing images based on these measurements using classical fully-connected neural networks and parameterized quantum circuits. Classical and quantum classifiers showed the best accuracies of 96% and 95% for MNIST and 84% and 81% for FashionMNIST, respectively, after 6 training epochs, which is a quite competitive result. In the area of intersection by the number of parameters of the quantum and classical classifiers, the quantum demonstrates results no worse than the classical one, even better by a value of about 1-3%. image reconstruction was also demonstrated using classical and quantum neural networks after 10 training epochs;the best structural similarity index measure values were 0.76 and 0.26 for MNIST and 0.73 and 0.22 for FashionMNIST, respectively, which indicates that the problem in such a formulation turned out to be too difficult for quantum neural networks in such a configuration for now.

关键词： Quantum machine learning Parameterized quantum circuits Single-pixel imaging Compressive sensing image classification image reconstruction

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A Survey on Automatic image Captioning Approaches: Contemporary Trends and Future Perspectives

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ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING 2025年第3期32卷 1459-1497页

作者： Salgotra, Garima Abrol, Pawanesh Selwal, Arvind Univ Jammu Dept Comp Sci & Informat Technol Jammu 180006 India Cent Univ Jammu Dept Comp Sci & Informat Technol Samba 181143 India

The automatic generation of image captions is one of the complex computer vision tasks that involve integration of object detection and natural language processing (NLP). In recent times, one of the significant aspects is to design image captioning approaches that accurately and efficiently generate appropriate image captions in a particular domain. With the emergence of deep learning paradigms, the task of image captioning becomes comparatively easier than traditional template-based approaches. In this article, we expound an in-depth examination of state of the art (SOTA) image captioning methods, along with the key conceptions. Besides, a comparative analysis of evaluation protocols is presented that are presently used to access the efficacy of the algorithms. Moreover, the study reveals open research issues in the existing methods that can be further investigated by the research community. One of the key challenges is to develop larger corpora of language specific dataset to design image captioning approaches in other regional languages such as Hindi, Marathi, Sanskrit, Telugu, and Gujarati etc. Furthermore, designing accurate and efficient image captioning approaches requisite the notion of attention mechanism in the images.

关键词： image captioning Text generation Deep learning machine translation neural networks

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Advances and Challenges in Computer Vision for image-Based Plant Disease Detection: A Comprehensive Survey of machine and Deep learning Approaches

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 2639-2670页

作者： Qadri, Syed Asif Ahmad Huang, Nen-Fu Wani, Taiba Majid Bhat, Showkat Ahmad Natl Tsing Hua Univ Coll Elect Engn & Comp Sci Hsinchu 300044 Taiwan Natl Tsing Hua Univ Dept Comp Sci Hsinchu 300044 Taiwan Sapienza Univ Rome Dept Comp Control & Management Engn I-00185 Rome Italy Natl Tsing Hua Univ Ctr Innovat Incubator Hsinchu 300044 Taiwan

As advancements in agricultural technology unfold, machine learning and deep learning approaches are gaining interest in robust plant disease identification. Early disease detection, integral to agricultural productivity, has propelled innovations across all phases of detection. This survey paper provides a meticulous examination of plant disease detection systems, elucidating data collection methodologies and underscoring the pivotal role of datasets in model training. The narrative navigates through the complex areas of data and image processing techniques, segueing into an exploration of various segmentation methods. The survey emphasizes the importance of feature extraction and selection techniques, illustrating their efficacy in increasing classification accuracy. It examines the classification process, embracing both traditional machine learning and avant-garde deep learning methods, with a particular spotlight on Convolutional neural networks (CNNs). The study examines over one hundred seminal papers, anatomizing their dataset utilizations, feature considerations, and classification strategies. Overall, the paper contemplates the challenges permeating this vibrant field, addressing critical issues such as dataset diversity, model generalization, and real-world applicability. Note to Practitioners-To ensure crop health and yield, timely and precise plant disease detection is crucial. Our research, titled "Advances And Challenges in Plant Disease Detection: A Comprehensive Survey of machine and Deep learning Approaches," examines the critical role of datasets, advanced image processing, and segmentation techniques in disease detection. This paper presents practitioners with a guide to the latest techniques for enhanced disease detection by emphasizing the significance of feature extraction and highlighting the capabilities of convolutional neural networks (CNNs). By understanding the highlighted challenges, such as dataset diversity and model generalization, in

关键词： Plant disease detection image processing machine learning deep learning convolutional neural network

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Accelerating convolutional neural networks on FPGA platforms: a high-performance design methodology using OpenCL

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JOURNAL OF REAL-TIME image processing 2025年第2期22卷 1-19页

作者： Gdaim, Soufien Mtibaa, Abdellatif Sousse Univ Prince Res Lab ISITCOM Sousse Tunisia Univ Sfax Syst Integrat & Emerging Energies Lab Sfax Tunisia

Convolutional neural networks (CNNs) are among the most promising algorithms, outperforming traditional methods in classification tasks with superior accuracy. They have been widely applied across various deep learning domains, including computer vision, speech recognition, image processing, and object detection. However, many CNNs require substantial computational resources, particularly within their convolutional layers. As high-performance CNNs continue to evolve, their processing and memory requirements are also increasing. To address these challenges, this paper proposes an effective design methodology for accelerating CNN algorithms on Field-Programmable Gate Array (FPGA) hardware architectures. The proposed methodology introduces a novel approach for accelerating CNN algorithms using FPGAs, addressing the significant processing and memory demands associated with CNNs. The implementation is based on Open Computing Language (OpenCL), which provides rapid implementation flows. This approach was chosen for its efficiency in reducing development time and eliminating the need to manually write hardware description language (HDL) code. The MNIST and the CIFAR-10 datasets on the Xilinx ZYNQ 7000 device were used to evaluate our approach. Our method achieved a 97% recognition rate on MNIST and an 86% recognition rate on CIFAR-10. We compared the execution time of our accelerated CNN kernel on the FPGA with that of a single-core Central processing Unit (CPU). The experimental results demonstrate that our proposed design is 10 times faster than a standard CPU, validating its effectiveness. Our model optimizes power consumption and performance, exceeding previous studies in accuracy and efficiency. It is well suited for real-world applications that demand both precision and energy efficiency.

关键词： CNN Hardware accelerator FPGA OpenCL machine learning Embedded system

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