The proceedings contain 120 papers. The topics discussed include: mobile device fingerprinting recognition using insensitive information;garbage image classification based on improved residual neural networks;object d...
ISBN:
(纸本)9781665464680
The proceedings contain 120 papers. The topics discussed include: mobile device fingerprinting recognition using insensitive information;garbage image classification based on improved residual neural networks;object detection in visible and infrared missile borne fusion image;augmented reality calibration with stereo image registration for surgical navigation;momentum contrast learning for aerial image segmentation and precision agriculture analysis;transformer with convolution for irregular image inpainting;image recognition of marine organisms based on convolutional neural networks;multiple recurrent attention convolutional neural network for fine-grained image recognition;oracle bone inscriptions detection based on standard evaluation metric;the application of square module elements in digital images from the sense of order;and image and lidar fusion mapping method based on joint adjustment.
The proceedings contain 64 papers. The special focus in this conference is on machinelearning, imageprocessing, Network Security and Data Sciences. The topics include: Sensorless Control Algorithm of Permanent Magne...
ISBN:
(纸本)9783031243660
The proceedings contain 64 papers. The special focus in this conference is on machinelearning, imageprocessing, Network Security and Data Sciences. The topics include: Sensorless Control Algorithm of Permanent Magnet Synchronous Motor on Account of neural Network;a Techno Aid to Ease in e-Rehabilitation;a Novel Approach to Analyse Lung Cancer Progression and Metastasis Using Page Rank Technique;homomorphic Encryption of neural networks;an Empirical Study to Enhance the Accuracy of an Ensemble learning Model for Crop Recommendation System by Using Bit-Fusion Algorithm;bayesian learning Model for Predicting Stability of System with Nonlinear Characteristics;Novel ABC: Aspect Based Classification of Sentiments Using Text Mining for COVID-19 Comments;topic Modeling, Sentiment Analysis and Text Summarization for Analyzing News Headlines and Articles;bioBodyComp: A machinelearning Approach for Estimation of Percentage Body Fat;a Computational Approach to Identify Normal and Abnormal Persons Gait Using Various machinelearning and Deep learning Classifier;wearable Technology for Early Detection of Hyperthermia Using machinelearning;intelligent Evaluation Framework of English Composition Based on Intelligent Algorithm;multimedia English Teaching System Based on Computer Information Technology;Correlation Analysis of Central Bank Communication Behavior and Monetary Policy Independence Based on VR Technology and machine learning;power Demand Data Analysis and Recovery for Management of Power Distribution systems;Optimization Design of Green Building Landscape Space Environment Based on LM-BP Algorithm;design of Computational Thinking Intelligent Training System Under Big Data Technology;creative Graphic Design System Based on Multi-objective Firefly Algorithm;next Generation Ultra-sensitive Surface Plasmon Resonance Biosensors.
The proceedings contain 64 papers. The special focus in this conference is on machinelearning, imageprocessing, Network Security and Data Sciences. The topics include: Sensorless Control Algorithm of Permanent Magne...
ISBN:
(纸本)9783031243516
The proceedings contain 64 papers. The special focus in this conference is on machinelearning, imageprocessing, Network Security and Data Sciences. The topics include: Sensorless Control Algorithm of Permanent Magnet Synchronous Motor on Account of neural Network;a Techno Aid to Ease in e-Rehabilitation;a Novel Approach to Analyse Lung Cancer Progression and Metastasis Using Page Rank Technique;homomorphic Encryption of neural networks;an Empirical Study to Enhance the Accuracy of an Ensemble learning Model for Crop Recommendation System by Using Bit-Fusion Algorithm;bayesian learning Model for Predicting Stability of System with Nonlinear Characteristics;Novel ABC: Aspect Based Classification of Sentiments Using Text Mining for COVID-19 Comments;topic Modeling, Sentiment Analysis and Text Summarization for Analyzing News Headlines and Articles;bioBodyComp: A machinelearning Approach for Estimation of Percentage Body Fat;a Computational Approach to Identify Normal and Abnormal Persons Gait Using Various machinelearning and Deep learning Classifier;wearable Technology for Early Detection of Hyperthermia Using machinelearning;intelligent Evaluation Framework of English Composition Based on Intelligent Algorithm;multimedia English Teaching System Based on Computer Information Technology;Correlation Analysis of Central Bank Communication Behavior and Monetary Policy Independence Based on VR Technology and machine learning;power Demand Data Analysis and Recovery for Management of Power Distribution systems;Optimization Design of Green Building Landscape Space Environment Based on LM-BP Algorithm;design of Computational Thinking Intelligent Training System Under Big Data Technology;creative Graphic Design System Based on Multi-objective Firefly Algorithm;next Generation Ultra-sensitive Surface Plasmon Resonance Biosensors.
Pavement condition assessment is essential for roadway maintenance and rehabilitation processes. image-based inspection methods provide information regarding the surface of the pavement and allow quantitative analyses...
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Pavement condition assessment is essential for roadway maintenance and rehabilitation processes. image-based inspection methods provide information regarding the surface of the pavement and allow quantitative analyses of pavement conditions. A methodology for the detection of damages in the pavement, by applying pattern recognition and image analysis and machine learningalgorithms, is presented in the paper. This methodology consists of image acquisition, imageprocessing using the wavelet scattering transform (WST), feature extraction employing the fractal dimension by box-counting method, and finally classification. The methodology was applied for the detection of three common types of damages: potholes, longitudinal and alligator cracks. Two different supervised learningalgorithms, Artificial neural networks (ANN) and Support Vector machine (SVM), were used for classification and results are compared training Convolutional neural networks (CNN). The multilayer ANN an overall accuracy of 98.36% and an F-score of 98.33%, while the SVM an accuracy of 97.22% and an F-score of 97.22%.
A subset of machinelearning 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 reward...
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A subset of machinelearning 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 imageprocessing 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 imageprocessing 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 imageprocessing and that it has the potential to solve complex imageprocessing tasks.
This paper presents the design of a comprehensive automatic fish processing line utilizing machinelearningalgorithms. The processing line encompasses several essential steps, including fish identification by type, f...
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This paper presents the design of a comprehensive automatic fish processing line utilizing machinelearningalgorithms. 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 machinelearningalgorithms. 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 machinelearningalgorithms. To provide a visual representation of these design concepts, we have created a 3D SolidWorks model.
As the 16th most common cancer globally, oral cancer yearly accounts for some 355,000 new cases. This study underlines that an early diagnosis can improve the prognosis and cut down on mortality. It discloses a multif...
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As the 16th most common cancer globally, oral cancer yearly accounts for some 355,000 new cases. This study underlines that an early diagnosis can improve the prognosis and cut down on mortality. It discloses a multifaceted approach to the detection of oral cancer, including clinical examination, biopsies, imaging techniques, and the incorporation of artificial intelligence and deep learning methods. This study is distinctive in that it provides a thorough analysis of the most recent AI-based methods for detecting oral cancer, including deep learning models and machine learningalgorithms that use convolutional neural networks. By improving the precision and effectiveness of cancer cell detection, these models eventually make early diagnosis and therapy possible. This study also discusses the importance of techniques in image pre-processing and segmentation in improving image quality and feature extraction, an essential component of accurate diagnosis. These techniques have shown promising results, with classification accuracies reaching up to 97.66% in some models. Integrating the conventional methods with the cutting-edge AI technologies, this study seeks to advance early diagnosis of oral cancer, thus enhancing patient outcomes and cutting down on the burden this disease is imposing on healthcare systems.
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 pro...
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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 imageprocessing techniques and vision algorithms,including color image analysis,three-dimensional depth percep-tion,and automatic object recognition technology that integrates machinelearning and deep learningalgorithms,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
The demand for solid wood is high in the construction and manufacturing industries, and the quality of the wood is crucial. Defects in solid wood can result in hazardous accidents or financial loss. While manual visua...
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The demand for solid wood is high in the construction and manufacturing industries, and the quality of the wood is crucial. Defects in solid wood can result in hazardous accidents or financial loss. While manual visual inspection of defects is time consuming and labor intensive, Automated Optical Inspection (AOI) systems provide a solution that is hindered by defect variations and environmental factors such as moisture content and lighting conditions. AOI systems coupled with machinelearningalgorithms have emerged as a promising approach for inspecting wood defects. Despite their promising results compared to manual visual inspection and AOI systems, machinelearningalgorithms have shown several limitations in terms of complex imageprocessing methods, feature engineering, and hyperparameter dependence. Deep learningalgorithms have tremendous potential and have become trends in wood defect inspection in recent years, particularly Convolutional neural networks (CNNs), single-shot detectors (SSD), You Only Look Once (YOLO), and faster region-based neural networks (Faster R-CNN) algorithms. The coupling of machine vision technology with deep learningalgorithms can enhance the efficiency and accuracy of wood defect inspection, and their impact has been proven in several studies. This study aims to provide a comprehensive overview of wood defect inspection approaches by analyzing related studies on machinelearning-based and deep learning-based defect inspection methods. Their principles, procedures, performance, and limitations were compared and discussed. Subsequently, future trends and challenges in wood defect inspection are also discussed to provide a detailed understanding and direction for related fields.
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 dec...
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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 machinelearningalgorithms. 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 machinelearningalgorithms 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
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