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Intelligent Cybernetics Technology & applications (ICICyTA), International Conference on

作者： Álvaro S. Careli Evandro C. Vilas Boas Eduardo H. Teixeira Elaine C. C. Silva Guilherme P. Aquino Felipe A. P. Figueiredo Cyber Security and IoT Laboratory CS&I Lab. National Institute of Telecommunication Inatel Santa Rita do Sapucaí Brazil IoT Research Group Laboratory National Institute of Telecommunication Inatel Santa Rita do Sapucaí Brazil Wireless and Artificial Intelligence Laboratory WAI Lab. National Institute of Telecommunication Inatel Santa Rita do Sapucaí Brazil

ISBN: (数字)9798331506490

ISBN: (纸本)9798331506506

This work evaluates deep learning segmentation models to propose a deforestation monitoring embedded system. The approach stands for environmental monitoring using remote sensing imagery, edge computing, and a deep learning segmentation model. Thus, the performance of you only look once architecture version 8 (YOLOv8) and Mask Region-based convolutional neural networks (Mask R-CNN) embedded in Raspberry Pi Model 4 regarding Intersection over Union (IoU), mean Average Precision (mAP), and time per image processing metrics is compared. The models are combined with a pixel-based algorithm that analyzes the temporal segmented images to define their forest area percentage for deforestation monitoring and detection. The results demonstrate YOLOv8x model achieved an IoU of 0.762, with a time per image of 0.4777 seconds, while Mask R-CNN R101 FPN 3x obtained an IoU of 0.763, with a time per image of 0.2669 seconds. The average times for YOLOv8 ranged from 0.0434 to 0.4777 seconds, and for Mask R-CNN from 0.1969 to 0.2669 seconds. Finally, this work proposes evaluating the model's performance when working with generative AI models Dall-e, Craiyon, and Tess-AI to create a synthetic dataset to augment the initial one with synthetic samples and improve the model's training with a large dataset. The Dall-e has been shown to outperform the others regarding the IoU metric, which was suggested to augment datasets with synthetic samples.

关键词： Deforestation Measurement Deep learning Training image segmentation Embedded systems Generative AI Computational modeling Environmental monitoring Synthetic data

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EDITORIAL SPECIAL ISSUE: PART IV-III-II-I SERIES - FRACTALS-FRACTIONAL AI-BASED ANALYSES AND applications TO COMPLEX SYSTEMS finestre of creativity, complexity and universality…

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FRACTALS-COMPLEX GEOMETRY PATTERNS AND SCALING IN NATURE AND SOCIETY 2023年第10期31卷 2302005-2302005页

作者： Karaca, Yeliz Baleanu, Dumitru Moonis, Majaz Zhang, Yu-dong Gervasi, Osvaldo Univ Massachusetts Chan Med Sch 55 Lake Ave North Worcester MA 01655 USA Lebanese Amer Univ Beirut 11022 Lebanon Inst Space Sci Bucharest 077125 Romania Univ Leicester Univ Rd Leicester LE1 7RH England Univ Perugia I-06123 Perugia Italy

Complex systems, as interwoven miscellaneous interacting entities that emerge and evolve through self-organization in a myriad of spiraling contexts, exhibit subtleties on global scale besides steering the way to understand complexity which has been under evolutionary processes with unfolding cumulative nature wherein order is viewed as the unifying framework. Indicating the striking feature of non-separability in components, a complex system cannot be understood in terms of the individual isolated constituents' properties per se, it can rather be comprehended as a way to multilevel approach systems behavior with systems whose emergent behavior and pattern transcend the characteristics of ubiquitous units composing the system itself. This observation specifies a change of scientific paradigm, presenting that a reductionist perspective does not by any means imply a constructionist view;and in that vein, complex systems science, associated with multiscale problems, is regarded as ascendancy of emergence over reductionism and level of mechanistic insight evolving into complex system. While evolvability being related to the species and humans owing their existence to their ancestors' capability with regards to adapting, emerging and evolving besides the relation between complexity of models, designs, visualization and optimality, a horizon that can take into account the subtleties making their own means of solutions applicable is to be entailed by complexity. Such views attach their germane importance to the future science of complexity which may probably be best regarded as a minimal history congruent with observable variations, namely the most parallelizable or symmetric process which can turn random inputs into regular outputs. Interestingly enough, chaos and nonlinear systems come into this picture as cousins of complexity which with tons of its components are involved in a hectic interaction with one another in a nonlinear fashion amongst the other related systems

关键词： Applied Mathematics Computational Mathematics Pure Mathematics Mathematical Sciences Discrete Mathematics Mathematical Physics Theoretical Sciences Exact Science Computational Methods Computational Intelligence Applied Sciences Computer Science Computational Science Data Science Engineering Electrical and Electronics Engineering Exact Sciences Natural Sciences Social Sciences Nonlinear Dynamics Nonlinear Systems Linear Systems Fractional Calculus AI Control Control Systems Optimization Complexity Chaos Uncertainty Causality Symmetry Oscillations Bifurcation Stability Sensitivity Quantum Medicine Biology Physics Chemistry Mathematical Bioengineering Mathematical Neuroscience Mathematical Biology artificial Intelligence neural networks artificial neural networks Data-Driven Models Encryption Soft Computing Coding Theory Machine Learning Deep Learning Intelligent Systems Probabilities and Stochastic Processes Game Theory Solitons, Fractals, Multifractals Fractional Calculus Fractional Dynamics Fractional Mathematics Fractional Systems Fractional Models Fractals Multifractals Waves Wavelet Entropy Algebraic Methods Computational (-Algorithmic) Complexity Fractional Order Differential Equations Fractional Differential Equations Fuzzy Fractional Differential Equations Special Functions Functional Analysis Error Estimations Fuzzy Calculus Differential/Integral Equations, Stochastic Differential Equations, Fractal-Fractional Differential Equations Fractional-Order Controllers Fractional-Order Systems Intelligent Fractional Order Systems Variational Principles image and/or Signal processing Mathematical Models Fractal AI Fractional AI Fractals-Fractional AI Quantum AI Dynamical systems Chaotic Systems Complex Systems

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利用脉冲耦合神经网络的纹理图像检索方法

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计算机工程与应用 2012年第7期48卷 201-204,241页

作者：王晓飞李柏年兰州大学信息科学与工程学院兰州730000

为提高基于内容的图像检索系统(CBIR)中纹理特征提取的有效性,进一步提升CBIR系统的整体性能。提出了一种基于脉冲耦合神经网络的纹理图像检索方法。脉冲耦合神经网络(PCNN)是新一代的人工神经网络,在数据处理上具有很多优势。特征提取... 详细信息

为提高基于内容的图像检索系统(CBIR)中纹理特征提取的有效性,进一步提升CBIR系统的整体性能。提出了一种基于脉冲耦合神经网络的纹理图像检索方法。脉冲耦合神经网络(PCNN)是新一代的人工神经网络,在数据处理上具有很多优势。特征提取时具有平移、旋转、尺度、扭曲等不变性,以及很好的抗噪性,而这一点非常适合于图像检索系统。利用PCNN及简化模型ICM得到对应于不同灰度值的二值图像序列,计算序列中每幅图像的熵序列,其一维的特征矢量作为纹理特征。采用Eu-clidean距离进行相似度计算,建立了一套基于示例查询图像的纹理图像检索系统。实验结果表明,与小波包等特征提取方法相比,该方法不仅对噪声具有较强的鲁棒性,同时能降低特征向量维数,具有尺度、平移和旋转不变性,而且能取得更高的检索率。

关键词：基于内容的图像检索(CBIR) 脉冲耦合神经网络(PCNN) 交叉皮层模型(ICM) 特征提取

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TU-D-204C-03: Machine Learning for Medical image processing and Pattern Recognition

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Medical Physics 2010年第6PART28期37卷 3396-3396页

作者： K Suzuki The University of Chicago Chicago IL

Machine learning plays an essential role in medical image analysis, because objects such as lesions and organs in medical images cannot be modeled accurately by simple equations; thus, tasks in medical image analysis require essentially “learning from examples.” One of the most popular uses of machine learning in medical image analysis is the classification of objects such as lesions into certain categories (e.g., abnormal or normal, lesions or non-lesions). This class of machine leaning uses features (e.g., diameter, contrast, and circularity) extracted from segmented objects as information for classifying the objects. Machine learning techniques in this class include linear discriminant analysis, a k-nearest neighbor classifier, an artificial neural network, and a support vector machine. Recently, as available computational power increased dramatically, a new class of machine learning called pixel/voxel-based machine learning emerged in medical image processing and analysis, which uses pixel/voxel values in images directly instead of calculated features as input information; thus, feature calculation is not required. Because pixel/voxel-based machine learning can avoid errors caused by inaccurate segmentation and feature calculation, the performance of the pixel/voxel-based machine learning can be better than common feature-based machine learning. Such advanced machine-learning techniques in this class include neural filters, neural edge enhancers, massive-training artificial neural networks, convolution neural networks, and shift-invariant neural networks. This lecture will provide an overview of techniques, methodologies, and applications of machine learning used in medical image processing and pattern recognition, which have been developed and advanced mainly in the field of diagnostic radiology in the past two decades or so. The applications of these machine-learning techniques include quantum noise reduction, enhancement of the boundary of an organ, lesion en

关键词： Machine learning Medical image processing Medical imaging artificial neural networks image analysis Pattern recognition Radiation therapy Anatomy Biological neural networks Medical image contrast

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GNet: 3D Object Detection from Point Cloud with Geometry-Aware Network

GNet: 3D Object Detection from Point Cloud with Geometry-Awa...

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IEEE International Conference on Cyborg and Bionic Systems (CBS)

作者： Hao Ruan Bingrong Xu Junbin Gao Lianguang Liu Junting Lv Yin Sheng Zhigang Zeng School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan China School of Automation Wuhan University of Technology Wuhan China

3D object detection based on point clouds in real-world applications has been a challenging task because the raw point cloud may be sparse or occluded. It may be caused by bad weather or limited sensor resolution, leading to decreasing detection performance. To this end, a novel highperformance Geometry-aware Network (GNet) is proposed and used to accurately detect 3D objects from sparse point clouds. Different from previous work that only regresses the offset of 3D bounding boxes numerically between ground truth (GT) and prediction, GNet considers the geometric information of 3D bounding boxes and combines the two effectively to achieve a better regression performance. In particular, Point Nets blocks learned voxel-wise features and the proposals are generated by the 3D voxel convolution neural network (voxel CNN). By utilizing the prior knowledge and the geometric properties of the 3D bounding boxes, the proposed novel OS-loss can improve detection performance and accelerate speed of inference. Furthermore, GNet takes advantage of prior geometry properties rather than only regressing the offset numerically. These speed up the convergence of the regression network. Finally, our proposed GNet was evaluated on the KITTI dataset for 3D objects and Bird’s Eye View (BEV) detection aspects. The results demonstrate that GNet can improve object detection performance for sparse point clouds.

关键词： Point cloud compression Location awareness Geometry Three-dimensional displays neural networks Object detection Proposals

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MFBGR: Multi-scale feature boundary graph reasoning network for polyp segmentation

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ENGINEERING applications OF artificial INTELLIGENCE 2023年第PartA期123卷

作者： Liu, Fangjin Hua, Zhen Li, Jinjiang Fan, Linwei Shandong Technol & Business Univ Coinnovat Ctr Shandong Coll Univ Future Intelligen Sch Comp Sci & Technol Yantai 264005 Peoples R China Shandong Univ Finance & Econ Sch Comp Sci & Technol Jinan 250014 Shandong Peoples R China

At present, adding Transformer to CNN has promoted the rapid development of colorectal polyp image processing. However, from the perspective of multi-scale feature interaction and boundary coherence, there are mainly some limitations: (1) ignore the local and global correlation within the scale feature, which may cause the missed detection of tiny polyps, (2) lack of multi-scale features to explore the target region, which hinders the learning of multi-variant polyps, and (3) the semantic connection between the target area and the boundary is ignored, cause incoherent segmentation boundaries. In this regard, we design a multi-scale feature boundary graph inference network for polyp segmentation, namely MFBGR. First, the Transformer block captures local- global cues inside the multi-scale information learned by the CNN branches. Second, for the multi-scale global information generated by the Transformer block, we design a cross-scale feature fusion module (CSFM). CSFM performs scale-variation interaction and cascaded fusion to capture the correlation between features across scales and solve the scale-variation problem of segmented objects. Finally, the traditional boundary refinement or enhancement idea is generalized to the graph convolutional reasoning layer (BGRM). BGRM receives CNN's low-level feature information and CSFM's fusion features, or intermediate prediction results, and propagates cross-domain feature information between graph vertices, explores information between target regions and boundary regions, and achieves more accurate boundary segmentation. On the CVC-300, CVC-ClinicDB, CVC-ColonDB, Kvasir-SEG, ETIS datasets, MFBGR and mainstream polyp segmentation networks were compared and tested. MFBGR achieved good results, and Dice, IOU, BAcc, and Haudo were the best. The values reached 94.16%, 89.35% and 97.42%, 3.7442, and the segmentation accuracy of colorectal polyp images has been improved to a certain extent.

关键词： Polyp segmentation Transformer Convolutional neural Network Cross-scale feature fusion module Boundary Graph Reasoning Module

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A defect detection method for topological phononic materials based on few-shot learning

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NEW JOURNAL OF PHYSICS 2022年第8期24卷 083012页

作者： Zhang, Beini Luo, Xiao Lyu, Yetao Wu, Xiaoxiao Wen, Weijia Hongkong Univ Sci & Technol Adv Mat Thrust Guangzhou 511458 Peoples R China HKUST Shenzhen Hong Kong Collaborat Innovat Res I Shenzhen 518000 Peoples R China Hong Kong Univ Sci & Technol Dept Phys Hong Kong 999077 Peoples R China Hong Kong Prod Council HKPC Robot & Artificial Intelligence Div Hong Kong 999077 Peoples R China

Topological phononic materials have been widely used in many fields, such as topological antennas, asymmetric waveguides, and noise insulation. However, due to the limitations of the manufacturing process, topological protection is vulnerable to some severe defects that may affect the application effect. Therefore, the quality inspection of topological materials is essential to ensure reliable results. Due to the low contrast and irregularity of defects and the similarity of topological phononics, they are difficult to recognize by traditional image processing algorithms, so manual detection is still mainstream at present. But manual detection requires experienced inspectors, which is expensive and time-consuming. In addition, topological materials are expensive to produce, and there is no large publicly available dataset, but deep learning usually relies on large datasets for training. To solve the above problems, we propose an automatic deep learning topology structure defect detection method (ADLTSDM) in this work, which could classify not only the structure of topological materials but also detect the defects of topological phononics based on a small dataset. ADLTSDM exploits the prior knowledge of the topological material structure and achieves an augmentation factor of more than 100 times through the random and fixed interval screenshot algorithm, thus enabling the training of deep neural networks with only two raw data. For defect detection, ADLTSDM has an accuracy of more than 97% and improves detection speed by more than 38% compared with manual detection. For structure classification, ADLTSDM can achieve an accuracy of over 99% and seven times faster speed compared with manual classification. Besides, the detection standard of ADLTSDM is unified, so the accuracy will not be affected by the experience of the inspectors, which has more potential in high-throughput industrial applications.

关键词： topological materials few-shot learning deep learning defect detection structure classification

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U-Infuse: Democratization of Customizable Deep Learning for Object Detection

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SENSORS 2021年第8期21卷 2611页

作者： Shepley, Andrew Falzon, Greg Lawson, Christopher Meek, Paul Kwan, Paul Univ New England Sch Sci & Technol Armidale NSW 2350 Australia Flinders Univ S Australia Coll Sci & Engn Adelaide SA 5001 Australia NSW Dept Primary Ind Vertebrate Pest Res Unit POB 530 Coffs Harbour NSW 2450 Australia Univ New England Sch Environm & Rural Sci Armidale NSW 2350 Australia Melbourne Inst Technol Sch IT & Engn Melbourne Vic 3000 Australia

image data is one of the primary sources of ecological data used in biodiversity conservation and management worldwide. However, classifying and interpreting large numbers of images is time and resource expensive, particularly in the context of camera trapping. Deep learning models have been used to achieve this task but are often not suited to specific applications due to their inability to generalise to new environments and inconsistent performance. Models need to be developed for specific species cohorts and environments, but the technical skills required to achieve this are a key barrier to the accessibility of this technology to ecologists. Thus, there is a strong need to democratize access to deep learning technologies by providing an easy-to-use software application allowing non-technical users to train custom object detectors. U-Infuse addresses this issue by providing ecologists with the ability to train customised models using publicly available images and/or their own images without specific technical expertise. Auto-annotation and annotation editing functionalities minimize the constraints of manually annotating and pre-processing large numbers of images. U-Infuse is a free and open-source software solution that supports both multiclass and single class training and object detection, allowing ecologists to access deep learning technologies usually only available to computer scientists, on their own device, customised for their application, without sharing intellectual property or sensitive data. It provides ecological practitioners with the ability to (i) easily achieve object detection within a user-friendly GUI, generating a species distribution report, and other useful statistics, (ii) custom train deep learning models using publicly available and custom training data, (iii) achieve supervised auto-annotation of images for further training, with the benefit of editing annotations to ensure quality datasets. Broad adoption of U-Infuse by ecological pra

关键词： animal identification artificial intelligence camera-trap images camera trapping deep convolutional neural networks deep learning environmental software wildlife ecology wildlife monitoring ecological object detection

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Traffic Signs Recognition in a mobile-based application using TensorFlow and Transfer Learning technics

Traffic Signs Recognition in a mobile-based application usin...

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IEEE International Conference on System of Systems Engineering (SoSE)

作者： Abdallah Benhamida Annamária R. Várkonyi-Kóczy Miklos Kozlovszky Doctoral School of Applied Informatics and Applied Mathematics BioTech Research Center EKIK Óbuda University Budapest Hungary Department of Mathematics and Informatics J. Selye University Komarno Slovakia John von Neumann Faculty of Informatics Institute of Biomatics Óbuda University Budapest Hungary

ISBN: (数字)9781728180502

ISBN: (纸本)9781728180519

Nowadays, Machine Learning applications are spreading widely in different science and research fields which gave, in fact, the possibility to enhance the results of all kind of both, automated tasks and further possible application areas. Autonomous smart driving cars presents one of the major fields that uses machine learning technics to push further the automated tasks inside the car systems. Many types of research related to this topic enabled real application to fully automate some parts of the car driving process. Road lane detection, pedestrian and car approximation detection, and fastest road finding using real-time traffic statistics present some of the possible application areas that could use the Machine learning technics to improve autonomous driving cars systems. Traffic signs present an important part of the daily driving routine, therefore, traffic signs recognition for mobile-based application is a great solution that provides a new layer for autonomous car driving systems. In this paper, we propose a powerful tool for traffic signs recognition in a mobile-based application. This tool uses TensorFlow together with transfer learning technic that makes it easier to train our dataset on a pre-trained Model using the convolutional network (ConvNet). The used model is a Single Shot MultiBox Detector (SSD) MobileNet V2 based model which uses one single deep network to train the model on multiple objects per image. This network uses 300x300 annotated input images with multiple objects to provide faster training time and faster detection results compared to other types of neural networks. The annotation is made by providing the coordinates of the rectangle that surrounds the given object together with its label which defines the name of the object. The coordinates are usually given by providing the (x,y) coordinates of the top-left and bottom-right points of the surrounding rectangle. This presents a powerful technic for real-time detection on mobile devices

关键词： machine learning traffic signs object detection TensorFlow SSD Convolutional network image processing traffic signs solid-state disk Silver Sulfadiazine Object detection Mobile devices image processing artificial Intelligence Machine Learning Automobile driving Coordinates Transfer (Psychology) Cars

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A contactless automated dynamic monitoring method for organoid morphology on the time axis

A contactless automated dynamic monitoring method for organo...

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IEEE International Conference on Data Mining Workshops (ICDM Workshops)

作者： Xun Deng Lun Hu Zekun Jiang Lu Liu Peng-Wei Hu The Xinjiang Technical Institute of Physics and Chemistry Chinese Academy of Sciences Urumqi China University of Chinese Academy of Sciences Beijing China Xinjiang Laboratory of Minority Speech and Language Information Processing Urumqi China Shanghai Artificial Intelligence Research Institute Shanghai China Shanghai Roche Pharmaceuticals Ltd Shanghai China

Organoids, as in vitro 3D cultures with structures and functional characteristics similar to corresponding tissues, offer crucial biotechnological support for physiology, pathology, and drug testing. In the field of organoid imaging, the primary approach involves observing the dynamic changes in organoid morphology and movement during cultivation. Currently, there are limited and somewhat restrictive methods for organoid image analysis. Common techniques, such as those based on fluorescence staining, can impact organoid viability. Methods relying on bounding-box detection and tracking fail to capture organoid morphological characteristics, while threshold-based or deep neural network methods often suffer from segmentation inaccuracies. this paper presents the MacrOrga model, which is based on a combination of a multi-axis attention mechanism and convolutional residual networks. MacrOrga represents an innovative approach to organoid segmentation. MacrOrga effectively captures long-distance pixel relationships within organoid images, facilitating precise organoid segmentation. It demonstrates robust generalization capabilities across various datasets. Based on the results of the exact segmentation, the tracking of individual organoids is achieved using the Kuhn– Munkres algorithm to minimize the cost matrix of organoid assignment. The combination of precise segmentation and tracking functions offers a contactless, automated monitoring method for organoids in drug experiments, physiological assessments, and other research applications.

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