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artificial Intelligence and Signal processing (AISP), INDIA, International Conference on

作者： Molkam Rakesh Pushparaj Shetty D Department of Mathematics and Computational Science National Institute of Technology Karnataka Surathkal India

ISBN: (数字)9798350350654

ISBN: (纸本)9798350350661

This paper explores the applications of Graph neural networks (GNN) for enhancing Medicare fraud detection. Graph convolutional network (GCN) is a type of graph neural network. Governments and insurance companies are continuously adapting new technologies to detect and prevent fraud activities and trying to minimize financial losses and improve services because every year they lose billions of dollars due to Medicare fraud. Machine learning algorithms fail to analyze the graph data structure but Graph neural networks are good at analyzing the complex relational data and they directly integrate with the learning process. Machine learning algorithms are facing scalability and generalization across diverse graphs. GNN works on graph data structure, using unique IDs as nodes in a graph, with edges illustrating their relationships. Graph neural networks is used to improve the accuracy and efficiency of fraud detection by learning the complex relational information obtained from providers, beneficiaries, and physicians. We created a graph database based on the healthcare provider dataset. In this graph database, two types of heterogeneous nodes are there that are beneficiary and medicare provider nodes. The connection between the beneficiary and medicare providers is a power edge and the connection between providers is a shared-physician edge. We developed a fraud detection model using both machine learning and graph neural networks. Our Graph convolutional Network (GCN) model performed well compared to the basic machine learning (Logistic regression) model. The complex relationships between provider and beneficiary, provider and physician helped to detect medicare fraud using our model.

关键词： Logistic regression Machine learning algorithms Graph convolutional networks Databases image edge detection Scalability Medical services Machine learning Data structures Fraud

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A 0.8V Intelligent Vision Sensor with Tiny Convolutional neural Network and Programmable Weights Using Mixed-Mode processing-in-Sensor Technique for image Classification

A 0.8V Intelligent Vision Sensor with Tiny Convolutional Neu...

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2022 IEEE International Solid-State Circuits Conference, ISSCC 2022

作者： Hsu, Tzu-Hsiang Chen, Guan-Cheng Chen, Yi-Ren Lo, Chung-Chuan Liu, Ren-Shuo Chang, Meng-Fan Tang, Kea-Tiong Hsieh, Chih-Cheng National Tsing Hua University Hsinchu Taiwan

ISBN: (纸本)9781665428002

Vision systems with artificial intelligence (AI) for applications requiring image classification are in growing demand. However, the imager plus dedicated AI accelerator solution [1] suffers from the burdens of power and latency caused by the raw image data traffic between the imager and the companion signal processor with a neural network accelerator, making it unsuitable for the real-time inference in low-power edge devices. Recently, imagers with near- or in-sensor processing capability have been developed [2]-[6] to improve the system efficiency for specific applications. In [2]-[4], the near-sensor Haar-like filtering operations are implemented in imagers to realize face detection (FD). However, unlike using convolutional neural networks (CNNs) with programmable weights for different tasks, the implemented features of such prior works are limited and not configurable. In [5], a convolutional CMOS image sensor (CIS) with near-sensor analog multiply-accumulate (MAC) operations was reported for assisting with the 1st-layer computations of a CNN. However, the convolutional CIS is inadequate for some tasks, due limits on the numbers of layers/kernels, and needs a companion digital accelerator for the required operations (Rectified Linear Unit: ReLU, Maximum-Pooling: MP, Fully-Connected layer: FC, etc.) of a complete CNN model. In [6], an analog convolutional CIS is reported with a 5-layer network for CNN implementation. However, the analog MAC operations using charge sharing with a capacitor array leads to gain loss, low weight resolution, and limited accuracy. Moreover, the ReLU+MP operation using a static winner-take-all circuit is power hungry. To address these issues, we present an intelligent vision sensor (IVS) with an embedded tiny CNN model and programmable weights to achieve configurable feature extraction and on-chip image classification using a mixed-mode processing-in-sensor (PIS) technique. © 2022 IEEE.

关键词： image classification

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Adversarial Multi-Path Residual Network for image Super-Resolution

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IEEE TRANSACTIONS ON image processing 2021年 30卷 6648-6658页

作者： Wang, Qianqian Gao, Quanxue Wu, Linlu Sun, Gan Jiao, Licheng Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Xidian Univ Sch Telecommun Engn Key Lab Minist Educ Intellisense & Image Understanding Xian 710071 Peoples R China Xidian Univ State Key Lab ISN Xian 710071 Shaanxi Peoples R China Xidian Ningbo Informat Technol Inst Ningbo 315000 Peoples R China Huawei Consumer Business Grp Xian Peoples R China Chinese Acad Sci Shenyang Inst Automat State Key Lab Robot Shenyang 110016 Peoples R China Chinese Acad Sci Inst Robot & Intelligent Mfg Shenyang 110016 Peoples R China Xidian Univ Sch Artificial Intelligence Minist Educ Intellisense & Image Understanding Key Lab Xian 710071 Peoples R China

Recently, deep convolutional neural networks have demonstrated remarkable progresses on single image super-resolution (SR) problem. However, most of them use more deeper and wider networks to improve SR performance, which is not practical in real-world applications due to large complexity, high computation cost, and low efficiency. In addition, they cannot provide high perception quality and guarantee objective quality simultaneously. To address these limitations, we in this paper propose a novel Adversarial Multi-path Residual Network (AMPRN), which can largely suppress the number of network parameters and achieve a higher SR performance compared with the state-of-the-art methods. More specifically, we propose a multi-path residual block (MPRB) for multi-path residual network (MPRN) with fewer network parameters, which can extract abundant local features by fully using features from different paths generated by channel slices. These hierarchical features from all the MPRBs are then jointly aggregated by global gradual feature fusion. Following MPRN, we construct an adversarial gradient network with a gradient loss to make the gradient distribution of the generated SR images and ground truth image closer. In this way, the generated SR images of our model can provide high perception quality and objective quality. Finally, several experimental results demonstrate that our AMPRN achieves better performance in comparison with fewer parameters than the state-of-the-art methods.

关键词： Feature extraction Residual neural networks Superresolution Generative adversarial networks image reconstruction Generators Training Single image super-resolution (SISR) residual learning deep convolutional neural network

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Research on Construction of BIM Building View based on Deep Convolutional neural Network

Research on Construction of BIM Building View based on Deep ...

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2022 IEEE International Conference on artificial Intelligence and Computer applications, ICAICA 2022

作者： He, Junmin Xiao, Kaixi Xiao, Baichao Chen, Zhimin China Construction Third Bureau Engineering Design Co. Ltd. Hubei Wuhan430000 China

ISBN: (数字)9781665499910

ISBN: (纸本)9781665499910

The practice of smart urban governance and building intelligence has become one of the important branches of BIM architectural view construction. At the same time, it puts forward requirements for the BIM architectural model and data construction, especially the surveying, mapping, and modeling of existing buildings. Because most traditional mathematical algorithms can only deal with certain special images, the view construction of existing buildings relies on building extraction, and it is difficult to deal with remote sensing image processing with complex backgrounds effectively. Therefore, researchers gradually try to use deep convolutional neural networks for building extraction and plan view construction. This paper proposes to use UNet to deal with this problem. In order to avoid overfitting, the traditional UNet model has been adjusted accordingly in view of the shortcomings of the traditional UNet model in terms of material adaptability, extraction and recognition accuracy, and building boundaries. At the same time, the architecture has been adjusted and optimized, and dropout has been added. After the above optimization, the deep convolutional neural network UNet can obtain better building information extraction performance and support the construction of BIM architectural views of existing buildings. © 2022 IEEE.

关键词： Remote sensing

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Stochastic Precision Ensemble: Self-Knowledge Distillation for Quantized Deep neural networks 35

Stochastic Precision Ensemble: Self-Knowledge Distillation f...

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35th AAAI Conference on artificial Intelligence / 33rd Conference on Innovative applications of artificial Intelligence / 11th Symposium on Educational Advances in artificial Intelligence

作者： Boo, Yoonho Shin, Sungho Choi, Jungwook Sung, Wonyong Seoul Natl Univ Seoul South Korea Hanyang Univ Seoul South Korea

ISBN: (纸本)9781577358664

The quantization of deep neural networks (QDNNs) has been actively studied for deployment in edge devices. Recent studies employ the knowledge distillation (KD) method to improve the performance of quantized networks. In this study, we propose stochastic precision ensemble training for QDNNs (SPEQ). SPEQ is a knowledge distillation training scheme;however, the teacher is formed by sharing the model parameters of the student network. We obtain the soft labels of the teacher by randomly changing the bit precision of the activation stochastically at each layer of the forward-pass computation. The student model is trained with these soft labels to reduce the activation quantization noise. The cosine similarity loss is employed, instead of the KL-divergence, for KD training. As the teacher model changes continuously by random bit-precision assignment, it exploits the effect of stochastic ensemble KD. SPEQ outperforms the existing quantization training methods in various tasks, such as image classification, question-answering, and transfer learning without the need for cumbersome teacher networks.

关键词： Deep neural networks

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Insights Into YOLOv9-Based Breast Mass Detection Using Ultrasound images

Insights Into YOLOv9-Based Breast Mass Detection Using Ultra...

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Workshops on image processing Theory, Tools and applications, IPTA

作者： Nouhaila Erragzi Abdelghani Moussaid Nabila Zrira Ibtissam Benmiloud Rajaa Sebihi Nabil Ngote Faculty of Science in Rabat LPHE-Modeling and Simulations Mohammed V University Morocco MECAtronique Team CPS2E Laboratory National Superior School of Mines Rabat Morocco ADOS Team LISTD Laboratory National Superior School of Mines Rabat Morocco Abulcasis International University of Health Sciences National Superior School of Mines Rabat Morocco

ISBN: (数字)9798331541842

ISBN: (纸本)9798331541859

The most prevalent form of cancer globally is breast cancer, which predominantly impacts women. Early detection ensures successful treatment of breast cancer, significantly improving patients' survival chances. Various imaging modalities, including mammography and ultrasound, are utilized for breast cancer screening. Incorporating new technologies is essential for better patient management, particularly for those with malignant masses. artificial intelligence can assist radiologists by training neural networks to detect breast lesions on mammograms or ultrasounds using deep learning techniques. In this article, the YOLOv9 network is trained on two public ultrasound databases, UDIAT and BUSIS. The network successfully localized malignant and benign masses with a precision of 83%, a recall of 82%, and a mAP of 87% in the UDIAT dataset. In the BUSIS dataset, our model achieved a precision of 75%, a recall of 88%, and a mAP of 90%. Furthermore, we used real Moroccan cases to evaluate the model's performance.

关键词： Training Deep learning Ultrasonic imaging Databases image processing neural networks Imaging Breast cancer Mammography Lesions

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Convolution Filter Equivariance/Invariance in Convolutional neural networks: A Survey 1st

Convolution Filter Equivariance/Invariance in Convolutional...

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1st Pan-African Conference on artificial Intelligence, PanAfriCon AI 2022

作者： Habte, Sinshaw Bekele Ibenthal, Achim Bekele, Ephrem Tehsale Debelee, Taye Girma Addis Ababa University Addis Ababa Ethiopia HAWK University of Applied Sciences and Arts Göttingen Germany Ethiopian Artificial Intelligence Institute Addis Ababa Ethiopia Addis Ababa Science and Technology University Addis Ababa Ethiopia

ISBN: (纸本)9783031313264

Models parameterized by Convolutional neural networks (CNNs), reportedly, have garnered a commanding position in learning based multidimensional signal processing. This feat is achieved to a large extent through the translation equivariance property of the convolutional filters employed. While popular, the required optimum level of model parameters, the limited availability of signal datasets, robustness of the model to input signal transformations, and generalization to out of distribution signal sets are some of the challenges of CNN models. The incorporation of equivariance or consistency to symmetry transformations in which invariance is a special case, have been suggested to not only alleviate these challenges but also bring about model interpretability. This work presents a systematic survey of the equivariant convolutional filter design for convolutional neural networks in image processing applications with the aim of bringing forth design methods in terms of group theory, discrete Fourier transform, discrete cosine transform, and wavelet transform. The theoretical foundations for translation, rotation, scale and affine equivariance under nonlinear transform, their efficient implementations especially in natural image processing applications has been thoroughly discussed. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Convolution

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Deep Learning Models Selection and Adaptation for a Typical Problem of X-Ray image processing

Deep Learning Models Selection and Adaptation for a Typical ...

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2021 Innovations in Intelligent Systems and applications Conference, ASYU 2021

作者： Sheshulin, Kirill Chuvakov, Aleksandr Ivaschenko, Anton Samara State Technical University Computer Science Department Samara Russia

ISBN: (纸本)9781665434058

The paper explores a problem of artificial neural networks comparative analysis and adaptation while solving a typical task of image analysis and pattern recognition. This article examines four neural networks using different optimizers to solve a problem of pneumonia detection on X-ray images. The study implements convolutional neural networks, as well as the method of their transfer learning. The architecture of neural networks and the results of their work with various metrics that assess the success of this model are presented. The problem of interpretability of the decision made by a neural network can be solved using a special visualization method presented in the study. Advantages of the proposed research are related with a possibility to use the convolutional neural networks in practical applications. © 2021 IEEE.

关键词： Intelligent systems

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Piktor-O-Bot: Integrated image processing algorithms for portrait drawing robot applications

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Measurement: Sensors 2025年 38卷

作者： Hoang, Anh Tuan Csempesz, János Cserteg, Tamás Viharos, Zsolt János Budapest Hungary Faculty of Economics and Business John von Neumann University Kecskemét Hungary Doctoral School of Informatics ELTE Eötvös Loránd University BudapestH-1117 Hungary

Collaborative robots, or cobots, have emerged as vital assets in numerous industries, reshaping operational paradigms and fostering human-machine collaboration. This paper introduces Piktor-o-bot, a Universal Robots 5 series portrait drawing robot, which integrates advanced image processing algorithms with collaborative robotic functionalities to streamline and enhance portrait creation. By utilizing neural networks for human face detection, background removal, edge detection, semantic segmentation, clothes segmentation, and eye detection, Piktor-o-bot aims to achieve lifelike depictions efficiently. The proposed system integrates these specialized neural networks, innovative algorithms, and collaborative robotic capabilities to offer a promising and optimized solution. The individual components and their interactions contribute to the overall success of the Pictor-o-bot system, as demonstrated by its performance at several exhibitions. © 2024

关键词： Semantic Segmentation

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A Survey on Test Input Selection and Prioritization for Deep neural networks

A Survey on Test Input Selection and Prioritization for Deep...

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International Symposium on System and Software Reliability (ISSSR)

作者： Shengrong Wang Dongcheng Li Hui Li Man Zhao W. Eric Wong School of Computer Science China University of Geosciences Wuhan China Department of Computer Science California State Polytechnic University - Humboldt Arcata USA Department of Computer Science University of Texas at Dallas Richardson USA

ISBN: (数字)9798350362930

ISBN: (纸本)9798350362947

With the breakthrough advancements of deep neural network technology in applications such as image processing, autonomous driving, and speech recognition, the testing of deep neural network models becomes crucial to ensure their performance and reliability. The selection of test inputs is a critical step in the testing process. Prioritizing test inputs and selecting the most impactful ones can help improve testing efficiency to identify potential problems and deficiencies in the model as early as possible, given the large size of the test dataset and the high cost of annotation. In order to gain a deeper understanding of the research progress in the field of test input selection for deep neural networks, this paper conducts a survey of academic papers in the field over the past years. A systematic review of existing research outcomes is presented, focusing on the criteria, methods, and priority ranking for the selection of test inputs in deep neural networks. Additionally, the paper offers insights into future challenges for test input selection in deep neural networks.

关键词： Surveys Uncertainty Systematics Reviews artificial neural networks Speech recognition Robustness

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