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检索条件"任意字段=Conference on Applications of Artificial Neural Networks in Image Processing II"

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5th EAI International conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

5th EAI International Conference on Cognitive Computing and ...

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5th EAI International conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

ISBN: (纸本)9783031770807

The proceedings contain 102 papers. The special focus in this conference is on Cognitive Computing and Cyber Physical Systems. The topics include: Comparative Analysis Between Fuzzy Theorem and KNN Methodology for Fault and Anomaly Detection;Production Classification in E-Commerce Based on Product Descriptions with Natural Language processing (NLP) and Machine Learning Models;defense Strategy Security Mechanism for Sensor networks;an Efficient Approach for Food Demand Forecasting Using an Ensemble Technique and Statistical Analysis;identification of Different Medicinal Plants Using Machine Learning and image processing;Leveraging the Power of MRMR in Machine Learning Models for Multi Class Classification of Rice to Promote Sustainable and Efficient Smart Farming;performance Analysis of Word Recognition System Using Tensor Flow;A Distinct artificial Feed Forward neural Network (AF2NN) Model for Predicting Compressive Strength of Geo-Polymer Concrete;Performance Assessment of Deep Learning-Models for Kidney Tumor Segmentation using CT images;investigation of Quantum Machine Learning for Smart Eco System Focusing on Energy Optimization;Classification of Skin Cancer Using CNN with Transformer Layer;a Unified Approach to Smart Coconut Farming with IoT and Deep Learning for Recommendation of Pesticides and Fertilizers;classifying the Severity of Diabetic Retinopathy Using Deep Learning;Exploring Prominent Convolutional neural Network Frameworks to Identify COVID-19 Deceases by Using Medical images;non-invasive Technique for Detecting Glycosuria Through image processing and Deep Learning Approaches;deep Learning Strategies for Multiclass Skin Disease Classification;AI-Driven Glaucoma Susceptibility Assessment and Lifestyle Guidance;Deep Learning in Cartoon Moderation: Distinguishing Child-Friendly Content with CNN Architectures;THz Wideband Metamaterial Absorber for Different applications.

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An Overview of Different Approaches used for processing Satellite images

An Overview of Different Approaches used for Processing Sate...

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2024 International conference on Distributed Systems, Computer networks and Cybersecurity, ICDSCNC 2024

作者： Chethana, V. Rashmi, S. Visvesveraya Technological University Dayananda Sagar College of Engineering Department of Information Science & Engineering Belagavi India Visvesveraya Technological University Dayananda Sagar College of Engineering Department of Computer Science & Engineering (Data Science) Belagavi India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

In country such as India due to monsoon and frequent climatic changes leads to natural disasters such as floods, drought, landslides, cyclones, forest fire, earthquake and so on. The post disaster badly impacts on human life, economy and environment. As remote sensing technology advanced, the satellite images with high resolution can be processed and analyzed to get the required results. These analyzed results can be used for rescue operations after disaster or to predict any disasters before their occurrences. Some of the applications such as post disaster event management widely use onboard processing to get faster analyzed results. Various operations such as satellite image- enhancement, compression, classification and segmentation are performed on satellite images. Results of these techniques mainly used in applications such as disaster management, Land Use Land Cover, Change detection, landslide and many more. In recent days, satellite image processing are performed using new technical aspects such as deep learning, artificial intelligence and machine learning to get fast and more accurate results. Edge-Computing-Enabled Deep Learning and Leveraging Deep Learning are the approaches used for satellite image enhancement. Panoptic Segmentation of Multispectral Satellite images approaches are mainly used for satellite image segmentation. Deep Learning based approach and Genererative Adverserial Network architecture are applied to get change detection in satellite images. Lossless algorithm for hyperspectral data compression and neural network lossy compression techniques are preferably used for satellite image compression. Approaches such as Hybrid data model centric approach and Multiscale Convolutional neural Network are applied to perform object detection. And also machine learning algorithms are mainly used for classification of satellite images. This paper provides comparative study survey on these various approaches used for processing of satellite images. ©

关键词： convolutional neural network deep learning disaster remote sensing satellite images

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5th EAI International conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

5th EAI International Conference on Cognitive Computing and ...

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5th EAI International conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

ISBN: (纸本)9783031770746

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Generative Adversarial networks for Domain Translation in Unpaired Breast DCE-MRI Datasets 4th

Generative Adversarial Networks for Domain Translation in Un...

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4th International conference on Deep Learning Theory and applications (DeLTA)

作者： Galli, Antonio Gravina, Michela Marrone, Stefano Sansone, Carlo Univ Naples Federico II Dept Elect Engn & Informat Technol DIETI Naples Italy

ISBN: (纸本)9783031390586;9783031390593

Generative Adversarial networks (GAN) are more and more gaining attention in the computer vision domain thanks to their ability to generate synthetic data, in particular in the context of domain adaptation and image-to-image translation. These properties are attracting the medical community too, in order to solve some complex biomedical challenges, such as the translation between different medical imaging acquisition protocols. Indeed, as the actual acquisition protocol is strongly dependent on factors such as the operator, the aim, the centre, etc., gathering cohorts of patients all sharing the same typology of imaging is an open challenge. In this paper, we propose to face this problem by using a GAN to realise a domain translation architecture in the case of breast Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI), considering two different acquisition protocols, in the context of automatic lesion classification. Despite this work wanting to be a first step toward artificial data generation in the medical domain, the obtained results have been analysed from both a quantitative and qualitative point of view, in order to evaluate the correctness and quality of the proposed architecture as well as its usability in a clinical scenario.

关键词： Breast MRI Generative adversarial networks image-to-image translation

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Split Computing based Quantized PoseNet Model for Distributed AI Architecture in IoT-Edge 15

Split Computing based Quantized PoseNet Model for Distribute...

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15th International conference on Communication Systems and networks (COMSNETS)

作者： Karjee, Jyotirmoy Naik, Praveen S. Anand, Kartik Byadgi, Chandrashekhar S. Dabbiru, Ramesh Babu Venkat Srinidhi, N. Samsung R&D Inst India Bangalore Karnataka India

ISBN: (纸本)9781665477062

In recent times, Internet of Things (IoT) devices is gaining popularity in advanced wireless technology (i.e., 5G). However, in 5G applications (say in edge platform), the IoT devices have limited computation & processing capabilities which makes it challenging to execute Deep neural Network (DNN) models on them. To address this, we introduce Split Computing technology, to partition DNN inference layers based on the computational capabilities (such as bandwidth, battery level and processing power, etc.) of IoT and edge (computationally powerful) devices, respectively. To validate split computing, we propose a framework called Distributed artificial Intelligence (DAI) architecture. We use the architecture for a fitness application (use-case) where we detect the pose of a person for our proposed Quantized Split PoseNet DNN (QSP-DNN) algorithm which partitions the DNN layers among IoT device and edge based on Wi-Fi bandwidth. We perform experiments to validate the QSP-DNN algorithm using DAI architecture. The QSP-DNN with DAI compares split execution (computed among IoT device & edge) for partial offload and full-offload executed on edge device. The result shows that using QSP-DNN in DAI architecture provides split execution performing 20.76 % improvement compared to the full offload case.

关键词： Performance evaluation Computational modeling image edge detection Computer architecture Bandwidth Inference algorithms Partitioning algorithms

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Segmentation of Retina Vessels in 2D OCT-Reconstructed Fundus images with 3D UNet 26

Segmentation of Retina Vessels in 2D OCT-Reconstructed Fundu...

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26th IEEE Signal processing: Algorithms, Architectures, Arrangements, and applications, SPA 2023

作者： Marciniak, Tomasz Stankiewicz, Agnieszka Zaradzki, Przemyslaw Poznan University of Technology Division of Electronic Systems and Signal Processing Institute of Automatic Control and Robotics Department of Control Robotics and Electrical Engineering Poznan Poland

ISBN: (纸本)9798350304985

This paper presents an analysis of the effectiveness of blood vessel segmentation in human retinal images. The segmentation process was carried out for three-dimensional optical coherence tomography (OCT) scans using the author's publicly available CAVRI-C database. A 3D version of the U-Net artificial neural network was used. Various dimensions of the input blocks were investigated to determine their influence on the segmentation process. The effectiveness of the proposed solutions was evaluated using various metrics and the results obtained were compared with the 2D solution, also based on neural networks. The use of the proposed solutions allowed us to improve the segmentation even by 15% (in the case of the F1-score). © 2023 Division of Signal processing and Electronic Systems, Poznan University of Technology (DSPES PUT).

关键词： Optical tomography

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Signup page using Face recognition authentication

Signup page using Face recognition authentication

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2025 IEEE International Students' conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Modi, Akash Rayar, Prajwal V Kavya, A.G. Vaishnavi, R. Dravya, B.S. Tamilarisi, R. Alliance School of Advanced Computing Department of Computer Science Bengaluru India

ISBN: (纸本)9798331529833

Improvements in artificial intelligence and machine learning have led to the growing integration of face recognition technology for a wide range of applications, from security systems to social media. Three related uses of facial processing technology are examined in this abstract: face detection, face recognition, and authentication through a face-based signup page. Finding and differentiating human faces in an image frame requires the first step, face detection. It also recognizes the facial features and separates them from other items in the frame using computer vision algorithms. Histogram of Oriented Gradients (HOG), and the Haar Cascade, are the methods used for face recognition. Still, due to failure in extracting facial features in harsh conditions, we use the more recent deep learning models like Convolutional neural networks (CNNs) to do this project. Building on detection and face recognition compares the observed faces to known faces in a database to identify or to confirm a person's identity. To assess distinctive facial traits (embeddings) or features and increase recognition accuracy across a variety of circumstances, like varying lighting and angles, modern face recognition systems employ deep learning models, such as Face Net and Deep Face. The Facial Recognition The signup page serves as an example of how face recognition can be used in authentication systems for secure apps. A user registers for this program by having their face captured, and this information is safely kept and acts as a unique identification. The solution gives a hands-free and password-less authentication mechanism during login by comparing the captured face with the stored data in the database. © 2025 IEEE.

关键词： Convolutional neural networks

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Chicken Quality Evaluation Using Deep Learning 2nd

Chicken Quality Evaluation Using Deep Learning

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2nd International conference on Emerging Trends in Expert applications and Security, ICE-TEAS 2023

作者： Madan, Rishi Choudhury, Tanupriya Sarkar, Tanmay Bansal, Nikunj Toe, Teoh Teik Uttarakhand Dehradun248007 India Adjunct Professor CSE Dept. Graphic Era Hill University Uttarakhand Dehradun248002 India The AI University CutbankMT59427 United States Department of Food Processing Malda Polytechnic West Bengal Malda732102 India Singapore Singapore

ISBN: (纸本)9789819919451

In recent years, the demand for food materials is reaching sky, thus making the artificial intelligence-based evaluation technique the research hot-spot now because the quality determination of these materials can be really difficult without proper understanding and experience. As a result, we have developed a classification model for artificial neural networks based on computer vision. On a primary dataset, we are utilizing a convolutional-based architecture to focus on shape and texture, which are the most crucial factors to experts. An example of a DNN (Deep neural Network) is a convolutional neural network, which has been shown to be particularly effective in extracting features from input images. This project demonstrates that deep learning improved the accuracy of classification tasks over traditional image analysis methods. And thus, it can be inferred that it is a potential future tool for grading the food quality. We also intend to do an analysis of different machine learning algorithms available and compare it with our model. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image analysis

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Physical neural network using skyrmion-based spin torque nano-oscillators

Physical neural network using skyrmion-based spin torque nan...

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2024 International conference on Electrical Technology and Automation Engineering, ETAE 2024

作者： Xiong, Shan Liang, Xue Xing, Xiangjun Zhou, Yan School of Physics & Optoelectronic Engineering Guangdong University of Technology Guangdong Guangzhou510006 China Guangdong Provincial Key Laboratory of Information Photonics Technology Guangdong University of Technology Guangdong Guangzhou510006 China School of Science & Engineering The Chinese University of HongKong Guangdong Shenzhen518172 China

Due to physical limitations on the miniaturization of traditional electronic devices, architectures based on emerging principles have become the focus of current research to meet the needs of rapidly developing information technologies in the post-Moore era. Neuromorphic devices hold huge potential for use in future artificial intelligence (AI) chips beyond conventional architectures. Benefiting from a wealth of nonlinear dynamic characteristics of spin torque nano-oscillators (STNOs), studies of neuromorphic computations and their applications based on STNOs are attracting growing attention. In this article, at first, we construct a magnetic skyrmion-based STNO and analyze its characteristics;on this basis, we propose a physical echo state network (ESN) including eight skyrmion-based STNOs, which is utilized to implement an image recognition task. Micromagnetic simulations of the nonlinear response of skyrmion-based STNOs to current pulses imply that such a physical neural network has remarked performance in handwritten digit recognition. The high precision, low energy consumption, and fast processing speed of STNO-based neuromorphic devices are desirable in multitudinous practical applications, possibly leveraging the use of STNO-based physical neural networks in the field of artificial intelligence. © Published under licence by IOP Publishing Ltd.

关键词： Energy utilization

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Provable Editing of Deep neural networks using Parametric Linear Relaxation 38

Provable Editing of Deep Neural Networks using Parametric Li...

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38th conference on neural Information processing Systems, NeurIPS 2024

作者： Tao, Zhe Thakur, Aditya V. University of California Davis DavisCA95616 United States

Ensuring that a DNN satisfies a desired property is critical when deploying DNNs in safety-critical applications. There are efficient methods that can verify whether a DNN satisfies a property, as seen in the annual DNN verification competition (VNN-COMP). However, the problem of provably editing a DNN to satisfy a property remains challenging. We present PREPARED,1 the first efficient technique for provable editing of DNNs. Given a DNN N with parameters θ, input polytope P, and output polytope Q, PREPARED finds new parameters θ such that ∀x ∈ P. N(x;θ) ∈ Q while minimizing the changes θ − θ. Given a DNN and a property it violates from the VNN-COMP benchmarks, PREPARED is able to provably edit the DNN to satisfy this property within 45 seconds. PREPARED is efficient because it relaxes the NP-hard provable editing problem to solving a linear program. The key contribution is the novel notion of Parametric Linear Relaxation, which enables PREPARED to construct tight output bounds of the DNN that are parameterized by the new parameters θ. We demonstrate that PREPARED is more efficient and effective compared to prior DNN editing approaches i) using the VNN-COMP benchmarks, ii) by editing CIFAR10 and TINYimageNET image-recognition DNNs, and BERT sentiment-classification DNNs for local robustness, and iii) by training a DNN to model a geodynamics process and satisfy physics constraints. © 2024 neural information processing systems foundation. All rights reserved.

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