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

共 20880 条记录，以下是4881-4890 订阅

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Enhancing Industrial Automation Flexibility Through neural Network-Empowered Machine Vision applications 1

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1st International conference on artificial Intelligence and its applications, ICAIA 2023

作者： George, Navin M. Rosero, Evelyn Mayakannan, S. Shameem, A. Divya, S. Arul, N. Electronics and Communication Engineering Karunya Institute of Technology and Sciences Tamil Nadu Coimbatore641114 India Riobamba060155 Ecuador Department of Mechanical Engineering Vidya Vikas College of Engineering and Technology Namakkal Tamilnadu Tiruchengode India AMET Business School AMET University Tamilnadu Chennai India Erode Tamil Nadu Perundurai638057 India Department of Mechatronics Engineering Sona College of Technology Tamilnadu Salem India

ISBN: (数字)9783031843976

ISBN: (纸本)9783031843969

Vision-based systems provide non-contact sensory input for processing and feedback, expanding the capabilities of industrial automation applications. Prediction using ANNs aids such conformities in navigating nonlinear computational landscapes. They map the plethora of potential outcomes or zones of uncertainty introduced by the system’s constituents onto a set of feasible options for action. A measure of intelligence is transferred to robotic systems through the use of trained networks. Two uses of machine vision are presented here. The 3 DOF robotic assembly enables precision cutting of fragile materials with the help of visual guiding and pixel elimination. The 6-degrees-of-freedom robot uses a combination of visual feedback from an onboard camera and supervision from an external camera to achieve its goals. Pick-and-place operations are executed with a switching control method. Both systems improved in terms of computing time and convergence once ANN was used to make the strategies intelligent. The retrieved scene image features are put to use by the networks in a variety of ways. The simulation and experimental results that back up the suggested approaches demonstrates the value of artificial neural network in machine vision applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Robotic assembly

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Detection of Anomalous Cryptocurrency Transactions Using neural networks with Decision-making Explanation

Detection of Anomalous Cryptocurrency Transactions Using Neu...

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Euromicro conference on Parallel, Distributed and Network-Based processing

作者： Dmitry Levshun Ksenia Zhernova Andrey Chechulin St. Petersburg Federal Research Center of the Russian Academy of Sciences Saint Petersburg Russia

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

The objective of this paper is to explore an effective approach for detecting anomalies in cryptocurrency transactions using artificial intelligence techniques. The proposed method is compared with existing approaches for identifying illegal transactions within cryptocurrency networks. This research systematically analyzes various data processing methods applicable to the problem of detecting illicit transactions, as well as reviewing relevant studies in cryptocurrency transaction analysis, data visualization, and computer vision-based anomaly detection. Experimental evaluations on a cryptocurrency transaction dataset show significant insights, with results benchmarked against those obtained in prior studies on the same dataset (BABD). The findings suggest that the proposed method, utilizing statistical graph characteristics, deep and ensemble learning technologies, enhances the accuracy of identifying fraudulent transactions. This approach could be instrumental in developing software solutions for monitoring cryptocurrency transactions, potentially aiding in digital forensics and cybersecurity applications. The findings may benefit researchers in computer security and developers focused on information security systems.

关键词： Adaptation models Accuracy Sensitivity neural networks Digital forensics Information security Cryptocurrency Fraud Ensemble learning Anomaly detection

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Revolutionizing Brain Tumor Diagnosis: Harnessing Convolutional neural networks for Enhanced Prediction and Classification

Revolutionizing Brain Tumor Diagnosis: Harnessing Convolutio...

引用

Computing, Power and Communication Technologies (IC2PCT), IEEE International conference on

作者： Aditya Kumar Singh Aman Mishra School of Computer Science and Engineering Galgotias University Greater Noida India

Diagnosing brain tumors correctly and on time is the only way of effectively planning suitable treatment. Advances made recently in the field of medical imaging and artificial intelligence are leading to better methods of diagnosis. This paper is going to explore how Convolutional neural networks (CNNs) can be used to predict and classify tumors in the brain. The challenges of diagnosing brain tumors, the importance of CNNs in medical image processing, a detailed survey about recent studies and techniques used in this area would also be covered. Moreover, we propose an innovative CNN architecture designed for brain tumor detection by using modern developments in deep *** said, a two stage analysis methodology was followed with a total focus towards prevalent challenges within image Restoration and image Enhancement. As such, this study introduces new techniques that address these issues *** study proposes an approach utilizing the power of Convolutional neural networks (CNNs). The novel CNN classification technique is applied, leveraging the Python and TensorFlow *** findings underscore the potential of this approach in revolutionizing brain tumor detection. By providing detailed insights into the strengths and limitations of the proposed model, this research contributes significantly to the *** paper concludes by emphasizing the transformative impact of this research, opening avenues for further exploration and innovation in this critical domain.

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Clothing image Classification Using VGG-19 Deep Learning Model For E-commerce Web Application

Clothing Image Classification Using VGG-19 Deep Learning Mod...

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Emerging Smart Technologies and applications (eSmarTA), International conference on

作者： Mohammed Hameed Amr Al- Wajih Mohammed Shaiea Mohammed Rageh Fahd A. Alqasemi Information Technology Dept. University of Science and Technology Yemen

ISBN: (数字)9798350354133

ISBN: (纸本)9798350354140

artificial Intelligence applications become one of the most important tools that help to increase the profits of e-commerce stores. It is expected that AI methods will push this aspiration to an even higher level. This study aims at classifying images of clothing products using deep learning (DL) techniques while embedding them in an e-commerce web application. We utilize deep learning techniques to determine the type of clothing products in the image, such as shirts, dresses, pants, shoes, etc. This study performs several steps, including requirements collecting and modeling, DL model training using two deep learning models, and then testing the models and the system’s accuracy on a set of images. We have used two deep learning models improved from classic Convolutional neural networks (CNN). The Convolutional neural Network models are achieving high accuracy on image classification tasks. Therefore, the literature proposes many suggested improvements to CNN architecture, such as Xception and VGG-19 architectures. In this study, we have selected a VGG-19-based clothing image classification. We found out that the VGG-19 model outperforms the Xception model. Therefore, the trained VGG-19 model is incorporated into a clothing store web application for classifying clothing image products. Testing accuracy is found, and then a manual test of the system accuracy is achieved using in-lab sample images.

关键词： Deep learning Training Accuracy Unified modeling language Clothing Data models Convolutional neural networks

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Multi-Musical Instrument Recognition neural Network

Multi-Musical Instrument Recognition Neural Network

引用

artificial Intelligence (WSAI), World Symposium on

作者： Bardia Timouri Ravdeep Aulakh Gathrean Dela Cruz Dept. of Computer Science British Columbia Institute of Technology Vancouver Canada

ISBN: (数字)9798350374162

ISBN: (纸本)9798350374179

artificial intelligence continues to evolve, particularly in the realms of natural language processing (LLMs), image generation, and task automation. Despite these advancements, multi-musical instrument recognition remains a challenging area with limited effective solutions. Addressing this, our research introduces an innovative methodology using a convolutional neural network (CNN) embedded within an artificial neural network framework. This method utilizes the OpenMIC-2018 dataset, meticulously refined for our purposes. We process entire songs by converting them into mel-spectrograms, which are instrumental in distinguishing subtle variations in pitch, dynamics, and timbre. Our approach sets a new benchmark in the field, adeptly identifying and categorizing up to 10 distinct musical instruments within complex audio recordings. It boasts an impressive F1 score of 56%. This significant achievement not only advances audio signal processing but also highlights the versatility and effectiveness of CNNs in handling sophisticated tasks like multi-instrument recognition. Our work serves as a stepping stone for future explorations in audio recognition, potentially paving the way for more nuanced and accurate audio analysis in various applications, from music production to sound engineering.

关键词： Training Accuracy Instruments Forensics Production Benchmark testing Robustness Natural language processing Convolutional neural networks Timbre

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Design-Space Exploration of Systolic Array for Edge Inferencing applications 3

Design-Space Exploration of Systolic Array for Edge Inferenc...

引用

3rd International conference on AI-ML Systems, AIMLSystems 2023

作者： Prashanth, H.C. Mogal, Yash Dharmesh Rao, Madhav IIIT-Bangalore Bangalore India

ISBN: (纸本)9798400716492

Systolic array (SA) architectures are suitable hardware accelerators to run artificial intelligence (AI) algorithms due to their scalable and pipelined structure. Inferencing on edge devices enables decision-making capability in a small, power-efficient form factor suitable for many IoT-based applications. Convolutional neural networks (CNNs), which form a major part of AI algorithms, are generally memory and compute-intensive algorithms. Optimizing resource-constrained SA-based edge-computing system architecture is crucial. The primary challenge is to find an optimal SA configuration with sufficient on-chip and off-chip memory, processing elements, and optimal memory-bandwidth interface for achieving minimum latency and stall cycles while maximizing compute utilization and mapping efficiency. This work characterizes the popular ResNet50 CNN model on different SA architecture configurations that are structured using a realistic designer tool SCALE-SIM. The design-space exploration of SA was carried out to run the Resnet50 model by configuring i) array sizes (processing elements matrix), ii) SRAM memory sizes available to the computing elements, iii) varying interface bandwidth of memory to the computing elements. The compute latency in clock cycles, SA utilization, Mapping efficiency, number of Stall cycles, and SRAM and DRAM bandwidth requirements were extracted using cycle accurate runs. All three types of data-flow models, including output stationary (OS), weight stationary (WS), and input stationary (IS), were investigated in this work. Unlike previous research that relied on benchmarking various mathematical models, this study focuses on characterizing SA configurations using the ResNet50 model. It presents comprehensive results for all the investigated configurations and establishes the most efficient SA architecture for running CNN architectures. Moreover, it provides valuable insights for designing resource-constrained edge-inferencing systems. © 2023 ACM.

关键词： Mapping

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A survey of Convolutional neural networks —From software to hardware and the applications in measurement

A survey of Convolutional Neural Networks —From software to...

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作者： Li, Hengyi Yue, Xuebin Wang, Zhichen Wang, Wenwen Tomiyama, Hiroyuki Meng, Lin Graduate School of Science and Engineering Ritsumeikan University KusatsuShiga Japan Department of Computer Science University of Georgia AthensGA United States

The convolutional neural network is a subfield of artificial neural networks and has made great achievements in various domains over the past decade. The technique has been widely applied including computer vision, natural language processing, image analysis, etc. The field of measurement also achieves exciting progress with convolutional neural networks. This paper gives a survey of convolutional neural networks from software algorithms to hardware architectures perspective and the applications in measurement. The survey hopes to give contributions to the cross-research of artificial intelligent and measurement. © 2021

关键词： Convolution

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A Novel artificial Intelligence Inspired Approach for Analysing Sentiments using neural Network 1

A Novel Artificial Intelligence Inspired Approach for Analys...

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1st International conference on Smart and Sustainable Technologies in Energy and Power Sectors, SSTEPS 2022

作者： Thakral, Deepti Sharma, Sachin Sharma, Khushi Ranjan, Sudhakar Apeejay Stya University School of Engineering and Technology Gurugram India Manav Rachna International Institute of Research and Studies Faculty of Computer Applications Faridabad India Amity University Department of Computer Engineering Noida India

ISBN: (纸本)9781665464147

Sentiment Analysis is a text classification system used to analyse the incoming text and determine if the sentiment evolved from the argument text is positive, negative or neutral. It holds specific significance in different areas of Technology and it has varied applications. It combines the power of two most significant fields of artificial Intelligence, viz. Deep Learning and Natural Language processing. Deep Learning has evolved as an advanced and efficient technology to target and solve real-world problems with greater accuracy and efficiency. neural networks are used to deal with enormous amounts of data without any considerable domain engineering and adapt from its own method of computing. Sentiments within a text can become misleading if the whole sentence is taken as a collection of independent words, since each and every word is connected to other in some sense. This research is used to propose and develop an efficient model that can analyse the sequential sentiments with greater prediction and accuracy. This research also compares the prediction accuracy of the proposed model with other existing novel methods. © 2022 IEEE.

关键词： Sentiment analysis

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An 1.38nJ/Inference Clock-Free Mixed-Signal Neuromorphic Architecture Using ReL-PSP Function and Computing-in-Memory

An 1.38nJ/Inference Clock-Free Mixed-Signal Neuromorphic Arc...

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2023 IEEE Biomedical Circuits and Systems conference, BioCAS 2023

作者： Fang, Wenbing Xuan, Zihao Chen, Song Kang, Yi University of Science and Technology of China School of Microelectronics Hefei China

ISBN: (纸本)9798350300260

Spiking neural networks (SNNs) associated with neuromorphic hardware architectures inspired by the human brain are considered to have promising prospects in ultra-low power applications such as Internet of Things (IoTs) and edge computing devices due to their spike-based information processing and transmission. In this paper, we propose a fully event-driven, clock-free and mixed-signal CMOS design of a highly energy-efficient spiking neural network hardware architecture for visual recognition tasks. The SNN algorithm we employ is based on the Rectified Linear Postsynaptic Potential function (ReL-PSP), which operates in the continuous time domain and dovetails perfectly with our completely clock-free hardware design. In combination with current domain SRAM CIM (Computing-in-Memory), energy expenditure of the I&F (Integrate-and-Fire) neuron and synaptic array has been notably reduced. Simulation results under 28nm CMOS technology indicate that our design achieves 96.92% classification accuracy on MNIST handwritten digit recognition, with only 1.38nJ energy consumption per image and 10us latency. © 2023 IEEE.

关键词： clock-free mixed-signal circuit Neuromorphic processor ReL-PSP function Spiking neural Network (SNN) SRAM Computing-in-Memory

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Performance Evaluation of Optic Disc Detection Using Faster RCNN with Alexnet, Resnet50 and Vgg19 Convolutional neural networks 11th

Performance Evaluation of Optic Disc Detection Using Faster ...

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11th International conference on Robotics, Vision, Signal processing and Power applications, RoViSP 2021

作者： Poh, Chyong Yi Teoh, Soo Siang School of Electrical and Electronic Engineering USM Engineering Campus Universiti Sains Malaysia Penang Nibong Tebal14300 Malaysia

ISBN: (纸本)9789811681288

Automatic detection of optic disc (OD) from retinal images is essential in the diagnosis of diabetic retinopathy (DR) and other eye diseases. For example, it is used in the detection of glaucoma and neovascularization on the optic disc. In this paper, the application of deep learning for automatic detection of OD from fundus images is investigated. The methods investigated are Faster Region Convolutional neural Network (Faster RCNN) using three different pre-trained networks: Alexnet, Resnet50 and Vgg19. The effect of using input images in RGB and CIEXYZ formats on the detection performance is also evaluated. The performance is compared using four metrics: precision, sensitivity, miss rate and processing time. Fundus image dataset from DIABETDB0 is used in the evaluation. The experiment results show that using Faster RCNN and a pre-trained network with deeper convolutional layers gives better results for OD detection. Faster RCNN with Restnet50 network produced the best overall results with the highest precision and sensitivity (1.0000 and 0.9474 respectively), and the lowest miss rate (0.0526). It was also found that using input image in CIEXYZ format can give better results compared to RGB image. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Convolutional neural networks

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