image classification in the domain of cultural heritage becomes extremely important with the development of digitisation practices. This study aims to analyze how classification performance on the small dataset repres...
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This paper focuses on the detection of surface defects in the steel manufacturing industry since these defects inhibit the quality of the manufactured products and the efficiency of the manufacturing processes. Conven...
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ISBN:
(数字)9798350367720
ISBN:
(纸本)9798350367737
This paper focuses on the detection of surface defects in the steel manufacturing industry since these defects inhibit the quality of the manufactured products and the efficiency of the manufacturing processes. Conventional inspection techniques require a extensive amount of work to be done at the inspection sites and perhaps associated with several kinds of errors. The current research work describes an automated system for determining the defects in the steel using imageprocessing and integrating it with computational intelligence in MATLAB. The proposed method consists of pre-processing where the images are enhanced, feature extraction where characteristics of the defects are obtained and classification utilizing the machinelearning models. The efficiency of the proposed approach is proved using experiments on the dataset of steel images and a substantial increase in the precision of the defects’ recognition compared to other techniques. Thus, the’ findings of the apply demonstrate that the use of imageprocessing and machinelearning can offer a strong system for real time defect identification in steel production.
The Agriculture plays a major part in any nation for economy through the production of various crops and it’s an essential source of income in India. The plant disease is one of the leading task of agriculture. When ...
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ISBN:
(数字)9798350356816
ISBN:
(纸本)9798350356823
The Agriculture plays a major part in any nation for economy through the production of various crops and it’s an essential source of income in India. The plant disease is one of the leading task of agriculture. When the infections occur in crops result in reduced quantity and quality of production of crops which will result in economic loss. Therefore, plague needs to be identified as quickly as possible for the healthy production of crops and plants. Various methods and solutions were proposed for crop disease detection by the researchers using imageprocessing, machinelearning, and deep learning. The deep learning plays an essential part in the field of imageprocessing using convolution neural networks and their architecture. In this experiment, we have examined the CNN, Transfer learning VGG16 model on the plant-village dataset which was available on the Kaggle. The dataset(records) includes images of various potato, tomato and pepper crops of 15 different classes. The detection was done using the leaf samples. Our goal is to detect the disease in plants with accurate without taking so much time so that people can take care of their plants and apply the appropriate treatment. In future this VGG16 can be used to identify specific objects, animals, plants, and more in a picture. to identify, recognition and classification of image, that will use in many applications. Medical, Banking, Transportations well as Agriculture.
Adaptive resonance theory ARTI neural network (unsupervised learning) is being implemented for the classification of feature values with the objective of carrying out a comparative study with the performance of other ...
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Object Detection is the most important mission in computer imaginative and prescient within the latest times. It is widely used in Autonomous driving, wildlife protection, security system, etc. The orthodox processing...
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Smart cities are planned to have millions of Internet-connected sensors and devices. Sensors can create a huge amount of data in a range of applications. In modern urban environments, quality of life in a Smart City i...
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Traditional methods for adapting pre-trained vision models to downstream tasks involve fine-tuning some or all of the model's parameters. There are a number of trade-offs with this approach. When too many paramete...
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ISBN:
(纸本)9783031064272;9783031064265
Traditional methods for adapting pre-trained vision models to downstream tasks involve fine-tuning some or all of the model's parameters. There are a number of trade-offs with this approach. When too many parameters are fine-tuned, the model may lose the benefits associated with pre-training, such as the ability to generalize to out-of-distribution data. But, if instead too few parameters are fine-tuned, the model may be unable to adapt effectively for the tasks downstream. In this paper, we propose Visual Prompt Tuning (VPT) as an alternative to fine-tuning for Transformer-based vision models. Our method is closely related to, and inspired by, prefix-tuning of language models [22]. We find that, by adding additional parameters to a pre-trained model, VPT offers similar performance to fine-tuning the final layer. In addition, for low-data settings and for specialized tasks, such as traffic sign recognition, satellite photo recognition and handwriting classification, the performance of Transformer-based vision models is improved with the use of VPT.
In this methodology paper, we address the challenge of optimizing the performance of a detector for diabetic retinopathy (DR) from ultra-widefield fundus images, focusing on balancing model accuracy and latency. This ...
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The proceedings contain 46 papers. The special focus in this conference is on Advanced Computing, machinelearning, Robotics and Internet Technologies. The topics include: Galactic Simulation: Visual Perception of Ani...
ISBN:
(纸本)9783031472237
The proceedings contain 46 papers. The special focus in this conference is on Advanced Computing, machinelearning, Robotics and Internet Technologies. The topics include: Galactic Simulation: Visual Perception of Anisotropic Dark Matter;Protocol Anomaly Detection in IIoT;Agricultural Informatics & ICT: The Foundation, Issues, Challenges and Possible Solutions—A Policy Work;a Guava Leaf Disease Identification Application;text to image Generation Using Attentional Generative Adversarial Network;attention-CoviNet: A Deep-learning Approach to Classify Covid-19 Using Chest X-Rays;a Deep learning Framework for Violence Detection in Videos Using Transfer learning;multi-focus image Fusion Methods: A Review;cache Memory and On-Chip Cache Architecture: A Survey;authenticating Smartphone Users Continuously Even if the Smartphone is in the User’s Pocket;Comparative Analysis of machinelearning Algorithms for COVID-19 Detection and Prediction;machinelearning Classifiers Explanations with Prototype Counterfactual;a Systematic study of Super-Resolution Generative Adversarial Networks: Review;stance Detection in Manipuri Editorial Article Using CRF;deep learning Based Software Vulnerability Detection in Code Snippets and Tag Questions Using Convolutional Neural Networks;a Comprehensive study of the Performances of Imbalanced Data learning Methods with Different Optimization Techniques;Smart Parking System Using Arduino and IR Sensor;quMaDe: Quick Foreground Mask and Monocular Depth Data Generation;fine-Grained Air Quality with Deep Air learning;enhancing Melanoma Skin Cancer Detection with machinelearning and imageprocessing Techniques;imageprocessing Technique and SVM for Epizootic Ulcerative Syndrome Fish image Classification.
The proceedings contain 46 papers. The special focus in this conference is on Advanced Computing, machinelearning, Robotics and Internet Technologies. The topics include: Galactic Simulation: Visual Perception of Ani...
ISBN:
(纸本)9783031472206
The proceedings contain 46 papers. The special focus in this conference is on Advanced Computing, machinelearning, Robotics and Internet Technologies. The topics include: Galactic Simulation: Visual Perception of Anisotropic Dark Matter;Protocol Anomaly Detection in IIoT;Agricultural Informatics & ICT: The Foundation, Issues, Challenges and Possible Solutions—A Policy Work;a Guava Leaf Disease Identification Application;text to image Generation Using Attentional Generative Adversarial Network;attention-CoviNet: A Deep-learning Approach to Classify Covid-19 Using Chest X-Rays;a Deep learning Framework for Violence Detection in Videos Using Transfer learning;multi-focus image Fusion Methods: A Review;cache Memory and On-Chip Cache Architecture: A Survey;authenticating Smartphone Users Continuously Even if the Smartphone is in the User’s Pocket;Comparative Analysis of machinelearning Algorithms for COVID-19 Detection and Prediction;machinelearning Classifiers Explanations with Prototype Counterfactual;a Systematic study of Super-Resolution Generative Adversarial Networks: Review;stance Detection in Manipuri Editorial Article Using CRF;deep learning Based Software Vulnerability Detection in Code Snippets and Tag Questions Using Convolutional Neural Networks;a Comprehensive study of the Performances of Imbalanced Data learning Methods with Different Optimization Techniques;Smart Parking System Using Arduino and IR Sensor;quMaDe: Quick Foreground Mask and Monocular Depth Data Generation;fine-Grained Air Quality with Deep Air learning;enhancing Melanoma Skin Cancer Detection with machinelearning and imageprocessing Techniques;imageprocessing Technique and SVM for Epizootic Ulcerative Syndrome Fish image Classification.
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