scholarly journals Industrial object and defect recognition utilizing multilevel feature extraction from industrial scenes with Deep Learning approach

2022 ◽  
Author(s):  
Ioannis D. Apostolopoulos ◽  
Mpesiana A. Tzani
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Learning Approach ◽  
Industrial Object ◽  
Defect Recognition

scholarly journals Underwater Target Recognition Based on Multi-Decision LOFAR Spectrum Enhancement: A Deep-Learning Approach

2021 ◽  
Vol 13 (10) ◽  
pp. 265
Author(s):  
Jie Chen ◽  
Bing Han ◽  
Xufeng Ma ◽  
Jian Zhang
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Recognition Accuracy ◽  
Target Recognition ◽  
Signal To Noise Ratio ◽  
Recognition Performance ◽  
Low Frequency ◽  
Learning Approach ◽  
Decision Algorithm ◽  
Underwater Target

Underwater target recognition is an important supporting technology for the development of marine resources, which is mainly limited by the purity of feature extraction and the universality of recognition schemes. The low-frequency analysis and recording (LOFAR) spectrum is one of the key features of the underwater target, which can be used for feature extraction. However, the complex underwater environment noise and the extremely low signal-to-noise ratio of the target signal lead to breakpoints in the LOFAR spectrum, which seriously hinders the underwater target recognition. To overcome this issue and to further improve the recognition performance, we adopted a deep-learning approach for underwater target recognition, and a novel LOFAR spectrum enhancement (LSE)-based underwater target-recognition scheme was proposed, which consists of preprocessing, offline training, and online testing. In preprocessing, we specifically design a LOFAR spectrum enhancement based on multi-step decision algorithm to recover the breakpoints in LOFAR spectrum. In offline training, the enhanced LOFAR spectrum is adopted as the input of convolutional neural network (CNN) and a LOFAR-based CNN (LOFAR-CNN) for online recognition is developed. Taking advantage of the powerful capability of CNN in feature extraction, the recognition accuracy can be further improved by the proposed LOFAR-CNN. Finally, extensive simulation results demonstrate that the LOFAR-CNN network can achieve a recognition accuracy of 95.22%, which outperforms the state-of-the-art methods.

scholarly journals Classification of Remote Sensing Image Scenes Using Double Feature Extraction Hybrid Deep Learning Approach

2021 ◽  
Vol 3 (2) ◽  
pp. 133-149
Author(s):  
Akey Sungheetha ◽  
Rajesh Sharma R
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
Deep Learning ◽  
Visual Recognition ◽  
Research Work ◽  
Recognition Rate ◽  
Remote Sensing Image ◽  
Quality Data ◽  
Learning Approach ◽  
Remote Sensing Images

Over the last decade, remote sensing technology has advanced dramatically, resulting in significant improvements on image quality, data volume, and application usage. These images have essential applications since they can help with quick and easy interpretation. Many standard detection algorithms fail to accurately categorize a scene from a remote sensing image recorded from the earth. A method that uses bilinear convolution neural networks to produce a lessweighted set of models those results in better visual recognition in remote sensing images using fine-grained techniques. This proposed hybrid method is utilized to extract scene feature information in two times from remote sensing images for improved recognition. In layman's terms, these features are defined as raw, and only have a single defined frame, so they will allow basic recognition from remote sensing images. This research work has proposed a double feature extraction hybrid deep learning approach to classify remotely sensed image scenes based on feature abstraction techniques. Also, the proposed algorithm is applied to feature values in order to convert them to feature vectors that have pure black and white values after many product operations. The next stage is pooling and normalization, which occurs after the CNN feature extraction process has changed. This research work has developed a novel hybrid framework method that has a better level of accuracy and recognition rate than any prior model.

Automatic feature extraction in large fusion databases by using deep learning approach

2016 ◽  
Vol 112 ◽  
pp. 979-983 ◽  
Author(s):  
Gonzalo Farias ◽  
Sebastián Dormido-Canto ◽  
Jesús Vega ◽  
Giuseppe Rattá ◽  
Héctor Vargas ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Learning Approach ◽  
Automatic Feature Extraction

scholarly journals Prediction of Recombination Spots Using Novel Hybrid Feature Extraction Method via Deep Learning Approach

2020 ◽  
Vol 11 ◽  
Author(s):  
Fatima Khan ◽  
Mukhtaj Khan ◽  
Nadeem Iqbal ◽  
Salman Khan ◽  
Dost Muhammad Khan ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Extraction Method ◽  
Learning Approach ◽  
Feature Extraction Method ◽  
Hybrid Feature Extraction

scholarly journals A new deep learning approach for the retinal hard exudates detection based on superpixel multi-feature extraction and patch-based CNN

2020 ◽  
Author(s):  
Chenxi Huang ◽  
Yongshuo Zong ◽  
Yimin Ding ◽  
Xin Luo ◽  
Kathy Clawson ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Learning Approach ◽  
Hard Exudates

scholarly journals GAIT RECOGNITION BASED ON CONVOLUTIONAL NEURAL NETWORKS

2017 ◽  
Vol XLII-2/W4 ◽  
pp. 207-212 ◽  
Author(s):  
A. Sokolova ◽  
A. Konushin
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Feature Extraction ◽  
Deep Learning ◽  
Deep Neural Network ◽  
Gait Recognition ◽  
Learning Approach ◽  
Network Architectures ◽  
Motion Information ◽  
Advantages And Disadvantages

In this work we investigate the problem of people recognition by their gait. For this task, we implement deep learning approach using the optical flow as the main source of motion information and combine neural feature extraction with the additional embedding of descriptors for representation improvement. In order to find the best heuristics, we compare several deep neural network architectures, learning and classification strategies. The experiments were made on two popular datasets for gait recognition, so we investigate their advantages and disadvantages and the transferability of considered methods.

scholarly journals A Tree Based Machine Learning Approach for PTB Diagnostic Dataset

2021 ◽  
Vol 2115 (1) ◽  
pp. 012042
Author(s):  
S Premanand ◽  
Sathiya Narayanan
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Primary Objective ◽  
Machine Learning Algorithms ◽  
Learning Approach ◽  
Related Data ◽  
Machine Learning Approach ◽  
Health Related

Abstract The primary objective of this particular paper is to classify the health-related data without feature extraction in Machine Learning, which hinder the performance and reliability. The assumption of our work will be like, can we able to get better result for health-related data with the help of Tree based Machine Learning algorithms without extracting features like in Deep Learning. This study performs better classification with Tree based Machine Learning approach for the health-related medical data. After doing pre-processing, without feature extraction, i.e., from raw data signal with the help of Machine Learning algorithms we are able to get better results. The presented paper which has better result even when compared to some of the advanced Deep Learning architecture models. The results demonstrate that overall classification accuracy of Random Forest, XGBoost, LightGBM and CatBoost, Tree-based Machine Learning algorithms for normal and abnormal condition of the datasets was found to be 97.88%, 98.23%, 98.03% and 95.57% respectively.

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