Study of Image Recognition Using Machine Learning

Deshmukh, Shubhangi

doi:10.22214/ijraset.2023.54063

Cited by 3 publications

(1 citation statement)

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“…Deep learning is able to learn high-level abstract representations of data through multi-level nonlinear transformations. ML and DL share many common applications, such as image recognition, [1][2][3][4] object detection, [5][6][7] face recognition [8][9][10][11][12] and other tasks. However, they differ in some ways.…”

Section: Introductionmentioning

confidence: 99%

Recent Advances in Machine Learning Assisted Hydrogel Flexible Sensing

Zhou,

Song,

et al. 2024

Zeitschrift anorg allge chemie

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Hydrogel flexible sensors are widely used in wearable devices, health care, intelligent robots and other fields due to their excellent flexibility, biocompatibility and high sensitivity. With the development of single sensor to multi‐channel and multi‐mode sensor network, the sensor data also presents the characteristics of multi‐dimension, complex and massive. Traditional data analysis methods can no longer meet the data analysis requirements of hydrogel flexible sensor networks. The introduction of machine learning (ML) technology optimizes the process of data analysis. With the continuous development of multi‐layer neural network technology and the improvement of computer performance, deep learning (DL) algorithm is increasingly used to achieve higher efficiency and accuracy, provides a powerful tool for data analysis of hydrogel flexible sensor, and accelerates the intelligent process of hydrogel flexible sensor equipment. This paper introduces the classification of hydrogel flexible sensors and the working mechanism and common algorithms of ML, and summarizes the application of ML technology to assist hydrogel flexible sensors in data analysis in the fields of health care and information recognition. This review will provide inspiration and reference for integrating ML technology into the field of hydrogel flexible sensors.

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Section: Introductionmentioning

confidence: 99%

Recent Advances in Machine Learning Assisted Hydrogel Flexible Sensing

Zhou,

Song,

et al. 2024

Zeitschrift anorg allge chemie

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A Comprehensive Methodology for Image Recognition Utilizing Machine Learning and Computer Vision: Automation of the Harvesting Process

Rajab,

Nor Azlan,

Wong

et al. 2024

Preprint

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A Comprehensive Methodology for Image Recognition Utilizing Machine Learning and Computer Vision: Automation of the Harvesting Process

Rajab,

Azlan,

Yew

et al. 2024

IJITEE

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This study aims to investigate the machine learning techniques implemented in image recognition technology for the identification and classification of oil palm fruit ripeness. The accurate determination of fruit ripeness is crucial for optimizing harvest time and improving oil yield. The palm oil industry is one of the major plantations in Malaysia. The harvesting process of oil palm fruit was conducted with traditional methods by relying on manual inspection, which can be subjective and inconsistent. Plus, it required several workers. A model of image recognition was developed using machine learning algorithms and computer vision to automate the harvesting process and overcome the shortage of labor issues. Implementing this technology in the field could lead to more consistent harvests and higher-quality oil production. Several machine learning models were developed, trained, and tested for their ability to classify the ripeness stages. The findings suggest the trending techniques in implementing image recognition which can provide a reliable and efficient tool for assessing oil palm fruit ripeness.

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Study of Image Recognition Using Machine Learning

Cited by 3 publications

References 0 publications

Recent Advances in Machine Learning Assisted Hydrogel Flexible Sensing

Recent Advances in Machine Learning Assisted Hydrogel Flexible Sensing

A Comprehensive Methodology for Image Recognition Utilizing Machine Learning and Computer Vision: Automation of the Harvesting Process

A Comprehensive Methodology for Image Recognition Utilizing Machine Learning and Computer Vision: Automation of the Harvesting Process

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