Diagnostic Accuracies of Laryngeal Diseases Using a Convolutional Neural Network‐Based Image Classification System

2021 ◽  
Author(s):  
Won Ki Cho ◽  
Yeong Ju Lee ◽  
Hye Ah Joo ◽  
In Seong Jeong ◽  
Yeonjoo Choi ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Classification System ◽  
Laryngeal Diseases

scholarly journals CNN based efficient image classification system for smartphone device

2021 ◽  
Author(s):  
Mayank Mishra ◽  
Tanupriya Choudhury ◽  
Tanmay Sarkar
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Classification System ◽  
Text Messaging ◽  
Motivational Messages ◽  
Social Media Text ◽  
Social Media Platforms ◽  
Optimum Model

Abstract In our work, we look to classify images that make their way into our smartphone devices through various social-media text-messaging platforms. We aim at classifying images into three broad categories: document-based images, quote-based images, and photographs. People, especially students, share many document-based images that include snapshots of essential emails, handwritten notes, articles, etc. Quote based images, consisting of birthday wishes, motivational messages, festival greetings, etc., are among the highly shared images on social media platforms. A significant share of images constitutes photographs of people, including group photographs, selfies, portraits, etc. We train various convolutional neural network (CNN) based models on our self-made dataset and compare their results to find our task’s optimum model.

scholarly journals Weeds Detection in Agricultural Fields using Convolutional Neural Network

2019 ◽  
Vol 8 (11) ◽  
pp. 292-296
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Weed Control ◽  
Classification System ◽  
Agricultural Fields ◽  
Use Of Technology ◽  
Neural Network Algorithm

Weeds are very annoying for farmers and also not very good for the crops. Its existence might damage the growth of the crops. Therefore, weed control is very important for farmers. Farmers need to ensure their agricultural fields are free from weeds for at least once a week, whether they need to spray weeds herbicides to their plantation or remove it using tools or manually. The aim of this research is to build an automated weed control robot using the Lego Mindstorm EV3 which connected to a computer. The robot consists of motors, servo motors and a camera which we use to capture the image of the crops and weeds. An automated image classification system has been designed to differentiate between weeds and crops. The robot will spray the weed herbicides directly to the area that have been detected weeds near or at it. For the image classification method, we employ the convolutional neural network algorithm to process the image of the object. Therefore, by the use of technology especially in artificial intelligence, farmers can reduce the amount of workload and workforce they need to monitor their plantation. In addition, this technology also can improve the quality of the crops.

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