scholarly journals Conversion of Image processing data of pipeline analysis using Machine learning Algorithm

2016 ◽  
Vol 3 (12) ◽  
pp. 223-224
Author(s):  
Dr. A. Prema Kirubakaran
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Processing Data

scholarly journals ONLINE LEARNING FOR IMAGE PROCESSING IN NETWORKED SETTING

2017 ◽  
Vol 10 (13) ◽  
pp. 284
Author(s):  
Ankush Rai ◽  
Jagadeesh Kannan R
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Online Learning ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
The Past ◽  
Slow Development ◽  
Online Learning Algorithm

In the past decade development of machine learning algorithm for network settings has witnessed little advancements owing to slow development of technologies for improving bandwidth and latency.  In this study we present a novel online learning algorithm for network based computational operations in image processing setting

scholarly journals Machine Learning and Fuzzy Logic based Detection, Classification and Grading of Leaf Disease in Plants

2020 ◽  
Vol 9 (7S) ◽  
pp. 38-42
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Image Processing ◽  
Fuzzy Logic ◽  
Learning Algorithm ◽  
Expert Advice ◽  
Support Vector ◽  
Machine Learning Algorithm ◽  
Image Processing Techniques ◽  
Processing Techniques

India is an agricultural country where most of people are depends on the agriculture. When Plants are infected by the virus, fungus and bacteria, they are mostly seen on leaves and stems of the plants. Because of that, plants production is decreased also economy of the country is decreased. The farmer has to identify the disease and decide which pesticide will be used to control the disease in plants. To finding out which disease affect the plants, the farmer contacts the expert for the solution. The expert gives the advice based on its knowledge and information but sometimes seeking the expert advice is time consuming, expensive and may be not accurate. So, to solve this problem, the image processing techniques and Machine Learning algorithm like Neural Network, Fuzzy Logic and Support Vector Machine gives the better, accurate and affordable solution to control the plants disease than manual method.

scholarly journals Motion Detection and Prediction Using Machine Learning Algorithm

2020 ◽  
pp. 220-226
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Logistic Regression ◽  
Linear Regression ◽  
21St Century ◽  
Motion Detection ◽  
Data Analytics ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Processing Data

Machine learning is a branch of Artificial Intelligence which is gaining importance in the 21st century with increasing processing speeds and miniaturization of sensors, the applications of Artificial Intelligence and cognitive technologies are growing rapidly. An array of ultrasonic sensors i.e., HCSR-04 is placed at different directions, collecting data for a particularinterval of a period during a particular day. The acquired sensor values are subjected to pre-processing, data analytics, and visualization. The prepared data is now split into test and train. A prediction model is designed using logistic regression and linear regression and checked for accuracy, F1 score, and precision compared.

scholarly journals Underwater Object Detection using Tensorflow

2020 ◽  
Vol 32 ◽  
pp. 03037
Author(s):  
Avinash Mahavarkar ◽  
Ritika Kadwadkar ◽  
Sneha Maurya ◽  
Smitha Raveendran
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Image Processing ◽  
Computer Vision ◽  
Object Detection ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Computer Vision Technology ◽  
Underwater Object ◽  
Suitable Environment

Object Detection is a popular technology that detects instances within an image. In order to eliminate the barriers in Computer Vision technology due to the dissolution of the BGR(Blue-Green-Red) constituents with the increase in depth, it has been a necessity that the accuracy and efficiency of detecting any object underwater is optimum. In this article, we conduct Underwater Object Detection using Machine Learning through Tensorflow and Image Processing along with Faster R-CNN (Regions with Convolution Neural Network) as an algorithm for implementation. A suitable environment will be created so that Machine Learning algorithm will be used to train different images of the object. Open source Computer Vision has various functions which can be used for the image processing needs when an image is captured.

scholarly journals Anomaly Detection at ATM Center using Machine Learning Algorithm

2021 ◽  
pp. 230-236
Author(s):  
Prarthana Sanas ◽  
Pradnya Kasbe ◽  
Ankita Shelke ◽  
Srushti Salunkhe
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Learning Algorithm ◽  
Abnormal Behavior ◽  
Machine Learning Algorithm ◽  
Social Infrastructure ◽  
Behavior Detection ◽  
Technical Base ◽  
Normal Behavior ◽  
Abnormal Behavior Detection

This paper proposes a new supervised algorithm for detecting abnormal events in confined areas like ATM room, server room etc. The aim of proposed work is to establish a technical base that will support a more secure and convenient social infrastructure, and one of the technologies that make up the technical base in the abnormal behavior detection using image processing. Generally, abnormal behavior detection is a method in which a model is created using normal behavior data and any behavior deviating from the model is deemed abnormal. In many cases, it is difficult to comprehensively collect abnormal behavior data in advance, thus being able to detect abnormalities with a model created using only normal behavior data is extremely useful for actual implementation. This article first shows application examples of abnormal behavior detection using image processing, which is followed by typical examples of abnormal behavior detection through motion image processing.

Machine Learning Algorithm to Predict Early Complications after Brain Tumor Surgery

2018 ◽  
Author(s):  
C.H.B. van Niftrik ◽  
F. van der Wouden ◽  
V. Staartjes ◽  
J. Fierstra ◽  
M. Stienen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Tumor Surgery ◽  
Early Complications ◽  
Brain Tumor Surgery

Feature extraction and prediction of Dengue Outbreaks

2020 ◽  
pp. 216-222
Author(s):  
Kunal Parikh ◽  
Tanvi Makadia ◽  
Harshil Patel
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Developing Countries ◽  
Feature Extraction ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Health Concerns ◽  
The World ◽  
Dengue Outbreaks

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.

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