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.

Crowd Abnormal Behavior Detection Based on Machine Learning

2013 ◽  
Vol 12 (6) ◽  
pp. 1199-1205 ◽  
Author(s):  
Dongping Zhang ◽  
Huailiang Peng ◽  
Yu Haibin ◽  
Yafei Lu
Keyword(s):  
Machine Learning ◽  
Abnormal Behavior ◽  
Behavior Detection ◽  
Abnormal Behavior Detection

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 Pet dog disease pre-diagnosis system for caregiver with possibilistic C-means clustering and disease database

2020 ◽  
Vol 20 (1) ◽  
pp. 300
Author(s):  
Kwang Baek Kim ◽  
Doo Heon Song
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Disease Symptom ◽  
Abnormal Behavior ◽  
Machine Learning Algorithm ◽  
Unsupervised Machine Learning ◽  
Diagnosis System ◽  
Pet Dogs ◽  
Pet Owners ◽  
Pet Owner

<p>While the population of pet dogs and veterinary clinics are increasing, there is no reliable and useful software for pet owners/caregivers who have limited knowledge on the pet diseases. In this paper, we propose a pre-diagnosis system working on the mobile platform that the pet owner can take a pre-diagnosis from his/her observation of pet dog’s abnormality. Technically, the system needs a reliable databases for disease-symptom association thus we provide it based on the textbook and encyclopedia. Then, we apply Possibilistic C-Means algorithm that is an unsupervised machine learning algorithm to form the connections between disease and symptoms from database. The system outputs five most probable diseases from the observed symptoms of pet dog. The utility of this system is to alert the owner’s attention on the pet dog’s abnormal behavior and try to find the diseases as soon as possible.</p>

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 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.

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