scholarly journals Calorie Detection of Food Image based on SVM Algorithm

2021 ◽  
Vol 9 (VI) ◽  
pp. 1836-1840
Author(s):  
Madhubala Kamble
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Training Model ◽  
Support Vector ◽  
Prototype System ◽  
Training Module ◽  
Text Data ◽  
Server Side ◽  
Machine Learning Model ◽  
Unique System

Nowadays, standard intake of healthy food is vital for keeping a diet to avoid obesity within the human body . In this paper, we present a totally unique system supported machine learning that automatically performs accurate classification of food images and estimates food attributes. This paper proposes a machine learning model consisting of a support vector machine that classifies food into specific categories within the training a part of the prototype system. The most purpose of the proposed method is to reinforce the accuracy of the pre-training model. The paper designs a prototype system supported the client server network model. The client sends an image detection request and processes it on the server side. The prototype system is meant with three main software components, including a pre-trained support vector machine training module for classification purposes, a text data training module for attribute estimation models, and a server-side module. We experimented with a selection of food categories, each containing thousands of images, and therefore the machine learning training to understand higher classification accuracy.

FINDING TEMPORAL STRUCTURE IN TEXT: MACHINE LEARNING OF SYNTACTIC TEMPORAL RELATIONS

2007 ◽  
Vol 01 (04) ◽  
pp. 441-457 ◽  
Author(s):  
STEVEN BETHARD ◽  
JAMES H. MARTIN ◽  
SARA KLINGENSTEIN
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Temporal Structure ◽  
Learning Model ◽  
High Accuracy ◽  
Support Vector ◽  
Automatic Extraction ◽  
Temporal Relations ◽  
Tense And Aspect ◽  
Machine Learning Model

This research proposes and evaluates a linguistically motivated approach to extracting temporal structure from text. Pairs of events in a verb-clause construction were considered, where the first event is a verb and the second event is the head of a clausal argument to that verb. All pairs of events in the TimeBank that participated in verb-clause constructions were selected and annotated with the labels BEFORE, OVERLAP and AFTER. The resulting corpus of 895 event-event temporal relations was then used to train a machine learning model. Using a combination of event-level features like tense and aspect with syntax-level features like the paths through the syntactic tree, support vector machine (SVM) models were trained which could identify new temporal relations with 89.2% accuracy. High accuracy models like these are a first step towards automatic extraction of temporal structure from text.

Support Vector Machine Learning Model for the Prediction of Sentinel Node Status in Patients With Cutaneous Melanoma

2006 ◽  
Vol 13 (8) ◽  
pp. 1113-1122 ◽  
Author(s):  
Simone Mocellin ◽  
Alessandro Ambrosi ◽  
Maria Cristina Montesco ◽  
Mirto Foletto ◽  
Giorgio Zavagno ◽  
...  
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Sentinel Node ◽  
Cutaneous Melanoma ◽  
Learning Model ◽  
Support Vector ◽  
Node Status ◽  
Machine Learning Model ◽  
Sentinel Node Status

scholarly journals Classification of Spam Text using SVM

2021 ◽  
Vol 23 (08) ◽  
pp. 616-624
Author(s):  
Gaddam Akhil Reddy ◽  
◽  
Dr. B. Indira Reddy ◽  
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Text Messages ◽  
Training Data ◽  
Support Vector ◽  
Spam Detection ◽  
Advantages And Disadvantages ◽  
Machine Learning Model ◽  
Model Support

The necessity for spam detection is particularly pertinent nowadays, as there is no quality control over social media, and users have the ability to distribute unverified material, therefore facilitating fraud and deceit. Spam detection can aid in the prevention of such fraud. This scenario has developed mostly as a result of the distribution of disparate, unconfirmed information via shopping websites, emails, and text messages (SMS). There are several ways of categorising and identifying spam. Each of them has certain advantages and disadvantages. The machine learning model “Support Vector Machine” is employed to detect spam in this case. SVM is a basic concept: the method proposes a line or hyperplane to classify the data. The model can categorise any type of text into a given category after being fed a set of labelled training data for each category.

scholarly journals FoRex Trading Using Supervised Machine Learning

2018 ◽  
Vol 7 (4.15) ◽  
pp. 400 ◽  
Author(s):  
Thuy Nguyen Thi Thu ◽  
Vuong Dang Xuan
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Exchange Rate ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Machine Learning Algorithm ◽  
Machine Learning Model

The exchange rate of each money pair can be predicted by using machine learning algorithm during classification process. With the help of supervised machine learning model, the predicted uptrend or downtrend of FoRex rate might help traders to have right decision on FoRex transactions. The installation of machine learning algorithms in the FoRex trading online market can automatically make the transactions of buying/selling. All the transactions in the experiment are performed by using scripts added-on in transaction application. The capital, profits results of use support vector machine (SVM) models are higher than the normal one (without use of SVM). 

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