scholarly journals AUTOMATIC TEXT SUMMARIZATION USING SUPERVISED MACHINE LEARNING TECHNIQUE FOR HINDI LANGAUGE

2016 ◽  
Vol 05 (06) ◽  
pp. 361-367
Author(s):  
Nikita Desai .
Keyword(s):  
Machine Learning ◽  
Text Summarization ◽  
Supervised Machine Learning ◽  
Machine Learning Technique ◽  
Automatic Text Summarization ◽  
Learning Technique ◽  
Automatic Text

An Efficient Supervised Machine Learning Technique for Forecasting Stock Market Trends

2021 ◽  
pp. 143-162
Author(s):  
Asad Khattak ◽  
Adil Khan ◽  
Habib Ullah ◽  
Muhammad Usama Asghar ◽  
Areeba Arif ◽  
...  
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Supervised Machine Learning ◽  
Machine Learning Technique ◽  
Learning Technique ◽  
Market Trends

scholarly journals Detection of Breast Cancer in the Bio Medical Image using Supervised Machine Learning

2019 ◽  
Vol 9 (2) ◽  
pp. 1140-1142
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Feature Extraction ◽  
Medical Image ◽  
Image Sensors ◽  
Supervised Machine Learning ◽  
Machine Learning Technique ◽  
Biomedical Image ◽  
Learning Technique ◽  
Learning Concept

The detection of disease for diagnosis in the medical image becomes easier, when the machine learning concept is used. In this article, the Breast cancer is detected from the biomedical image using supervised machine learning technique. The bio medical image is acquired from the image sensors and that acquired image is pre processed for further processing. From that pre-processed image, the object detection is performed. The next processes are segmentation and feature extraction. Finally, the supervised learning is implemented in the image for image classification. With the help of machine learning, the different cases of cells are also detected, differentiated and spotted for the further diagnosis

scholarly journals Machine learning Techniques for Hotel Online Reputation

2019 ◽  
Vol 8 (9) ◽  
pp. 3004-3007
Keyword(s):  
Machine Learning ◽  
Online Reviews ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Machine Learning Technique ◽  
Learning Techniques ◽  
Learning Technique ◽  
Learning Reinforcement ◽  
Online Reputation ◽  
Fake Reviews

Now days when someone decide to book a hotel, previous online reviews of the hotels play a major role in determining the best hotel within the budget of the customer. Previous Online reviews are the most important motivation for the information that are used to analyse public opinion. Because of the high impact of the reviews on business, hotel owners are always highly concerned and focused about the customer feedback and past online reviews. But all reviews are not true and trustworthy, sometime few people may intentionally generate the fake reviews to make some hotel famous of to defame. Therefore it is essential to develop and propose the techniques for analysis of reviews. With the help of various machine learning techniques viz. Supervised machine learning technique, Text mining, Unsupervised machine learning technique, Semi-supervised learning, Reinforcement learning etc we may detect the fake reviews. This paper gives some notions of using machine learning techniques in analysis of past online reviews of hotels, Based on the observation it also suggest the optimal machine learning technique for a particular situation

scholarly journals Identification of Core Suppliers Based on E-Invoice Data Using Supervised Machine Learning

2018 ◽  
Vol 11 (4) ◽  
pp. 70 ◽  
Author(s):  
Jung-sik Hong ◽  
Hyeongyu Yeo ◽  
Nam-Wook Cho ◽  
Taeuk Ahn
Keyword(s):  
Machine Learning ◽  
Random Forests ◽  
Area Under The Curve ◽  
High Accuracy ◽  
Supervised Machine Learning ◽  
Machine Learning Method ◽  
Learning Method ◽  
Machine Learning Technique ◽  
Novel Approach ◽  
Learning Technique

Since not all suppliers are to be managed in the same way, a purchasing strategy requires proper supplier segmentation so that the most suitable strategies can be used for different segments. Most existing methods for supplier segmentation, however, either depend on subjective judgements or require significant efforts. To overcome the limitations, this paper proposes a novel approach for supplier segmentation. The objective of this paper is to develop an automated and effective way to identify core suppliers, whose profit impact on a buyer is significant. To achieve this objective, the application of a supervised machine learning technique, Random Forests (RF), to e-invoice data is proposed. To validate the effectiveness, the proposed method has been applied to real e-invoice data obtained from an automobile parts manufacturer. Results of high accuracy and the area under the curve (AUC) attest to the applicability of our approach. Our method is envisioned to be of value for automating the identification of core suppliers. The main benefits of the proposed approach include the enhanced efficiency of supplier segmentation procedures. Besides, by utilizing a machine learning method to e-invoice data, our method results in more reliable segmentation in terms of selecting and weighting variables.

Breast cancer prediction using an optimal machine learning technique for next generation sequences

2021 ◽  
pp. 1063293X2199180
Author(s):  
Babymol Kurian ◽  
VL Jyothi
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Decision Tree ◽  
Classification Model ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Next Generation ◽  
Machine Learning Technique ◽  
Cancer Prediction ◽  
Learning Technique

A wide reach on cancer prediction and detection using Next Generation Sequencing (NGS) by the application of artificial intelligence is highly appreciated in the current scenario of the medical field. Next generation sequences were extracted from NCBI (National Centre for Biotechnology Information) gene repository. Sequences of normal Homo sapiens (Class 1), BRCA1 (Class 2) and BRCA2 (Class 3) were extracted for Machine Learning (ML) purpose. The total volume of datasets extracted for the process were 1580 in number under four categories of 50, 100, 150 and 200 sequences. The breast cancer prediction process was carried out in three major steps such as feature extraction, machine learning classification and performance evaluation. The features were extracted with sequences as input. Ten features of DNA sequences such as ORF (Open Reading Frame) count, individual nucleobase average count of A, T, C, G, AT and GC-content, AT/GC composition, G-quadruplex occurrence, MR (Mutation Rate) were extracted from three types of sequences for the classification process. The sequence type was also included as a target variable to the feature set with values 0, 1 and 2 for classes 1, 2 and 3 respectively. Nine various supervised machine learning techniques like LR (Logistic Regression statistical model), LDA (Linear Discriminant analysis model), k-NN (k nearest neighbours’ algorithm), DT (Decision tree technique), NB (Naive Bayes classifier), SVM (Support-Vector Machine algorithm), RF (Random Forest learning algorithm), AdaBoost (AB) and Gradient Boosting (GB) were employed on four various categories of datasets. Of all supervised models, decision tree machine learning technique performed most with maximum accuracy in classification of 94.03%. Classification model performance was evaluated using precision, recall, F1-score and support values wherein F1-score was most similar to the classification accuracy.

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