scholarly journals Classification of Cardiotocogram Data using Neural Network based Machine Learning Technique

2012 ◽  
Vol 47 (14) ◽  
pp. 19-25 ◽  
Author(s):  
Sundar. C ◽  
M.Chitradevi M.Chitradevi ◽  
G. Geetharamani
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Machine Learning Technique ◽  
Learning Technique

scholarly journals An Ensemble Machine Learning Technique for Functional Requirement Classification

Symmetry ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 1601
Author(s):  
Nouf Rahimi ◽  
Fathy Eassa ◽  
Lamiaa Elrefaei
Keyword(s):  
Machine Learning ◽  
Requirement Engineering ◽  
Support Vector ◽  
Functional Requirement ◽  
Machine Learning Technique ◽  
Engineering Software ◽  
Ensemble Machine Learning ◽  
Learning Technique ◽  
Combined Models

In Requirement Engineering, software requirements are classified into two main categories: Functional Requirement (FR) and Non-Functional Requirement (NFR). FR describes user and system goals. NFR includes all constraints on services and functions. Deeper classification of those two categories facilitates the software development process. There are many techniques for classifying FR; some of them are Machine Learning (ML) techniques, and others are traditional. To date, the classification accuracy has not been satisfactory. In this paper, we introduce a new ensemble ML technique for classifying FR statements to improve their accuracy and availability. This technique combines different ML models and uses enhanced accuracy as a weight in the weighted ensemble voting approach. The five combined models are Naïve Bayes, Support Vector Machine (SVM), Decision Tree, Logistic Regression, and Support Vector Classification (SVC). The technique was implemented, trained, and tested using a collected dataset. The accuracy of classifying FR was 99.45%, and the required time was 0.7 s.

scholarly journals 2030. How Machine-Learning Technique Using Artificial Neural Network Determines Whether the Fever Is Actually Related to the Bacteremia

2018 ◽  
Vol 5 (suppl_1) ◽  
pp. S591-S591
Author(s):  
Kyoung Hwa Lee ◽  
Seul Gi Yoo ◽  
Da Eun Kwon ◽  
Soon Young Park ◽  
Jae June Dong ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Machine Learning Technique ◽  
Learning Technique ◽  
Artificial Neural

Polarity-based Classification of Subjective Micro-posts using Machine Learning Technique

2018 ◽  
Vol 9 (7) ◽  
pp. 373
Author(s):  
Arshad Arain ◽  
Rajesh kumar ◽  
Nudra Siddiquie ◽  
Komal Naz ◽  
Sabeen gul ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Learning Technique ◽  
Learning Technique

scholarly journals Applying a Machine Learning Technique to Classification of Japanese Pressure Patterns

2009 ◽  
Vol 8 ◽  
pp. S59-S67 ◽  
Author(s):  
H Kimura ◽  
H Kawashima ◽  
H Kusaka ◽  
H Kitagawa
Keyword(s):  
Machine Learning ◽  
Machine Learning Technique ◽  
Learning Technique

scholarly journals Classification of specialty coffees using machine learning techniques

2021 ◽  
Vol 10 (5) ◽  
pp. e13110514732
Author(s):  
Paulo César Ossani ◽  
Diogo Francisco Rossoni ◽  
Marcelo Ângelo Cirillo ◽  
Flávio Meira Borém
Keyword(s):  
Machine Learning ◽  
Supervised Classification ◽  
Consumer Acceptance ◽  
Machine Learning Techniques ◽  
Machine Learning Technique ◽  
Learning Techniques ◽  
Learning Technique ◽  
Evaluation Test ◽  
New Methodologies

Specialty coffees have a big importance in the economic scenario, and its sensory quality is appreciated by the productive sector and by the market. Researches have been constantly carried out in the search for better blends in order to add value and differentiate prices according to the product quality. To accomplish that, new methodologies must be explored, taking into consideration factors that might differentiate the particularities of each consumer and/or product. Thus, this article suggests the use of the machine learning technique in the construction of supervised classification and identification models. In a sensory evaluation test for consumer acceptance using four classes of specialty coffees, applied to four groups of trained and untrained consumers, features such as flavor, body, sweetness and general grade were evaluated. The use of machine learning is viable because it allows the classification and identification of specialty coffees produced in different altitudes and different processing methods.

Effectiveness of a Hybrid Deep Learning Model Integrated with a Hybrid Parameterisation Model in Decision-Making Analysis

2020 ◽  
Author(s):  
Masurah Mohamad ◽  
Ali Selamat
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Decision Making ◽  
Deep Learning ◽  
Missing Data ◽  
Machine Learning Technique ◽  
Processing Times ◽  
Learning Technique ◽  
Neural Network Algorithm ◽  
Deep Learning Model

Deep learning has recently gained the attention of many researchers in various fields. A new and emerging machine learning technique, it is derived from a neural network algorithm capable of analysing unstructured datasets without supervision. This study compared the effectiveness of the deep learning (DL) model vs. a hybrid deep learning (HDL) model integrated with a hybrid parameterisation model in handling complex and missing medical datasets as well as their performance in increasing classification. The results showed that 1) the DL model performed better on its own, 2) DL was able to analyse complex medical datasets even with missing data values, and 3) HDL performed well as well and had faster processing times since it was integrated with a hybrid parameterisation model.

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