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 Cascaded Machine-Learning Technique for Debris Classification in Floor-Cleaning Robot Application

2018 ◽  
Vol 8 (12) ◽  
pp. 2649 ◽  
Author(s):  
Balakrishnan Ramalingam ◽  
Anirudh Lakshmanan ◽  
Muhammad Ilyas ◽  
Anh Le ◽  
Mohan Elara
Keyword(s):  
Binary Classification ◽  
Support Vector ◽  
Single Shot ◽  
Machine Learning Technique ◽  
Cleaning Robot ◽  
Svm Model ◽  
Learning Technique ◽  
Essential Function ◽  
Cleaning Robots

Debris detection and classification is an essential function for autonomous floor-cleaning robots. It enables floor-cleaning robots to identify and avoid hard-to-clean debris, specifically large liquid spillage debris. This paper proposes a debris-detection and classification scheme for an autonomous floor-cleaning robot using a deep Convolutional Neural Network (CNN) and Support Vector Machine (SVM) cascaded technique. The SSD (Single-Shot MultiBox Detector) MobileNet CNN architecture is used for classifying the solid and liquid spill debris on the floor through the captured image. Then, the SVM model is employed for binary classification of liquid spillage regions based on size, which helps floor-cleaning devices to identify the larger liquid spillage debris regions, considered as hard-to-clean debris in this work. The experimental results prove that the proposed technique can efficiently detect and classify the debris on the floor and achieves 95.5% percent classification accuracy. The cascaded approach takes approximately 71 milliseconds for the entire process of debris detection and classification, which implies that the proposed technique is suitable for deploying in real-time selective floor-cleaning applications.

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.

scholarly journals Modeling surface roughness of point robot laser hardening, with emphasis on the surface

Politehnika ◽  
2021 ◽  
Vol 5 (1) ◽  
pp. 6-9
Author(s):  
Matej Babič
Keyword(s):  
Machine Learning ◽  
Surface Roughness ◽  
Intelligent System ◽  
Future Trend ◽  
Support Vector ◽  
Machine Learning Technique ◽  
Wear And Corrosion ◽  
Learning Technique ◽  
Wear And Corrosion Resistance ◽  
Laser Cell

The topic of Machine Learning is so popular that it is not only the future trend, but also the money tide. Machine learning technique and intelligent system methods are very popular in mechanical engineering. Robot laser surface hardening is one of the most promising techniques for surface modification of the microstructure of a material to improve wear and corrosion resistance. For predicting the surface roughness of the hardened specimens, the support vector machine and multiple regression is used. The aim of this paper is to present modeling roughness of point robot laser hardened specimens with different parameters of robot laser cell.

Density Based Clustering with Integrated One-Class SVM for Noise Reduction

2017 ◽  
Vol 6 (3) ◽  
pp. 199
Author(s):  
K. Nafees Ahmed ◽  
T. Abdul Razak
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Data Analysis ◽  
Noise Reduction ◽  
Spatial Clustering ◽  
Support Vector ◽  
Machine Learning Technique ◽  
Learning Classifier ◽  
Density Based Clustering ◽  
Learning Technique

<p>Information extraction from data is one of the key necessities for data analysis. Unsupervised nature of data leads to complex computational methods for analysis. This paper presents a density based spatial clustering technique integrated with one-class Support Vector Machine (SVM), a machine learning technique for noise reduction, a modified variant of DBSCAN called Noise Reduced DBSCAN (NRDBSCAN). Analysis of DBSCAN exhibits its major requirement of accurate thresholds, absence of which yields suboptimal results. However, identifying accurate threshold settings is unattainable. Noise is one of the major side-effects of the threshold gap. The proposed work reduces noise by integrating a machine learning classifier into the operation structure of DBSCAN. The Experimental results indicate high homogeneity levels in the clustering process.</p>

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