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.

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.

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>

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.

scholarly journals Application of Support Vector Machine in Designing Theo Jansen Linkages

2019 ◽  
Vol 9 (3) ◽  
pp. 371 ◽  
Author(s):  
Min-Chan Hwang ◽  
Chiou-Jye Huang ◽  
Feifei Liu
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Circular Orbit ◽  
Energy Efficient ◽  
Support Vector ◽  
Legged Robot ◽  
Machine Learning Technique ◽  
Learning Technique ◽  
Four Bar Linkage ◽  
Lateral Axis

Theo Jansen linkage is an appealing mechanism to implement a bio-inspired motion for a legged robot. The oval orbit that is generated by the Theo Jansen linkage, possessing a transversal axis longer than a lateral axis, achieves energy efficient walking comparing to the circular orbit that is generated by the four-bar linkage. However, the ensemble of its links can produce different patterns of orbits other than oval orbits, some of which are not qualified to be the foot trajectories. It is vital to give a guideline, to which one can refer, to ensure the design of a Theo Jansen leg always possessing its eligibility. In this paper, the machine learning technique, called SVM (Support Vector Machine) along with machine vision serving as a classifier to distinguish desired trajectories from undesired ones, is employed and two databases gathering all eligible data concerned with properties of orbits and dimensions of Theo Jansen linkages are established. Based upon SVM to delimit the eligible designs, one can seek the improvement of a Theo Jansen linkage by resizing its links without rendering an ineligible design. The ensemble dimensions of Theo Jansen linkage can be determined by searching the orbits in compliance with the specification of obliqueness and slenderness from the database of properties and using their correspondent identity numbers to list all candidates of TJLs from the database of dimensions. With the aid of this proposed method, the TJLs have been successfully designed and implemented on a legged robot.

scholarly journals Rainrate Estimation from FY-4A Cloud Top Temperature for Mesoscale Convective Systems by Using Machine Learning Algorithm

2021 ◽  
Vol 13 (16) ◽  
pp. 3273
Author(s):  
Ping Lao ◽  
Qi Liu ◽  
Yuhao Ding ◽  
Yu Wang ◽  
Yuan Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Mesoscale Convective Systems ◽  
Support Vector ◽  
Positive Contribution ◽  
Machine Learning Technique ◽  
Convective Systems ◽  
Learning Technique ◽  
Mesoscale Convective ◽  
Cloud Top Temperature

Satellite rainrate estimation is a great challenge, especially in mesoscale convective systems (MCSs), which is mainly due to the absence of a direct physical connection between observable cloud parameters and surface rainrate. The machine learning technique was employed in this study to estimate rainrate in the MCS domain via using cloud top temperature (CTT) derived from a geostationary satellite. Five kinds of machine learning models were investigated, i.e., polynomial regression, support vector machine, decision tree, random forest, and multilayer perceptron, and the precipitation of Climate Prediction Center morphing technique (CMORPH) was used as the reference. A total of 31 CTT related features were designed to be the potential inputs for training an algorithm, and they were all proved to have a positive contribution in modulating the algorithm. Random forest (RF) shows the best performance among the five kinds of models. By combining the classification and regression schemes of the RF model, an RF-based hybrid algorithm was proposed first to discriminate the rainy pixel and then estimate its rainrate. For the MCS samples considered in this study, such an algorithm generates the best estimation, and its accuracy is definitely higher than the operational precipitation product of FY-4A. These results demonstrate the promising feasibility of applying a machine learning technique to solve the satellite precipitation retrieval problem.

scholarly journals Prediction of Heart Disease using Machine Learning

2020 ◽  
pp. 150-166
Author(s):  
Ankit Singh
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Mean Squared Error ◽  
Classification Algorithm ◽  
Support Vector ◽  
Machine Learning Technique ◽  
Machine Learning Classification ◽  
Squared Error ◽  
Clinical Data Analysis ◽  
Learning Technique

Cardiovascular Disease is the leading cause of death (Approximately, 17 million people every year) in the all the area of the world. Prediction of heart disease is the critical challenge in the area of the clinical data analysis. The objective of paper is to build the model for predicting the Heart Disease using various machine learning classification algorithm. Classification is a powerful machine learning technique that is commonly used for prediction. Some of the classification algorithm are Logistic Regression, Support Vector Machine, Naïve Bayes, Decision Tree, Random Forest Classifier, KNN. This paper investigate which algorithm is used for the improving the accuracy in the prediction of heart disease. And, a comparative analysis on the accuracy and mean squared error is to done for predicting the best model. The result of the study indicates that KNN algorithm is effective in predicting the model with the accuracy of the 85.71% and having a very low mean squared error.

scholarly journals A Comparative Study of Parameters Measuring in Data Mining Function Using SVM

2021 ◽  
Vol 10 (8) ◽  
pp. 15-22
Author(s):  
Meghna Utmal
Keyword(s):  
Machine Learning ◽  
Support Vector ◽  
Machine Learning Technique ◽  
Vector Machines ◽  
Learning Technique ◽  
Evaluation Of Parameters ◽  
Statistical Approaches ◽  
Using Data ◽  
Improving Accuracy ◽  
Learning Issues

Due to the vast amount of data available on the internet nowadays, it is necessary to categorise the data, and fast, accurate, and resilient algorithms for data analysis are required. Support vector machines (SVMs) are a form of machine learning technique that is commonly used to solve a variety of statistical learning issues. It's been designed as a reliable categorization tool, and it's especially useful when there's a lot of data. Machine learning is an area of artificial intelligence (AI) and computer science that focuses on using data and algorithms to mimic the way humans learn, with the goal of steadily improving accuracy. Algorithms are trained to create classifications by using statistical approaches. These should ideally have an impact on important growth measures. In this study, we found that employing the Support Vector Machine technique provides the best accuracy and efficiency for our dataset. Our work is based on the evaluation of parameters like accuracy, recall and precision.

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