scholarly journals Improved Salp Swarm Optimization Algorithm: Application in Feature Weighting for Blind Modulation Identification

Electronics ◽  
2021 ◽  
Vol 10 (16) ◽  
pp. 2002
Author(s):  
Sarra Ben Chaabane ◽  
Akram Belazi ◽  
Sofiane Kharbech ◽  
Ammar Bouallegue ◽  
Laurent Clavier
Keyword(s):  
Optimization Algorithm ◽  
Distance Measure ◽  
Learning Algorithms ◽  
Classification Problem ◽  
Machine Learning Algorithms ◽  
Feature Weighting ◽  
Swarm Optimization ◽  
Opposition Based Learning ◽  
Modulation Identification ◽  
Detection Approach

In modulation identification issues, like in any other classification problem, the performance of the classification task is significantly impacted by the feature characteristics. Feature weighting boosts the performance of machine learning algorithms, particularly the class of instance-based learning algorithms such as the Minimum Distance (MD) classifier, in which the distance measure is highly sensitive to the magnitude of features. In this paper, we propose an improved version of the Salp Swarm optimization Algorithm (SSA), called ISSA, that will be applied to optimize feature weights for an MD classifier. The aim is to improve the performance of a blind digital modulation detection approach in the context of multiple-antenna systems. The improvements introduced to SSA mainly rely on the opposition-based learning technique. Computer simulations show that the ISSA outperforms the SSA as well as the algorithms that derive from it. The ISSA also exhibits the best performance once it is applied for feature weighting in the above context.

A Confidence-Guided Anomaly Detection Approach Jointly Using Multiple Machine Learning Algorithms

2019 ◽  
pp. 93-100 ◽  
Author(s):  
Xueshuo Xie ◽  
Zongming Jin ◽  
Qingqi Han ◽  
Shenwei Huang ◽  
Tao Li
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Detection Approach

scholarly journals Enhancing Surface Fault Detection Using Machine Learning for 3D Printed Products

2021 ◽  
Vol 4 (2) ◽  
pp. 34
Author(s):  
Vaibhav Kadam ◽  
Satish Kumar ◽  
Arunkumar Bongale ◽  
Seema Wazarkar ◽  
Pooja Kamat ◽  
...  
Keyword(s):  
Machine Learning ◽  
Fault Detection ◽  
Computer Aided Design ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Fused Deposition Modelling ◽  
Layer By Layer ◽  
Fused Deposition ◽  
Detection Approach ◽  
3D Printed

In the era of Industry 4.0, the idea of 3D printed products has gained momentum and is also proving to be beneficial in terms of financial and time efforts. These products are physically built layer-by-layer based on the digital Computer Aided Design (CAD) inputs. Nonetheless, 3D printed products are still subjected to defects due to variation in properties and structure, which leads to deterioration in the quality of printed products. Detection of these errors at each layer level of the product is of prime importance. This paper provides the methodology for layer-wise anomaly detection using an ensemble of machine learning algorithms and pre-trained models. The proposed combination is trained offline and implemented online for fault detection. The current work provides an experimental comparative study of different pre-trained models with machine learning algorithms for monitoring and fault detection in Fused Deposition Modelling (FDM). The results showed that the combination of the Alexnet and SVM algorithm has given the maximum accuracy. The proposed fault detection approach has low experimental and computing costs, which can easily be implemented for real-time fault detection.

scholarly journals A Survey on Predicting Advanced Liver Fibrosis Using Different Machine Learning Algorithms

2020 ◽  
pp. 177-183
Author(s):  
Krishnendu K B ◽  
Deepa S S
Keyword(s):  
Machine Learning ◽  
Liver Fibrosis ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Swarm Optimization ◽  
Advanced Liver Fibrosis ◽  
Forest Genetic ◽  
Multi Linear Regression ◽  
Artificial Neural Network Ann

Machine learning (ML) is a subsection of AI. The goal of ML is to understand the structure of data and fit that data into models that can be used for prediction, classification etc. Although machine learning is an area within computer science, it differs from traditional computational approaches. In recent years, different machine learning algorithms are used for disease prediction. Algorithms like Decision Tree (DT), Support Vector Machine (SVM), Particle Swarm Optimization (PSO), Multi- Linear Regression, Random Forest, Genetic Algorithm (GA), Artificial Neural Network (ANN), Naive Bayes, etc. are used for classification. Using these algorithms liver fibrosis stages can be predicted. This paper discusses different machine learning algorithms for the prediction of liver fibrosis stage and the performance analysis of these algorithms in various studies.

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