Performance Comparison of Machine Learning Algorithms in Symbol Detection Using OFDM

2022 ◽  
pp. 455-466
Author(s):  
Siva Satya Sri Ganesh Seeram ◽  
Avuthu Yuvaraja Reddy ◽  
N. J. Basil ◽  
Akella Viswa Sai Suman ◽  
K. Anuraj ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Performance Comparison ◽  
Machine Learning Algorithms ◽  
Symbol Detection

scholarly journals Performance Improvement of Decision Tree: A Robust Classifier Using Tabu Search Algorithm

2021 ◽  
Vol 11 (15) ◽  
pp. 6728
Author(s):  
Muhammad Asfand Hafeez ◽  
Muhammad Rashid ◽  
Hassan Tariq ◽  
Zain Ul Abideen ◽  
Saud S. Alotaibi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Tabu Search ◽  
Decision Tree ◽  
Decision Trees ◽  
Search Algorithm ◽  
Learning Algorithms ◽  
Performance Comparison ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Tabu Search Algorithm

Classification and regression are the major applications of machine learning algorithms which are widely used to solve problems in numerous domains of engineering and computer science. Different classifiers based on the optimization of the decision tree have been proposed, however, it is still evolving over time. This paper presents a novel and robust classifier based on a decision tree and tabu search algorithms, respectively. In the aim of improving performance, our proposed algorithm constructs multiple decision trees while employing a tabu search algorithm to consistently monitor the leaf and decision nodes in the corresponding decision trees. Additionally, the used tabu search algorithm is responsible to balance the entropy of the corresponding decision trees. For training the model, we used the clinical data of COVID-19 patients to predict whether a patient is suffering. The experimental results were obtained using our proposed classifier based on the built-in sci-kit learn library in Python. The extensive analysis for the performance comparison was presented using Big O and statistical analysis for conventional supervised machine learning algorithms. Moreover, the performance comparison to optimized state-of-the-art classifiers is also presented. The achieved accuracy of 98%, the required execution time of 55.6 ms and the area under receiver operating characteristic (AUROC) for proposed method of 0.95 reveals that the proposed classifier algorithm is convenient for large datasets.

scholarly journals PERFORMANCE COMPARISON OF MACHINE LEARNING ALGORITHMS FOR PREDICTIVE MAINTENANCE

2020 ◽  
Vol 10 (3) ◽  
pp. 32-35
Author(s):  
Jakub Gęca
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Performance Comparison ◽  
Machine Learning Algorithms ◽  
Predictive Maintenance ◽  
Model Parameters ◽  
Data Set ◽  
Reduction Techniques ◽  
Machine Reliability ◽  
Dimensionality Reduction Techniques

The consequences of failures and unscheduled maintenance are the reasons why engineers have been trying to increase the reliability of industrial equipment for years. In modern solutions, predictive maintenance is a frequently used method. It allows to forecast failures and alert about their possibility. This paper presents a summary of the machine learning algorithms that can be used in predictive maintenance and comparison of their performance. The analysis was made on the basis of data set from Microsoft Azure AI Gallery. The paper presents a comprehensive approach to the issue including feature engineering, preprocessing, dimensionality reduction techniques, as well as tuning of model parameters in order to obtain the highest possible performance. The conducted research allowed to conclude that in the analysed case , the best algorithm achieved 99.92% accuracy out of over 122 thousand test data records. In conclusion, predictive maintenance based on machine learning represents the future of machine reliability in industry.

scholarly journals Performance Evaluation of Machine Learning Techniques for Identifying Forged and Phony Uniform Resource Locators (URLs)

2019 ◽  
Vol 16 (4) ◽  
pp. 155-169
Author(s):  
N. A. Azeez ◽  
A. A. Ajayi
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Financial Institutions ◽  
Traditional Approach ◽  
Learning Algorithms ◽  
Performance Comparison ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Reliable Solution ◽  
F Measure

Since the invention of Information and Communication Technology (ICT), there has been a great shift from the erstwhile traditional approach of handling information across the globe to the usage of this innovation. The application of this initiative cut across almost all areas of human endeavours. ICT is widely utilized in education and production sectors as well as in various financial institutions. It is of note that many people are using it genuinely to carry out their day to day activities while others are using it to perform nefarious activities at the detriment of other cyber users. According to several reports which are discussed in the introductory part of this work, millions of people have become victims of fake Uniform Resource Locators (URLs) sent to their mails by spammers. Financial institutions are not left out in the monumental loss recorded through this illicit act over the years. It is worth mentioning that, despite several approaches currently in place, none could confidently be confirmed to provide the best and reliable solution. According to several research findings reported in the literature, researchers have demonstrated how machine learning algorithms could be employed to verify and confirm compromised and fake URLs in the cyberspace. Inconsistencies have however been noticed in the researchers’ findings and also their corresponding results are not dependable based on the values obtained and conclusions drawn from them. Against this backdrop, the authors carried out a comparative analysis of three learning algorithms (Naïve Bayes, Decision Tree and Logistics Regression Model) for verification of compromised, suspicious and fake URLs and determine which is the best of all based on the metrics (F-Measure, Precision and Recall) used for evaluation. Based on the confusion metrics measurement, the result obtained shows that the Decision Tree (ID3) algorithm achieves the highest values for recall, precision and f-measure. It unarguably provides efficient and credible means of maximizing the detection of compromised and malicious URLs. Finally, for future work, authors are of the opinion that two or more supervised learning algorithms can be hybridized to form a single effective and more efficient algorithm for fake URLs verification.Keywords: Learning-algorithms, Forged-URL, Phoney-URL, performance-comparison

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