Hybridization of Fuzzy and Hard Semi-Supervised Clustering Algorithms Tuned with Ant Lion Optimizer Applied to Higgs Boson Search

Abstract

This paper focuses on the unsupervised detection of the Higgs boson particle using the most informative features and variables which characterize the "Higgs machine learning challenge 2014" data set. This unsupervised detection goes in this paper analysis through 4 steps: (1) selection of the most informative features from the considered data; (2) definition of the number of clusters based on the elbow criterion. The experimental results showed that the optimal number of clusters that group the considered data in an unsupervised manner corresponds to 2 clusters; (3) proposition of a new approach for hybridization of both hard and fuzzy clustering tuned with Ant Lion Optimization (ALO); (4) comparison with some existing metaheuristic optimizations such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). By employing a multi-angle analysis based on the cluster validation indices, the confusion matrix, the efficiencies and purities rates, the average cost variation, the computational time and the Sammon mapping visualization, the results highlight the effectiveness of the improved Gustafson-Kessel algorithm optimized with ALO (ALOGK) to validate the proposed approach. Even if the paper gives a complete clustering analysis, its novel contribution concerns only the Steps (1) and (3) considered above. The first contribution lies in the method used for Step (1) to select the most informative features and variables. We used the t-Statistic technique to rank them. Afterwards, a feature mapping is applied using Self-Organizing Map (SOM) to identify the level of correlation between them. Then, Particle Swarm Optimization (PSO), a metaheuristic optimization technique, is used to reduce the data set dimension. The second contribution of this work concern the third step, where each one of the clustering algorithms as K-means (KM), Global K-means (GlobalKM), Partitioning Around Medoids (PAM), Fuzzy C-means (FCM),

A Design and Implementation of a New Control Based on Petri Nets for Three Phase PWM-Rectifier

“…It focuses on the process that governs how the components and system change over time. In the literature, there are many applications of dynamic modeling in machine learning, deep learning, computational intelligence, control systems, robotics, sensor network and cyber-security (Ben Smida et al, 2018;Lamamra et al, 2017;Grassi et al, 2017;Mohanty et al, 2021 ;Ghoudelbourk et al, 2022Ghoudelbourk et al, , 2021Ghoudelbourk et al, , 2016Mekki et al, 2015;Dudekula et al, 2023 ;Hussain et al, 2023 ;El-Shorbagy et al, 2023 ;Ramadan et al, 2022 ;Ashfaq et al, 2022a,b;Waleed et al, 2022 ;Jothi et al, 2022Jothi et al, , 2020Jothi et al, , 2019Jothi et al, , 2013Lavanya et al, 2022 ;Inbarani et al, , 2018Inbarani et al, , 2014Inbarani et al, , 2015Boulmaiz et al, 2022 ;Fouad et al, 2021 ;Elfouly et al, 2021 ;Khan et al, 2021 ;Aslam et al, 2021 ;Nasser et al, 2021 ;Hussien et al, 2020 ;Kumar et al, 2019Kumar et al, , 2015aMjahed et al, 2020 ;Banu et al, 2017 ;Ben Abdallah et al, 2016Emary et al, 2014a,b;Anter et al, 2015Anter et al, , 2013Elshazly et al, 2013a,b ;Azar et al, 2013…”

Novel Adaptive Histogram Binning-Based Lesion Segmentation for Discerning Severity in COVID-19 Chest CT Scan Images

“…Deep learning technology, which evolved from Artificial Neural Networks (ANN), has become a major issue in the computer world and is widely used in fields such as healthcare, image identification, text analytics, cybersecurity, and many more (Dudekula et al, 2023;Fati et al, 2022;Boulmaiz et al, 2022, Zaidi et al 2022Ganesan et al, 2022;Abbas et al, 2022;Azar et al, 2021a,b;Ibrahim et al, 2020;Ramadan et al, 2022;Aslam et al, 2021). Machine Learning (ML) is an artificial intelligence subset that generates dynamic algorithms capable of making data-driven judgments (Hussain et al, 2023;Atteia et al, 2023;Salam et al, 2021Salam et al, , 2022Mathiyazhagan et al, 2022;Ashfaq et al, 2022a,b;Inbarani et al, 2022Inbarani et al, , 2020Inbarani et al, , 2018Inbarani et al, , 2015aInbarani et al, ,b, 2014aFekik et al, 2021Fekik et al, , 2018aEl Kafazi et al, 2021;Sundaram et al, 2021;Hussien et al, 2020;Mjahed et al, 2020 ;Sayed et al, 2019;Aboamer et al, 2019Aboamer et al, , 2014aSallam et al, 2020;Kumar et al, 2017Banu et al, 2017Banu et al, , 2014Ben Abdallah et al, 2016Fredj et al, 2016 ;Malek and Azar, 2016a,b;Malek et al, 2015a,b;…”

k‐Means Clustering Algorithm and Its Simulation Based on Distributed Computing Platform

“…Cluster analysis can divide the data set into several clusters [9]. e k-means is suitable for data sets with large amounts of data and high feature dimensions, and its dependence on data is low.…”