Hybrid optimization for k-means clustering learning enhancement

In recent years, combinational optimization issues are introduced as critical problems in clustering algorithms to partition data in a way that optimizes the performance of clustering. K-means algorithm is one of the famous and more popular clustering algorithms which can be simply implemented and it can easily solve the optimization issue with less extra information. But the problems associated with Kmeans algorithm are high error rate, high intra cluster distance and low accuracy. In this regard, researchers have worked to improve the problems computationally, creating efficient solutions that lead to better data analysis through the K-means clustering algorithm. The aim of this study is to improve the accuracy of the Kmeans algorithm using hybrid and meta-heuristic methods. To this end, a metaheuristic approach was proposed for the hybridization of K-means algorithm scheme. It obtained better results by developing a hybrid Genetic Algorithm-K-means (GAK- means) and a hybrid Partial Swarm Optimization-K-means (PSO-K-means) method. Finally, the meta-heuristic of Genetic Algorithm-Partial Swarm Optimization (GAPSO) and Partial Swarm Optimization-Genetic Algorithm (PSOGA) through the K-means algorithm were proposed. The study adopted a methodological approach to achieve the goal in three phases. First, it developed a hybrid GA-based K-means algorithm through a new crossover algorithm based on the range of attributes in order to decrease the number of errors and increase the accuracy rate. Then, a hybrid PSO-based K-means algorithm was mooted by a new calculation function based on the range of domain for decreasing intra-cluster distance and increasing the accuracy rate. Eventually, two meta-heuristic algorithms namely GAPSO-K-means and PSOGA-K-means algorithms were introduced by combining the proposed algorithms to increase the number of correct answers and improve the accuracy rate. The approach was evaluated using six integer standard data sets provided by the University of California Irvine (UCI). Findings confirmed that the hybrid optimization approach enhanced the performance of K-means clustering algorithm. Although both GA-K-means and PSO-K-means improved the result of K-means algorithm, GAPSO-K-means and PSOGA-K-means meta-heuristic algorithms outperformed the hybrid approaches. PSOGA-K-means resulted in 5%- 10% more accuracy for all data sets in comparison with other methods. The approach adopted in this study successfully increased the accuracy rate of the clustering analysis and decreased its error rate and intra-cluster distance

Shortest Route at Dynamic Location with Node Combination-Dijkstra Algorithm

Abstract— Online transportation has become a basic requirement of the general public in support of all activities to go to work, school or vacation to the sights. Public transportation services compete to provide the best service so that consumers feel comfortable using the services offered, so that all activities are noticed, one of them is the search for the shortest route in picking the buyer or delivering to the destination. Node Combination method can minimize memory usage and this methode is more optimal when compared to A* and Ant Colony in the shortest route search like Dijkstra algorithm, but can’t store the history node that has been passed. Therefore, using node combination algorithm is very good in searching the shortest distance is not the shortest route. This paper is structured to modify the node combination algorithm to solve the problem of finding the shortest route at the dynamic location obtained from the transport fleet by displaying the nodes that have the shortest distance and will be implemented in the geographic information system in the form of map to facilitate the use of the system. Keywords— Shortest Path, Algorithm Dijkstra, Node Combination, Dynamic Location (key words

Advances in Theoretical and Computational Energy Optimization Processes

The paradigm in the design of all human activity that requires energy for its development must change from the past. We must change the processes of product manufacturing and functional services. This is necessary in order to mitigate the ecological footprint of man on the Earth, which cannot be considered as a resource with infinite capacities. To do this, every single process must be analyzed and modified, with the aim of decarbonising each production sector. This collection of articles has been assembled to provide ideas and new broad-spectrum contributions for these purposes

Control Theory in Engineering

The subject matter of this book ranges from new control design methods to control theory applications in electrical and mechanical engineering and computers. The book covers certain aspects of control theory, including new methodologies, techniques, and applications. It promotes control theory in practical applications of these engineering domains and shows the way to disseminate researchers’ contributions in the field. This project presents applications that improve the properties and performance of control systems in analysis and design using a higher technical level of scientific attainment. The authors have included worked examples and case studies resulting from their research in the field. Readers will benefit from new solutions and answers to questions related to the emerging realm of control theory in engineering applications and its implementation

ATHENA Research Book, Volume 2

ATHENA European University is an association of nine higher education institutions with the mission of promoting excellence in research and innovation by enabling international cooperation. The acronym ATHENA stands for Association of Advanced Technologies in Higher Education. Partner institutions are from France, Germany, Greece, Italy, Lithuania, Portugal and Slovenia: University of Orléans, University of Siegen, Hellenic Mediterranean University, Niccolò Cusano University, Vilnius Gediminas Technical University, Polytechnic Institute of Porto and University of Maribor. In 2022, two institutions joined the alliance: the Maria Curie-Skłodowska University from Poland and the University of Vigo from Spain. Also in 2022, an institution from Austria joined the alliance as an associate member: Carinthia University of Applied Sciences. This research book presents a selection of the research activities of ATHENA University's partners. It contains an overview of the research activities of individual members, a selection of the most important bibliographic works of members, peer-reviewed student theses, a descriptive list of ATHENA lectures and reports from individual working sections of the ATHENA project. The ATHENA Research Book provides a platform that encourages collaborative and interdisciplinary research projects by advanced and early career researchers

Bowdoin College Catalogue and Academic Handbook (2023-2024)

https://digitalcommons.bowdoin.edu/course-catalogues/1321/thumbnail.jp

Bowdoin College Catalogue and Academic Handbook (2022-2023)

https://digitalcommons.bowdoin.edu/course-catalogues/1320/thumbnail.jp

WSU Course Catalog, 2024

School catalog delineating classes offered, credits, requirements, and other academic information believed to be necessary for incoming students at Washington State University

Bowdoin College Catalogue (2008-2009)

https://digitalcommons.bowdoin.edu/course-catalogues/1289/thumbnail.jp