Search Results - (( centered learning algorithm ) OR ((( self learning algorithm ) OR ( e learning algorithm ))))*
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Advancements and challenges in mobile robot navigation: a comprehensive review of algorithms and potential for self-learning approaches
Published 2024“…In this review paper, a comprehensive review of mobile robot navigation algorithms has been conducted. The findings suggest that, even though the self-learning algorithms require huge amounts of training data and have the possibility of learning erroneous behavior, they possess huge potential to overcome challenges rarely addressed by the other traditional algorithms. …”
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Feasibility comparison of HAC algorithm on usability performance and self-reported metric features for MAR learning
Published 2023Conference Paper -
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The Evolutionary Convergent Algorithm: A Guiding Path of Neural Network Advancement
Published 2025“…In this study, a new idea centered on seed growth, Seed Growth Algorithm (SGA), as a conditional convergent evolutionary algorithm is proposed for optimizing several discrete and continuous optimization problems. …”
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Lexicon-based and immune system based learning methods in Twitter sentiment analysis
Published 2016“…The aim of this article attempts to study the potential of this method in text classification for sentiment analysis.This study consists of three phases; data preparation; classification model development using three selected Immune System based algorithms i.e. Negative Selection algorithm (NSA), Clonal Selection algorithm (CSA) and Immune Network algorithm (INA); and model analysis. …”
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Machine learning: tasks, modern day applications and challenges
Published 2019“…Machine learning algorithms learned from available data. Further, this learning laid the foundation to develop AI for the various systems around us. …”
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Prediction of Ground Surface Deformation Induced by Earthquake on Urban Area Using Machine Learning
Published 2023“…Overall, the four machine learning algorithms have outstanding performance, with a coefficient determinant of more than 0.9. …”
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Contrastive Self-Supervised Learning for Image Classification
Published 2021“…Thus, people have introduced a new paradigm that falls under unsupervised learning – self-supervised learning. Through self-supervised learning, pretraining of the model can be conducted without any human-labelled data and the model can learn from the data itself. …”
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An Improved Pheromone-Based Kohonen Self- Organising Map in Clustering and Visualising Balanced and Imbalanced Datasets
Published 2021“…However, similar to other clustering algorithms, this algorithm requires sufficient data for its unsupervised learning process. …”
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Topological Clustering via Adaptive Resonance Theory With Information Theoretic Learning
Published 2019“…This paper proposes a topological clustering algorithm by integrating topological structure and information theoretic learning, i.e., correntropy, into adaptive resonance theory (ART). …”
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An improved pheromone-based kohonen self-organising map in clustering and visualising balanced and imbalanced datasets
Published 2021“…However, similar to other clustering algorithms, this algorithm requires sufficient data for its unsupervised learning process. …”
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Lithium-ion Battery State of Charge Estimation Method Using Optimized Deep Recurrent Neural Network Algorithm
Published 2023“…Alumina; Aluminum oxide; Backpropagation; Battery management systems; Bioluminescence; Charging (batteries); Cobalt compounds; Deep neural networks; Genetic algorithms; Ions; Lithium compounds; Machine learning; Nickel oxide; Radial basis function networks; Recurrent neural networks; Back-propagation neural networks; Computation intelligences; Electrochemical batteries; Firefly algorithms; Manganese-cobalt oxides; Radial basis function neural networks; Self-learning capability; State of charge; Lithium-ion batteries…”
Conference Paper -
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Intelligent agent for e-commerce using genetic algorithm / Kok Sun Sun
Published 2000“…In order to develop an intelligent agent, various programming techniques are used in achieving the property of self learning, information retrieval and searching algorithm. …”
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Hybridization of metaheuristic algorithm in training radial basis function with dynamic decay adjustment for condition monitoring / Chong Hue Yee
Published 2023“…In this research work, the motivation is to develop an autonomous learning model based on the hybridization of an adaptive ANN and a metaheuristic algorithm for optimizing ANN parameters so that the network could perform learning and adaptation in a more flexible way and handle condition classification tasks more accurately in industries, such as in power systems. …”
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Triangulating the implementation of hierarchical agglomerative clustering on MAR-learning usability data
Published 2023“…Augmented reality; Clustering algorithms; Machine learning; Usability engineering; English language teaching; Hierarchical agglomerative clustering; Mobile augmented reality; Performance metrics; Self-reported metrics; Usability; Hierarchical clustering…”
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A Mininet emulation study for SDN fat tree data center sleep mode routing algorithms
Published 2025“…2024 by the authors; licensee Learning Gate.…”
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Self-driving car with Artificial Intelligence (AI) technology / Asrul Amin Mohd Isa ... [et al.]
Published 2023“…It is based on the use of sensors, actuators, sophisticated algorithms, machine learning systems, and robust processors. …”
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Academic leadership bio-inspired classification model using negative selection algorithm
Published 2015“…Negative selection algorithm has been successfully used in several purposes such as in fault detection, data integrity protection, virus detection and etc.due to the unique ability in self-recognition by classifying self or non-self’s detectors. …”
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Conference or Workshop Item -
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Automated Nutritional Guidance for Obesity Management: Insights from Machine Learning, Naïve Bayes, Random Forest
Published 2025“…This paper presents the design and development of an Automated Nutritional Guidance System aimed at enhancing obesity management through intelligent, user-centered dietary recommendations. The system utilizes a combination of machine learning algorithms, nutritional databases, and user input to provide personalized dietary plans aligned with individual health goals, dietary preferences, and lifestyle patterns. …”
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