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Algorithmic design issues in adaptive differential evolution schemes: Review and taxonomy
Published 2018“…These fine-tuning techniques continue to be the object of ongoing research. …”
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Feature selection optimization using hybrid relief-f with self-adaptive differential evolution
Published 2017“…Hence, feature selection is embedded to select the most meaningful features based on their rank. Differential evolution (DE) is one of the evolutionary algorithms that are widely used in various classification domains. …”
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Development of obstable avoidance system for 3D robot navigation
Published 2024“…In conclusion, the developed algorithm enables the robot to detect obstacles that are not on the same plane as the 2D LiDAR and cannot be detected using depth camera data.…”
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Cloud Worm Detection and Response Technique By Integrating The Enhanced Genetic Algorithm An Threat Level
Published 2024thesis::doctoral thesis -
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Design Of Navigation Algorithm For F_Bot For Patrolling Using Fuzzy Logic
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Monograph -
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A Review: Current Trend of Immersive Technologies for Indoor Navigation and the Algorithms
Published 2024“…Based on the findings of this review, we can conclude that an efficient solution for indoor navigation that uses the capabilities of embedded data and technological advances in immersive technologies can be achieved by training the shortest path algorithm with a deep learning algorithm to enhance the indoor navigation system.…”
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Sistem navigasi berasaskan algoritma Dijkstra di dalam persekitaran tiga dimensi
Published 2008“…Dijkstra's algorithm is one of the algorithms that could be used in network analysis process especially for 2D shortest route data. …”
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Thesis -
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Email spam classification based on deep learning methods: A review
Published 2025“…Deep learning has become a potent collection of techniques for addressing intricate issues such as spam classification in recent times. …”
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Analysis on target detection and classification in LTE based passive forward scattering radar
Published 2016“…By utilizing the forward scattering technique and procedure into the specific mode of PBR can provide an improvement in target detection and classification. …”
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Improving Ant Swarm Optimization With Embedded Vaccination For Optimum Reducts Generation
Published 2011“…Unlike a conventional PSOIACO algorithm, this hybrid algorithm shows improvement of the classification accuracy in its generated rough reducts to solve NP-Hard problem. …”
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Design Of Robot Motion Planning Algorithm For Wall Following Robot
Published 2006“…Algorithms are developed for a simulated mobile robot that uses an array of range finders for navigation. …”
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Monograph -
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Classification with degree of importance of attributes for stock market data mining
Published 2004“…The SVM is a training algorithm for learning classification and regression rules from data [7]. …”
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Stock market turning points rule-based prediction / Lersak Photong … [et al.]
Published 2021“…From news classification and news sentiment, a rule-based algorithm was used to predict the stock market turning points. …”
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Book Section -
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Innovative algorithm for easing VIP's navigation by avoiding obstacles and finding safe routes
Published 2023Conference Paper -
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Mobile robot navigation using GPS to solve target location achieving problem / Saiful Zaimy Yahaya, Rozan Boudville and Mohamad Faizal Abdul Rahman
Published 2010“…The data received from GPS is processed and hence the decision is made for the robot navigation path by the algorithm applied in the microcontroller. …”
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Research Reports -
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Vision-based robot indoor navigation
Published 2022“…A navigation robot is built to test the workability and efficiency of the algorithms. …”
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Final Year Project / Dissertation / Thesis -
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Artificial neural network learning enhancement using Artificial Fish Swarm Algorithm
Published 2011“…Artificial Neural Network (ANN) is a new information processing system with large quantity of highly interconnected neurons or elements processing parallel to solve problems.Recently, evolutionary computation technique, Artificial Fish Swarm Algorithm (AFSA) is chosen to optimize global searching of ANN.In optimization process, each Artificial Fish (AF) represents a neural network with output of fitness value.The AFSA is used in this study to analyze its effectiveness in enhancing Multilayer Perceptron (MLP) learning compared to Particle Swarm Optimization (PSO) and Differential Evolution (DE) for classification problems.The comparative results indeed demonstrate that AFSA show its efficient, effective and stability in MLP learning.…”
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