Search Results - (( java implication based algorithm ) OR ( knowledge formation learning algorithm ))
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Optimisation of fed-batch fermentation process using deep reinforcement learning
Published 2023“…Fed-batch fermentation process has always been a challenge for optimisation because it is highly non-linear and complex. Deep reinforcement learning is a self-learning algorithm through trial and error and experience, without any prior knowledge. …”
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Thesis -
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MFA-OSELM Algorithm For Wifi-Based Indoor Positioning System
Published 2019“…Facilitating the use of WiFi for this purpose requires fingerprint formation and the implementation of a learning algorithm with the aim of using the fingerprint to determine locations. …”
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MFA-OSELM algorithm for WiFi-based indoor positioning system
Published 2019“…Facilitating the use of WiFi for this purpose requires fingerprint formation and the implementation of a learning algorithm with the aim of using the fingerprint to determine locations. …”
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A Survey Of Supervised Machine Learning In Wireless Sensor Network: A Power Management Perspective
Published 2013“…Machine learning algorithms are iteration based algorithms, as the new knowledge is based on the previous predicted /calculated knowledge which helps to decrease errors in order to increase efficiency. …”
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Conference or Workshop Item -
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Clustering Student Performance Data Using k-Means Algorithms
Published 2023“…Clustering, an unsupervised learning technique, is one of the most powerful machine- learning tools for discovering patterns and unseen data. …”
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Brain stroke detection using medical images
Published 2025“…In order to enable automated brain clot detection and adapt to various image variations, machine learning algorithms are integrated for classification. …”
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Final Year Project / Dissertation / Thesis -
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Teaching students interdisciplinary knowledge through compilation of differential models within the framework of course projects / Duisebek Nurgabyl ... [et al.]
Published 2023“…Thus, the need to study and solve these problems, their theoretical and practical significance in the formation of interdisciplinary knowledge among future mathematics teachers determines the relevance of the research topic. …”
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Cluster identification and separation in the growing self-organizing map: Application in protein sequence classification
Published 2010“…Growing self-organizing map (GSOM) has been introduced as an improvement to the self-organizing map (SOM) algorithm in clustering and knowledge discovery. Unlike the traditional SOM, GSOM has a dynamic structure which allows nodes to grow reflecting the knowledge discovered from the input data as learning progresses. …”
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Prediction of PVT properties in crude oil systems using support vector machines
Published 2023Conference paper -
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Digital Quran With Storage Optimization Through Duplication Handling And Compressed Sparse Matrix Method
Published 2024thesis::doctoral thesis -
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Design, development and evaluation of a game-based learning application for room housekeeping
Published 2024“…The paper concludes with a discussion on potential enhancements such as incorporating adaptive learning algorithms and multiplayer features to support collaborative learning. …”
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An immunological-based simulation: A case study of risk concentration for mobile spam context assessment
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journal::journal article -
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Developing students' mathematical thinking: how far have we came?
Published 2015“…Mathematical thinking is the foundation to do reasoning and problem solving and to develop conceptual knowledge, as opposed to procedural knowledge. Several findings from studies that focused on students' ability to provide reasoning and give meanings to concepts and algorithms are highlighted. …”
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Inaugural Lecture -
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Zero distortion-based steganography for handwritten signature
Published 2018“…To the best of our knowledge, this is the first time that a human handwritten signature sample is used for steganography application. …”
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Artificial Neural Network-Forecasted Compression Strength of Alkaline-Activated Slag Concretes
Published 2022“…The prediction accuracy of the optimal ANN model was then compared to existing ANN-based models, while the variable selection was compared to existing AASC models with other machine learning algorithms, due to limitations in the ANN-based model. …”
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