Search Results - (( knowledge creation learning algorithm ) OR ( java application optimisation algorithm ))
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Study and Implementation of Data Mining in Urban Gardening
Published 2019“…The system is essentially a three-part development, utilising Android, Java Servlets, and Arduino platforms to create an optimised and automated urban-gardening system. …”
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An Artificial Intelligence-Based Knowledge Management System for Outcome-Based Education Implementing in Higher Education Institutions
Published 2025“…Based on the results of the knowledge creation in KMS, it succeeded in meeting learners' learning needs. …”
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Web-based expert system for material selection of natural fiber- reinforced polymer composites
Published 2015“…Finally, the developed expert system was deployed over the internet with central interactive interface from the server as a web-based application. As Java is platform independent and easy to be deployed in web based application and accessible through the World Wide Web (www), this expert system can be one stop application for materials selection.…”
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A framework of modified adaptive neuro-fuzzy inference engine
Published 2012“…One of the vital significant issues for constructing a high quality neuro-fuzzy system is the creation of the knowledge base, which mainly consists of membership functions and fuzzy rules. …”
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Allah’s wisdom in creating the universe as a source of inspiration in artificial intelligence
Published 2024“…This tradition of learning from nature not only advances knowledge and technology but also aligns with the Islamic view of humanity's role as Khalifah on Earth, integrating spiritual and scientific exploration.…”
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Developing framework for natphoric computer-aided web-based kansei engineering / Mohammad Bakri Che Haron
Published 2013“…The Natphoric algorithm learns the process done by training with sets of training data from previous KE research works. …”
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Physiological signals as predictors of mental workload: Evaluating single classifier and ensemble learning models
Published 2023“…A comprehensive evaluation was conducted on several ML algorithms, including both single (Support Vector Machine – SVM, and Naïve Bayes) and ensemble learning (Gradient Boost and AdaBoost) classiers and incorporating selected features and validation approaches. …”
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