Search Results - (( knowledge profiling learning algorithm ) OR ( java adaptation optimization algorithm ))

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    Optimisation of fed-batch fermentation process using deep reinforcement learning by Chai, Wan Ying

    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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  3. 3

    Ontology based approach to Fuzzy student model design by Muhammad Sani, Salisu, Mohd Aris, Teh Noranis

    Published 2014
    “…ITS is a software system designed using artificial intelligent techniques (comprising of Fuzzy Logic, Neural-Networks, Bayesian networks, Ontology, Genetic Algorithms and Software Agents) to provide a personalized tutoring adaptable to each student based on his/her profile or characteristics. …”
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  4. 4

    Proposal for ontology based approach to fuzzy student model design by Muhammad Sani, Salisu, Mohd Aris, Teh Noranis

    Published 2014
    “…In this paper we intend to employ the use of Fuzzy logic and Ontology techniques to model the student's learning behaviour with the aim of improving the learning path and increase the system's adaptability. …”
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  5. 5

    Anfis Modelling On Diabetic Ketoacidosis For Unrestricted Food Intake Conditions by Saraswati, Galuh Wilujeng

    Published 2017
    “…The project has also implemented the optimization process onto the proposed ANFIS model through the hybrid of Genetic Algorithm on the fuzzy membership function of the ANFIS model. …”
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  6. 6

    Methods for identification of the opportunistic gut mycobiome from colorectal adenocarcinoma biopsy tissues by Aisyah, Yunus, Norfilza, Mohd Mokhtar, Raja Affendi, Raja Ali *, Siti Maryam, Ahmad Kendong, Hajar, Fauzan Ahmad

    Published 2024
    “…•Application of machine learning algorithms to the identification of potential mycobiome biomarkers for non-invasive colorectal cancer screening. …”
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  7. 7

    Pattern extraction for programming performance evaluation using directed apriori by Mohamad Mohsin, Mohamad Farhan, Zaiyadi, Mohd Fairuz, Md Norwawi, Norita, Abd Wahab, Mohd Helmy

    Published 2009
    “…Several researches indicated that there are many factors which can affect student programming performance.Thus, the objective of this paper is to investigate the significant factors that may influence students programming performance using information from previous student performance.Since data mining data analysis able to discover hidden knowledge in database, a programming dataset which comprises information about performance profile of Bachelor of Information Technology students of Faculty of IT, Universiti Utara Malaysia in the year 2004-2005 were explored using data mining technique.The dataset consists of 421 records with 70 mixture type of attributes were pre-processed and then mined using directed association rule (AR) mining algorithm namely apriori.The result indicated that the student who has a programming experience in advanced before starts learn programming in university and scored well in Mathematics and English subject during SPM were among the factor that contributes to a good programming grades.…”
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  8. 8

    Fuzzy expert system modeling of laser processing by Subramonian, Sivarao, Ahmad, Kamely, Md Palil, Md Dan

    Published 2009
    “…The objective of the paper is: to design knowledge based rules, algorithm, architecture & learning ability to develop fuzzy surface roughness predictive model for laser machining; to develop fuzzy predictive model using design parameters which were critically analyzed; to make a comparative study of the observed and modelled surface roughness output of 5mm Mn-Mo pressure vessel plate. …”
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    Three-dimensional craniometrics identification model and cephalic index classification of Malaysian sub-adults: A multi-slice computed tomography study / Sharifah Nabilah Syed Mohd... by Sharifah Nabilah , Syed Mohd Hamdan

    Published 2024
    “…Discriminant function analysis (DFA), binary logistic regression (BLR), and several machine learning (ML) algorithms (random forest (RF), support vector machines (SVM), and linear discriminant analysis (LDA)) were used to statistically analyse the data. …”
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