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Modified And Ensemble Intelligent Water Drop Algorithms And Their Applications
Published 2015“…Pertama, algoritma TAC yang diubahsuai, diperkenalkan. The Intelligent Water Drop (IWD) algorithm is a swarm-based model that is useful for undertaking optimization problems. …”
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Hybrid model to improve the river streamflow forecasting utilizing multi-layer perceptron-based intelligent water drop optimization algorithm
Published 2023“…Complex networks; Drops; Forecasting; Iterative methods; Network architecture; Network layers; Optimization; Rivers; Stochastic models; Stochastic systems; Stream flow; Time series; Engineering applications; Gradient-decent algorithm; Intelligent Water Drops (IWD); Multi layer perceptron; Multi-layer perceptron neural networks; Optimization algorithms; Streamflow forecasting; Time series prediction; Multilayer neural networks…”
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Inverse modeling of mass transferprocess in the RDC column by fuzzy approach
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Developing an intelligent system to acquire meeting knowledge in problem-based learning environments
Published 2006“…MALESAbrain1-3 is an intelligent algorithm which originally is designed for problem-based learning (PBL) environment. …”
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Heuristic factors in ant system algorithm for course timetabling problem
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An Intelligent System Approach to the Dynamic Hybrid Robot Control
Published 1996“…The objective of this study was to solve the robot dynamic hybrid control problem using intelligent computational processes. In the course of problem- solving, biologically inspired models were used. …”
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Artificial intelligence to predict pre-clinical dental student academic performance based on pre-university results: a preliminary study
Published 2024“…Purpose/Objectives: Admission into dental school involves selecting applicants for successful completion of the course. This study aimed to predict the academic performance of Kulliyyah of Dentistry, International Islamic University Malaysia pre-clinical dental students based on admission results using artificial intelligence machine learning (ML) models, and Pearson correlation coefficient (PCC). …”
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IOT Based Smart Wastewater Treatment Model for Industry 4.0 Using Artificial Intelligence
Published 2022“…Artificial neural networks (ANNs) offer accurate technique modeling for complex systems using an artificial intelligence technique. …”
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A comparative study and simulation of object tracking algorithms
Published 2020“…The algorithms using convolution features and multi-features fusion algorithms have more advantages in tracking accuracy than the algorithm using a single feature, but the tracking speed will also drop rapidly. …”
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Hybrid Metaheuristic Algorithm and Metaheuristic Performance Measurement for Solving University Course Timetabling Problem
Published 2007“…Metaheuristics have received considerable interest in the fields of applied artificial intelligence and combinatorial optimization such as university course timetabling problem (UCTP). …”
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Assessing the potential of laboratory instructional tool through Synthesia AI: a case study on student learning outcome / Jacqueline Joseph
Published 2023“…The course was divided into two groups, an experimental group and a control group. …”
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A multi-nets ANN model for real-time performance-based automatic fault diagnosis of industrial gas turbine engines
Published 2017“…Using the trained multi-nets ANN model, two case studies were conducted pertaining to the detection of drop in compressor flow capacity and compressor fouling in an industrial 18.7-MW twin-shaft gas turbine engine. …”
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Intelligent traffic lights using Q-learning
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Proceeding Paper -
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Simulated Kalman Filter algorithms for solving optimization problems
Published 2019“…The proposed population-based SKF algorithm and the single solution-based SKF algorithm use the scalar model of discrete Kalman filter algorithm as the search strategy to overcome these flaws. …”
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Blood cell classification using deep learning
Published 2022“…The learning rate was fine-tuned using the Tuner library from Keras to find the optimal value that generates a well-established model. As a result, the finalized model which consists of batch size of 16, learning rate of 0.0009, global pooling layer, 4 number of classes dropped to 3 number of classes and 3 dense layers achieved a testing accuracy of approximately 63% and validation accuracy of 99%.…”
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Final Year Project / Dissertation / Thesis -
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Enhancing teaching and learning through data-driven optimization of servicing code demand and lecturer allocation using WEKA analysis
Published 2025“…Complementary K-Means clustering grouped the data into two major clusters, indicating that a clear differentiation between economic-based and entrepreneurship-based courses in terms of student enrolment volume and approval distribution. …”
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