Search Results - (( variable effective learning algorithm ) OR ( java machine learning algorithm ))
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1
Teaching and learning via chatbots with immersive and machine learning capabilities
Published 2019“…These chatbots support learning of Java via problem-solving steps through “learning by doing”. …”
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Development Of Machine Learning User Interface For Pump Diagnostics
Published 2022“…The features extracted of time domain and frequency domain in vibration and acoustic will use as database of a Support Vector Machine (SVM) algorithms by using MATLAB R2021a. The result from the SVM algorithms will be used as database for the machine learning in Microsoft Azure. …”
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Adoption of machine learning algorithm for analysing supporters and non supporters feedback on political posts / Ogunfolajin Maruff Tunde
Published 2022“…This work employed the use of machine learning approach. Four conventional classification algorithms: naïve bayes (NB), support vector machines (SVM), nearest neighbor (k-NN), and decision trees (J48) classifiers are implemented in identifying and categorizing tweet data of three political figures in Malaysia: Dato Seri Anwar, Dato Hadi Awang, and Lim Guang Eng, as either positive, negative, or neutral perceptions. …”
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4
AI powered asthma prediction towards treatment formulation: an android app approach
Published 2022“…We utilized eight robust machine learning algorithms to analyze this dataset. …”
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Landslide Susceptibility Mapping with Stacking Ensemble Machine Learning
Published 2024“…While numerous methods have been proposed, machine learning (ML) is the most popular approach that has been applied across the globe. …”
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AI powered asthma prediction towards treatment formulation : An android app approach
Published 2022“…We utilized eight robust machine learning algorithms to analyze this dataset. …”
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7
Features selection for intrusion detection system using hybridize PSO-SVM
Published 2016“…Features selection process can be considered a problem of global combinatorial optimization in machine learning. Genetic algorithm GA had been adopted to perform features selection method; however, this method could not deliver an acceptable detection rate, lower accuracy, and higher false alarm rates. …”
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8
Mathematical simulation for 3-dimensional temperature visualization on open source-based grid computing platform
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Web-based clustering tool using fuzzy k-mean algorithm / Ahmad Zuladzlan Zulkifly
Published 2019“…On the Construction phase, the development of the prototype has been started. All the algorithm for the engine has been developed by using Java script language. …”
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10
Dynamic Bayesian Networks and Variable Length Genetic Algorithm for Dialogue Act Recognition
Published 2007“…In the selection phase, a new variable length genetic algorithm is applied to select the lexical cues. …”
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Prediction of hydropower generation via machine learning algorithms at three Gorges Dam, China
Published 2024“…Machine learning models have been effectively applied to predict certain variable in several engineering applications where the variable is highly stochastic in nature and complex to identify utilizing the classical mathematical models. …”
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Fine-scale predictive modeling of Aedes mosquito abundance and dengue risk indicators using machine learning algorithms with microclimatic variables
Published 2025“…Integrating time-lagged microclimatic variables into machine learning frameworks enhances the predictive accuracy of dengue risk indicators at a fine spatial scale. …”
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Feasibility analysis for predicting the compressive and tensile strength of concrete using machine learning algorithms
Published 2024“…Furthermore, in the prediction of tensile strength the number of variables utilized had an insignificant effect on the performance of the models. …”
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Dynamic Bayesian networks and variable length genetic algorithm for designing cue-based model for dialogue act recognition
Published 2010“…The model is, essentially, a dynamic Bayesian network induced from manually annotated dialogue corpus via dynamic Bayesian machine learning algorithms. Furthermore, the dynamic Bayesian network's random variables are constituted from sets of lexical cues selected automatically by means of a variable length genetic algorithm, developed specifically for this purpose. …”
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15
Modeling time series data using Genetic Algorithm based on Backpropagation Neural network
Published 2018“…The first objective is to study the effects of varying the architecture designs and parameter values of the backpropagation neural network (BPNN) learning algorithm. …”
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16
Enhancing wind power forecasting accuracy with hybrid deep learning and teaching-learning-based optimization
Published 2024“…The variations in the performance of other algorithms across instances highlight the robustness and effectiveness of TLBO-DL in achieving accurate wind power forecasts. …”
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Hyper-heuristic approaches for data stream-based iIntrusion detection in the Internet of Things
Published 2022“…Here, the memory consumption can be reduced by enabling a feature selection algorithm that excludes nonrelevant features and preserves the relevant ones. the algorithm is developed based on the variable length of the PSO. …”
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18
A New Quadratic Binary Harris Hawk Optimization For Feature Selection
Published 2019“…A comparative study is conducted to compare the effectiveness of QBHHO with other feature selection algorithms such as binary differential evolution (BDE), genetic algorithm (GA), binary multi-verse optimizer (BMVO), binary flower pollination algorithm (BFPA), and binary salp swarm algorithm (BSSA). …”
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Enhanced Adaptive Confidence-Based Q Routing Algorithms For Network Traffic
Published 2004“…The ECDRQ Routing Algorithm integrates the ECQ and Dual Reinforcement Q (DRQ) Routing Algorithms with Alternative Q Value Approach to minimise the effect of partially learning cycle. …”
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Green building valuation based on machine learning algorithms / Thuraiya Mohd ... [et al.]
Published 2021“…In the cycle of Industrial Revolution 4.0 (IR 4.0), many issues in the industries can be solved with implementation of artificial intelligence approaches, including machine learning models. Designing an effective machine learning model for prediction and classification problems is a continuous effort. …”
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