Search Results - (( parallel optimization sensor algorithm ) OR ( variable active learning algorithm ))
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Simulated kalman filter (SKF) based image template matching for distance measurement by using stereo vision system
Published 2018“…Stereo vision sensor consists of two stereo cameras, mounted parallel in stationary position. …”
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2
Single-objective and multi-objective optimization algorithms based on sperm fertilization procedure / Hisham Ahmad Theeb Shehadeh
Published 2018“…The obtained results are compared with the results of four algorithms. These algorithms are Genetic Algorithms (GA), Parallel Genetic Algorithm (PGA), Particle Swarm Optimization (PSO) and Accelerated Particle Swarm Optimization (APSO). …”
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Performance monitoring algorithm for optimizing electrical power generated by using photovoltaic system
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Algorithm enhancement for host-based intrusion detection system using discriminant analysis
Published 2004“…Anomaly detection algorithms model normal behavior. Anomaly detection models compare sensor data to normal patterns learned from the training data by using statistical method and try to detect activity that deviates from normal activity. …”
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5
A study on advanced statistical analysis for network anomaly detection
Published 2005“…Anomaly detection algorithms model normal behavior. Anomaly detection models compare sensor data to normal patterns learned from the training data by using statistical method and try to detect activity that deviates from normal activity. …”
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Monograph -
6
Estimation of core size distribution of magnetic nanoparticles using high-Tc SQUID magnetometer and particle swarm optimizer-based inversion technique
Published 2023“…In this work, the core size estimation technique of magnetic nanoparticles (MNPs) using the static magnetization curve obtained from a high-Tc SQUID magnetometer and a metaheuristic inversion technique based on the Particle Swarm Optimizer (PSO) algorithm is presented. The high-Tc SQUID magnetometer is constructed from a high-Tc SQUID sensor coupled by a flux transformer to sense the modulated magnetization signal from a sample. …”
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Online teleoperation of writing manipulator through graphics processing unit based accelerated stereo vision
Published 2021“…These algorithms are then parallelized using Compute Unified Device Architecture CUDA C language to run on Graphics Processing Unit GPU for hardware acceleration. …”
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Examining the potential of machine learning for predicting academic achievement: A systematic review
Published 2023“…Predicting student academic performance is a critical area of education research. Machine learning (ML) algorithms have gained significant popularity in recent years. …”
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Examining the potential of machine learning for predicting academic achievement: A systematic review
Published 2023“…Predicting student academic performance is a critical area of education research. Machine learning (ML) algorithms have gained significant popularity in recent years. …”
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Classification of students' performance in computer programming course according to learning style
Published 2024Conference Paper -
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Modeling time series data using Genetic Algorithm based on Backpropagation Neural network
Published 2018“…The performance of ANNs depend on many factors, including the network structure, the selection of activation function, the learning rate of the training algorithm, and initial synaptic weight values, the number of input variables, and the number of units in the hidden layer. …”
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Classification of Students' Performance in Computer Programming Course According to Learning Style
Published 2024“…The findings show that that student's good performance in programming courses has a visual, active and sequential learning style.…”
Proceedings Paper -
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Classifying corporates default and non-default using machine learning Artificial Neural Network: multilayer perceptron / Nur Insyirah Mohamad Radzi, Murni Salina Rosidi and Nur Asy...
Published 2023“…Asset volatility is found to be the most significant independent variable. Therefore, ANN is a machine learning algorithm that uses multiple layers perceptron to solve complex problems and predict analytics.…”
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An evolutionary based features construction methods for data summarization approach
Published 2015“…Here, feature construction methods are applied in order to improve the descriptive accuracy of the DARA algorithm.This research proposes novel feature construction methods, called Variable Length Feature Construction without Substitution (VLFCWOS) and Variable Length Feature Construction with Substitution(VLFCWS), in order to construct a set of relevant features in learning relational data. …”
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15
Forecasting hydrological parameters for reservoir system utilizing artificial intelligent models and exploring their influence on operation performance
Published 2019“…The three different optimization algorithms used in this study are the genetic algorithm (GA), particle swarm optimization (PSO) algorithm and shark machine learning algorithm (SMLA). …”
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16
Enhancing understanding of programming concepts through physical games
Published 2017“…The activities were conducted involving first and fourth year undergraduate students and Master students in Programming 1 (31 students), Data Structure (6 students), Analysis of Algorithm (12 students) and Advanced Algorithm (22 students) courses respectively. …”
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Characterization of water quality conditions in the Klang River Basin, Malaysia using self organizing map and K-means algorithm
Published 2015“…The self organizing map (SOM) combined with the K-means algorithm arranged the data based on the relationships of 25 variables. …”
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Evaluation of postgraduate academic performance using artificial intelligence models
Published 2022“…Exploring other variables such as research activities, marital status, and living conditions would have improved the overall accuracy of the proposed ML models. …”
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Evaluation of postgraduate academic performance using artificial intelligence models
Published 2022“…Exploring other variables such as research activities, marital status, and living conditions would have improved the overall accuracy of the proposed ML models. …”
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