Search Results - (( data application using algorithm ) OR ( variable active learning algorithm ))
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1
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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2
Modeling time series data using Genetic Algorithm based on Backpropagation Neural network
Published 2018“…This study showed the task of optimizing the topology structure and the parameter values (e.g., weights) used in the BPNN learning algorithm by using the GA. …”
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3
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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4
An evolutionary based features construction methods for data summarization approach
Published 2015“…In other words, this research will discuss the application of genetic algorithm to optimize the feature construction process from the Coral Reefs data to generate input data for the data summarization method called Dynamic Aggregation of Relational Attributes (DARA). …”
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Research Report -
5
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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7
Edge detection and contour segmentation for fruit classification in natural environment / Khairul Adilah Ahmad
Published 2018“…This learning algorithm represents an automatic generation of membership functions and rules from the data. …”
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8
Dynamics and control of underactuated systems with applications in robotics / Ahmad Azlan Mat Isa … [et al.]
Published 2011“…An ANN modal was trained using the experimentally obtained data and then used to predict the path of the passive joint that is positioned by the dynamic coupling of the active joint. …”
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9
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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10
Modeling flood occurences using soft computing technique in southern strip of Caspian Sea Watershed
Published 2012“…Multilayer Feedforward Back Propagation (MLFFBP) was used. Among the available learning algorithms in the Neural Network Toolbox of MATLAB, three algorithms, gradient descent back propagation (TRAINGD), gradient descent with adaptive learning rule back propagation (TRAINGDA) and the Levenberg-Marquardt (TRAINLM) were studied. …”
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11
Input significance analysis: Feature selection through synaptic weights manipulation for EFuNNs classifier
Published 2017“…Specifically for the classification process, Big Data can cause the classifiers to process longer than necessary, and the redundant or irrelevant data may misguide the learning classification algorithms to learn the random error or noise related to them. …”
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12
Data-Driven Approach to Modeling Biohydrogen Production from Biodiesel Production Waste: Effect of Activation Functions on Model Configurations
Published 2022“…The gradient descent optimization algorithm was observed to help improve the modelâ��s performance. …”
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13
Tangible interaction learning model to enhance learning activity processes among children with dyslexia
Published 2024“…This method has the ability to impute both categorical and continuous data features for large datasets. In addition, an application is presented for public users to utilize the proposed method using standard and autonomous data imputation. …”
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14
Multi-sensor fusion and deep learning framework for automatic human activity detection and health monitoring using motion sensor data / Henry Friday Nweke
Published 2019“…This is further worsen by the use of single sensors modality and machine learning algorithms. …”
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15
DESIGN AND IMPLEMENTATION OF INTELLIGENT MONITORING SYSTEMS FOR THERMAL POWER PLANT BOILER TRIPS
Published 2011“…For this hybrid intelligent system, the selection of appropriate variables from hundreds of boiler operation variables with optimal neural network topology combinations to monitor boiler trips was a major concern. …”
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16
Short-Term forecasting of floating photovoltaic power generation using machine learning models
Published 2024“…This study investigates the application of machine learning models for predicting FPV power generation using data from the Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA) solar installation, which has a capacity of 157.20 kWp. …”
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Rough Neural Networks Architecture For Improving Generalization In Pattern Recognition
Published 2004“…A novel feature extraction algorithm was developed to extract the feature vectors. …”
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19
Optimization of Lipase Catalysed Synthesis of Sugar Alcohol Esters Using Taguchi Method and Neural Network Analysis
Published 2011“…Various feedforward neural networks were performed using different learning algorithms. The best algorithm was found to be Levenberg–Marquardt (LM) for a network composed of two hidden layers with six and seven neurons in the first and second layers, respectively for xylitol stearate and xylitol palmitate and also seven and five neurons in the first and second layers for xylitol caprate, with hyperbolic tangent sigmoid transfer function. …”
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20
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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