Search Results - (( model validating a algorithm ) OR ( text classification based algorithm ))
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1
Integrated combined layer algorithm of jamming detection and classification in manet / Ahmad Yusri Dak
Published 2019“…Finally, both algorithms are validated against the findings in various literatures. …”
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2
Hybrid weight deep belief network algorithm for anomaly-based intrusion detection system
Published 2022“…The optimized DBN algorithm, known as the HW-DBN algorithm, integrated through feature learning based on a Gaussian–Bernoulli Restricted Boltzmann Machine as well as classification task through a weight neuron network. …”
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3
Improvement of fuzzy neural network using mine blast algorithm for classification of Malaysian Small Medium Enterprises based on strength
Published 2015“…The performance of the proposed classification model is validated on SME dataset obtained from SME Corporation Malaysia, and also on real-world benchmark classification problems like Breast Cancer, Iris, and Glass. …”
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4
Development of classification model between clean water and polluted water based on capacitance properties using Levenberg Marquardt (LM) algorithm of artificial neural network / M...
Published 2020“…These measurement inputs were then going through the process of classification in ANN to generate the optimized models by using LM algorithm. The model is being trained, tested, and validated to differentiate between clean water and polluted water. …”
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5
Modified word representation vector based scalar weight for contextual text classification
Published 2024“…For this experiment, the modified word vectors serve as input to train a Machine Learning (ML) model for the text classification process, aiming for the developed ML model to have a significantly smaller parameter count. …”
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6
Residual Attention Network for Brain Tumour Classification
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Final Year Project Report / IMRAD -
7
Compact Convolutional neural network (CNN) based on SincNet for end-to-end motor imagery decoding and analysis
Published 2021“…In order to validate the performance of proposed algorithms, two datasets were used; the first is the publicly available BCI Competition IV dataset 2a, which was often used as a benchmark in validating motor imagery classification algorithms, and the second is a dataset consists of primary data initially collected to study the difference between motor imagery and mental-task associated motor imagery BCI and was used to test the plausibility of the proposed algorithm in highlighting the differences in terms of cortical rhythms. …”
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8
An integrated model to email spam classification using an enhanced grasshopper optimization algorithm to train a multilayer perceptron neural network
Published 2021“…This study introduces an email detection model that is designed based on use of an improved version of the grasshopper optimization algorithm to train a Multilayer Perceptron in classifying emails as ham and spam. …”
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9
Performance of Levenberg-Marquardt neural network algorithm in power quality disturbances classification / Adibah I’zzah Mohamad Kasim
Published 2025“…A simulation-based methodology was adopted, leveraging MATLABO/Simulink to model a power grid and generate synthetic PQD waveforms. …”
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10
Green building valuation based on machine learning algorithms / Thuraiya Mohd ... [et al.]
Published 2021“…This experiment used five common machine learning algorithms namely 1) Linear Regressor, 2) Decision Tree Regressor, 3) Random Forest Regressor, 4) Ridge Regressor and 5) Lasso Regressor tested on a real estate data-set of covering Kuala Lumpur District, Malaysia. 3 set of experiments was conducted based on the different feature selections and purposes The results show that the implementation of 16 variables based on Experiment 2 has given a promising effect on the model compare the other experiment, and the Random Forest Regressor by using the Split approach for training and validating data-set outperformed other algorithms compared to Cross-Validation approach. …”
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11
Development of Phasor Measurement Unit Based Fault Detection and Faulty Line Classification in Electrical Power System
Published 2019“…This model implements Non-Recursive Phasor Estimation (NRPE) algorithm. …”
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12
Detection of corneal arcus using rubber sheet and machine learning methods
Published 2019“…The classification algorithms such as the Lavenberg-Marquardt (LM), Bayesian regularization (BR), scaled conjugate gradient (SCG) and one model of bag-of-features (BoF) are used in this research. …”
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13
AN AGENT-BASED DOCUMENT CLASSIFICATION MODEL TO IMPROVE THE EFFICIENCY OF THE AUTOMATED SYSTEMATIC REVIEW PROCESS
Published 2023“…Firstly, the document classification algorithm analyses a full text of research articles and evaluates relevancy. …”
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Poverty risk prediction based on socioeconomic factors using machine learning approach
Published 2025“…This study seeks to develop a predictive model of measuring poverty risk using socioeconomic factors based on a machine learning framework. …”
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A new intelligent multilayer framework for insider threat detection
Published 2021“…However, the selection of the most appropriate ML classification model for insider threats detection remains a challenge. …”
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Integrated approach using data mining-based decision tree and object-based image analysis for high-resolution urban mapping of WorldView-2 satellite sensor data
Published 2016“…Many features were explored by using a DM algorithm, which created the classification rules as a decision tree (DT) structure from the first study area. …”
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Heart sound diagnosis using nonlinear ARX model / Noraishah Shamsuddin
Published 2011“…A Lipschitz method and Leven berg Marquardt algorithm is used to determine the model order number and train the network respectively. …”
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18
Activity recognition based on accelerometer sensor using combinational classifiers
Published 2015“…The result of classification was validated using 10-fold cross validation algorithm in order to make sure all the experiments perform well.…”
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19
Personalized Prediction Clothing Algorithm Based on Multi-modal Feature Fusion
Published 2024“…To assess the generalization ability of the two models, cross-validation was performed. The experimental results indicate that the 1DCNN-2DCNN model exhibits superior generalization, achieving a recall rate of 97.20%, an F1 score of 98.60%, a macro average of 98.62%, a weighted average of 98.63%, and model accuracy of 98.59%. …”
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