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1
A Model for Evaluation of Cryptography Algorithm on UUM Portal
Published 2004“…The purpose of this project are to construct and provide guidelines to develop a simulation model to evaluate cryptography algorithm in terms of encryption speed and descryption speed on UUM portal. …”
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Thesis -
2
A Feature Ranking Algorithm in Pragmatic Quality Factor Model for Software Quality Assessment
Published 2013“…This thesis describes original research in the field of software quality model by presenting a Feature Ranking Algorithm (FRA) for Pragmatic Quality Factor (PQF) model. …”
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3
Performance analysis of machine learning algorithms for classification of infection severity levels on rubber leaves
Published 2023“…The chlorophyll content of each leaf was measured using SPAD meter. Four classification algorithms investigated in this study were artificial neural network (ANN), support vector machine (SVM), knearest neighbour (kNN) and random forest (RF). …”
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Book Section -
4
Factor Strategy Model: Proofs of Prototype Concept for Software Quality Evaluation
Published 2010“…The objective of this study is to provide software developers some measures to evaluate software quality of a Java program. Studies on software quality requirements, software quality models (as a quantifying measurement technique) and various good design rules are required for. …”
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Article -
5
Fuzzy modeling using Bat Algorithm optimization for classification
Published 2018“…A Sazonov Engine which is a fuzzy java engine is use to apply Bat Algorithm in the experiment. …”
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Undergraduates Project Papers -
6
A Predictive Classification Model For Running Injury
Published 2022“…The J48, SMO, Random Forest, and Simple Logistic algorithms were used for 10-fold cross validation mode classification benchmarked on the ZeroR baseline algorithm. …”
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Monograph -
7
Classification of basal stem rot disease in oil palm using dielectric spectroscopy
Published 2018“…First, features selection algorithms (genetic algorithm (GA), random forest (RF), and support vector machine-feature selection (SVM-FS)) were used to select the most significant frequencies. …”
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8
Non-invasive gliomas grading using swarm intelligence algorithm / Muhammad Harith Ramli
Published 2017“…Bat algorithm is chosen for the development of the prototype for segmentation and classification purpose. …”
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9
Software metrics selection model for predicting maintainability of object-oriented software using genetic algorithms
Published 2016“…The latest effort to solve this selection problem is the development of the metrics selection model that uses genetic algorithm (GA). However, the process failed to state clearly the encoding strategy in its initial stage. …”
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10
An intra-severity classification and adaptation technique to improve dysarthric speech recognition accuracy / Bassam Ali Qasem Al-Qatab
Published 2020“…Secondly, the identified severity level of a particular dysarthric speaker in the first stage is applied to the corresponding intra-severity adaptation of dysarthric speech. For the classification part, there are six algorithms used to classify the intra-severity of dysarthric speakers. …”
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11
Mid-infrared spectroscopy for early detection of basal stem rot disease in oil palm
Published 2014“…The selected principal component scores were used in classification using linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), k-nearest neighbor (kNN) and Naive-Bayes (NB) multivariate classification algorithms. …”
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Article -
12
Classification of Distracted Male Driver Based on Driving Performance Indicator (DPI)
Published 2024“…We applied these algorithms on their datasets using its GUI or command-line parameters. …”
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Conference or Workshop Item -
13
Features selection for intrusion detection system using hybridize PSO-SVM
Published 2016“…The simulation will be carried on WEKA tool, which allows us to call some data mining methods under JAVA environment. The proposed model will be tested and evaluated on both NSL-KDD and KDD-CUP 99 using several performance metrics.…”
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Thesis -
14
Enhancement of Ant Colony Optimization for Grid Job Scheduling and Load Balancing
Published 2011“…A simulation environment was developed using Java programming to test the performance of the proposed EACO algorithm against existing grid resource management algorithms such as Antz algorithm, Particle Swarm Optimization algorithm, Space Shared algorithm and Time Shared algorithm, in terms of processing time and resource utilization. …”
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15
Backpropagation vs. radial basis function neural model : Rainfall intensity classification for flood prediction using meteorology data
Published 2016“…Among the various soft computing methods, Artificial Neural Network (ANN) is the most commonly used methodology. While numerous ANN algorithms were applied, the most commonly applied are the Backpropagation (BPN) and Radial Basis Function (RFN) models. …”
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Article -
16
Classification of Mental Health Level of Students Using SMOTE and Soft Voting Ensemble Classifier and the DASS-21 Profile
“…It leverages the Synthetic Minority Over-sampling Technique (SMOTE) to address the class imbalance in the dataset and employs a Voting Ensemble with soft voting to combine several base algorithms (Logistic Regression, Random Forest, Gradient Boosting, and XGBoost/SVM) for accurate prediction of mental health levels (normal, mild, moderate, severe, very severe). …”
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17
Moving vehicle noise classification using backpropagation algorithm
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Working Paper -
18
Automatic diabetic retinopathy detection and classification system
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Conference or Workshop Item -
19
Diabetic retinopathy detection using fusion of textural and optimized convolutional neural network features / Uzair Ishtiaq
Published 2024“…Combining Local Binary Patterns (LBP) based texture features and deep learning features resulted in the creation of the fused features vector which was then optimized using Binary Dragonfly Algorithm (BDA) and Sine Cosine Algorithm (SCA). …”
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Thesis -
20
Landslide Susceptibility Mapping with Stacking Ensemble Machine Learning
Published 2024“…One of the prominent methods to improve machine learning accuracy is by using ensemble method which basically employs multiple base models. In this paper, the stacking ensemble method is used to increase the accuracy of the machine learning model for LSM where the base (first-level) learners use five ML algorithms namely decision tree (DT), k-nearest neighbor (KNN), AdaBoost, extreme gradient boosting (XGB) and random forest (RF). …”
Conference Paper
