Search Results - (( cross validation study algorithm ) OR ( early identification using algorithm ))*
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Evaluation of K-fold value in breast cancer diagnosis technique using SVM and bio-inspired optimization algorithm (JA-ABC5)
Published 2023“…The purpose of this study is to determine how K-fold cross validation affects breast cancer classification performance. …”
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2
Design of intelligent Qira’at identification algorithm
Published 2017“…To evaluate the algorithm, 350 samples for 10 types of Qira’at recitation are in used, and for justifying the best pattern classification, few algorithms are tested in the early preliminary evaluation with K-Nearest Neighbour, GMM and PPCA. …”
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3
Identification of microcalcification in mammographic images / Noor Azliza Abdul Shauti
Published 2008“…By using this identification, it can simulate in flexible images post processing and analysis in easy image management using Java aplication. …”
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4
Early Detection Of ADHD Among Children Using Machine Learning
Published 2023“…Early identification using fMRI and machine learning holds great potential for improving the lives of children with ADHD through timely interventions and targeted support.…”
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Undergraduates Project Papers -
5
A case study of microarray breast cancer classification using machine learning algorithms with grid search cross validation
Published 2023“…Grid search cross validation (CV) is applied for hyperparameter tuning of the algorithms. …”
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Phylogenetic tree classification system using machine learning algorithm
Published 2015“…In addition to that, 10-fold cross-validation is also conducted in the evaluation. …”
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Final Year Project Report / IMRAD -
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Machine-learning approach using thermal and synthetic aperture radar data for classification of oil palm trees with basal stem rot disease
Published 2021“…The main benefit of this study is the development of an appropriate model for early identification and severity classification of BSR disease in oil palms via remote sensing and data mining approaches rapidly and cost-effectively.…”
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9
Methods for identification of the opportunistic gut mycobiome from colorectal adenocarcinoma biopsy tissues
Published 2024“…•Detailed method to identify the gut mycobiome in colorectal cancer patients using ITS-specific amplicon sequencing. •Application of machine learning algorithms to the identification of potential mycobiome biomarkers for non-invasive colorectal cancer screening. …”
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Classification of credit card holder behavior using K Nearest Neighbor algorithm / Ahmad Faris Rahimi
Published 2017“…In the implementation phase, Bubble Sort, Euclidean Distance, 10-Fold Cross validation, and K Nearest Neighbor algorithm are developed. …”
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Development of MyCGPA for early predicting students’ academic performance
Published 2025“…One of the primary concerns in higher education is the early identification of underperforming students. …”
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Prediction of life expectancy for Asian population using machine learning ALGORITHMS / Nurul Shahira Pisal, Shuzlina Abdul-Rahman, Mastura Hanafiah and Saidatul Izyanie Kamarudin
Published 2022“…This study presents machine learning algorithms for life expectancy based on the Asian population dataset. …”
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Regression study for thyroid disease prediction Comparison of crossing-over approaches and multivariate analysis
Published 2022“…For the multivariate analysis, we found that the number of variable is not the key element to determine the performance of a model, rather than a suitable combination of strong predictors. Future studies could explore the effects of cross-validation and multivariate analysis on other machine learning algorithms.…”
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14
Investigation of An Early Prediction System of Cardiac Arrest Using Machine Learning Techniques
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Undergraduates Project Papers -
15
AI-Enabled Deep Learning Model for COVID-19 Identification Leveraging Internet of Things
Published 2023“…A chest X-ray dataset is used to compare the deep ensemble model against six transfer learning algorithms. …”
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A comparative study of the classification of skin burn depth in human
Published 2017“…The dataset was evaluated using both a supplied test set and 10-fold cross validation methods. Empirical results showed that the best classification algorithms that were able to classify most of the burn depths using a supplied test set were Logistic, Simple Logistic, MultiClassClassifier, OneR, and LMT, with an average accuracy of 68.9% whereas for 10-fold cross validation evaluation, the best result was obtained through the Simple Logistic algorithm with an average accuracy of 73.2%. …”
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A Comparison Study On Pca_Modular Pca And Lda For Face Recognition
Published 2017“…The performance of these face recognition algorithms will be evaluated by 10-fold cross validation using ORL database. …”
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Monograph -
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Identification of transformer fault based on dissolved gas analysis using hybrid support vector machine-modified evolutionary particle swarm optimisation
Published 2018“…It was found that the proposed hybrid SVM-Modified EPSO (MEPSO)-Time Varying Acceleration Coefficient (TVAC) technique results in the highest correct identification percentage of faults in a power transformer compared to other PSO algorithms. …”
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Validation assessments on resampling method in imbalanced binary classification for linear discriminant analysis
Published 2021“…This manuscript attempted to shed more light on the effect of a resampling method (ROS or RUS) on the performance of LDA based on true positive rate and true negative rate through five validation strategies, i.e. leave-one-out cross-validation, k-fold cross-validation, repeated k-fold cross-validation, naive bootstrap, and .632+ bootstrap. 100 twogroup bivariate normally distributed simulated and four real data sets with severe class imbalance ratio were utilised. …”
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Validation assessments on Resampling Method in Imbalanced Binary Classification for Linear Discriminant Analysis
Published 2021“…This manuscript attempted to shed more light on the effect of a resampling method (ROS or RUS) on the performance of LDA based on true positive rate and true negative rate through five validation strategies, i.e. leave-one-out cross-validation, k-fold cross-validation, repeated k-fold cross-validation, naive bootstrap, and .632+ bootstrap. 100 two-group bivariate normally distributed simulated and four real data sets with severe class imbalance ratio were utilised. …”
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