Search Results - (( re evaluation tree algorithm ) OR ( writer identification using algorithm ))

Improved random forest for feature selection in writer identification by Sukor, Nooraziera Akmal

“…Writer Identification (WI) is a process to determine the writer of a given handwriting sample. …”
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A New Swarm-Based Framework for Handwritten Authorship Identification in Forensic Document Analysis by Draman @ Muda, Azah Kamilah, Choo, Yun Huoy, Draman @ Muda, Noor Azilah

“…However in this chapter, feature selection is used to obtain the unique individual significant features which are proven very important in handwriting analysis of Writer Identification domain. …”
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A new swarm-based framework for handwritten authorship identification in forensic document analysis by Pratama, Satrya Fajri, Draman @ Muda, Azah Kamilah, Choo, Yun Huoy, Draman @ Muda, Noor Azilah

“…The use of feature selection as one of the important machine learning task is often disregarded in Writer Identification domain, with only a handful of studies implemented feature selection phase. …”
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Discretization of integrated moment invariants for writer identification by Draman @ Muda, Azah Kamilah, Shamsuddin, Siti Maryam, Darus, Maslina

“…Hence, in this study, an integrated scaling formulation of Aspect Scaling Invariant is presented in Writer Identification to hunt for the individuality perseverance. …”
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Invariants discretization for individuality representation in handwritten authorship by Draman @ Muda, Azah Kamilah, Shamsuddin, Siti Mariyam, Darus, Maslina

“…Writer identification is one of the areas in pattern recognition that have created a center of attention by many researchers to work in. …”
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An Improved C4.5 Data Mining Driven Algorithm for the Diagnosis of Coronary Artery Disease by Haruna, A.A., Muhammad, L.J., Yahaya, B.Z., Garba, E.J., Oye, N.D., Jung, L.T.

“…This research used an im-proved C4.5 data mining algorithm for the diagnosis of CAD. A performance evaluation of the improved algorithm was carried out against the traditional C4.5 Algorithm. …”
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Prediction of COVID-19 patient using supervised machine learning algorithm by M., Buvana, K., Muthumayil

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Customer behavior analysis based on purchasing history and reviews using automated decision-making systems by Allur, Naga Sushma, Deevi, Durga Praveen, Dondapati, Koteswararao, Chetlapalli, Himabindu, Kodadi, Sharadha, Perumal, Thinagaran

“…According to the provided tables, the proposed methods (KNN and ADMS) outperform the other algorithms (ID3, Decision Tree, and Logistic Regression) across a range of customer review counts in terms of accuracy (98.95%), precision (94.62%), recall (99.24%), and F1-score (96.87%) while processing customer reviews more quickly (0.063). …”
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Automatic extraction of digital terrain model and Building Footprint from airborne LiDAR data using rule-based learning techniques by Jifroudi, Hamidreza Maskani

“…In the next step, noise and roof errors were removed using KNN filter and a new network was created and re-evaluated based on the shortest distance in the LiDAR point cloud to create an integrated DTM. …”
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Factors with retirement behaviour among retirees and pre-retirees identified with a machine learning method / Muhammad Aizat Zainal Alam by Muhammad Aizat , Zainal Alam

“…This study uses 3,067 responses which are then be coupled with a machine learning methodology (ranging from Naïve Bayesian, Generalised Linear Model, Logistic Regression, Artificial Neural Network, Decision Tree, Random Forest, and Gradient Boosted Trees) via RapidMiner Studio to expand the understanding of how categories of wealth and expenditures can affect retirement behaviour, given the increasingly important role of machine learning algorithms within the context of behavioural economics where it has been demonstrated to describe patterns and relationships in behavioural data better than standard statistical analysis. …”
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State-of-charge estimation for lithium-ion batteries with optimized self-supervised transformer deep learning model by Dickson Neoh Tze How, Dr.

“…To select the optimal hyperparameters for the Transformer model, the Tree Parzen Estimator(TPE) optimization in combination with the Hyperband pruning algorithm is employed to search for the best combination that yields the lowest Root Mean Squared Error(RMSE)and Mean Absolute Error (MAE) error metrics. …”