A review on the computational methods for emotional state estimation from the human EEG
A growing number of affective computing researches recently developed a computer system
that can recognize an emotional state of the human user to establish affective human …
that can recognize an emotional state of the human user to establish affective human …
Maximizing AUC to learn weighted naive Bayes for imbalanced data classification
T Kim, JS Lee - Expert Systems with Applications, 2023 - Elsevier
Imbalanced data classification is a challenging problem frequently encountered in many real-
world applications. Traditional classification algorithms are generally designed to maximize …
world applications. Traditional classification algorithms are generally designed to maximize …
[PDF][PDF] Comparative analysis of text classification algorithms for automated labelling of Quranic verses
The ultimate goal of labelling a Quranic verse is to determine its corresponding theme.
However, the existing Quranic verse labelling approach is primarily depending on the …
However, the existing Quranic verse labelling approach is primarily depending on the …
[PDF][PDF] Machine learning classification technique for famine prediction
W Okori, J Obua - Proceedings of the world congress on engineering, 2011 - iaeng.org
Learning techniques for famine prediction. Early detection of famine reduces vulnerability of
the society at risk. The dataset used in the study was collected between 2004 to 2005 across …
the society at risk. The dataset used in the study was collected between 2004 to 2005 across …
[HTML][HTML] A new fusion model for classification of the lung diseases using genetic algorithm
C Bhuvaneswari, P Aruna, D Loganathan - Egyptian Informatics Journal, 2014 - Elsevier
Automatic classification of lung diseases in computed tomography (CT) images is an
important diagnostic tool for computer-aided diagnosis system. In this study, we propose a …
important diagnostic tool for computer-aided diagnosis system. In this study, we propose a …
Anomaly detection combining one-class SVMs and particle swarm optimization algorithms
J Tian, H Gu - Nonlinear Dynamics, 2010 - Springer
Anomalies are patterns in data that do not conform to a well-defined notion of normal
behavior. One-class Support Vector Machines calculate a hyperplane in the feature space to …
behavior. One-class Support Vector Machines calculate a hyperplane in the feature space to …
[PDF][PDF] A Dataset-Driven Parameter Tuning Approach for Enhanced K-Nearest Neighbour Algorithm Performance.
The number of Neighbours (k) and distance measure (DM) are widely modified for improved
kNN performance. This work investigates the joint effect of these parameters in conjunction …
kNN performance. This work investigates the joint effect of these parameters in conjunction …
[PDF][PDF] An Automated CAD System of CT Chest Images for COVID-19 Based on Genetic Algorithm and K-Nearest Neighbor Classifier.
Accepted: 26 September 2020 The detection of COVID-19 from computed tomography (CT)
scans suffered from inaccuracies due to its difficulty in data acquisition and radiologist …
scans suffered from inaccuracies due to its difficulty in data acquisition and radiologist …
Incorporating receiver operating characteristics into naive Bayes for unbalanced data classification
T Kim, BD Chung, JS Lee - Computing, 2017 - Springer
Naive Bayesian classification has been widely used in data mining area because of its
simplicity and robustness to missing values and irrelevant attributes. However, naive Bayes …
simplicity and robustness to missing values and irrelevant attributes. However, naive Bayes …
Feature weighted SVMs using receiver operating characteristics
Abstract Support Vector Machines (SVMs) are a leading tool in classification and pattern
recognition and the kernel function is one of its most important components. This function is …
recognition and the kernel function is one of its most important components. This function is …