Classification of focal and non-focal EEG signals using optimal geometrical features derived from a second-order difference plot of FBSE-EWT rhythms.

Background/introduction: Manual detection and localization of the brain's epileptogenic areas using electroencephalogram (EEG) signals is time-intensive and error-prone. An automated detection system is, thus, highly desirable for support in clinical diagnosis. A set of relevant and significant non-linear features plays a major role in developing a reliable, automated focal detection system.

Withdrawn: A Novel Continuous-Time Hidden Markov Model Based on a Bag of Features Extracted from MR Brain Images for Alzheimer's Stage Progression and Detection.

Unlabelled: Since the authors are not responding to the editor’s requests to fulfill the editorial requirement, the article has been withdrawn. Bentham Science apologizes to the readers of the journal for any inconvenience this may have caused. The Bentham Editorial Policy on Article Withdrawal can be found at https://benthamscience.

Augmented sequence features and subcellular localization for functional characterization of unknown protein sequences.

Advances in high-throughput techniques lead to evolving a large number of unknown protein sequences (UPS). Functional characterization of UPS is significant for the investigation of disease symptoms and drug repositioning. Protein subcellular localization is imperative for the functional characterization of protein sequences.

Automated FBSE-EWT based learning framework for detection of epileptic seizures using time-segmented EEG signals.

Epilepsy is a neurological disorder that has severely affected many people's lives across the world. Electroencephalogram (EEG) signals are used to characterize the brain's state and detect various disorders. The EEG signals are non-stationary and non-linear in nature.

Multi-objective Evolutionary Approach for the Performance Improvement of Learners using Ensembling Feature Selection and Discretization Technique on Medical Data.

Background: Biomedical data is filled with continuous real values; these values in the feature set tend to create problems like underfitting, the curse of dimensionality and increase in misclassification rate because of higher variance. In response, pre-processing techniques on dataset minimizes the side effects and have shown success in maintaining the adequate accuracy.

Aims: Feature selection and discretization are the two necessary preprocessing steps that were effectively employed to handle the data redundancies in the biomedical data.

Compositional framework for multitask learning in the identification of cleavage sites of HIV-1 protease.

Inadequate patient samples and costly annotated data generations result into the smaller dataset in the biomedical domain. Due to which the predictions with a trained model that usually reveal a single small dataset association are fail to derive robust insights. To cope with the data sparsity, a promising strategy of combining data from the different related tasks is exercised in various application.

Compositional model based on factorial evolution for realizing multi-task learning in bacterial virulent protein prediction.

The ability of multitask learning promulgated its sovereignty in the machine learning field with the diversified application including but not limited to bioinformatics and pattern recognition. Bioinformatics provides a wide range of applications for Multitask Learning (MTL) methods. Identification of Bacterial virulent protein is one such application that helps in understanding the virulence mechanism for the design of drug and vaccine.