Mel frequency cepstral coefficient and its applications: A review
ZK Abdul, AK Al-Talabani - IEEE Access, 2022 - ieeexplore.ieee.org
Feature extraction and representation has significant impact on the performance of any
machine learning method. Mel Frequency Cepstrum Coefficient (MFCC) is designed to …
machine learning method. Mel Frequency Cepstrum Coefficient (MFCC) is designed to …
A systematic review on metaheuristic optimization techniques for feature selections in disease diagnosis: open issues and challenges
There is a need for some techniques to solve various problems in today's computing world.
Metaheuristic algorithms are one of the techniques which are capable of providing practical …
Metaheuristic algorithms are one of the techniques which are capable of providing practical …
An efficient marine predators algorithm for feature selection
DS Abd Elminaam, A Nabil, SA Ibraheem… - IEEE …, 2021 - ieeexplore.ieee.org
Feature Selection (FS) reduces the number of features by removing unnecessary,
redundant, and noisy information while keeping a relatively decent classification accuracy …
redundant, and noisy information while keeping a relatively decent classification accuracy …
Binary grey wolf optimizer with mutation and adaptive k-nearest neighbour for feature selection in Parkinson's disease diagnosis
RR Rajammal, S Mirjalili, G Ekambaram… - Knowledge-Based …, 2022 - Elsevier
Disease identification and classification relies on Feature Selection (FS) to find the relevant
features for accurate medical diagnosis. FS is an optimization problem solved with the help …
features for accurate medical diagnosis. FS is an optimization problem solved with the help …
Automatic and non-invasive Parkinson's disease diagnosis and severity rating using LSTM network
Deep learning has a huge potential in healthcare for uncovering the hidden patterns from
large volume of clinical data to diagnose different diseases. This paper presents a novel …
large volume of clinical data to diagnose different diseases. This paper presents a novel …
A robust intelligence regression model for monitoring Parkinson's disease based on speech signals
Parkinson's disease (PD) is a degenerative neurological disease, and early diagnosis of PD
is crucial. Monitoring PD progression from voice records is a promising technique, which is …
is crucial. Monitoring PD progression from voice records is a promising technique, which is …
Optimizing multi-objective PSO based feature selection method using a feature elitism mechanism
M Amoozegar, B Minaei-Bidgoli - Expert Systems with Applications, 2018 - Elsevier
Feature selection is an important preprocessing task in classification that eliminates the
irrelevant, redundant, and noisy features. Improving the performance of model, decreasing …
irrelevant, redundant, and noisy features. Improving the performance of model, decreasing …
Time-varying hierarchical chains of salps with random weight networks for feature selection
Feature selection (FS) is considered as one of the most common and challenging tasks in
Machine Learning. FS can be considered as an optimization problem that requires an …
Machine Learning. FS can be considered as an optimization problem that requires an …
Stacking-based multi-objective evolutionary ensemble framework for prediction of diabetes mellitus
Diabetes mellitus (DM) is a combination of metabolic disorders characterized by elevated
blood glucose levels over a prolonged duration. Undiagnosed DM can give rise to a host of …
blood glucose levels over a prolonged duration. Undiagnosed DM can give rise to a host of …
An automatic non-invasive method for Parkinson's disease classification
D Joshi, A Khajuria, P Joshi - Computer methods and programs in …, 2017 - Elsevier
Background and objective The automatic noninvasive identification of Parkinson's disease
(PD) is attractive to clinicians and neuroscientist. Various analysis and classification …
(PD) is attractive to clinicians and neuroscientist. Various analysis and classification …