In machine learning, an efficient classifier model design is mostly based on effective feature extraction and appropriate feature selection. This work mainly focused on different optimized feature selection algorithms for automatic biometric recognition system with the use of electrocardiogram (ECG) signals. Initially, the features are extracted from P-QRS-T segments of the ECG signal with position normalization. The extracted features are processed through different optimization algorithms for quality assessment prior to the classification stage. In this work, two different methods are proposed based on wrapper feature selection and embedded feature selection to get optimized feature datasets for the perfect identification of human biometrics. In the first proposed method, wrapper-oriented feature selection is implemented with genetic algorithm (GA) and particle swarm optimization. In the second proposed method, the embedded method is included as the least absolute shrinkage selection operator and elastic net (EN). The processed optimized feature vectors from the optimization phase are fed to popular machine learning techniques, such as support vector machine and random forest (RF) classifiers, for automatic biometric human recognition. Classifier performances are investigated using two publicly available open-source databases, Electrocardiogram identification and large Physikalisch-Technische Bundesanstalt diagnostic database with a dynamic number of subjects. The experimental results indicate that the proposed combination of feature selection with machine learning algorithms has enhanced the classification accuracy. Finally, GA and EN with RF classifier provide improved recognition rates of 95.30% and 94.90%, respectively.

Segmentation is one of the important steps for image analysis. Multilevel thresholding image segmentation was more popular in image segmentation. Otsu and Kapur based methods are most popular for multilevel threshold image segmentation. Many authors implemented evolutionary algorithms for the optimal multilevel threshold selection based on the above methods. In this paper, an efficient approach for multilevel image segmentation has been proposed and implemented based on novel evolutionary algorithm Improved Differential Search (IDS). The feasibility of proposed approach/algorithm has been tested on standard gray scale images. To check the effectiveness of the proposed approach/algorithm, all experimental results are analyzed quantitatively and qualitatively.

An Electrocardiogram (ECG) graphically records changes in electrical potentials between different sites on the skin due to cardiac activity. The heart’s electrical activity is a depolarization and depolarization sequence. ECGs help in identifying cardiac arrhythmia because they have diagnostic information. ECG arrhythmia detection accuracy improves by using machine learning and data mining methods. This study proposes multi-layer perceptron neural network optimization using Genetic Algorithm (GA) to classify ECG arrhythmia. Symlet extracts RR intervals from ECG data as features while symmetric uncertainty assures feature reduction. GA optimizes learning rate and momentum.