No abstract

In recent years, the electroencephalography (EEG) signal identification of epileptic seizures has developed into a routine procedure to determine epilepsy. Since physically identifying epileptic seizures by expert neurologists becomes a labor-intensive, time-consuming procedure that also produces several errors. Thus, efficient, and computerized detection of epileptic seizures is required. The disordered brain function that causes epileptic seizures can have an impact on a patient's condition. Epileptic seizures can be prevented by medicine with great success if they are predicted before they start. Electroencephalogram (EEG) signals are utilized to predict epileptic seizures by using machine learning algorithms and complex computational methodologies. Furthermore, two significant challenges that affect both expectancy time and genuine positive forecast rate are feature extraction from EEG signals and noise removal from EEG signals. As a result, we suggest a model that offers trustworthy preprocessing and feature extraction techniques. To automatically identify epileptic seizures, a variety of ensemble learning-based classifiers were utilized to extract frequency-based features from the EEG signal. Our algorithm offers a higher true positive rate and diagnoses epileptic episodes with enough foresight before they begin. On the scalp EEG CHB-MIT dataset on 24 subjects, this suggested framework detects the beginning of the preictal state, the state that occurs before a few minutes of the onset of the detention, resulting in an elevated true positive rate of (91%) than conventional methods and an optimum estimation time of 33 minutes and an average time of prediction is 23 minutes and 36 seconds. Depending on the experimental findings' The maximum accuracy, sensitivity, and specificity rates in this research were 91 %, 98%, and 84%.

Research for automatic recognition and identification of paper currency (banknote) has gained popularity in recent years due to its potential applications, e.g., electronic banking, currency monitoring systems, money exchange machines, etc. Existing research work for identification of currency has some constraints that limit their accuracy. We are proposing a pattern recognition-based approach for the classification of Pakistani paper currency. The dataset used for our research work consists of 1750 banknotes, including light variated, torn, worn, dirty, and marked banknotes. The proposed approach was based on extraction of 371 textural features from entire image, as well as from 4 regions of interest. High dimensional feature set was then reduced to most discriminating features. Four classification models, i.e., K*, LogitBoost, PART, and Random Forest were used to evaluate the accuracy of our proposed approach. It was observed that using region of interest with reduced feature set resulted in better performance and lesser computational time as compared to existing approaches. The highest accuracy achieved was 100 % with Kstar classifier. The novelty of our research work lies in the fact that the proposed approach was capable of successfully classifying banknotes, even when the denomination was occluded or completely missing, as compared to existing approaches.