Congenital heart disease (CHD) is a heart disorder associated with the devastating indications that result in increased mortality, increased morbidity, increased healthcare expenditure, and decreased quality of life. Ventricular Septal Defects (VSDs) and Arterial Septal Defects (ASDs) are the most common types of CHD. CHDs can be controlled before reaching a serious phase with an early diagnosis. The phonocardiogram (PCG) or heart sound auscultation is a simple and non-invasive technique that may reveal obvious variations of different CHDs. Diagnosis based on heart sounds is difficult and requires a high level of medical training and skills due to human hearing limitations and the non-stationary nature of PCGs. An automated computer-aided system may boost the diagnostic objectivity and consistency of PCG signals in the detection of CHDs. The objective of this research was to assess the effects of various pattern recognition modalities for the design of an automated system that effectively differentiates normal, ASD, and VSD categories using short term PCG time series. The proposed model in this study adopts three-stage processing: pre-processing, feature extraction, and classification. Empirical mode decomposition (EMD) was used to denoise the raw PCG signals acquired from subjects. One-dimensional local ternary patterns (1D-LTPs) and Mel-frequency cepstral coefficients (MFCCs) were extracted from the denoised PCG signal for precise representation of data from different classes. In the final stage, the fused feature vector of 1D-LTPs and MFCCs was fed to the support vector machine (SVM) classifier using 10-fold cross-validation. The PCG signals were acquired from the subjects admitted to local hospitals and classified by applying various experiments. The proposed methodology achieves a mean accuracy of 95.24% in classifying ASD, VSD, and normal subjects. The proposed model can be put into practice and serve as a second opinion for cardiologists by providing more objective and faster interpretations of PCG signals.

Accurate and early detection of machine faults is an important step in the preventive maintenance of industrial enterprises. It is essential to avoid unexpected downtime as well as to ensure the reliability of equipment and safety of humans. In the case of rotating machines, significant information about machine’s health and condition is present in the spectrum of its vibration signal. This work proposes a fault detection system of rotating machines using vibration signal analysis. First, a dataset of 3-dimensional vibration signals is acquired from large induction motors representing healthy and faulty states. The signal conditioning is performed using empirical mode decomposition technique. Next, multi-domain feature extraction is done to obtain various combinations of most discriminant temporal and spectral features from the denoised signals. Finally, the classification step is performed with various kernel settings of multiple classifiers including support vector machines, K-nearest neighbors, decision tree and linear discriminant analysis. The classification results demonstrate that a hybrid combination of time and spectral features, classified using support vector machines with Gaussian kernel achieves the best performance with 98.2% accuracy, 96.6% sensitivity, 100% specificity and 1.8% error rate.

Electrocardiogram (ECG) is an electric signal of cardiac activity posing highly discriminative properties related to human recognition. ECG based authentication has gained much success in recent times however discriminant feature extraction and efficient pattern classification still encounter numerous challenges. This paper proposed a novel methodology for ECG based biometric authentication system. Proposed method first denoise single lead raw ECG signal through empirical mode decomposition (EMD). Region of interest from ECG signals having maximum characteristic information related to subject’s recognition is also extracted through EMD. Next, feature extraction is performed by combination of five features from statistical, time and frequency domains. Finally, selected features were categorized with range of different classification methods such as Support Vector Machines (SVM), K-nearest neighbor (KNN) and Decision Tree (DT). 10-fold cross validation based classification evaluation reveals that SVM with cubic kernel achieves best accuracy of 98.7%, sensitivity of 100% and 98.8% specificity for successful classification of 14 subjects.

Electricity theft is a criminal practice of stealing electricity. In the country like Pakistan where the consumption is more than the production, the electricity theft can be hazardous for the economy. During the year 2017–18, there was a loss of 53 billion Rs. to the economy due to electricity theft. A novel system for the detection of electricity thefts is designed. The dataset provided by State Grid Corporation of China (SGCC) was used which contained two classes i.e. normal and theft. The dataset comprised of data collected for 1,035 days. The dataset included various missing and erroneous values. Preprocessing techniques such as interpolation was used to get the missing values and for the breakdown of signal, empirical mode decomposition was employed. After that the features were extracted from the signals of both classes. After a number of experiments combinations of features were found that gave maximum accuracy. K-nearest neighbors (KNN) classifier was used because of its advantage that it is very fast and simple. System was able to detect the electricity theft with accuracy of 91.0%. The system is very reliable and can be helpful in reducing the losses due to electricity theft. It is a very easy to use system as well as cost efficient.