Cardiotocography (CTG), which measures the fetal heart rate (FHR) and maternal uterine contractions (UC) simultaneously, is used for monitoring fetal well-being during delivery or antenatally at the third trimester. Baseline FHR and its response to uterine contractions can be used to diagnose fetal distress, which may necessitate therapeutic intervention. In this study, a machine learning model based on feature extraction (autoencoder), feature selection (recursive feature elimination), and Bayesian optimization, was proposed to diagnose and classify the different conditions of fetuses (Normal, Suspect, Pathologic) along with the CTG morphological patterns. The model was evaluated on a publicly available CTG dataset. This research also addressed the imbalance nature of the CTG dataset. The proposed model has a potential application as a decision support tool to manage pregnancies. The proposed model resulted in good performance analysis metrics. Using this model with Random Forest resulted in a model accuracy of 96.62% for fetal status classification and 94.96% for CTG morphological pattern classification. In rational terms, the model was able to accurately predict 98% Suspect cases and 98.6% Pathologic cases in the dataset. The combination of predicting and classifying fetal status as well as the CTG morphological patterns shows potential in monitoring high-risk pregnancies.

This paper presents a software solution for the in-car voice control of the vehicles using an interactive educational application in MATLAB. The comparison of various digital filters for speech analysis gives the best possible solution for vehicles to opt for in-car voice features (car start, car stop, navigation on, etc). The comparative analysis of voice control is efficiently presented using adaptive, wiener, matched, lowpass, high pass and bandpass filters. This paper also deals with the fundamental concepts of digital signal processing in the form of students' learning from the educational tool. The basic concepts in the form of fast Fourier transform (FFT), power spectral density (PSD), joint time-frequency analysis (JTFA), comparison of voice samples with and without noise, and signal to noise ratio (SNR) of the voice samples before and after filtration could enable students to perform well in the signal processing modules.

In emergencies, X (formerly Twitter) has emerged as an essential and fast information-sharing tool. A mechanism based on TF-IDF (Term Frequency-Inverse Document Frequency), WordNet, an ensemble (stacking) learning model, and multilingual support is proposed to classify disaster-related tweets obtained from the X platform. Moreover, this proposed model is evaluated on eight popular classifiers for classifying textual data. An ensemble of MLP, MNB, CNB, and SVC yields a good overall model with 81.36% accuracy and an AUC of 0.80. The proposed model can predict whether a tweet is related to a disaster by using an unlabeled dataset of tweets. It can classify tweets in multiple languages due to its multilingual support. The model uses Turkish, a low-resource language, to classify disaster-related tweets. Current research shows the application of this model in English and Turkish. This research uses two publicly available datasets of disaster-related tweets. The proposed model achieved good performance metrics on both labeled and unlabeled datasets. The model can be utilized by disaster relief authorities to filter out actual disaster-related tweets automatically and efficiently from many other fake and "clickbait tweets."