Polycystic Ovary Syndrome (PCOS) is a complex disorder predominantly defined by biochemical hyperandrogenism, oligomenorrhea, anovulation, and in some cases, the presence of ovarian microcysts. This endocrinopathy inhibits ovarian follicle development causing symptoms like obesity, acne, infertility, and hirsutism. Artificial Intelligence (AI) has revolutionized healthcare, contributing remarkably to science and engineering domains. Therefore, we have demonstrated an AI approach using heterogeneous Machine Learning (ML) and Deep Learning (DL) classifiers to predict PCOS among fertile patients. We used an Open-source dataset of 541 patients from Kerala, India. Among all the classifiers, the final multi-stack of ML models performed best with accuracy, precision, recall, and F1-score of 98%, 97%, 98%, and 98%. Explainable AI (XAI) techniques make model predictions understandable, interpretable, and trustworthy. Hence, we have utilized XAI techniques such as SHAP (SHapley Additive Values), LIME (Local Interpretable Model Explainer), ELI5, Qlattice, and feature importance with Random Forest for explaining tree-based classifiers. The motivation of this study is to accurately detect PCOS in patients while simultaneously proposing an automated screening architecture with explainable machine learning tools to assist medical professionals in decision-making.

Epilepsy (EP) is a persistent neurological condition of chronic brain disorder characterized by repeated seizures and causes psychological issues such as anxiety and depression. There is a need to detect the presence of epilepsy at an earlier stage with the help of technological intervention. Early detection of epilepsy can help medical practitioners treat patients effectively and in a better way. Electroencephalography (EEG) signals are more suitable for monitoring brain activity and detecting brain disorders. In this paper, we propose a deep learning based approach for the early detection of epilepsy via EEG Spectrogram images. The proposed approach is 3-fold. First, we propose an algorithm to generate spectrogram images from the EEG signals, and then, we adapt an efficient Convolutional Neural Network (CNN) model to classify the spectrogram images. Finally, we utilized SmoothGradCAM++ and saliency maps to interpret the decision-making process of the deep learning models. We examined the use of three different pretrained CNN architectures, namely, EfficientNetB0, MobileNetV2, and ResNet18. The methodology is tested on two publicly available datasets to validate the performance of the classifiers in terms of sensitivity, accuracy, specificity, precision, and F1-Score. We observed that the modified MobileNetV2 architecture achieved a state-of-the-art accuracy of 99.24%. The proposed approach can be instrumental in the early detection of epilepsy and other neurological disorders using EEG.

Sentiment Analysis (SA) has achieved significant growth in recent decades among the evolving applications, including social networks and decision-support systems. Recently, the proliferation of data volumes for Dravidian languages has made SA a significant task to retrieve useful information. The prevailing works are focused more on the monolingual analysis and lack a complete study of the recent trends. The research presents a survey on the evolving landscape of SA in Indian languages with a specific emphasis on Dravidian languages. An overview of SA, emphasizing the levels and the purpose of the analysis is presented. Moreover, the generic process is explained with the major advancements of pre-trained architectures applied in recent works. Along with the various classification methodologies, this study analyzes the strengths conforming to the various training databases and languages, mainly focused on sentiment classification. Furthermore, an exhaustive view of the existing challenges and the applications is provided, offering unique linguistic guidance in the field of SA.

Bharatanatyam is a classical dance form of India that upholds the rich culture of India. This dance is learned under the supervision of Guru, the teacher traditionally called in India. The scarcity of experts resulted in the decline of people practicing this dance. There is a need for leveraging technology in preserving and promoting this traditional dance and propagating it amongst the youth. In this research, it is attempted to develop a methodology for automated classification of Bharatanatyam dance postures. The methodology involves extraction of existing features such as speeded up robust features (SURF) and histogram of oriented gradients (HOG), which are used to train and test an artificial neural network (ANN). The results are corroborated with deep learning architectures such as AlexNet and GoogleNet. The proposed methodology has yielded a classification accuracy of 99.85% as compared with 93.10% and 94.25% of AlexNet and GoogleNet respectively. The proposed method finds applications such as assistance to Bharatanatyam dance teachers, e-learning of dance, and evaluating the correctness of the postures.