Dementia affects the patient’s memory and leads to language impairment. Research has demonstrated that speech and language deterioration is often a clear indication of dementia and plays a crucial role in the recognition process. Even though earlier studies have used speech features to recognize subjects suffering from dementia, they are often used along with other linguistic features obtained from transcriptions. This study explores significant standalone speech features to recognize dementia. The primary contribution of this work is to identify a compact set of speech features that aid in the dementia recognition process. The secondary contribution is to leverage machine learning (ML) and deep learning (DL) models for the recognition task. Speech samples from the Pitt corpus in Dementia Bank are utilized for the present study. The critical speech feature set of prosodic, voice quality and cepstral features has been proposed for the task. The experimental results demonstrate the superiority of machine learning (87.6 percent) over deep learning (85 percent) models for recognizing Dementia using the compact speech feature combination, along with lower time and memory consumption. The results obtained using the proposed approach are promising compared with the existing works on dementia recognition using speech.

The growing popularity of microblogging websites has transformed these into rich resources for sentiment mining. Even though opinion mining has more than a decade of research to boost about, it is mostly confined to the exploration of formal text patterns like online reviews, news articles etc. Exploration of the challenges offered by informal and crisp microblogging have taken roots but there is scope for a large way ahead. The proposed work aims at developing a hybrid model for sentiment classification that explores the tweet specific features and uses domain independent and domain specific lexicons to offer a domain oriented approach and hence analyze and extract the consumer sentiment towards popular smart phone brands over the past few years. The experiments have proved that the results improve by around 2 points on an average over the unigram baseline.

Healthcare in simplest form is all about diagnosis and prevention of disease or treatment of any injury by a medical practitioner. It plays an important role in providing quality life for the society. The concern is how to provide better service with less expensive therapeutically equivalent alternatives. Machine Learning techniques (ML) help in achieving this goal. Healthcare has various categories of data like clinical data, claims data, drugs data and hospital data. This paper focuses on clinical and claims data for studying 11 chronic diseases such as kidney disease, osteoporosis, arthritis etc. using the claims data. The correlation between the chronic diseases and the corresponding diagnostic tests is analyzed, by using ML techniques. An effective conclusion on various diagnostics for each chronic disease is made, keeping in mind the clinical relevance.