Validation of the Language ENvironment Analysis (LENA) Automated Speech Processing Algorithm Labels for Adult and Child Segments in a Sample of Families From India.

Purpose: The Language ENvironment Analysis (LENA) technology uses automated speech processing (ASP) algorithms to estimate counts such as total adult words and child vocalizations, which helps understand children's early language environment. This ASP has been validated in North American English and other languages in predominantly monolingual contexts but not in a multilingual context like India. Thus, the current study aims to validate the classification accuracy of the LENA algorithm specifically focusing on speaker recognition of adult segments (AdS) and child segments (ChS) in a sample of bi/multilingual families from India.

Integrating deep learning techniques for effective river water quality monitoring and management.

Effective river water quality monitoring is essential for sustainable water resource management. In this study, we established a comprehensive monitoring system along the Kaveri River, capturing real-time data on multiple critical water quality parameters. The parameters collected encompassed water contamination levels, turbidity, pH measurements, temperature, and total dissolved solids (TDS), providing a holistic view of river water quality.

Feasibility and acceptability of a caregiver-mediated early support program, delivered online, for infants at elevated familial likelihood for autism: A feasibility randomized controlled trial.

Preliminary evidence indicates potential benefit of providing caregiver-mediated intervention, prior to diagnosis, for infants at elevated familial likelihood for autism and related developmental delays including language delay (EL-A). However, delivering such interventions online and in low-resource settings like India has not been reported. This study aimed to evaluate the feasibility and acceptability of delivering a novel manualized caregiver-mediated early support program, the "LiL' STEPS," online in India, for EL-A infants.

Factors associated with delay in diagnosis of oral cancers.

Background: Oral cancer is one of the ten most common malignancies in the world and approximately 90 % of cases are OSCC. Despite the progress in available treatment modalities, the mortality of patients with OSCC has remained steadily high during the last 20 years. Survival data is strongly influenced by the timing of diagnosis: with more than 50 % of patients being diagnosed at an advanced stage, and their 5-year survival rate being less than 50 %.

GIT-Net: An Ensemble Deep Learning-Based GI Tract Classification of Endoscopic Images.

This paper presents an ensemble of pre-trained models for the accurate classification of endoscopic images associated with Gastrointestinal (GI) diseases and illnesses. In this paper, we propose a weighted average ensemble model called GIT-NET to classify GI-tract diseases. We evaluated the model on a KVASIR v2 dataset with eight classes.