A STUDY ON THE KNOWLEDGE PRACTICE GAP OF ASHAS ABOUT HER WORK PROFILE FROM NORTHERN INDIA

Objective: The objective of the study was to study the knowledge practice gap of Accredited Social Health Activist (ASHAs) about her work profile from northern India. Methods: The study was carried out between June 2021 and November 2021. The study comprised all 97 local ASHA employees, who were all questioned using a self-made semi-structured questionnaire. Results: Data were collected from 94 ASHA workers. 91 (96.8%) of ASHA workers completed 8th standard or more of schooling. 92 (97.87%) of ASHA workers completed training before working as ASHA. Almost all the study subjects had knowledge about immunization activities, accompanying delivery cases, and participation in family planning activities. Very few ASHAs knew that active participation in village health planning, providing counseling to the residents on various health issues and addressing adolescence health issues with residents of the village were part of her work profile. Drug kit stock register, format for individual birth preparedness plans, format for first examination of the new born, and home visit form for high risk baby were relatively deficient with respect to their maintenance and completeness. Conclusion: ASHAs do offer a variety of services and have the ability to contribute to the provision of primary healthcare, but they must still put their knowledge to use when offering services and/or advise to negotiate access to healthcare for underprivileged women and children

COMPUTERIZED SOFTWARE QUALITY EVALUATION WITH NOVEL ARTIFICIAL INTELLIGENCE APPROACH

Software quality assurance has grown in importance in the fast-paced world of software development. One of trickiest parts of creating and maintaining software is predicting how well it will perform. The term "computer evaluation" refers to use of advanced AI techniques in software quality assurance, replacing human evaluations and paving the way for a new era in software evaluation. We proposed Hybrid Elephant herding optimized Conditional Long short-term memory (HEHO-CLSTM) to estimate Software Quality Prediction. Software quality prediction and assurance has grown in importance in ever-changing world of software development. Software quality prediction encompasses a wide range of activities aimed at improving the quality of software systems via the use of data-driven approaches for prediction, evaluation and enhancement. We have collected Software Defects data and we feature extracted the attributes using linear discriminant Analysis (LDA). The suggested system improves the accuracy, AUC and Buggy instance compared with the current methods