D-GENE-Based Discovery of Frequent Occupational Diseases among Female Home-Based Workers

Abstract

A considerable fraction of the female workforce worldwide is making ends meet by doing various jobs informally at home or in nearby places, rather than at employers’ premises. The contribution of these female home-based workers (FHBWs) is significant to the country’s economic growth. FHBWs are often confronted with numerous occupational diseases due to a lack of awareness of occupational safety and health measures, and unhealthy living and working conditions. The informality of FHBWs prevents them from getting proper healthcare, safety, and other dispensations enjoyed by formal employees. Despite their undeniable importance, health issues of FHBWs are still overlooked. This study is an attempt to discover the frequent co-occurring occupational diseases encountered by FHBWs in Punjab, a province of Pakistan. Frequent itemset mining (FIM) or co-occurrence grouping is a technique of data science that identifies the associations among different entities in the data. Based on FIM, the D-GENE algorithm is applied in this study to efficiently discover frequent co-occurring diseases in the data obtained from the Punjab Home-based Workers Survey (2016). The far-reaching goal of the study is to bring awareness of the occupational health issues and safety risks to the health authorities as well as to the FHBWs.

Hardware accelerators for processing clusters in binary vectors

“…Therefore, the most frequent item appears four times (the computed Hamming weight plus 1) in the original set and this is item 21 (the item whose index is 7 in the sorted set). Other practical applications are described in [16][17][18][19][20][21][22][23][24]. Since many systems have hard real-time constraints, performance is important and hardware accelerators may provide significant assistance.…”