Shweta Jain
Biographic Data
| ID | 3973405 |
|---|---|
| NAME | Shweta Jain |
| GIVEN NAMES | Shweta |
| FAMILY NAME | Jain |
| SIGNATURE | JAIN S |
| AFFILIATIONS | Indian Institute of Technology Ropar |
| ORCID | 0000-0002-2666-9058 |
| VERIFIED | Yes |
| TOTAL WORKS | 4 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 4 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2022 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
From bias to business insight
Machine Learning (ML) can reveal the complex nonlinear relationships between applicant attributes, offering new opportunities to strengthen underwriting risk classification in insurance. However, concerns about algorithmic opacity and subgroup bias have hindered the use of ML in underwriting. Existing studies have mostly concentrated on claims prediction and pricing, with limited attention to classification, interpretability, and fairness. We eva…
Integrating vision and language
Cross-lingual learning provides an excellent chance for knowledge transfer across multiple languages. However, the substantial resource disparity between high- and low-resource languages creates considerable issues. This study focuses on two Indic language families, Indo-Aryan, and Dravidian, as well as a definitely endangered low-resource language that often lacks the extensive training data available in high-resource languages, such as English.…
Aging and Health Challenges of Tribal Population
The aim of the present study was to examine the aging and associated health challenges among the Car Nicobarese. The study was conducted among 165 Car Nicobarese (20–80 years old), a tribal population of Nicobar Island of India. A comparative assessment of this tribal population with other island/coastal regions tribal population groups in India and in global context has been highlighted. Anthropometric variables, physiological functions, and adi…
Multi-disease prediction with machine learning
In the present era, Machine learning (ML) algorithms are extensively used in computer assisted diagnosis of the disease based on the symptoms of the disease. The widespread use of healthcare applications in the pandemic time, provides a motivation to further develop new computer assisted diagnostic application in the healthcare domain. Prevention and treatment of disease, accurate and timely diagnosis of any health-related problem is essential. I…
No prominent works on this page.
Multi-disease prediction with machine learning
In the present era, Machine learning (ML) algorithms are extensively used in computer assisted diagnosis of the disease based on the symptoms of the disease. The widespread use of healthcare applications in the pandemic time, provides a motivation to further develop new computer assisted diagnostic application in the healthcare domain. Prevention and treatment of disease, accurate and timely diagnosis of any health-related problem is essential. I…
Aging and Health Challenges of Tribal Population
The aim of the present study was to examine the aging and associated health challenges among the Car Nicobarese. The study was conducted among 165 Car Nicobarese (20–80 years old), a tribal population of Nicobar Island of India. A comparative assessment of this tribal population with other island/coastal regions tribal population groups in India and in global context has been highlighted. Anthropometric variables, physiological functions, and adi…
Integrating vision and language
Cross-lingual learning provides an excellent chance for knowledge transfer across multiple languages. However, the substantial resource disparity between high- and low-resource languages creates considerable issues. This study focuses on two Indic language families, Indo-Aryan, and Dravidian, as well as a definitely endangered low-resource language that often lacks the extensive training data available in high-resource languages, such as English.…
From bias to business insight
Machine Learning (ML) can reveal the complex nonlinear relationships between applicant attributes, offering new opportunities to strengthen underwriting risk classification in insurance. However, concerns about algorithmic opacity and subgroup bias have hindered the use of ML in underwriting. Existing studies have mostly concentrated on claims prediction and pricing, with limited attention to classification, interpretability, and fairness. We eva…
Medicine (2 works) · Anthropometry (1 work) · Artificial Intelligence (1 work) · Artificial Intelligence in Healthcare (1 work) · Artificial Intelligence in Healthcare and Education (1 work) · Bayes' theorem (1 work) · Bayesian probability (1 work) · Body mass index (1 work) · Categorization, perception, and language (1 work) · Child Nutrition and Water Access (1 work)