Applied Science and Biotechnology Journal for Advanced Research

A Deep Learning-based Predictive Analytics Model for Remote Patient Monitoring and Early Intervention in Diabetes Care

2024-11-30T12:37:23+0530

This paper presents a deep learning-based predictive analytics model for remote diabetes monitoring and early intervention. The proposed method combines photoplethysmography (PPG) signals with population and clinical data by combining LSTM-CNN architecture, achieving the best glucose monitoring results in real time. Manage the inability to care. The system architecture includes a custom-designed wearable device for data acquisition, cloud-based infrastructure, and real-time intervention mechanisms. Validation tests, including 139 subjects (69 diabetics and 70 non-diabetic), showed a 91.2% prediction accuracy over the continuous product to check glucose. The application has achieved 99.7% uptime with a response time of 2.3 seconds, ensuring adequate monitoring time and quick response. The early warning system demonstrated 97.8% accuracy in detecting potential complications through innovative feature extraction methodologies and adaptive learning algorithms. Performance evaluation through Clarke Error Grid analysis indicated clinically acceptable predictions, with all readings falling within zones A and B. The system's cost-effectiveness and reduced invasiveness promote widespread adoption potential, particularly in resource-limited settings. Integrating existing medical systems enables data collection and analysis, facilitating personalized treatment strategies and improving patient outcomes. The research has advanced the level of diabetes management through new contributions to theoretical frameworks and practical applications in remote patient care.

Genetic Variability, Heritability and Genetic Advance of Dry Matter Yield and Yield Contributing Characters in Rhodes Grass (Chloris gayana) Genotypes

2024-10-25T10:51:44+0530

Information on the communal association of traits is important for effective selection in forage-breeding program. Twenty four genotypes of Rhodes grass and one check were evaluated at Mechara Agricultural Research site (Onstation) with lattice design in 2023/24 main rainy season to evaluate Genetic variability, heritability and genetic advance of dry matter yield and yield contributing characters in Rhodes grass genotypes. The mean sum of squares of genotypes showed significant differences (p < 0.05) for stand vigor, days to 50% emergence, date to 50% flowering and Plant height and highly significant (p < 0.001) for biomass yield, dry matter and number of leaf per plant. Maximum phenotypic variance and genotypic variance value was recorded for days to maturity. The range observed for heritability (H²bs) was from (0.0%) to (55%). Stand vigor exhibited highest value of genetic advance as percentage of mean followed by number of leaf per plant. Highest genotypic coefficient variation were recorded from days to maturity (89.8%) flowed by Plant height (62.3%) and Highest phenotypic coefficient variation were recorded from plot cover (184.9%) followed by days to maturity (225.4%). Phenotypically and genotypically dry matter yield was highly positive significant associated with of Plot cover (0.546**), stand vigor (0.566**), leaf per plant (0.439**) and showed highly negative significant with days to 50% emergence. The results of phenotypic path coefficient analysis showed that stand vigor (0.378) and leaf per plant had exerted moderate positive direct effect on dry matter. stand vigor followed by plant height, plot cover and leaf per plant had exerted high and positive direct effect on dry matter yield and genotypic path analysis showed stand vigor followed by plant height, plot cover and leaf per plant had exerted high and positive direct effect on dry matter yield. This indicates that selection based on these traits could be more effective to maximize dry yield.

Assessing Agricultural Health through FinTech Data - An Analytical Approach

2024-12-13T10:53:43+0530

The agricultural sector plays a crucial role in the economic development of many countries, particularly those with large rural populations. However, traditional methods of assessing the health of the agricultural sector can be limited in scope and timeliness. The rapid advancements in financial technology have transformed the way financial services are delivered, particularly in rural areas. FinTech solutions, such as digital payments, lending, and insurance, can provide valuable insights into the financial activities and challenges faced by farmers and agricultural enterprises.This paper explores the transformative potential of FinTech in the agricultural sector, examining its impact on financial inclusion, resilience, and efficiency. By integrating quantitative FinTech data with qualitative insights from stakeholders, the study provides a comprehensive assessment of the sector's health. Findings indicate that FinTech complements traditional agricultural data, offering a dynamic view of financial activities and performance. Increased FinTech adoption in rural areas can drive financial inclusion, improve credit access, and foster innovation. However, challenges such as digital literacy, infrastructure gaps, and regulatory frameworks need to be addressed. The study emphasizes the importance of investments in digital infrastructure, capacity building, and collaboration between FinTech and agricultural sectors.These insights have implications for policymakers, financial institutions, and agricultural stakeholders, enabling data-driven decision-making, targeted interventions, and the promotion of sustainable agricultural development.