RESUMO
To compare fracture risk assessment (FRAX) calculation with and without bone mineral density (BMD) in predicting 10-year probability of hip and major osteoporotic fracture in patients of rheumatic diseases. Methodology: A cross-sectional was conducted at outpatient Department of Rheumatology. Eighty-one Patients of more than 40 years of age having either sex. Diagnosed case of Rheumatic diseases were according to American College of Rheumatology (ACR) /European Alliance of Associations for Rheumatology (EULAR) criteria were included in our study. FRAX score without BMD was calculated and information was recorded in proforma. These patients were advised dual energy X-ray absorptiometry Scan and after that FRAX with BMD was calculated, after which comparison between result of two scores was made. The data were analyzed by SPSS software version 24. Effect modifiers were controlled by stratification. Post-stratification χ2 test were applied. P value less than 0.05 was considered as significant. Results: This study consisted of 63 participants, who were assessed for osteoporotic risk fracture, with and without BMD. Data analysis revealed a significant association between the type of fracture and age (p value=0.009), previous fracture (p value=0.25), parent fractured hip (p values) and treatment with bone mineral dismissal. There was no statistically significant association seen of fractures with bone deterioration with sex, weight, height, or current smoking. Conclusion: FRAX may be crucial in rural areas where dual energy X-ray absorptiometry scanning is not available since it is a readily available instrument. FRAX is a useful substitute for estimating osteoporosis risk when funds are scarce. Given the possible effect it will have on healthcare costs, this is extremely pertinent.
RESUMO
Effective software cost estimation significantly contributes to decision-making. The rising trend of using nature-inspired meta-heuristic algorithms has been seen in software cost estimation problems. The constructive cost model (COCOMO) method is a well-known regression-based algorithmic technique for estimating software costs. The limitation of the COCOMO models is that the values of these coefficients are constant for similar kinds of projects whereas, in reality, these parameters vary from one organization to another organization. Therefore, for accurate estimation, it is necessary to fine-tune the coefficients. The research community is now examining deep learning (DL) as a forward-looking solution to improve cost estimation. Although deep learning architectures provide some improvements over existing flat technologies, they also have some shortcomings, such as large training delays, over-fitting, and under-fitting. Deep learning models usually require fine-tuning to a large number of parameters. The meta-heuristic algorithm supports finding a good optimal solution at a reasonable computational cost. Additionally, heuristic approaches allow for the location of an optimum solution. So, it can be used with deep neural networks to minimize training delays. The hybrid of ant colony optimization with BAT (HACO-BA) algorithm is a hybrid optimization technique that combines the most common global optimum search technique for ant colonies (ACO) in association with one of the newest search techniques called the BAT algorithm (BA). This technology supports the solution of multivariable problems and has been applied to the optimization of a large number of engineering problems. This work will perform a two-fold assessment of algorithms: (i) comparing the efficacy of ACO, BA, and HACO-BA in optimizing COCOMO II coefficients; and (ii) using HACO-BA algorithms to optimize and improve the deep learning training process. The experimental results show that the hybrid HACO-BA performs better as compared to ACO and BA for tuning COCOMO II. HACO-BA also performs better in the optimization of DNN in terms of execution time and accuracy. The process is executed upto 100 epochs, and the accuracy achieved by the proposed DNN approach is almost 98% while NN achieved accuracy of up to 85% on the same datasets.