Home healthcare agencies (HHCAs) provide clinical care and rehabilitation services to patients in their own homes. The organization’s rules regulate several connected practitioners, doctors, and licensed skilled nurses. Frequently, it monitors a physician or licensed nurse for the facilities and keeps track of the health histories of all clients. HHCAs’ quality of care is evaluated using Medicare’s star ratings for in-home healthcare agencies. The advent of technology has extensively evolved our living style. Online businesses’ ratings and reviews are the best representatives of organizations’ trust, services, quality, and ethics. Using data mining techniques to analyze HHCAs’ data can help to develop an effective framework for evaluating the finest home healthcare facilities. As a result, we developed an automated predictive framework for obtaining knowledge from patients’ feedback using a combination of statistical and machine learning techniques. HHCAs’ data contain twelve performance characteristics that we are the first to analyze and depict. After adequate pattern recognition, we applied binary and multi-class approaches on similar data with variations in the target class. Four prominent machine learning models were considered: SVM, Decision Tree, Random Forest, and Deep Neural Networks. In the binary class, the Deep Neural Network model presented promising performance with an accuracy of 97.37%. However, in the case of multiple class, the random forest model showed a significant outcome with an accuracy of 91.87%. Additionally, variable significance is derived from investigating each attribute’s importance in predictive model building. The implications of this study can support various stakeholders, including public agencies, quality measurement, healthcare inspectors, and HHCAs, to boost their performance. Thus, the proposed framework is not only useful for putting valuable insights into action, but it can also help with decision-making.
Aim of our work was to compare the performance of the tomato varieties under tunnel conditions and evaluate the yield and fruit quality for both fresh consumption, processing. The experiment was laid out according to completely randomized design. The research consisted of two year (2010)(2011)(2012) experiments. Both were conducted under tunnel conditions. Data was taken after 30, 60 and 90 days of planting. During the first year experiment V1 gives the maximum plant height after 30 (33.525 cm), 60 (70.20 cm) and 90 (96.537 cm) days of planting while during second year V2 gives the maximum plant height after 30 (50.850 cm), 60 (89.102 cm), and 90 (113.01 cm) day of planting. V4 gives the maximum number of leaves after 30 (66.337), 60 (96.755) and 90 (126.33) after planting during first year while V4 gives the maximum number of leaves after 30 (86.420), 60 (106.95) and 90 (135.09). During first V3 takes maximum time in first flower (39.50 days) and first fruit (78.98 days) initiation wile during second year V5 was taken maximum time (23.08 days, 44.08) in flower and fruit initiation. During first year V4 (11.57) while during second year V4 (12.75) gives maximum flowers per branch. V1gives maximum fruits per bunch and fruit diameter, while mortality was recorded maximum in V2 during both years. During first year maximum fresh fruit weight, dry fruit weight, total fresh plant weight, total dry plant weight was recodes in V1 while during second year V2 gives maximum fresh fruit weight and total fresh plant weight. Yield per plant was maximum in V1 during first year while during second year V2gives maximum yield per plant.
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