Machine Learning is seeing its growth more rapidly in this decade. Many applications and algorithms evolve in Machine Learning day to day. One such application found in journals is house price prediction. House prices are increasing every year which has necessitated the modeling of house price prediction. These models constructed, help the customers to purchase a house suitable for their need. Proposed work makes use of the attributes or features of the houses such as number of bedrooms available in the house, age of the house, travelling facility from the location, school facility available nearby the houses and Shopping malls available nearby the house location. House availability based on desired features of the house and house price prediction are modeled in the proposed work and the model is constructed for a small town in West Godavari district of Andhrapradesh. The work involves decision tree classification, decision tree regression and multiple linear regression and is implemented using Scikit-Learn Machine Learning Tool.
Aim of the Study:To assess the level of knowledge and attitude on insulin therapy in patients with diabetes mellitus in a teaching hospital.Objectives:1. To assess the level of knowledge and attitude on insulin therapy among patients with diabetes mellitus. 2. To find out the association between the knowledge and attitude on insulin therapy with the selected study variables.Materials and Methods:This is a nonexperimental, cross-sectional study. A total of 100 participants were recruited from the outpatients and inpatients attending the department of general medicine and general surgery. Adult male and female with diabetes mellitus receiving insulin injection and willing to participate in the study were included in the study. The data was collected using 20 structured questionnaires to assess the level of knowledge and modified 4 point Likert scale was used, which contains 10 statements to assess the level of attitude on insulin therapy by interview method.Results:It was observed from the results that the level of knowledge among the study participants was as follows: 4% of them had adequate knowledge, 44% had moderately adequate knowledge, and more than half (52%) of them had inadequate knowledge. The level of attitude among the study participants (27%) with diabetes mellitus had favorable attitude, more than half (69%) had moderately favorable attitude, and 4% had unfavorable attitude on insulin therapy. It is observed that there is no statistically significant correlation (r) =0.038, P = 0.707 between the level of knowledge and attitude on insulin therapy among patients with diabetes mellitus.Conclusion:This study showed that there was inadequate knowledge and unfavorable attitude among the diabetic patients regarding insulin therapy. The people with diabetes should receive ongoing need-based quality diabetic education by using innovative methods that is tailored to their needs, delivered by skilled healthcare providers.
Systolic tree structure is a reconfigurable architecture in Field-programmable gate arrays (FPGA) which provide performance advantages. It is used for frequent pattern mining operations. High throughput and cost effective performance are the highlights of the systolic tree based reconfigurable architecture. Frequent pattern mining algorithms are used to find frequently occurring item sets in databases. However, space and computational time requirements are very high in frequent pattern mining algorithms. In the proposed system, systolic tree based hardware mechanism is employed with Weighted Association Rule Mining (WARM) for frequent item set extraction process of the Web access logs. Weighted rule mining is to mine the items which are assigned with weights based on user’s interest and the importance of the items. In the proposed system, weights are assigned automatically to Web pages that are visited by the users. Hence, systolic tree based rule mining scheme is enhanced for WARM process, which fetches the frequently accessed Web pages with weight values. The dynamic Web page weight assignment scheme uses the page request count and span time values. The proposed system improves the weight estimation process with span time, request count and access sequence details. The user interest based page weight is used to extract the frequent item sets. The proposed system will also improve the mining efficiency on sparse patterns. The goal is to drive the mining focus to those significant relationships involving items with significant weights.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.