Using K-Means Algorithm for Description Analysis of Text in RSS News Format

This article shows the use of different techniques for the extraction of information through text mining. Through this implementation, the performance of each of the techniques in the dataset analysis process can be identified, which allows the reader to recommend the most appropriate technique for the processing of this type of data. This article shows the implementation of the K-means algorithm to determine the location of the news described in RSS format and the results of this type of grouping through a descriptive analysis of the resulting clusters

Investigation of software maintainability prediction models

Software must be well developed and maintainable to adapt to the constantly changing requirement of the competitive world. In this article, we distinct different software maintainability prediction models and techniques which can help us to predict the maintainability of software, and can lead us to minimum the effort required to fix the faults in the software and the software will be more maintainable. We have gathered our data from different studies focused on the accuracy of the prediction models as criteria. The results of our study showed that there is a little evidence on the accuracy results of the software maintainability prediction models

The application of artificial intelligence (AI) techniques to identify frailty within a residential aged care administrative data set

Introduction: Research has shown that frailty, a geriatric syndrome associated with an increased risk of negative outcomes for older people, is highly prevalent among residents of residential aged care facilities (also called long term care facilities or nursing homes). However, progress on effective identification of frailty within residential care remains at an early stage, necessitating the development of new methods for accurate and efficient screening. Objectives: We aimed to determine the effectiveness of artificial intelligence (AI) algorithms in accurately identifying frailty among residents aged 75 years and over in comparison with a calculated electronic Frailty Index (eFI) based on a routinely-collected residential aged care administrative data set drawn from 10 residential care facilities located in Queensland, Australia. A secondary objective included the identification of best-performing candidate algorithms. Methods: We designed a frailty prediction system based on the eFI identification of frailty, allocating 84.5% and 15.5% of the data to training and test data sets respectively. We compared the performance of 18 specific scenarios to predict frailty against eFI based on unique combinations of three ML algorithms (support vector machines [SVM], decision trees [DT] and K-nearest neighbours [KNN]) and six cases (6, 10, 11, 14, 39 and 70 input variables). We calculated accuracy, percentage positive and negative agreement, sensitivity, specificity, Cohen’s kappa and Prevalence- and Bias- Adjusted Kappa (PABAK), table frequencies and positive and negative predictive values. Results: Of 592 eligible resident records, 500 were allocated to the training set and 92 to the test set. Three scenarios (10, 11 and 70 input variables), all based on SVM algorithm, returned overall accuracy above 75 %. Conclusions: There is some potential for AI techniques to contribute towards better frailty identification within residential care. However, potential benefits will need to be weighed against administrative burden, data quality concerns and presence of potential bias.R.C. Ambagtsheer, N. Shafiabady, E. Dent, C. Seiboth, J. Beilb