Quantification of HIV-1 RNA is essential for clinical management of HIV patients. The limited throughput and significant hands-on time required by most HIV Viral load (VL) tests makes it challenging for laboratories with high test volume, to turn around patient results quickly. The Hologic Aptima HIV-1 Quant Dx Assay (Aptima), has the potential to alleviate this burden as it is high throughput and fully automated. This assay is validated for both plasma and dried blood spots (DBS), which are commonly used in resource limited settings. The objective of this study was to compare the performance of Aptima to Abbott RealTime HIV-1 Assay (Abbott RT), which was used as reference. This was a cross-sectional prospective study where HIV VL in finger stick (FS) DBS, venous blood (VB) DBS and plasma, collected from 258 consenting adults visiting 5 medical facilities in Kenya, Africa were tested in Aptima. The results were compared to plasma VL in Abbott RT at the medical decision point (MDP) of 1000 copies/mL and across Aptima assay range. The total agreement at MDP between plasma HIV VL in Abbott RT and plasma, FS and VB DBS tested in Aptima were 97.7%, 92.2% and 95.3% respectively with kappa statistic of 0.95, 0.84 and 0.90. The positive and negative agreement for all 3 sample types were >92%. Regression analysis between VL in Abbott RT plasma and various sample types tested in Aptima had a Pearson’s correlation coefficient ≥0.91 with systematic bias of < 0.20 log copies/mL on Bland-Altman analysis. The high level of agreement in Aptima HIV VL results for all 3 sample types with Abbott RT plasma VL along with the high throughput, complete automation, and ease of use of the Panther platform makes Aptima a good option for HIV VL monitoring for busy laboratories with high volume of testing.
Aim/Purpose: Although cassava is one of the crops that can be grown during the dry season in Northeastern Thailand, most farmers in the region do not know whether the crop can grow in their specific areas because the available agriculture planning guideline provides only a generic list of dry-season crops that can be grown in the whole region. The purpose of this research is to develop a predictive model that can be used to predict suitable areas for growing cassava in Northeastern Thailand during the dry season. Background: This paper develops a decision support system that can be used by farmers to assist them determine if cassava can be successfully grown in their specific areas. Methodology: This study uses satellite imagery and data on land characteristics to develop a machine learning model for predicting suitable areas for growing cassava in Thailand’s Nakhon-Phanom province. Contribution: This research contributes to the body of knowledge by developing a novel model for predicting suitable areas for growing cassava. Findings: This study identified elevation and Ferric Acrisols (Af) soil as the two most important features for predicting the best-suited areas for growing cassava in Nakhon-Phanom province, Thailand. The two-class boosted decision tree algorithm performs best when compared with other algorithms. The model achieved an accuracy of .886, and .746 F1-score. Recommendations for Practitioners: Farmers and agricultural extension agents will use the decision support system developed in this study to identify specific areas that are suitable for growing cassava in Nakhon-Phanom province, Thailand Recommendation for Researchers: To improve the predictive accuracy of the model developed in this study, more land and crop characteristics data should be incorporated during model development. The ground truth data for areas growing cassava should also be collected for a longer period to provide a more accurate sample of the areas that are suitable for cassava growing. Impact on Society: The use of machine learning for the development of new farming systems will enable farmers to produce more food throughout the year to feed the world’s growing population. Future Research: Further studies should be carried out to map other suitable areas for growing dry-season crops and to develop decision support systems for those crops.
With the increased dependence on the Internet, Network Intrusion Detection system (NIDs) becomes an indispensable part of information security system. NIDs aims at distinguishing the network traffic as either normal or abmormal. Due to the variety of network behaviors and the rapid development of attack strategies, it is necessary to build an intelligent and effective intrusion detection system with high detection rates and low false-alarm rates. One of the major developments in machine learning in the past decade is the ensemble method that generates a set of accurate and diverse classifiers that combine their outputs such that the resultant classifier outperforms all the single classifiers. In this work a comparative analysis on performance of three different ensemble methods, bagging, boosting and stacking is performed in order to determine the algorithm with high detection accuracy and low false positive rate. Three different experiments on NSL KDD data set are conducted and their performance evaluated based on accuracy, false alarms and computation time. The overall performance of the different types of classifiers used proved that ensemble machine learning classifiers outperformed the single classifiers with high detection accuracy and low false rates.
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