Spark Configurations to Optimize Decision Tree Classification on UNSW-NB15

This paper looks at the impact of changing Spark’s configuration parameters on machine learning algorithms using a large dataset—the UNSW-NB15 dataset. The environmental conditions that will optimize the classification process are studied. To build smart intrusion detection systems, a deep understanding of the environmental parameters is necessary. Specifically, the focus is on the following environmental parameters: the executor memory, number of executors, number of cores per executor, execution time, as well as the impact on statistical measures. Hence, the objective was to optimize resource usage and minimize processing time for Decision Tree classification, using Spark. This shows whether additional resources will increase performance, lower processing time, and optimize computing resources. The UNSW-NB15 dataset, being a large dataset, provides enough data and complexity to see the changes in computing resource configurations in Spark. Principal Component Analysis was used for preprocessing the dataset. Results indicated that a lack of executors and cores result in wasted resources and long processing time. Excessive resource allocation did not improve processing time. Environmental tuning has a noticeable impact

DOI: 10.1080/10549811.2011.577400 Predicting Forest Regeneration in the Central Appalachians Using the REGEN Expert System

REGEN is an expert system designed by David Loftis to predict the future species composition of dominant and codominant stems in forest stands at the onset of stem exclusion following a proposed harvest. REGEN predictions are generated using competitive rankings for advance reproduction along with other existing stand conditions. These parameters are contained within modular REGEN knowledge bases (RKBs). To extend REGEN coverage into hardwood stands of the Central Appalachians, RKBs were developed for four site classes (xeric, subxeric, submesic, mesic) based on literature and expert opinion. Data were collected from 48 paired stands in Virginia and West Virginia to calibrate the initial RKBs. Paired stands consisted of one mature uncut hardwood stand adjacent to a regenerating clear-cut stand with similar Joint funding for this project was provided by the United States Forest Service an

Neuropsychological performance of a sample of adults with ADHD, developmental reading disorder, and controls

In this study, we investigated the performance of adults with Attention Deficit Hyperactivity Disorder (ADHD), relative to adults with Developmental Reading Disorder (DRD), and controls on a battery of executive function tasks (Wisconsin Card Sorting Test [WCST], Test of Variables of Attention, Tower of Hanoi, and Ravens Progressive Matrices) and several self‐report ADHD rating scales (Wender Utah Rating Scale, Patient Behavior Checklist, and the Adult Rating Scale). Sixty‐four participants took part in the study (21 with ADHD, 19 with DRD, and 24 controls). Kruskall‐Wallis one‐way analysis of variance results revealed a significant difference between groups, with the DRD group committing more WCST errors (total and perseveration) than the remaining groups. Group differences were also found on the ADHD ratings scales, with the ADHD group reporting higher ratings. Discriminant Function Analyses (using the rating scales and the neuropsychological tasks) correctly classified 67% and 44% of the cases, respectively. The psychometric properties of the ADHD rating scales were also explored