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PREDICTING ABSENTEEISM OF FEMALE STUDENTS IN ALABAMA
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Students are chronically absent when they miss at least 15 days of the school year. Past researchers have identified income and environment as factors that affect school absenteeism. Alabama is a poor state with a high crime rate. The hypothesis for this research is that the absenteeism of female students in Alabama is high. Do we reject or fail to reject this hypothesis. If we fail to reject this hypothesis, then what other factors can affect absenteeism in schools? How can we best predict the absenteeism of female students in Alabama? What is the effect of bad data on predictive models? This research aims to answer the above questions.
Machine learning has proven to be one of the best methods in making good predictions for better decision-making. Different machine learning models are used for making predictions, but the outstanding question is how to identify the best model to predict dependent features. Are features very essential when considering the type of model to be used for prediction?
This research aims to analyze and compare the percentage of prediction and accuracy of prediction using supervised machine learning models while considering features. Based on findings, the recommendation for the best model to predict female students\u27 absenteeism in Alabama school districts was made. Also, the hypothesis that the absenteeism of female students in Alabama is high was rejected. This research is limited to only supervised machine learning models. Information on male students in Alabama is not included in this research
Keywords: Absenteeism, Machine Learning, Predictive Model, Naïve, Random Forest, Borut
Machine Learning in High-performance Networking
Get PDFApply machine learning methods to improve high-performance networkin
A Comprehensive Comparative Study of Big Data Transfer Methods
Get PDFHPN technologies, including the recent proposals on big data, transfer in HPNs to improve data transfer throughput performanc
Development of HU Cloud-based Spark Applications for Streaming Data Analytics
Get PDFNowadays, streaming data overflows from various sources and technologies such as Internet of Things (IoT), making conventional data analytics methods unsuitable to manage the latency of data processing relative to the growing demand for high processing speed and algorithmically scalability [1]. Real-time streaming data analytics, which processes data while it is in motion, is required to allow many organizations to analyze streaming data effectively and efficiently for being more active in their strategies. To analyze real time “Big” streaming data, parallel and distributed computing over a cloud of computers has become a mainstream solution to allow scalability, resiliency to failure, and fast processing of massive data sets. Several open source data analytics frameworks have been proposed and developed for streaming data analytics successfully. Apache Spark is one such framework being developed at the University of California, Berkley and gains lots of attentions due to reducing IO by storing data in a memory and a unique data executing model. In Computer & Information Sciences (CISC) at Harrisburg University (HU), we have been working on building a private Cloud Computing for future research and planning to involve industry collaboration where high volumes of real time streaming data are used to develop solutions to practical problems in industry. By developing a HU Cloud based environment for Apache Spark applications for streaming data analytics with batch processing on Hadoop Distributed File System (HDFS), we can prepare future big data era that can turn big data into beneficial actions for industry needs. This research aims to develop Spark applications supporting an entire streaming data analytics workflow, which consists of data ingestion, data analytics, data visualization and data storing. In particular, we will focus on a real time stock recommender system based on state-of-the-art Machine Learning (ML)/Deep Learning (DL) frameworks such as mllib, TensorFlow, Apache mxnet and pytorch. The plan is to gather real time stock market data from Google/Yahoo finance data streams to build a model to predict a future stock market trend. The proposed Spark applications on the HU cloud-based architecture will give emphasis to finding time-series forcating module for a specific period, typically based on selected attributes. In addition, we will test scale-out architecture, efficient parallel processing and fault tolerance of Spark applications on the HU Cloud based HDFS. We believe that this research will bring the CISC program at HU significant competitive advantages globally
Android Application for MNIST Handwritten Digits Classification
Get PDFUse Neural Network architecture to classify MNIST handwritten digits dataset, student/s should implement a phone application (Android) to demonstrate their work, application will then be published to the app store for other students and CISC faculty members for evaluation and feedback
The effect of switching costs on choice-inertia and its consequences
No full textIn two studies we provide a novel investigation into the effects of monetary switching costs on choice-inertia (i.e., selection of the same option on consecutive choices). Study 1 employed a static decisions-from-feedback task and found that the introduction of, as well as larger, monetary switching costs led to increases in choice-inertia. While experience and decreases in the similarity of options average payouts (expected value: EV) increased choice-inertia for the option with a higher EV (the EV maximizing option), switching costs increased choice-inertia for the inferior option (the lower EV option): The proportion of total participants showing choice-inertia for the EV maximizing option also increased with switching costs. Study 2 employed a dynamic decisions-from-feedback task where halfway through the task the EV maximizing option became the inferior option. The effect of switching costs increasing choice-inertia for both the EV maximizing and the inferior option was replicated with little impact of the change in options values being detected. In sum, decision makers appear to be sensitive to switching costs, and this sensitivity can bias them towards inferior or superior options, revealing the good and the bad of choice-inertia
Research the geometry of the brain (Part 1)
Get PDFWe want to explore how the brain creates meaning, understanding and allocates information into memory. From groups we distinguishes patterns and generalizes patterns into concepts. Does the brain use triangulation? Is there a trivariant system analogous to covariants? Why does pattern recognition require three elements? What is memory? What is the geometry of the brain
Performance Optimization of Big Data Transfer in High-performance Networks: A ReinforcementLearning Approach
Get PDFChoosing optimal parameter values for big data transfer in HPN
SpacEscape – How a Mobile Game Impact Science Learning - 2019 Presidential Research Grant Report
Get PDFThis project used a laboratory trial to examine learner problem-solving in a mobile Serious Game (SG) environment designed for learning space science in middle school. It intends to understand if and how mobile game could impact learner problem-solving. To conduct the study, a team of 12 members worked together for six months on the design, development and testing the SpaceEscape mobile game for Android devices. The data was collected in a local middle school, and over 250 students participated in the study. We will share the highlights, findings, and future research in this report