Florida Polytechnic University

About: Florida Polytechnic University is a education organization based out in Lakeland, Florida, United States. It is known for research contribution in the topics: Computer science & Catalysis. The organization has 302 authors who have published 538 publications receiving 6549 citations. The organization is also known as: Florida Poly.

Machine Learning-based Pre-discharge Prediction of Hospital Readmission

Abstract: In this paper, we describe the application of data mining techniques in relation to an important clinical care quality indicator: the prediction of hospital readmission within 30 days of discharge. We retrieved six months of encounter data between dates April through September, 2017, from five inpatient hospitals in a large US metropolitan area. Each encounter includes both administrative and clinical data. We utilized a feature reduction technique to replace thousands of clinical features with a much smaller number of proxy features. The dimensionally reduced dataset was then used in the development, training and evaluation of numerous readmission predictive models. Using standard implementation techniques, our model can function within the hospital EHR from where the data was sourced, in real- time, for all patients, prior to discharge. Model performance of the best performing model compares favorably to existing comparable pre-discharge, all-patient predictive model studies.

Real time estimation and suppression of hand tremor for surgical robotic applications

Abstract: In this work, an algorithm was developed to record and suppress the physiological tremor present in the hands of surgeon doing robotic surgical procedure due to fatigue or otherwise. A prototype setup of master handle having six degree of freedom with a vibration motor was designed and fabricated to record the hand tremor. The work involved recording the composite simulated motion consisting of both voluntary motion of surgeon’s hand and associated involuntary motion of tremor in real time, determination of peak frequencies of both the motions and providing necessary information on the graphical user interface. The adaptive algorithm is capable to cancel out the involuntary motion from the recorded raw signal in real time. After filtration, only voluntary motion remains for further processing. The developed algorithm promises potential to make robotic surgery more precise and error free.

A UML and MLN based approach to implementing a networking laboratory on a scalable Linux cluster

Abstract: The teaching of a practical laboratory component of certain computer science courses such as networking has, in the past, required dedicated laboratories, isolated from the campus networking infrastructure. During the past few years, virtualization has emerged as a practical alternative to this resource-intensive and very limiting solution. User-Mode Linux (UML) is a virtualization technology that offers many advantages; MLN is an application that allows implementation of predefined or student-designed virtual networks of arbitrary complexity. We discuss the use of these tools on a low-cost, scalable load-balancing Linux cluster, illustrating how our implementation fulfills necessary and desirable goals for an effective networking lab.

Identifying Android Malware Families Using Android-Oriented Metrics

Abstract: Android malware (malicious apps) families share common attributes and behavior through sharing core malicious code. However, as the number of new malware increases, the task of identifying the correct family becomes more challenging. Two prominent approaches tackle this problem, either using dynamic analysis that captures the runtime behavior of the malware or using static analysis methods that can reveal malicious behavior by analyzing the underlying logic and code patterns. A third emerging way is to use the various sources of identification features to analyze the architectural and external attributes of a malicious app. For example, two malicious apps can have different behavioral patterns but share common attributes. We hypothesize that this malware can belong to the same family but attempt to mislead dynamic and code-level static analysis tools by randomizing their behavior. In this work, we utilize a promising set of Android-oriented code metrics that guide a supervised classification learning process for identifying malware families in Android. Our empirical results on 2,869 malware apps, across 35 different malware families, show that these metrics are very effective to identify malware families. In particular, we achieve low false positive rate (1.2%) and AUC score of 0.984 for family identification by using Random Forest (RF) classifier.