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Articles 1 - 3 of 3
Full-Text Articles in Technology and Innovation
Self-Learning Algorithms For Intrusion Detection And Prevention Systems (Idps), Juan E. Nunez, Roger W. Tchegui Donfack, Rohit Rohit, Hayley Horn
Self-Learning Algorithms For Intrusion Detection And Prevention Systems (Idps), Juan E. Nunez, Roger W. Tchegui Donfack, Rohit Rohit, Hayley Horn
SMU Data Science Review
Today, there is an increased risk to data privacy and information security due to cyberattacks that compromise data reliability and accessibility. New machine learning models are needed to detect and prevent these cyberattacks. One application of these models is cybersecurity threat detection and prevention systems that can create a baseline of a network's traffic patterns to detect anomalies without needing pre-labeled data; thus, enabling the identification of abnormal network events as threats. This research explored algorithms that can help automate anomaly detection on an enterprise network using Canadian Institute for Cybersecurity data. This study demonstrates that Neural Networks with Bayesian …
Analysis Of First-Time Completion In The Field Service Environment, Gavin Rick, Scott Englerth, Marc Carter, Hayley Horn
Analysis Of First-Time Completion In The Field Service Environment, Gavin Rick, Scott Englerth, Marc Carter, Hayley Horn
SMU Data Science Review
First-time completion is an important measure of service quality and efficiency in the field service industry. Customers call upon field service providers to repair their equipment in a timely manner so it can be put back into service for their business demands. Responsiveness can be measured through first-time completion and is defined as completing the repair on the first visit of a service call. This research is exploring the first-time completion in the forklift service industry. This research found the primary factors that impact first-time completion percentage in this industry include parts on hand, parts backorder process, technician experience, and …
Comparative Study: Reducing Cost To Manage Accessibility With Existing Data, Claire Chu, Bill Kerneckel, Eric C. Larson, Nathan Mowat, Christopher Woodard
Comparative Study: Reducing Cost To Manage Accessibility With Existing Data, Claire Chu, Bill Kerneckel, Eric C. Larson, Nathan Mowat, Christopher Woodard
SMU Data Science Review
“Project Sidewalk” is an existing research effort that focuses on mapping accessibility issues for handicapped persons to efficiently plan wheelchair and mobile scooter friendly routes around Washington D.C. As supporters of this project, we utilized the data “Project Sidewalk” collected and used it to confirm predictions about where problem sidewalks exist based on real estate and crime data. We present a study that identifies correlations found between accessibility data and crime and housing statistics in the Washington D.C. metropolitan area. We identify the key reasons for increased accessibility and the issues with the current infrastructure management system. After a thorough …