Open Access. Powered by Scholars. Published by Universities.®
- Keyword
-
- Adherence (1)
- Anomaly detection (1)
- Big Data. (1)
- Cloud computing (1)
- Computational Path (1)
-
- Computer Vision (1)
- Convolutional Neural Network (1)
- DTW (1)
- Data mining (1)
- Deep learning (1)
- Dimensionality reduction (1)
- Dynamic Time Warping (1)
- Energy forecasting (1)
- Gamification (1)
- Interdependency (1)
- Intrusion detection (1)
- K-nearest Neighbors (1)
- Latency-aware Placement (1)
- Machine Learning (1)
- Machine learning (1)
- Measurement (1)
- Microservices (1)
- Migration (1)
- Neck (1)
- Network function visualization (1)
- Network security (1)
- Object detection (1)
- Pain (1)
- Quality of Service (1)
- Re-instantiation (1)
Articles 1 - 6 of 6
Full-Text Articles in Engineering
Leveraging Cloud-Based Nfv And Sdn Platform Towards Quality-Driven Next-Generation Mobile Networks, Hassan Hawilo
Leveraging Cloud-Based Nfv And Sdn Platform Towards Quality-Driven Next-Generation Mobile Networks, Hassan Hawilo
Electronic Thesis and Dissertation Repository
Network virtualization has become a key approach for Network Service Providers (NSPs) to mitigate the challenge of the continually increasing demands for network services. Tightly coupled with their software components, legacy network devices are difficult to upgrade or modify to meet the dynamically changing end-user needs. To virtualize their infrastructure and mitigate those challenges, NSPs have started to adopt Software Defined Networking (SDN) and Network Function Virtualization (NFV). To this end, this thesis addresses the challenges faced on the road of transforming the legacy networking infrastructure to a more dynamic and agile virtualized environment to meet the rapidly increasing demand …
Cluster-Based Chained Transfer Learning For Energy Forecasting With Big Data, Yifang Tian
Cluster-Based Chained Transfer Learning For Energy Forecasting With Big Data, Yifang Tian
Electronic Thesis and Dissertation Repository
Smart meter popularity has resulted in the ability to collect big energy data and has created opportunities for large-scale energy forecasting. Machine Learning (ML) techniques commonly used for forecasting, such as neural networks, involve computationally intensive training typically with data from a single building/group to predict future consumption for that same building/group. With hundreds of thousands of smart meters, it becomes impractical or even infeasible to individually train a model for each meter. Consequently, this paper proposes Cluster-Based Chained Transfer Learning (CBCTL), an approach for building neural network-based models for many meters by taking advantage of already trained models through …
Classifying Appliances Operation Modes Using Dynamic Time Warping (Dtw) And K Nearest Neighbors (Knn), Abdelkareem M. Jaradat
Classifying Appliances Operation Modes Using Dynamic Time Warping (Dtw) And K Nearest Neighbors (Knn), Abdelkareem M. Jaradat
Electronic Thesis and Dissertation Repository
In the Smart Grid environment, the advent of intelligent measuring devices facilitates monitoring appliance electricity consumption. This data can be used in applying Demand Response (DR) in residential houses through data analytics, and developing data mining techniques. In this research, we introduce a smart system approach that is applied to user's disaggregated power consumption data. This system encourages the users to apply DR by changing their behaviour of using heavier operation modes to lighter modes, and by encouraging users to shift their usages to off-peak hours. First, we apply Cross Correlation to detect times of the occurrences when an appliance …
Quantifying The Outcomes Of A Virtual Reality (Vr)-Based Gamified Neck Rehabilitation, Shahan Salim
Quantifying The Outcomes Of A Virtual Reality (Vr)-Based Gamified Neck Rehabilitation, Shahan Salim
Electronic Thesis and Dissertation Repository
Neck pain is a major global public health concern and adds a significant financial burden to both the healthcare system as well as people suffering from it. Additionally, it presents measurement and evaluation challenges for clinicians as well as adherence challenges and treatment barriers for the patients. We have developed a virtual reality (VR)-based video game that can be used to capture outcomes that may aid in the assessment and treatment of neck pain. We investigated: (i) performance metrics of overall accuracy, accuracy based on movement difficulty, duration, and total envelope of movement; (ii) stability across sessions; (iii) accuracy across …
Adaptation Of A Deep Learning Algorithm For Traffic Sign Detection, Jose Luis Masache Narvaez
Adaptation Of A Deep Learning Algorithm For Traffic Sign Detection, Jose Luis Masache Narvaez
Electronic Thesis and Dissertation Repository
Traffic signs detection is becoming increasingly important as various approaches for automation using computer vision are becoming widely used in the industry. Typical applications include autonomous driving systems, mapping and cataloging traffic signs by municipalities. Convolutional neural networks (CNNs) have shown state of the art performances in classification tasks, and as a result, object detection algorithms based on CNNs have become popular in computer vision tasks. Two-stage detection algorithms like region proposal methods (R-CNN and Faster R-CNN) have better performance in terms of localization and recognition accuracy. However, these methods require high computational power for training and inference that make …
Towards Efficient Intrusion Detection Using Hybrid Data Mining Techniques, Fadi Salo
Towards Efficient Intrusion Detection Using Hybrid Data Mining Techniques, Fadi Salo
Electronic Thesis and Dissertation Repository
The enormous development in the connectivity among different type of networks poses significant concerns in terms of privacy and security. As such, the exponential expansion in the deployment of cloud technology has produced a massive amount of data from a variety of applications, resources and platforms. In turn, the rapid rate and volume of data creation in high-dimension has begun to pose significant challenges for data management and security. Handling redundant and irrelevant features in high-dimensional space has caused a long-term challenge for network anomaly detection. Eliminating such features with spectral information not only speeds up the classification process, but …