Physical Sciences and Mathematics Commons^™

Distributed Load Testing By Modeling And Simulating User Behavior, Chester Ira Parrott

LSU Doctoral Dissertations

Modern human-machine systems such as microservices rely upon agile engineering practices which require changes to be tested and released more frequently than classically engineered systems. A critical step in the testing of such systems is the generation of realistic workloads or load testing. Generated workload emulates the expected behaviors of users and machines within a system under test in order to find potentially unknown failure states. Typical testing tools rely on static testing artifacts to generate realistic workload conditions. Such artifacts can be cumbersome and costly to maintain; however, even model-based alternatives can prevent adaptation to changes in a system …

Base-Package Recommendation Framework Based On Consumer Behaviours In Iptv Platform, Kuruparan Shanmugalingam, Ruwinda Ranganayanke, Chanka Gunawardhaha, Rajitha Navarathna

Research Collection School Of Computing and Information Systems

Internet Protocol TeleVision (IPTV) provides many services such as live television streaming, time-shifted media, and Video On Demand (VOD). However, many customers do not engage properly with their subscribed packages due to a lack of knowledge and poor guidance. Many customers fail to identify the proper IPTV service package based on their needs and to utilise their current package to the maximum. In this paper, we propose a base-package recommendation model with a novel customer scoring-meter based on customers behaviour. Initially, our paper describes an algorithm to measure customers engagement score, which illustrates a novel approach to track customer engagement …

An Explainable And Statistically Validated Ensemble Clustering Model Applied To The Identification Of Traumatic Brain Injury Subgroups, Dacosta Yeboah, Louis Steinmeister, Daniel B. Hier, Bassam Hadi, Donald C. Wunsch, Gayla R. Olbricht, Tayo Obafemi-Ajayi

Electrical and Computer Engineering Faculty Research & Creative Works

We present a framework for an explainable and statistically validated ensemble clustering model applied to Traumatic Brain Injury (TBI). The objective of our analysis is to identify patient injury severity subgroups and key phenotypes that delineate these subgroups using varied clinical and computed tomography data. Explainable and statistically-validated models are essential because a data-driven identification of subgroups is an inherently multidisciplinary undertaking. In our case, this procedure yielded six distinct patient subgroups with respect to mechanism of injury, severity of presentation, anatomy, psychometric, and functional outcome. This framework for ensemble cluster analysis fully integrates statistical methods at several stages of …

Global Optimization Algorithms For Image Registration And Clustering, Cuicui Zheng

Dissertations

Global optimization is a classical problem of finding the minimum or maximum value of an objective function. It has applications in many areas, such as biological image analysis, chemistry, mechanical engineering, financial analysis, deep learning and image processing. For practical applications, it is important to understand the efficiency of global optimization algorithms. This dissertation develops and analyzes some new global optimization algorithms and applies them to practical problems, mainly for image registration and data clustering.

First, the dissertation presents a new global optimization algorithm which approximates the optimum using only function values. The basic idea is to use the points …

Slashing Quality Index Modeling And Simulation Based On Data Dispersion Clustering, Yuxian Zhang, Xiaoyi Qian, Dong Xiao, Jianhui Wang

Journal of System Simulation

Abstract: For the sensitivity of noise and outliers data in the typical partitioning clustering algorithm, a clustering algorithm based on data dispersion was proposed. The data dispersion was defined and introduced to a non-Euclidean distance. The similarity metric was established, and the data clustering was realized. The optimal clustering number was obtained by the validity function based on improved partition coefficient. Then the proposed clustering algorithm was applied to quality index model in slashing process. A size add-on quality index model was built by radial basis function neural networks. The node number of hidden layer was determined and the center …

An Energy Efficient Routing Approach For Iot Enabled Underwater Wsns In Smart Cities, Nighat Usman, Omar Alfandi, Saeeda Usman, Asad Masood Khattak, Muhammad Awais, Bashir Hayat, Ahthasham Sajid

All Works

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. Nowadays, there is a growing trend in smart cities. Therefore, Terrestrial and Internet of Things (IoT) enabled Underwater Wireless Sensor Networks (TWSNs and IoT-UWSNs) are mostly used for observing and communicating via smart technologies. For the sake of collecting the desired information from the underwater environment, multiple acoustic sensors are deployed with limited resources, such as memory, battery, processing power, transmission range, etc. The replacement of resources for a particular node is not feasible due to the harsh underwater environment. Thus, the resources held by the node needs to be used …

The K-Means Algorithm: A Comprehensive Survey And Performance Evaluation, Mohiuddin Ahmed, Raihan Seraj, Syed Mohammed Shamsul Islam

Research outputs 2014 to 2021

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. The k-means clustering algorithm is considered one of the most powerful and popular data mining algorithms in the research community. However, despite its popularity, the algorithm has certain limitations, including problems associated with random initialization of the centroids which leads to unexpected convergence. Additionally, such a clustering algorithm requires the number of clusters to be defined beforehand, which is responsible for different cluster shapes and outlier effects. A fundamental problem of the k-means algorithm is its inability to handle various data types. This paper provides a structured and synoptic overview of …

Reinforcement Learning In Large, Structured Action Spaces: A Simulation Study Of Decision Support For Spinal Cord Injury Rehabilitation, Nathan Phelps

Electronic Thesis and Dissertation Repository

Reinforcement learning (RL) has helped improve decision-making in several applications. However, applying traditional RL is challenging in some applications, such as rehabilitation of people with a spinal cord injury (SCI). Among other factors, using RL in this domain is difficult because there are many possible treatments (i.e., large action space) and few patients (i.e., limited training data). Treatments for SCIs have natural groupings, so we propose two approaches to grouping treatments so that an RL agent can learn effectively from limited data. One relies on domain knowledge of SCI rehabilitation and the other learns similarities among treatments using an embedding …

Key Technologies Of Precaution And Prediction Of Abnormal Spatial-Temporal Trajectory: A Review Of Recent Advances, Gongda Qiu, He Ming, Yang Jie, Yuting Cao, Jihong Sun

Journal of System Simulation

Abstract: The ex-post disposition of a major incident, which is expected to transform into prediction and precaution of abnormal behavior, is increasingly unable to meet the urgent needs of the society.Therapid development and popularization of sensor network and positioning technology lay the foundation for mining spatial-temporal trajectory data. With the key objective of prediction and precaution of abnormal trajectory based on big data mining, the future research directions and prospects on trajectory clustering and recognitionareanalyzed, discussed and elaboratedinthis paper.Temporal trajectory prediction applied in prediction and precaution of abnormal spatial-temporal trajectory is also presented, providing a reference for further research on …

Robust Graph Learning From Noisy Data, Zhao Kang, Haiqi Pan, Steven C. H. Hoi, Zenglin Xu

Research Collection School Of Computing and Information Systems

Learning graphs from data automatically have shown encouraging performance on clustering and semisupervised learning tasks. However, real data are often corrupted, which may cause the learned graph to be inexact or unreliable. In this paper, we propose a novel robust graph learning scheme to learn reliable graphs from the real-world noisy data by adaptively removing noise and errors in the raw data. We show that our proposed model can also be viewed as a robust version of manifold regularized robust principle component analysis (RPCA), where the quality of the graph plays a critical role. The proposed model is able to …

Developing Agent-Based Models To Study Financial Markets, Saurav Chakraborty

USF Tampa Graduate Theses and Dissertations

This dissertation presents research that employs agent-based modelling to provide a framework to support simulation as a complement to traditional economic models for policy evaluation. It consists of three studies. The first study employs cluster analysis to capture the different types of banks and the associated business models that define their decision-making. The results from study one will help us get an understanding of how different banks behave and provide an insight into their lending practices. Hence, it would be very helpful in evaluating and analyzing the impact of future policies. Study two develops a fine-grained interbank lending model based …

A New Intra-Cluster Scheduling Scheme For Real-Time Flows In Wireless Sensor Networks, Gohar Ali, Fernando Moreira, Omar Alfandi, Babar Shah, Mohammed Ilyas

All Works

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. Real-time flows using time division multiple access (TDMA) scheduling in cluster-based wireless sensor networks try to schedule more flows per time frame to minimize the schedule length to meet the deadline. The problem with the previously used cluster-based scheduling algorithm is that intra-cluster scheduling does not consider that the clusters may have internal or outgoing flows. Thus, intra-cluster scheduling algorithms do not utilize their empty time-slots and thus increase schedule length. In this paper, we propose a new intra-cluster scheduling algorithm by considering that clusters may have having internal or outgoing …

Machine Learning And Data Mining-Based Methods To Estimate Parity Status And Age Of Wild Mosquito Vectors Of Infectious Diseases From Near-Infrared Spectra, Masabho Peter Milali

Dissertations (1934 -)

Previous studies show that a trained partial least square regresser [sic] (PLSR) from near-infrared spectra classify laboratory and semi-field raised mosquitoes into less than or ≥ to seven days old with an average accuracy of 80%. This dissertation demonstrates that training models on near-infrared spectra (NIRS) using artificial neural network (ANN) as an architecture yields models with higher accuracies than training models using partial least squares (PLS) as an architecture. In addition, irrespective of the model architecture used, direct training of a binary classifier scores higher accuracy than training a regresser and interpreting it as a binary classifier. Furthermore, for …

Bibsqlqc: Brown Infomax Boosted Sql Query Clustering Algorithm To Detectanti-Patterns In The Query Log, Vinothsaravanan Ramakrishnan, Palanisamy Chenniappan

Turkish Journal of Electrical Engineering and Computer Sciences

Discovery of antipatterns from arbitrary SQL query log depends on the static code analysis used to enhance the quality and performance of software applications. The existence of antipatterns reduces the quality and leads to redundant SQL statements. SQL log includes a large load on the database and it is difficult for an analyst to extract large patterns in a minimal time. Existing techniques which discover antipatterns in SQL query face a lot of innumerable challenges to discover the normal sequences of queries within the log. In order to discover the antipatterns in the log, an efficient technique called Brown infomax …

Optimizing Cluster Sets For The Scan Statistic Using Local Search, James Shulgan

Graduate Research Theses & Dissertations

In recent years, scattering sensors to produce wireless sensor networks (WSN) has been proposed for detecting localized events in large areas. Because sensor measurements are noisy, the WSN needs to use statistical methods such as the scan statistic. The scan statistic groups measurements into various clusters, computes a cluster statistic for each cluster, and decides that an event has happened if any of the statistics exceeds a threshold. Previous researchers have investigated the performance of the scan statistic to detect events; however, little attention was given to the optimization of which clusters the scan statistic should use. Using the scan …

Development Of A Modeling Algorithm To Predict Lean Implementation Success, Richard Charles Barclay

Doctoral Dissertations

”Lean has become a common term and goal in organizations throughout the world. The approach of eliminating waste and continuous improvement may seem simple on the surface but can be more complex when it comes to implementation. Some firms implement lean with great success, getting complete organizational buy-in and realizing the efficiencies foundational to lean. Other organizations struggle to implement lean. Never able to get the buy-in or traction needed to really institute the sort of cultural change that is often needed to implement change. It would be beneficial to have a tool that organizations could use to assess their …

An Efficient Storage-Optimizing Tick Data Clustering Model, Haleh Amintoosi, Masood Niazi Torshiz, Yahya Forghani, Sara Alinejad

Turkish Journal of Electrical Engineering and Computer Sciences

Tick data is a large volume of data, related to a phenomenon such as stock market or weather change, with data values changing rapidly over time. An important issue is to store tick data table in a way that it occupies minimum storage space while at the same time it can provide fast execution of queries. In this paper, a mathematical model is proposed to partition tick data tables into clusters with the aim of minimizing the required storage space. The genetic algorithm is then used to solve the mathematical model which is indeed a clustering model. The proposed method …