Physical Sciences and Mathematics Commons^™

20-Year Assessment Of Total Suspended Sediment (Tss) Variability In Barataria Bay From Modis Ocean Color Using A Combination Of Adaptive Semi-Analytical And Neural Network Algorithms, Bijaylaxmi Sahoo

LSU Master's Theses

Barataria Bay, a hydrologically dynamic basin in the northern Gulf of Mexico, exhibits a distinct spatio-temporal distribution of total suspended sediment (TSS). However, studies on sediment distribution are limited by availability of in-situ data as well as the limitation of ocean color algorithms for suspended sediments in shallow optically complex waters. Barataria Bay TSS concentration profile is complex, influenced in the upper basin by fresh water sources such as the Davis Pond Freshwater Diversion, and in the lower bay by the river plume and marine influence near the mouth of the bay. To efficiently study the sediment dynamics in the …

Hybrid Machine And Deep Learning-Based Cyberattack Detection And Classification In Smart Grid Networks, Adedayo Aribisala

Electronic Theses and Dissertations

Power grids have rapidly evolved into Smart grids and are heavily dependent on Supervisory Control and Data Acquisition (SCADA) systems for monitoring and control. However, this evolution increases the susceptibility of the remote (VMs, VPNs) and physical interfaces (sensors, PMUs LAN, WAN, sub-stations power lines, and smart meters) to sophisticated cyberattacks. The continuous supply of power is critical to power generation plants, power grids, industrial grids, and nuclear grids; the halt to global power could have a devastating effect on the economy's critical infrastructures and human life.

Machine Learning and Deep Learning-based cyberattack detection modeling have yielded promising results when …

Smart City Management Using Machine Learning Techniques, Mostafa Zaman

Theses and Dissertations

In response to the growing urban population, "smart cities" are designed to improve people's quality of life by implementing cutting-edge technologies. The concept of a "smart city" refers to an effort to enhance a city's residents' economic and environmental well-being via implementing a centralized management system. With the use of sensors and actuators, smart cities can collect massive amounts of data, which can improve people's quality of life and design cities' services. Although smart cities contain vast amounts of data, only a percentage is used due to the noise and variety of the data sources. Information and communication technology (ICT) …

Determining Power System Fault Location Using Neural Network Approach, Edward O. Ojini

Theses and Dissertations--Electrical and Computer Engineering

Fault location remains an extremely pivotal feature of the electric power grid as it ensures efficient operation of the grid and prevents large downtimes during fault occurrences. This will ultimately enhance and increase the reliability of the system. Since the invention of the electric grid, many approaches to fault location have been studied and documented. These approaches are still effective and are implemented in present times, and as the power grid becomes even more broadened with new forms of energy generation, transmission, and distribution technologies, continued study on these methods is necessary. This thesis will focus on adopting the artificial …

Building Effective Network Security Frameworks Using Deep Transfer Learning Techniques, Harsh Dhillon

Electronic Thesis and Dissertation Repository

Network traffic is growing at an outpaced speed globally. According to the 2020 Cisco Annual Report, nearly two-thirds of the global population will have internet connectivity by the year 2023. The number of devices connected to IP networks will also triple the total world population's size by the same year. The vastness of forecasted network infrastructure opens opportunities for new technologies and businesses to take shape, but it also increases the surface of security vulnerabilities. The number of cyberattacks are growing worldwide and are becoming more diverse and sophisticated. Classic network intrusion detection architectures monitor a system to detect malicious …

A Predictive Model For Diabetes Using Machine Learning Techniques (A Case Studyof Some Selected Hospitals In Kaduna Metropolis), A E. Evwiekpaefe, Nafisat Abdulkadir

Master of Science in Computer Science Theses

Diabetes Mellitus (DM) which refers to a metabolic disorder that occurs when the level of blood sugar in the body is considered high, which could be a resulting effect of inadequate availability of insulin in the body. It is a chronic disease which may lead to myriads of complications in the body system. Statistics by the World Health Organization (WHO) in 2013, indicated that DM was the cause of death of over 1.5 million people around the world and in 2016, 8.5% of adults within age seventeen (17) and above were reported to be diabetic and diabetic patients have continued …

A Novel Feature Maps Covariance Minimization Approach For Advancing Convolutional Neural Network Performance, Bikram Basnet

UNLV Theses, Dissertations, Professional Papers, and Capstones

We present a method for boosting the performance of the Convolutional Neural Network (CNN) by reducing the covariance between the feature maps of the convolutional layers.

In a CNN, the units of a hidden layer are segmented into the feature/activation maps. The units within a feature map share the weight matrix (filter), or in simple terms look for the same feature. A feature map is the output of one filter applied to the previous layer. CNN search for features such as straight lines, and as these features are spotted, they get reported to the feature map. During the learning process, …

A Deep Learning Approach To Recognizing Bees In Video Analysis Of Bee Traffic, Astha Tiwari

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Colony Collapse Disorder (CCD) has been a major threat to bee colonies around the world which affects vital human food crop pollination. The decline in bee population can have tragic consequences, for humans as well as the bees and the ecosystem. Bee health has been a cause of urgent concern for farmers and scientists around the world for at least a decade but a specific cause for the phenomenon has yet to be conclusively identified.

This work uses Artificial Intelligence and Computer Vision approaches to develop and analyze techniques to help in continuous monitoring of bee traffic which will further …

Optimized Multilayer Perceptron With Dynamic Learning Rate To Classify Breast Microwave Tomography Image, Chulwoo Pack

Electronic Theses and Dissertations

Most recently developed Computer Aided Diagnosis (CAD) systems and their related research is based on medical images that are usually obtained through conventional imaging techniques such as Magnetic Resonance Imaging (MRI), x-ray mammography, and ultrasound. With the development of a new imaging technology called Microwave Tomography Imaging (MTI), it has become inevitable to develop a CAD system that can show promising performance using new format of data. The platform can have a flexibility on its input by adopting Artificial Neural Network (ANN) as a classifier. Among the various phases of CAD system, we have focused on optimizing the classification phase …

Optimization Of Neural Network Architecture For Classification Of Radar Jamming Fm Signals, Alberto Soto

Open Access Theses & Dissertations

Radar jamming signal classification is valuable when situational awareness of radar systems is sought out for timely deployment of electronic support measures. Our Thesis shows that artificial neural networks can be utilized for effective and efficient signal classification. The goal is to optimize an artificial Neural Network (NN) approach capable of distinguishing between two common radar waveforms, namely bandlimited white Gaussian jamming noise (BWGN) and the ubiquitous linearly frequency modulated (LFM) signal. This is made possible by creating a theoretical framework for NN architecture testing that leads to a high probability of detection (PD) and a low probability of false …

Classification Of Radar Jammer Fm Signals Using A Neural Network Approach, Ariadna Estefania Mendoza

Open Access Theses & Dissertations

A Neural Network (NN) used to classify radar signals is proposed for the purpose of military survivability and lethality analysis. The goal of the NN is to correctly differentiate Frequency-Modulated (FM) signals from Additive White Gaussian Noise (AWGN) using limited signal pre-processing. The FM signals used to test the NN approach are the linear or chirp FM and the power-law FM. Preliminary simulations using the moments of the signals in the time and frequency domain yielded better results in the frequency domain, suggesting that time domain training would not be as effective frequency domain training. To test this hypoThesis, we …

Developing An Automated Forecasting Framework For Predicting Operation Room Block Time, Azad Sadr Haghighi

Wayne State University Theses

Operating rooms are the most important part of the hospitals, since they have highest influence on financial state of the hospital. Because of high uncertainty in surgery cases demands and their durations, the scheduling of the surgeries becomes a very challenging and critical issue in hospitals. One of the most common approaches to overcome this uncertainty is applying block times which is the time intervals allocated to surgery groups in the hospital. Assigning sufficient amount of the time to each block, is very important, since overestimating lead to wasting resources and on the other hand underestimation causes the overtime staffing …