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 …

Application Of Rule Learner Classifier For Studying Bioethanol Fuelled Spark Ignition Engine Out Emissions And Classification, D Y Dhande Mr., D P Gaikwad Mr, C S Choudhari

ASEAN Journal on Science and Technology for Development

In this paper, preparation of ethanol from waste pomegranate fruit using fermentation and distillation method have discussed. The spark ignition engine out emissions were tested at various operating speeds using four different ethanol blends. Experimental results showed that the ethanol mixing in pure Gasoline increases quality of engine out emissions except Nitrogen Oxides. The 15% ethanol blend and 1500 rpm engine speed were found as optimal input values providing enhanced performance. Based on the experimental results, training datasets were prepared in which emission characteristics of engine were mapped with engine speed and ethanol combination with petrol. These datasets are used …

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 …

Prediction Of Body Fat Percentage Based On Anthropometric Measurements Using Data Mining Approach, Hamsa Amro, Prof. Mohammed Awad

Journal of the Arab American University مجلة الجامعة العربية الامريكية للبحوث

In recent years, heart disease, diabetes, and some types of cancers have been reported as some main causes of death in most countries of the world, and obesity, which is often attributed to excess body fat, is one of the most common risk factors for these diseases. To make the vast amounts of data produced by health care information systems useful to the potential, the researchers applied knowledge discovery through predictive modeling. This study used anthropometric measurements as input data to different data mining techniques to predict body fat percentage. Fisher’s Method of Scoring was used to select the most …

Predicting Bus Travel Times In Washington, Dc Using Artificial Neural Networks (Anns), Stephen Arhin, Babin Manandhar, Hamdiat Baba Adam, Adam Gatiba

Mineta Transportation Institute

Washington, DC is ranked second among cities in terms of highest public transit commuters in the United States, with approximately 9% of the working population using the Washington Metropolitan Area Transit Authority (WMATA) Metrobuses to commute. Deducing accurate travel times of these metrobuses is an important task for transit authorities to provide reliable service to its patrons. This study, using Artificial Neural Networks (ANN), developed prediction models for transit buses to assist decision-makers to improve service quality and patronage. For this study, we used six months of Automatic Vehicle Location (AVL) and Automatic Passenger Counting (APC) data for six Washington …

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 …

Prediction Of Novel Anti-Ebola Virus Compounds Utilizing Artificial Neural Network (Ann), Ronald Bartzatt

Chemistry Faculty Publications

Artificial Neural Network (ANN) analysis is shown to predict the molecular properties of new anti-EBOLA compounds following training/learning by use of 60 previously known and studied drugs. Following training/learning by applying properties of 60 known drugs the TIBERIUS ANN system can efficiently predict the molecular properties of comparable new drugs. Molecular weight (MW) is an important and dominant property of perspective drugs considered for clinical trials. TIBERIUS ANN was able to predict comparable values of MW for drugs following training cycles. One-way ANOVA, F and T tests indicate that actual and predicted MW have the same means (P=.99). Passing-Bablok regression …

Development Of An Electronic Nose For Olfactory System Modelling Using Artificial Neural Network, Proceso L. Fernandez Jr, Mary Anne Sy Roa

Department of Information Systems & Computer Science Faculty Publications

Electronic nose (e-nose) devices have received considerable attention in the field of sensor technology because of their many potential uses such as in identification of toxic wastes, monitoring air quality, examining odors in infected wounds and in inspection of food. Notwithstanding the vast amount of literature on the usage of e-noses for specific purposes, the technology originally and ultimately aims to mimic the capability of mammals to discriminate odors from all sorts of objects. This study demonstrates the theoretical and practical feasibility of designing an e-nose towards general odor classification. A multi-sensor array hardware unit was carefully constructed for data …

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 …

Variable Selection In Regression Using Multilayer Feedforward Network, Tejaswi S. Kamble, Dattatraya N. Kashid

Journal of Modern Applied Statistical Methods

The selection of relevant variables in the model is one of the important problems in regression analysis. Recently, a few methods were developed based on a model free approach. A multilayer feedforward neural network model was proposed for developing variable selection in regression. A simulation study and real data were used for evaluating the performance of proposed method in the presence of outliers, and multicollinearity.

Assessment Of Surface Soil Moisture Using High-Resolution Multi-Spectral Imagery And Artificial Neural Networks, Leila Hassan-Esfahani, Alfonso Torres-Rua, Austin Jensen, Mac Mckee

AggieAir Publications

Many crop production management decisions can be informed using data from high-resolution aerial images that provide information about crop health as influenced by soil fertility and moisture. Surface soil moisture is a key component of soil water balance, which addresses water and energy exchanges at the surface/atmosphere interface; however, high-resolution remotely sensed data is rarely used to acquire soil moisture values. In this study, an artificial neural network (ANN) model was developed to quantify the effectiveness of using spectral images to estimate surface soil moisture. The model produces acceptable estimations of surface soil moisture (root mean square error (RMSE) = …

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 …

Fast Neural Network Surrogates For Complex Groundwater Flow Models, Niels Schütze, Tirthankar Roy

International Conference on Hydroinformatics

Surrogate modeling approach has been adopted in the study to replace computationally expensive physical-based numerical flow and transport model. Two approximate surrogate models namely, Artificial Neural Network (ANN) and Gaussian Process Model (GPM) are developed individually using a scenario database generated from the density dependent numerical flow and transport model OpenGeoSys (OGS). The state-space surrogates have the flexibility to move freely from one point to another within a time frame of decades and also to allow for moderate extrapolation in the case of extreme abstractions. The performance of the GPM was better in many cases with a little compromise on …

Development And Testing Of Data Driven Nowcasting Models Of Beach Water Quality, Jainy Mavani, Lianghao Chen, Darko Joksimovic, Songnian Li

International Conference on Hydroinformatics

Beach water issues are gaining worldwide attention due to their impact on health and other environmental problems. The Ontario beaches require beach managers to issue swimming advisories when water quality standards are exceeded since users of recreational waters may be exposed to elevated pathogen levels through various point and non-point sources. Typical daily notifications rely on microbial analysis of indicator organisms (e.g. Escherichia coli), which require 18-24 hours to provide an adequate response. This research evaluated the use of Artificial Neural Networks (ANNs) and Evolutionary Polynomial Regression (EPR) for real time prediction of E.coli in the beach waters of Toronto …

An Artificial Neural Network-Based Rainfall Runoff Model For Improved Drainage Network Modelling, David Walker, Edward C. Keedwell, Dragan A. Savić, Richard Kellagher

International Conference on Hydroinformatics

Modelling rainfall-runoff processes enables hydrologists to plan their response to flooding events. Urban drainage catchment modelling requires rainfall-runoff models as a prerequisite. In the UK, one of the main software tools used for drainage modelling is InfoWorks CS, based on relatively simple methods which are relatively robust in predicting runoff. This paper presents an alternative approach to modelling runoff that will allow for the complex inter-relation of runoff that occurs from impermeable areas, permeable areas, local surface storage and variation in rainfall induced infiltration. Apart from the uncertainties associated with the measurement of connected surfaces to the drainage system, the …

Would Bangkok Be More Vulnerable To The Anticipated Changing Climate?, Thannob Aribarg, Minh Tue Vu, Siriporn Supratid, Seree Supratid, Shie-Yui Liong

International Conference on Hydroinformatics

The severe flooding in Thailand in 2011 was triggered by the tropical storm Nock-ten at end of July along the Mekong and Chao Phraya river basin. There are 4 additional storms that caused medium to heavy rainfall from June to October in the north and north-east of Thailand. Due to limited capacity of the Chao Phraya river and also Pasak river, several overbank flows occurred and also dikes along the river were broken causing excessive flow to many communities beside the river and downstream. The consequence was a total of 815 deaths with 13.6 million people affected and over 20,000 …

Precipitation Forecasting With Wavelet-Based Empirical Orthogonal Function And Artificial Neural Network (Weof-Ann) Model, Sanaz Imen, Ni-Bin Chang, Y. Jeffrey Yang

International Conference on Hydroinformatics

Western drought began since 2000 caused sharp decrease by about 100 feet in the largest reservoir of North America, Lake Mead due to the precipitation pattern shift in the upstream lower Virgin River Basin. Oceans play an important role on earth’s climate via oceanic-atmospheric interactions known as climate teleconnections, which deeply affect the terrestrial precipitation patterns. This issue signifies the necessity of developing a modern hydroinformatics tool - precipitation forecasting model - to account for teleconnection signals from climate change and mitigate drought hazards impact on lake water, quantitatively and qualitatively, which cannot be achieved by using traditional Global Circulation …

Portable Gpu-Based Artificial Neural Networks For Data-Driven Modeling, Zheng Yi Wu

International Conference on Hydroinformatics

Artificial neural network (ANN) is widely applied as data-driven modeling tool in hydroinformatics due to its broad applicability of handing implicit and nonlinear relationships between the input and output data. To obtain a reliable ANN model, training ANN using the data is essential, but the training is usually taking many hours for large data set and/or for large systems with many variants. This may not be a concern when ANN is trained for offline applications, but it is of great importance when ANN is trained or retrained for real-time and near real-time applications, which are becoming an increasingly interested research …

Application Of Data Mining For Reverse Osmosis Process In Seawater Desalination, Jaewuk Koo, Yonghyun Shin, Sangho Lee, Juneseok Choi

International Conference on Hydroinformatics

Reverse osmosis (RO) membrane process has been considered a promising technology for water treatment and desalination. However, it is difficult to predict the performance of pilot- or full-scale RO systems because numerous factors are involved in RO performance, including variations in feed water (quantity, quality, temperature, etc), membrane fouling, and time-dependent changes (deteriorations). Accordingly, this study intended to develop a practical approach for the analysis of operation data in pilot-scale reverse osmosis (RO) processes. Novel techniques such as artificial neural network (ANN) and genetic programming (GP) technique were applied to correlate key operating parameters and RO permeability statistically. The ANN …

Integrating Soil And Plant Tissue Tests And Using An Artificial Intelligence Method For Data Modelling Is Likely To Improve Decisions For In-Season Nitrogen Management, Andreas Neuhaus, Leisa Armstrong, Jinsong Leng, Dean Diepeveen, Geoff Anderson

Research outputs 2014 to 2021

This paper hypothesizes that there is value in combining soil, climate and plant tissue data to give more reliable advice on nitrogen top-ups in-season when compared with models that are currently available. The benefit of soil and climate data is to factor in N mineralisation and potential yield while plant test data is a more direct approach of yield estimates when considering firstly plant N uptake from the whole soil profile and secondly biomass (important yield component). Plant test data are closer to yield in time and space than soil test data, shortening the time period for any yield prognosis …

Estimating Software Effort Using An Ann Model Based On Use Case Points, Ali Bou Nassif, Luiz Fernando Capretz, Danny Ho

Electrical and Computer Engineering Publications

In this paper, we propose a novel Artificial Neural Network (ANN) to predict software effort from use case diagrams based on the Use Case Point (UCP) model. The inputs of this model are software size, productivity and complexity, while the output is the predicted software effort. A multiple linear regression model with three independent variables (same inputs of the ANN) and one dependent variable (effort) is also introduced. Our data repository contains 240 data points in which, 214 are industrial and 26 are educational projects. Both the regression and ANN models were trained using 168 data points and tested using …