Physical Sciences and Mathematics Commons^™

High Performance And Machine Learning Algorithms For Brain Fmri Data, Taban Eslami

Dissertations

Brain disorders are very difficult to diagnose for reasons such as overlapping nature of symptoms, individual differences in brain structure, lack of medical tests and unknown causes of some disorders. The current psychiatric diagnostic process is based on behavioral observation and may be prone to misdiagnosis.

Noninvasive brain imaging technologies such as Magnetic Resonance Imaging (MRI) and functional Magnetic Resonance Imaging (fMRI) make the process of understanding the structure and function of the brain easier. Quantitative analysis of brain imaging data using machine learning and data mining techniques can be advantageous not only to increase the accuracy of brain disorder …

Assessment Of The Impact Of Climate Change On Land Use In The Emirate Of Abu Dhabi - An Environmental And Socio-Economic Perspective, Latifa Saeed Al Blooshi

Dissertations

This dissertation focuses on the impact of climate change on land use in the Emirate of Abu Dhabi – UAE. Climate change is a significant challenge resulting from natural and anthropogenic causes. Land use can stimulate changes in communities under climate change. The main objective of this dissertation is to assess the impact of climate change from an environmental and socio-economic perspective. In 2001, coastal sabkhas, mixed class and urbanized areas experienced an increase in temperature by (0.67, 1.14 and 1.16°C) respectively. In cities, urban areas are warmer than neighbouring rural areas. Unexpectedly, urbanization in desert areas in UAE led …

Extremal Problems On Induced Graph Colorings, James Hallas

Dissertations

Graph coloring is one of the most popular areas of graph theory, no doubt due to its many fascinating problems and applications to modern society, as well as the sheer mathematical beauty of the subject. As far back as 1880, in an attempt to solve the famous Four Color Problem, there have been numerous examples of certain types of graph colorings that have generated other graph colorings of interest. These types of colorings only gained momentum a century later, however, when in the 1980s, edge colorings were studied that led to vertex colorings of various types, led by the introduction …

Cross Language Information Transfer Between Modern Standard Arabic And Its Dialects – A Framework For Automatic Speech Recognition System Language Model, Tiba Zaki Abdulhameed

Dissertations

Significant advances have been made with Modern Standard Arabic (MSA) Automatic Speech Recognition (ASR) applications. Yet, dialectal conversation ASR is still trailing behind due to limited language resources. As is the case in most cultures, the formal Modern Standard Arabic language is not used in daily life. Instead, varieties of regional dialects are spoken, which creates a dire need to address dialect ASR systems. Processing MSA language naturally poses considerable challenges that are passed on to the processing of its derived dialects. In dialects, many words have gradually morphed from MSA pronunciations and at many times have different usages. Also, …

New Catalytic Reactions In Carbohydrate Chemistry, Scott Geringer

Dissertations

Carbohydrates or sugars are some of the most diverse and abundant biological molecules. They are involved in a multitude of processes in the body such as fertilization, cell-cell communication, and cancer metathesis. Because of these vital functions, the study of sugars is rapidly growing field. The field however is limited due to the complex nature of sugars which results in difficulties in obtaining large quantities for study.

Protecting group manipulation is a large emphasis area in carbohydrate chemistry due to the need to selectively protect different functional groups of each molecule during synthesis. Catalytic and selective cleavage of protecting groups …

High-Order Adaptive Synchrosqueezing Transform, Jawaher Alzahrani

Dissertations

The prevalence of the separation of multicomponent non-stationary signals across many elds of research makes this concept an important subject of study. The synchrosqueezing transform (SST) is a particular type of reassignment method. It aims to separate and recover the components of a multicomponent non-stationary signal. The short time Fourier transform (STFT)-based SST (FSST) and the continuous wavelet transform (CWT)based SST (WSST) have been used in engineering and medical data analysis applications. The current study introduces the dierent versions of FSST and WSST to estimate instantaneous frequency (IF) and to recover components. It has a good concentration and reconstruction for …

Noise Reduction Of Eeg Signals Using Autoencoders Built Upon Gru Based Rnn Layers, Esra Aynali

Dissertations

Understanding the cognitive and functional behaviour of the brain by its electrical activity is an important area of research. Electroencephalography (EEG) is a method that measures and record electrical activities of the brain from the scalp. It has been used for pathology analysis, emotion recognition, clinical and cognitive research, diagnosing various neurological and psychiatric disorders and for other applications. Since the EEG signals are sensitive to activities other than the brain ones, such as eye blinking, eye movement, head movement, etc., it is not possible to record EEG signals without any noise. Thus, it is very important to use an …

Metal Ions Impact On Shewanella Oneidensis Mr-1 Adhesion To Ito Electrode And The Enhancement Of Current Output, Aisha Awad Alshahrani

Dissertations

The goal of this study is to enhance the efficiency of bacterial extracellular electron transfer (EET) in Shewanella oneidensis MR-1 by enhancing adhesion to the electrode's surface. Our results clearly show a major difference in the attachment and behavior of Shewanella oneidensis MR-1 for Ca2+, Pb2+, Cd2+, and Mg2+, compared to the control. the final microbial coverage, as measured by confocal microscopy and cathodic peak charge in cyclic voltammetry (Qpc), increases with increasing metal ion concentrations. We found the cells attached to the electrode increased more with the addition of metal ion concentrations in the following order of metals: Ca2+ …

Optimization Of Microbial Fuel Cells, Norberto M. Gonzalez

Dissertations

Optimization of microbial fuel cells is investigated by utilizing Shewanella oneidensis as a model microorganism. the microbe's ability to grow and use glucose as a carbon source is explored under varying oxygen environments through an offline PMP derivatization method and HPLC analysis. Shewanella growth and glucose utilization is enhanced under aerobic environments; however, under microaerobic environments the addition of ferric iron results in a faster exponential growth initialization. a flavin mononucleotide modified indium tin oxide electrode is prepared and characterized for its usefulness in microbial fuel cells by controlled potential electrolysis, cyclic voltammetry, and electrochemical impedance spectroscopy studies. the electrode …

Lewis Acid-Carbonyl Solution Interactions And Their Implications In Catalytic Systems, Carly Soren Hanson

Dissertations

The utilization of Lewis acids to activate substrates containing carbonyls is ubiquitous in organic synthetic methods. in order to facilitate the development of novel reaction pathways and understand existing methods, it is necessary to determine the solution interactions between Lewis acids and Lewis bases. Historically, the characterization of the interactions of Lewis pairs has relied on solid state infrared (IR) spectroscopy and X-ray crystallography, as well as in situ NMR. I have developed a method utilizing in situ IR spectroscopy and solution conductivity towards the identification of the solution structures formed when a range of carbonyl compounds are combined with …

Development Of Methods For The Removal Of Selected Pollutants From Several Matrices And Identification Of Unknown Pollutants Adsorbed Onto Plastics Collected From Freshwater, Kathryn Marie Renyer

Dissertations

Plastic pollution represents one of greatest anthropogenic threats to the environment. Five to ten billion tons of plastic are manufactured every year. Currently, Earth's ecosystem is contaminated with billions of tons of plastic debris, much of which cannot be recycled. Over time, this plastic debris decomposes into small particles. Small plastic particles are known to adsorb toxic compounds in marine environments. My research is concerned with creating novel methods for the detection and quantification of selected persistent organic pollutants from several media. Specifically, I developed methods for the detection and quantification of endosulfan sulfate (ESS) from Lumbricus terrestris tissue and …

Electron Transfer Of Shewanella Oneidensis Mr-1 At Clay-Modified Ito Electrode, Reem Faez Alshehri

Dissertations

Various strategies have been established to enhance the extracellular electron transfer and energy output capability of microbial fuel cells, with the majority being aimed at anode modification. The anode has a significant impact on the electricity generation performance of MFCs because it is in direct contact with the microorganisms. The materials of the anode should be favorable for the bacterial cell and capable to facilitate the electron transfer. Developing of an electrode using low-cost and effective materials assists to enhance the bacterial cell attachment and extracellular electron transfer. This provides a significant improvement in MFC performance. In this study, clay …

Image Instance Segmentation: Using The Cirsy System To Identify Small Objects In Low Resolution Images, Orghomisan William Omatsone

Dissertations

The CIRSY system (or Chick Instance Recognition System) is am image processing system developed as part of this research to detect images of chicks in highly-populated images that uses the leading algorithm in instance segmentation tasks, called the Mask R-CNN. It extends on the Faster R-CNN framework used in object detection tasks, and this extension adds a branch to predict the mask of an object along with the bounding box prediction. Mask R-CNN has proven to be effective ininstance segmentation and object de-tection tasks after outperforming all existing models on evaluation of the Microsoft Common Objects in Context (MS COCO) …

Brain Disease Detection From Eegs: Comparing Spiking And Recurrent Neural Networks For Non-Stationary Time Series Classification, Hristo Stoev

Dissertations

Modeling non-stationary time series data is a difficult problem area in AI, due to the fact that the statistical properties of the data change as the time series progresses. This complicates the classification of non-stationary time series, which is a method used in the detection of brain diseases from EEGs. Various techniques have been developed in the field of deep learning for tackling this problem, with recurrent neural networks (RNN) approaches utilising Long short-term memory (LSTM) architectures achieving a high degree of success. This study implements a new, spiking neural network-based approach to time series classification for the purpose of …

An Evaluation Of Text Representation Techniques For Fake News Detection Using: Tf-Idf, Word Embeddings, Sentence Embeddings With Linear Support Vector Machine., Sangita Sriram

Dissertations

In a world where anybody can share their views, opinions and make it sound like these are facts about the current situation of the world, Fake News poses a huge threat especially to the reputation of people with high stature and to organizations. In the political world, this could lead to opposition parties making use of this opportunity to gain popularity in their elections. In the medical world, a fake scandalous message about a medicine giving side effects, hospital treatment gone wrong or even a false message against a practicing doctor could become a big menace to everyone involved in …

Drug Reviews: Cross-Condition And Cross-Source Analysis By Review Quantification Using Regional Cnn-Lstm Models, Ajith Mathew Thoomkuzhy

Dissertations

Pharmaceutical drugs are usually rated by customers or patients (i.e. in a scale from 1 to 10). Often, they also give reviews or comments on the drug and its side effects. It is desirable to quantify the reviews to help analyze drug favorability in the market, in the absence of ratings. Since these reviews are in the form of text, we should use lexical methods for the analysis. The intent of this study was two-fold: First, to understand how better the efficiency will be if CNN-LSTM models are used to predict ratings or sentiment from reviews. These models are known …

Classification Of Animal Sound Using Convolutional Neural Network, Neha Singh

Dissertations

Recently, labeling of acoustic events has emerged as an active topic covering a wide range of applications. High-level semantic inference can be conducted based on main audioeffects to facilitate various content-based applications for analysis, efficient recovery and content management. This paper proposes a flexible Convolutional neural network-based framework for animal audio classification. The work takes inspiration from various deep neural network developed for multimedia classification recently. The model is driven by the ideology of identifying the animal sound in the audio file by forcing the network to pay attention to core audio effect present in the audio to generate Mel-spectrogram. …

A Comparative Study Of Text Summarization On E-Mail Data Using Unsupervised Learning Approaches, Tijo Thomas

Dissertations

Over the last few years, email has met with enormous popularity. People send and receive a lot of messages every day, connect with colleagues and friends, share files and information. Unfortunately, the email overload outbreak has developed into a personal trouble for users as well as a financial concerns for businesses. Accessing an ever-increasing number of lengthy emails in the present generation has become a major concern for many users. Email text summarization is a promising approach to resolve this challenge. Email messages are general domain text, unstructured and not always well developed syntactically. Such elements introduce challenges for study …

Content-Based Filtering Recommendation Approach To Label Irish Legal Judgements, Sandesh Gangadhar

Dissertations

Machine learning approaches are applied across several domains to either simplify or automate tasks which directly result in saved time or cost. Text document labelling is one such task that requires immense human knowledge about the domain and efforts to review, understand and label the documents. The company Stare Decisis summarises legal judgements and labels them as they are made available on Irish public legal source www.courts.ie. This research presents a recommendation-based approach to reduce the time for solicitors at Stare Decisis by reducing many numbers of available labels to pick from to a concentrated few that potentially contains the …

Customer Churn Prediction, Deepshikha Wadikar

Dissertations

Churned customers identification plays an essential role for the functioning and growth of any business. Identification of churned customers can help the business to know the reasons for the churn and they can plan their market strategies accordingly to enhance the growth of a business. This research is aimed at developing a machine learning model that can precisely predict the churned customers from the total customers of a Credit Union financial institution. A quantitative and deductive research strategies are employed to build a supervised machine learning model that addresses the class imbalance problem handled feature selection and efficiently predict the …

An Examination Of The Smote And Other Smote-Based Techniques That Use Synthetic Data To Oversample The Minority Class In The Context Of Credit-Card Fraud Classification, Eduardo Parkinson De Castro

Dissertations

This research project seeks to investigate some of the different sampling techniques that generate and use synthetic data to oversample the minority class as a means of handling the imbalanced distribution between non-fraudulent (majority class) and fraudulent (minority class) classes in a credit-card fraud dataset. The purpose of the research project is to assess the effectiveness of these techniques in the context of fraud detection which is a highly imbalanced and cost-sensitive dataset. Machine learning tasks that require learning from datasets that are highly unbalanced have difficulty learning since many of the traditional learning algorithms are not designed to cope …

Machine Learning Assisted Gait Analysis For The Determination Of Handedness In Able-Bodied People, Hugh Gallagher

Dissertations

This study has investigated the potential application of machine learning for video analysis, with a view to creating a system which can determine a person’s hand laterality (handedness) from the way that they walk (their gait). To this end, the convolutional neural network model VGG16 underwent transfer learning in order to classify videos under two ‘activities’: “walking left-handed” and “walking right-handed”. This saw varying degrees of success across five transfer learning trained models: Everything – the entire dataset; FiftyFifty – the dataset with enough right-handed samples removed to produce a set with parity between activities; Female – only the female …

Identifying Online Sexual Predators Using Support Vector Machine, Yifan Li

Dissertations

A two-stage classification model is built in the research for online sexual predator identification. The first stage identifies the suspicious conversations that have predator participants. The second stage identifies the predators in suspicious conversations. Support vector machines are used with word and character n-grams, combined with behavioural features of the authors to train the final classifier. The unbalanced dataset is downsampled to test the performance of re-balancing an unbalanced dataset. An age group classification model is also constructed to test the feasibility of extracting the age profile of the authors, which can be used as features for classifier training. The …

Transformer Neural Networks For Automated Story Generation, Kemal Araz

Dissertations

Towards the last two-decade Artificial Intelligence (AI) proved its use on tasks such as image recognition, natural language processing, automated driving. As discussed in the Moore’s law the computational power increased rapidly over the few decades (Moore, 1965) and made it possible to use the techniques which were computationally expensive. These techniques include Deep Learning (DL) changed the field of AI and outperformed other models in a lot of fields some of which mentioned above. However, in natural language generation especially for creative tasks that needs the artificial intelligent models to have not only a precise understanding of the given …