Engineering Commons^™

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 …

Adapting Microservices In The Cloud With Faas, Mateusz Pietraszewski

Dissertations

This project involves benchmarking, microservices and Function-as-a-service (FaaS) across the dimensions of performance and cost. In order to do a comparison this paper proposes a benchmark framework.

Lightgwas: A Novel Genome-Wide Association Study Procedure, Bruno Ambrozio

Dissertations

This dissertation proposes LightGWAS, a novel machine learning procedure for genome-wide association study (GWAS) based on LightGBM and k-fold cross-validation. The conducted literature review identified that the currently available GWAS implementations rely on massive manual quality control steps to address statistical issues, such as controlling for false-positive inflation and power reduction. It also showed they demand a specific GWAS method for each type of genomic dataset morphology, which consequently increases the human dependency and open margins for misleadings. LightGWAS is a potential single, resilient, autonomous and scalable solution to address such concerns. Through this research, LightGWAS was contrasted against the …

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) …

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 …

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 …

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 …

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 …

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 …

Detection Of Pathological Hfo Using Supervised Machine Learning And Ieeg Data, Isabel L. Sicardi Rosell

Dissertations

Epilepsy is the second most common neurological disorder and it affects approxi mately 50 million people worldwide. One of the main characteristics of this disorder is the presence of recurrent seizures which tend to be controlled through medication. Nonetheless, 20% of the patients with this disorder are resistant to drug treatment meaning that they need to go through alternative procedures.

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. …

Synthetic Data Generation Using Wasserstein Conditional Gans With Gradient Penalty (Wcgans-Gp), Manhar Singh Walia

Dissertations

With data protection requirements becoming stricter, the data privacy has become increasingly important and more crucial than ever. This has led to restrictions on the availability and dissemination of real-world datasets. Synthetic data offers a viable solution to overcome barriers of data access and sharing. Existing data generation methods require a great deal of user-defined rules, manual interactions and domainspecific knowledge. Moreover, they are not able to balance the trade-off between datausability and privacy. Deep learning based methods like GANs have seen remarkable success in synthesizing images by automatically learning the complicated distributions and patterns of real data. But they …

Confusion Modelling - An Estimation By Semantic Embeddings, Praveen Mohanprasad

Dissertations

Approaching the task of coherence assessment of a conversation from its negative perspective ‘confusion’ rather than coherence itself, has been attempted by very few research works. Influencing Embeddings to learn from similarity/dissimilarity measures such as distance, cosine similarity between two utterances will equip them with the semantics to differentiate a coherent and an incoherent conversation through the detection of negative entity, ‘confusion’. This research attempts to measure coherence of conversation between a human and a conversational agent by means of such semantic embeddings trained from scratch by an architecture centralising the learning from the distance between the embeddings. State of …

Finetuning Pre-Trained Language Models For Sentiment Classification Of Covid19 Tweets, Arjun Dussa

Dissertations

It is a common practice in today’s world for the public to use different micro-blogging and social networking platforms, predominantly Twitter, to share opinions, ideas, news, and information about many things in life. Twitter is also becoming a popular channel for information sharing during pandemic outbreaks and disaster events. The world has been suffering from economic crises ever since COVID-19 cases started to increase rapidly since January 2020. The virus has killed more than 800 thousand people ever since the discovery as per the statistics from Worldometer [1] which is the authorized tracking website. So many researchers around the globe …

Improving Transfer Learning For Use In Multi-Spectral Data, Yuvraj Sharma

Dissertations

Recently Nasa as well as the European Space Agency have made observational satellites images public. The main reason behind opening it to public is to foster research among university students and corporations alike. Sentinel is a program by the European Space Agency which has plans to release a series of seven satellites in lower earth orbit for observing land and sea patterns. Recently huge datasets have been made public by the Sentinel program. Many advancements have been made in the field of computer vision in the last decade. Krizhevsky, Sutskever & Hinton, 2012, revolutionized the field of image analysis by …

Investigating Effect Of Amount Of Augmented Data On Performance Of Convolutional Neural Network For Multiclass Image Classification, Shivam Khandelwal

Dissertations

This research project seeks to investigate the use of Image Data augmentation that generates synthetic data by adding distortions to original images, as a means of replacement to a large amount of real data used to train the Convolutional Neural Networks. The purpose of the research project is to assess the effectiveness of augmented data over the real data by comparing the performance of the model trained with various amounts of augmented training and validation data ratio. Deep learning tasks involving convolutional neural networks have difficulty in generalizing the models effectively for computer vision tasks when the training dataset is …

Investigating The Predictability Of A Chaotic Time-Series Data Using Reservoir Computing, Deep-Learning And Machine- Learning On The Short-, Medium- And Long-Term Pricing Of Bitcoin And Ethereum., Molly Kenny

Dissertations

This study will investigate the predictability of a Chaotic time-series data using Reservoir computing (Echo State Network), Deep-Learning(LSTM) and Machine- Learning(Linear, Bayesian, ElasticNetCV , Random Forest, XGBoost Regression and a machine learning Neural Network) on the short (1-day out prediction), medium (5-day out prediction) and long-term (30-day out prediction) pricing of Bitcoin and Ethereum Using a range of machine learning tools, to perform feature selection by permutation importance to select technical indicators on the individual cryptocurrencies, to ensure the datasets are the best for predictions per cryptocurrency while reducing noise within the models. The predictability of these two chaotic time-series …

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 …

Evaluating Bert Embeddings For Text Classification In Bio-Medical Domain To Determine Eligibility Of Patients In Clinical Trials, Saurabh Khodake

Dissertations

Clinical Trials are studies conducted by researchers in order to assess the impact of new medicine in terms of its efficacy and most importantly safety on human health. For any advancement in the field of medicine it is very important that clinical trials are conducted with right ethics supported by scientific evidence. Not all people who volunteer or participate in clinical trials are allowed to undergo the trials. Age, comorbidity and other health issues present in a patient can be a major factor to decide whether the profile is suitable or not for the trial. Profiles selected for clinical trials …

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 …

Optimization Of Home Mortgage Mover Predictive Model Applying Geo-Spatial Analysis And Machine Learning Techniques, Natalia Riscovaia

Dissertations

In the last decade digital innovations and online banking services have significantly changed customers banking preferences and behaviour. Banking industry is going through the changes and developments in the provision of banking services that are affecting the structure and the organization of the bank network. However, private home loan, referred as Home Mortgage hereinafter, continue to remain among the products, that customers prefer to have personal interaction about with professional advisors prior making the decision to apply for the loan with financial institution.

Enhanced Label Free Normal And Cancer Cells Classification Using Combined Parametric Modeling And Optical Techniques, Ayshathul Fouzia Abdul Gani

Dissertations

The development of label-free methods for cell classifications has been driven by the importance of early detection and identification of cancer disease. The future point-of-care (POC) treatment methods require rapid and real-time cancer screening techniques. As the labelled methods of cell classification are time-consuming processes and require a large amount of sample preparation along with skilled persons, they do not appear to be suitable for POC treatment methods. This necessitates the importance of such development. The label-free methods incorporate the biophysical properties of cells instead of biomarkers. The optical properties of cells have been frequently utilized for cell classification. This …

Quantitative Metrics For Mutation Testing, Amani M. Ayad

Dissertations

Program mutation is the process of generating versions of a base program by applying elementary syntactic modifications; this technique has been used in program testing in a variety of applications, most notably to assess the quality of a test data set. A good test set will discover the difference between the original program and mutant except if the mutant is semantically equivalent to the original program, despite being syntactically distinct.

Equivalent mutants are a major nuisance in the practice of mutation testing, because they introduce a significant amount of bias and uncertainty in the analysis of test results; indeed, mutants …

Customized Boron And Magnesium-Based Reactive Materials Prepared By High Energy Mechanical Milling, Xinhang Liu

Dissertations

New reactive materials need to be developed having biocidal combustion products. When ignited, such material can add chemical biocidal effects to the common effects of high temperature and pressure. Biocidal combustion products are capable of deactivating harmful spores or bacteria, which can be released by targets containing biological weapons of mass destruction. Research showed that halogens, especially iodine, are effective as biocidal components of reactive material formulations. Recently, magnesium combustion product MgO is also found to have a biocidal effect. Thus, advanced formulations containing both magnesium and iodine are of interest; such formulations are prepared and investigated here.

Reactive materials …

Perivascular Waste Metabolites Clearance In Central Nervous System (Cns), Yiming Cheng

Dissertations

Efficient clearance of interstitial waste metabolites is essential for normal brain homeostasis. Such effective clearance is hampered by the lack of a lymphatic system in the brain, and the cerebrospinal fluid (CSF) is unable to clear large size waste metabolites in the brain. Here, a novel idea that brain arterial endothelium and smooth muscle cells reactivity regulates the clearance of these water-insoluble large size waste metabolites through the perivascular dynamic exchange, and that low dose ethanol promotes this perivascular clearance is proposed.

In Aim 1, the biodistribution of a large size waste metabolite (Amyloid-β protein mimic) in rat perivascular space …

Bio-Inspired Learning And Hardware Acceleration With Emerging Memories, Shruti R. Kulkarni

Dissertations

Machine Learning has permeated many aspects of engineering, ranging from the Internet of Things (IoT) applications to big data analytics. While computing resources available to implement these algorithms have become more powerful, both in terms of the complexity of problems that can be solved and the overall computing speed, the huge energy costs involved remains a significant challenge. The human brain, which has evolved over millions of years, is widely accepted as the most efficient control and cognitive processing platform. Neuro-biological studies have established that information processing in the human brain relies on impulse like signals emitted by neurons called …

Cerebro-Vascular Disruption Mediated Initiation And Propagation Of Traumatic Brain Injury In A Fluid Percussion Injury Model, Xiaotang Ma

Dissertations

Traumatic brain injury (TBI) is a major health problem for over 3.17 million people in the US. There is no FDA-approved drug for the treatment because the injury mechanisms have not been clearly identified. The knowledge gap is addressed here by the lateral fluid percussion injury (FPI) rat model, through the understanding of layer-structured mechanisms from physical vascular rupture to acute necrosis, as well as biochemical changes in perivascular space as secondary events.

Firstly, the cerebrovascular hemorrhage and related infarct volume are investigated as the primary events in moderate FPI, which is found to be increased with injury severity in …

Using Machine Learning Classification Methods To Detect The Presence Of Heart Disease, Nestor Pereira

Dissertations

Cardiovascular disease (CVD) is the most common cause of death in Ireland, and probably, worldwide. According to the Health Service Executive (HSE) cardiovascular disease accounting for 36% of all deaths, and one important fact, 22% of premature deaths (under age 65) are from CVD.

Using data from the Heart Disease UCI Data Set (UCI Machine Learning), we use machine learning techniques to detect the presence or absence of heart disease in the patient according to 14 features provide for this dataset. The different results are compared based on accuracy performance, confusion matrix and area under the Receiver Operating Characteristics (ROC) …

Factor Analysis Of Mixed Data (Famd) And Multiple Linear Regression In R, Nestor Pereira

Dissertations

In the previous projects, it has been worked to statistically analysis of the factors to impact the score of the subjects of Mathematics and Portuguese for several groups of the student from secondary school from Portugal.

In this project will be interested in finding a model, hypothetically multiple linear regression, to predict the final score, dependent variable G3, of the student according to some features divide into two groups. One group, analyses the features or predictors which impact in the final score more related to the performance of the students, means variables like study time or past failures. The second …

The Effect Of Precursor Design And Processing On The Semi-Crystalline Morphologies Of Polyacrylonitrile-Based Carbon Fiber, Katelyn Cordell

Dissertations

Basic research to control the morphology of polyacrylonitrile (PAN)-based carbon fiber is crucial for next generation composites as it determines their mechanical properties and final use. Poor molecular design of PAN-based precursors and fiber processing causes morphological defects and mechanical limitations.^1,2 This research focused on utilizing the controlled polymerization technique, reversible addition-fragmentation chain transfer (RAFT), of novel acrylamide comonomers to afford well-defined precursors with precisely controlled molecular design. This controlled RAFT technique improved the overall precursor graphitic structure as evident by the increased extent of stabilization and reduced activation energy as compared to precursors prepared by traditional free radical …