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Articles 1 - 28 of 28
Full-Text Articles in Engineering
Stacked Convolutional Recurrent Auto-Encoder For Noise Reduction In Eeg, Eoghan Keegan
Stacked Convolutional Recurrent Auto-Encoder For Noise Reduction In Eeg, Eoghan Keegan
Dissertations
Electroencephalogram (EEG) can be used to record electrical potentials in the brain by attaching electrodes to the scalp. However, these low amplitude recordings are susceptible to noise which originates from several sources including ocular, pulse and muscle artefacts. Their presence has a severe impact on analysis and diagnoses of brain abnormalities. This research assessed the effectiveness of a stacked convolutional-recurrent auto-encoder (CR-AE) for noise reduction of EEG signal. Performance was evaluated using the signal-to-noise ratio (SNR) and peak signal-to-noise ratio (PSNR) in comparison to principal component analysis (PCA), independent component analysis (ICA) and a simple auto-encoder (AE). The Harrell-Davis quantile …
Discover Influential Mental Workload Attributes Impacting Learners Performance In Third-Level Education, Amisha Mehta
Discover Influential Mental Workload Attributes Impacting Learners Performance In Third-Level Education, Amisha Mehta
Dissertations
Human Mental Workload is an intervening variable and a fundamental concept in the discipline of Ergonomics. It is deduced from variations in performance. High or low mental workload leads to hampering of performance. Mental workload in an educational setting has been extensively researched. It is applied in instructional design but it is obscure as to which factors are majorly driving mental workload in learners. This dissertation investigates the importance of the features used in the the NASA-Task Load Index mental workload assessment instrument and their impact on the performance of learners as assessed by multiple-choice tests conducted in classrooms of …
Team Creation Methods In Practice: Understanding The Potential Effects Of Nonverbal Communication In The Leadership Of Team Formation, Troy Allen Robertson
Team Creation Methods In Practice: Understanding The Potential Effects Of Nonverbal Communication In The Leadership Of Team Formation, Troy Allen Robertson
Dissertations
Teams require leadership, even if they are self-managed. The group of individuals who make up a team must be gathered in some form or another. For self managed teams to function successfully, the first step is the process of creating the team. Many aspects may factor into the creation process. Often time is of the essence and methods to quickly assess and form teams show merit. First impressions in general are based largely on nonverbal communication. The focus of this mixed-methods concurrent embedded study is to analyze the potential effects of nonverbal communication on influencing team creation. A group of …
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 …
Finetuning Pre-Trained Language Models For Sentiment Classification Of Covid19 Tweets, Arjun Dussa
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 …
Image Instance Segmentation: Using The Cirsy System To Identify Small Objects In Low Resolution Images, Orghomisan William Omatsone
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 …
Bimodal Emotion Classification Using Deep Learning, Ashutosh Kumar Singh
Bimodal Emotion Classification Using Deep Learning, Ashutosh Kumar Singh
Dissertations
Multimodal Emotion Recognition is an emerging associative field in the area of Human Computer Interaction and Sentiment Analysis. It extracts information from each modality to predict the emotions accurately. In this research, Bimodal Emotion Recognition framework is developed with the decision-level fusion of Audio and Video modality using RAVDES dataset. Designing such frameworks are computationally expensive and require more time to train the network. Thus, a relatively small dataset has been used for the scope of this research. The conducted research is inspired by the use of neural networks for emotion classification from multimodal data. The developed framework further confirmed …
Synthetic Data Generation Using Wasserstein Conditional Gans With Gradient Penalty (Wcgans-Gp), Manhar Singh Walia
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 …
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
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
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
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
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
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
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
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
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
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 …
Detection Of Pathological Hfo Using Supervised Machine Learning And Ieeg Data, Isabel L. Sicardi Rosell
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.
Lightgwas: A Novel Genome-Wide Association Study Procedure, Bruno Ambrozio
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 …
Optimization Of Home Mortgage Mover Predictive Model Applying Geo-Spatial Analysis And Machine Learning Techniques, Natalia Riscovaia
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.
Confusion Modelling - An Estimation By Semantic Embeddings, Praveen Mohanprasad
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 …
Evaluating Bert Embeddings For Text Classification In Bio-Medical Domain To Determine Eligibility Of Patients In Clinical Trials, Saurabh Khodake
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 …
Improving Transfer Learning For Use In Multi-Spectral Data, Yuvraj Sharma
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
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 …
A Discrimination Aware Model To Predict Childhood Literacy Levels, Kate Byrne
A Discrimination Aware Model To Predict Childhood Literacy Levels, Kate Byrne
Dissertations
It is illegal in Ireland to discriminate in the provision of education on the basis of multiple characteristics including gender, race and religion. While the increased use of machine learning models can open multiple avenues to identify early intervention strategies in education, caution must be exercised to ensure that any intervention does not discriminate with respect to a protected class. Poor literacy in childhood can have long term effects as the child ages, including on employment and mental health outcomes. Early intervention is key in mitigating this. In this dissertation, a model was created that predicted the outcome of a …
Adapting Microservices In The Cloud With Faas, Mateusz Pietraszewski
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.