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Articles 1291 - 1320 of 4606
Full-Text Articles in Engineering
Meaningful Age-Friendly Design. Case Studies On Enabling Assistive Technology., Matteo Zallio, Damon Berry, Larry J. Leifer
Meaningful Age-Friendly Design. Case Studies On Enabling Assistive Technology., Matteo Zallio, Damon Berry, Larry J. Leifer
Conference papers
The world population is steadily ageing and the World Health Organization recently stated that 8.5 percent of people worldwide are aged 65 and over. This cohort is projected to account for 1.6 billion people by 2050. Assistive Technology has been developed over previous decades with a particular aim to support people with disabilities. With the evolution of the market and the introduction of wearable technologies and IoT-based (Internet of Things) appliances, Assistive Technology has been influenced by the discipline of Age-Friendly Design, which has been applied to meaningfully improve the autonomy of a larger segment of the population, including older …
Modern Techniques For Discovering Digital Steganography, Michael Hegarty, Anthony Keane
Modern Techniques For Discovering Digital Steganography, Michael Hegarty, Anthony Keane
Conference papers
Digital steganography can be difficult to detect and as such is an ideal way of engaging in covert communications across the Internet. This research paper is a work-in-progress report on instances of steganography that were identified on websites on the Internet including some from the DarkWeb using the application of new methods of deep learning algorithms. This approach to the identification of Least Significant Bit (LSB) Steganography using Convolutional Neural Networks (CNN) has demonstrated some efficiency for image classification. The CNN algorithm was trained using datasets of images with known steganography and then applied to datasets with images to identify …
Evaluating The Impact Of Defeasible Argumentation As A Modelling Technique For Reasoning Under Uncertainty, Lucas Rizzo
Evaluating The Impact Of Defeasible Argumentation As A Modelling Technique For Reasoning Under Uncertainty, Lucas Rizzo
Doctoral
Limited work exists for the comparison across distinct knowledge-based approaches in Artificial Intelligence (AI) for non-monotonic reasoning, and in particular for the examination of their inferential and explanatory capacity. Non-monotonicity, or defeasibility, allows the retraction of a conclusion in the light of new information. It is a similar pattern to human reasoning, which draws conclusions in the absence of information, but allows them to be corrected once new pieces of evidence arise. Thus, this thesis focuses on a comparison of three approaches in AI for implementation of non-monotonic reasoning models of inference, namely: expert systems, fuzzy reasoning and defeasible argumentation. …
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) …
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 …
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 …
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 …
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 …
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 …
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.
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. …
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 …
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 …
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 …
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 …
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 …
Saperi: Approaching Gender Gap Using Spatial Ability Training Week In High-School Context, Maria Giulia Ballatore, Gavin Duffy, Sheryl Sorby, Anita Tabacco
Saperi: Approaching Gender Gap Using Spatial Ability Training Week In High-School Context, Maria Giulia Ballatore, Gavin Duffy, Sheryl Sorby, Anita Tabacco
Conference papers
The purpose of this paper is to describe the structure of a girls summer school, “SAperI – Spatial Ability per l’Ingegneria” (in English, “Knowledge – Spatial Ability for Engineering”), and to illustrate its impact on spatial ability development and future career preferences on those who participated in the week long summer school compared to a control group that did not participate.The 5 days school,
organized by Politecnico di Torino (Italy), was included in a larger project addressing 17 years old high-school students. Thirtyseven girls actively took part in a summer school, while 167 students (both males and females) were tested …
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 …
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 …
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 …
Forecasting The Short-Term Value Of Wind Power For Risk-Aware Bidding Strategies In Single-Imbalance Price Electricity Markets, Gianni Goretti
Forecasting The Short-Term Value Of Wind Power For Risk-Aware Bidding Strategies In Single-Imbalance Price Electricity Markets, Gianni Goretti
Doctoral
The participation of wind energy in electricity markets and strategic bidding in the day-ahead market has been investigated with growing interest in recent years. However, markets adopting a single-price imbalance settlement where participants can increase their profits if they help to put the system back into balance have received very limited attention in the academic literature. In this thesis, new probabilistic models forecasting the short-term value of wind power are developed and their use in bidding in these types of markets is investigated. The proposed strategies are designed for participants who want to bid strategically in the day-ahead market to …
Tracing Sources Of Natural Organic Matter, Trihalomethanes And Metals In Groundwater From A Karst Region, Connie O'Driscoll, Eoin Mcgillicuddy, Peter Croot, Pamela Bartley, John Mcmyler, Jerome Sheahan, Liam Morrison
Tracing Sources Of Natural Organic Matter, Trihalomethanes And Metals In Groundwater From A Karst Region, Connie O'Driscoll, Eoin Mcgillicuddy, Peter Croot, Pamela Bartley, John Mcmyler, Jerome Sheahan, Liam Morrison
Articles
Groundwater offers an important source for drinking water around the world; however, groundwater quality is under increasing pressure and is particularly vulnerable in karst areas. Total organic carbon (TOC) is significantly related to groundwater quality and when not removed by water treatment processes can give rise to the formation of disinfection by-products trihalomethanes (THMs) above the level of compliance. This study investigated the source of organic matter giving rise to the THM exceedances in a groundwater supply in a karst area. Results highlighted that source water for this groundwater supply was prone to surface water infiltration linked to rainfall events; …
Can The Integration Of Bim And Gis Enhance Information Management For Large Scale Linear Infrastructure Projects?, Bim Tudublin, Brendan Kennedy
Can The Integration Of Bim And Gis Enhance Information Management For Large Scale Linear Infrastructure Projects?, Bim Tudublin, Brendan Kennedy
Capstone Reports
The integration of BIM and GIS is the subject of considerable research, particularly as the two coalesce for the purpose of information management. Large scale infrastructure projects require best practice in terms of how information is managed given the volume of information generated throughout the project lifecycle and the numerous parties requiring access to project content. This paper investigates whether the integration of BIM and GIS can enhance information management on large scale linear infrastructure projects. The research comprised a literature review and interviews with a number of BIM/GIS professionals actively working on linear infrastructure projects across the world. Some …
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.
Development Of Integrated Water Resources Planning Model For Dublin Using Weap21, Mohammed Yassin
Development Of Integrated Water Resources Planning Model For Dublin Using Weap21, Mohammed Yassin
Doctoral
Population growth, urbanisation, and climate change are predicted to impose huge pressure on water resource systems of many cities around the world including Dublin. Integrated water resources management is seen as a viable approach to address these challenges. This approach examines the water resources system in a more interconnected manner, focusing on reducing water demands, reducing reliance on fresh water supplies, reducing discharges into receiving water bodies, and creating water supply assets from storm water and wastewater. The role of mathematical modelling in designing an integrated water resources management plan is paramount as it provides a tool whereby performances of …
The Temporal And Spatial Dynamics Of Fluvial Carbon Cycling In Irish Blanket Peatland Pools And Soils, Mariya Radomski
The Temporal And Spatial Dynamics Of Fluvial Carbon Cycling In Irish Blanket Peatland Pools And Soils, Mariya Radomski
Doctoral
Blanket peatlands in Ireland are reservoirs of organic matter and have effects on carbon dioxide (CO2) flux regulation. Data on dissolved CO2 in Ireland is limited. In blanket peatlands, the drainage systems are well connected with pools, hummocks and lawns and this connectivity leads to significant variations in outflows of carbon. The excess carbon dioxide in blanket peatlands could originate from soil organic matter decomposition. Dissolved CO2 could be transported into pools via surface run-off and lateral throughflow. Peatland pools are typically supersaturated in CO2. As part of a project described in this thesis, the role of spatial and seasonal …
Strategic Decision-Making In A Turbulent Business Environment: A Dynamic Capabilities Framework For Quantity Surveying Professional Service Firms In Ireland, Michael Adesi
Doctoral
The construction industry contributes significantly to the socio-economic development of nations through infrastructure development, and job creation culminating into the growth of Gross Domestic Product (GDP). Quantity Surveying Professional Service Firms (QSPSFs) play a critical role in the construction industry by ensuring that projects are delivered within cost, required quality and duration by providing technical and knowledge-intensive services to clients, contractors and stakeholders. Irish QSPSFs are facing challenges such as tender price inflation, intense competition, a skills shortage and disruptive technology. These challenges coupled with the cyclicality of the sector create a turbulent business environment for Irish QSPSFs, yet there …
The Integration Between Strategy Formulation And Implementationin Construction Contracting Firms In Ireland, Ahmed Hassan
The Integration Between Strategy Formulation And Implementationin Construction Contracting Firms In Ireland, Ahmed Hassan
Doctoral
The role of the construction sector in Ireland is critical in terms of the economic and social development however the economic, competitive, and regulatory business environment present a myriad of complexities to firms operating within the sector. The importance of strategic planning for business survival and prosperity has been well documented over several decades of research but there remains a paucity of empirical evidence to determine the in strategic decision-making process under these volatile environmental conditions.This study addresses the perceptible gap in existing knowledge by investigating the characteristics of the strategy strategic decision-making process, strategic choices, and implementation process in …