Engineering Commons^™

An Evaluation Of The Eeg Alpha-To-Theta And Theta-To-Alpha Band Ratios As Indexes Of Mental Workload, Bujar Raufi, Luca Longo

Articles

Many research works indicate that EEG bands, specifically the alpha and theta bands, have been potentially helpful cognitive load indicators. However, minimal research exists to validate this claim. This study aims to assess and analyze the impact of the alpha-to-theta and the theta-to-alpha band ratios on supporting the creation of models capable of discriminating self-reported perceptions of mental workload. A dataset of raw EEG data was utilized in which 48 subjects performed a resting activity and an induced task demanding exercise in the form of a multitasking SIMKAP test. Band ratios were devised from frontal and parietal electrode clusters. Building …

Schizo-Net: A Novel Schizophrenia Diagnosis Framework Using Late Fusion Multimodal Deep Learning On Electroencephalogram-Based Brain Connectivity Indices, Nitin Grover, Aviral Chharia, Rahul Upadhyay, Luca Longo

Articles

Schizophrenia (SCZ) is a serious mental condition that causes hallucinations, delusions, and disordered thinking. Traditionally, SCZ diagnosis involves the subject’s interview by a skilled psychiatrist. The process needs time and is bound to human errors and bias. Recently, brain connectivity indices have been used in a few pattern recognition methods to discriminate neuro-psychiatric patients from healthy subjects. The study presents Schizo-Net , a novel, highly accurate, and reliable SCZ diagnosis model based on a late multimodal fusion of estimated brain connectivity indices from EEG activity. First, the raw EEG activity is pre-processed exhaustively to remove unwanted artifacts. Next, six brain …

Ml-Based Online Traffic Classification For Sdns, Mohammed Nsaif, Gergely Kovasznai, Mohammed Abboosh, Ali Malik, Ruairí De Fréin

Articles

Traffic classification is a crucial aspect for Software-Defined Networking functionalities. This paper is a part of an on-going project aiming at optimizing power consumption in the environment of software-defined datacenter networks. We have developed a novel routing strategy that can blindly balance between the power consumption and the quality of service for the incoming traffic flows. In this paper, we demonstrate how to classify the network traffic flows so that the quality of service of each flow-class can be guaranteed efficiently. This is achieved by creating a dataset that encompasses different types of network traffic such as video, VoIP, game …

Exploring The Concept Of The Digital Educator During Covid-19, Fernando Jimenez, Gracia Sanchez, Jose Palma, Luis Miralles-Pechuán, Juan A. Botia

Articles

T In many machine learning classification problems, datasets are usually of high dimensionality and therefore require efficient and effective methods for identifying the relative importance of their attributes, eliminating the redundant and irrelevant ones. Due to the huge size of the search space of the possible solutions, the attribute subset evaluation feature selection methods are not very suitable, so in these scenarios feature ranking methods are used. Most of the feature ranking methods described in the literature are univariate methods, which do not detect interactions between factors. In this paper, we propose two new multivariate feature ranking methods based on …

Human Age And Gender Classification Using Convolutional Neural Networks, Eamon Kelliher

Dissertations

In a world relying ever more on human classification, this papers aims to improve on age and gender image classification through the use of Convolutional Neural Networks (CNN). Age and gender classification has become a popular area of study in the past number of years however there are still improvements to be made, particularly in the area of age classification. This research paper aims to test the currently accepted fact that CNN models are the superior model type for image classification by comparing CNN performance against Support Vector Machine performance on the same dataset. Using the Adience image classification dataset, …

A Comparison Of Instructional Efficiency Models In Third Level Education, Murali Rajendran

Dissertations

This study investigates the validity and sensitivity of a novel model of instructional efficiency: the parabolic model. The novel model is compared against state-of-the-art models present in instructional design today; Likelihood model, Deviational model and Multidimensional model. This models is based on the assumption that optimal mental workload and high performance leads to high efficiency, while other models assume that low mental workload and high performance leads to high efficiency. The investigation makes use of two instructional design conditions: a direct instructions approach to learning and its extension with a collaborative activity. A control group received the former instructional design …

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 …

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. …

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 …

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) …

Investigation Into The Application Of Personality Insights And Language Tone Analysis In Spam Classification, Colm Mcgetrick

Dissertations

Due to its persistence spam remains as one of the biggest problems facing users and suppliers of email communication services. Machine learning techniques have been very successful at preventing many spam mails from arriving in user mailboxes, however they still account for over 50% of all emails sent. Despite this relative success the economic cost of spam has been estimated as high as $50 billion in 2005 and more recently at $20 billion so spam can still be considered a considerable problem. In essence a spam email is a commercial communication trying to entice the receiver to take some positive …

An Evaluation Of The Use Of Diversity To Improve The Accuracy Of Predicted Ratings In Recommender Systems, Gillian Browne

Dissertations

The diversity; versus accuracy trade off, has become an important area of research within recommender systems as online retailers attempt to better serve their customers and gain a competitive advantage through an improved customer experience. This dissertation attempted to evaluate the use of diversity measures in predictive models as a means of improving predicted ratings. Research literature outlines a number of influencing factors such as personality, taste, mood and social networks in addition to approaches to the diversity challenge post recommendation. A number of models were applied included DecisionStump, Linear Regression, J48 Decision Tree and Naive Bayes. Various evaluation metrics …