Physical Sciences and Mathematics Commons^™

Offline Handwritten Chinese Character Using Convolutional Neural Network: State-Of-The-Art Methods, Yingna Zhong, Kauthar Mohd Daud, Ain Najiha Binti Mohamad Nor, Richard Adeyemi Ikuesan, Kohbalan Moorthy

All Works

Given the presence of handwritten documents in human transactions, including email sorting, bank checks, and automating procedures, handwritten characters recognition (HCR) of documents has been invaluable to society. Handwritten Chinese characters (HCC) can be divided into offline and online categories. Online HCC recognition (HCCR) involves the trajectory movement of the pen tip for expressing linguistic content. In contrast, offline HCCR involves analyzing and categorizing the sample binary or grayscale images of characters. As recognition technology develops, academics' interest in Chinese character recognition has continuously increased, as it significantly affects social and economic development. Recent development in this area is promising. …

Survey On Outdoor Navigation Applications For People With Visual Impairment, Fatmaelzahraa Eltaher, Luis Miralles-Pechuán, Jane Courtney, Susan Mckeever

Datasets

Outdoor navigation is a very challenging activity for People who suffer from Blindness or Visually Impairment (PBVI). Having examined the current literature, we conclude that there are very few publications providing a nuanced understanding of how PBVI undertake a journey in an outdoor environment and what their main challenges and obstacles are. To throw some light on this gap, we conducted a questionnaire in collaboration with the National Council for the Blind Ireland (NCBI) for 49 PBVI. Our questionnaire gathers information about key aspects related to PBVI outdoor navigation such as support tools/devices, hazards, journey preparation, crossing roads, and understanding …

Csc 71010/Csci 77100: Programming Languages/Software Engineering, Raffi T. Khatchadourian

Open Educational Resources

No abstract provided.

Wala Quick Start, Raffi T. Khatchadourian

Open Educational Resources

Setting up and trying the TJ Watson Library for Analysis (WALA).

Building An Ast Eclipse Plug-In, Raffi T. Khatchadourian

Open Educational Resources

Complete the Building an AST Eclipse Plug-in assignment. Once it works, find a medium-sized open-source Java project to run your plugin on. You may want to explore GitHub. Import the project into Eclipse and run your plug-in on it. Report on the following, which may require you to change some of the source code so that it is convenient:

1. Project name.
2. Project URL.
3. Project description.
4. The number of classes in the project.
5. The number of user-defined methods in the project.
6. For each class, the number of method calls.
7. Statistics about the method calls:
   1. The total number of method calls …

Working With Control-Flow Graphs, Raffi T. Khatchadourian

Open Educational Resources

No abstract provided.

Application Of A Gene Modular Approach For Clinical Phenotype Genotype Association And Sepsis Prediction Using Machine Learning In Meningococcal Sepsis, Asrar Rashid, Arif R. Anwary, Feras Al-Obeidat, Joe Brierley, Mohammed Uddin, Hoda Alkhzaimi, Amrita Sarpal, Mohammed Toufiq, Zainab A. Malik, Raziya Kadwa, Praveen Khilnani, M. Guftar Shaikh, Govind Benakatti, Javed Sharief, Syed Ahmed Zaki, Abdulrahman Zeyada, Ahmed Al-Dubai, Wael Hafez, Amir Hussain

All Works

Sepsis is a major global health concern causing high morbidity and mortality rates. Our study utilized a Meningococcal Septic Shock (MSS) temporal dataset to investigate the correlation between gene expression (GE) changes and clinical features. The research used Weighted Gene Co-expression Network Analysis (WGCNA) to establish links between gene expression and clinical parameters in infants admitted to the Pediatric Critical Care Unit with MSS. Additionally, various machine learning (ML) algorithms, including Support Vector Machine (SVM), Naive Bayes, K-Nearest Neighbors (KNN), Decision Tree, Random Forest, and Artificial Neural Network (ANN) were implemented to predict sepsis survival. The findings revealed a transition …

An Experiment On The Effects Of Using Color To Visualize Requirements Analysis Tasks: Supplemental Material, Yesugen Baatartogtokh, Irene Foster, Alicia M. Grubb

Computer Science: Faculty Publications

Supplemental material for the paper: "An Experiment on the Effects of using Color to Visualize Requirements Analysis Tasks".
This paper is a scientific evaluation of the effectiveness and usability of EVO. We conduct an experiment to measure any effect of using colors to represent evidence pairs.

Visualizations For User-Supported State Space Exploration Of Goal Models: Supplemental Material, Yesugen Baatartogtokh, Irene Foster, Alicia M. Grubb

Computer Science: Faculty Publications

Supplemental material for the research paper entitled, "Visualizations for User-supported State Space Exploration of Goal Models". This paper presents a technique for valuation-based filtering and coloring to assist users in understanding a solution space and selecting custom states from it. This supplement contains the data from our initial evaluation and associated models.

Synthetic Heart Sound Dataset, Davoud Shariat Panah, Andrew Hines, Susan Mckeever

Datasets

The repository contains synthetic heart sound recordings. The publication related to this dataset is "Exploring the impact of noise and degradations on heart sound classification models", Biomedical Signal Processing and Control journal.

Dynamic Data Sample Selection And Scheduling In Edge Federated Learning, Mohamed Adel Serhani, Haftay Gebreslasie Abreha, Asadullah Tariq, Mohammad Hayajneh, Yang Xu, Kadhim Hayawi

All Works

Federated Learning (FL) is a state-of-the-art paradigm used in Edge Computing (EC). It enables distributed learning to train on cross-device data, achieving efficient performance, and ensuring data privacy. In the era of Big Data, the Internet of Things (IoT), and data streaming, challenges such as monitoring and management remain unresolved. Edge IoT devices produce and stream huge amounts of sample sources, which can incur significant processing, computation, and storage costs during local updates using all data samples. Many research initiatives have improved the algorithm for FL in homogeneous networks. However, in the typical distributed learning application scenario, data is generated …

Forecasting Networks Links With Laplace Characteristic And Geographical Information In Complex Networks, Muhammad Wasim, Feras Al-Obeidat, Fernando Moreira, Haji Gul, Adnan Amin

All Works

Forecasting links in a network is a crucial task in various applications such as social networks, internet traffic management, and data mining. Many studies on forecasting links in social networks and on other networks have been conducted over the last decade. In this paper, we propose a novel method based on graph Laplacian eigenmaps for predicting the geographic location of nodes in complex networks. Our method utilizes the adjacency matrix of the network and generates a scoring matrix that captures the similarity between nodes in terms of their geographic location. By transforming the distance matrices into score matrices using exponential …

Explainable Machine Learning For Evapotranspiration Prediction, Bamory Koné, Rima Grati, Bassem Bouaziz, Khouloud Boukadi

All Works

No abstract provided.

Accuracy Of Spectral Indices Assessing Fire Severity Utilizing Maximum And Minimum Pixel Values, Jarrad Mckercher, David Blake, Eddie Van Etten

Research Datasets

This data set contains all Spectral Indices created using Google Earth Engine through Google Collaborate. 16 Spectral Indices were created that utilise different image collection and pixel value parameters to map the burn severity of the 2021 Wooroloo Bushfire.