Computer Engineering Commons^™

Imparting 3d Representations To Artificial Intelligence For A Full Assessment Of Pressure Injuries., Sofia Zahia

Electronic Theses and Dissertations

During recent decades, researches have shown great interest to machine learning techniques in order to extract meaningful information from the large amount of data being collected each day. Especially in the medical field, images play a significant role in the detection of several health issues. Hence, medical image analysis remarkably participates in the diagnosis process and it is considered a suitable environment to interact with the technology of intelligent systems. Deep Learning (DL) has recently captured the interest of researchers as it has proven to be efficient in detecting underlying features in the data and outperformed the classical machine learning …

Wetting-Driven Formation Of Present-Day Loess Structure, Yanrong Li, Weiwei Zhang, Shengdi He, Adnan Aydin

Faculty and Student Publications

© 2020 The Authors Present-day loess, especially Malan loess formed in Later Quaternary, has a characteristic structure composed of vertically aligned strong units and weak segments. Hypotheses describing how this structure forms inside original loess deposits commonly relate it to wetting-drying process. We tested this causal relationship by conducting unique experiments on synthetic samples of initial loess deposits fabricated by free-fall of loess particles. These samples were subjected to a wetting-drying cycle, and their structural evolutions were documented by close-up photography and CT scanning. Analysis of these records revealed three key stages of structural evolution: initiation (evenly distributed cracks appear …

A Mathematical Framework For Estimating Risk Of Airborne Transmission Of Covid-19 With Application To Face Mask Use And Social Distancing, Rajat Mittal, Charles Meneveau, Wen Wu

Faculty and Student Publications

© 2020 Author(s). A mathematical model for estimating the risk of airborne transmission of a respiratory infection such as COVID-19 is presented. The model employs basic concepts from fluid dynamics and incorporates the known scope of factors involved in the airborne transmission of such diseases. Simplicity in the mathematical form of the model is by design so that it can serve not only as a common basis for scientific inquiry across disciplinary boundaries but it can also be understandable by a broad audience outside science and academia. The caveats and limitations of the model are discussed in detail. The model …

A Review Paper: Analysis Of Weka Data Mining Techniques For Heart Disease Prediction System, Basma Jumaa Saleh, Ahmed Yousif Falih Saedi, Ali Talib Qasim Al-Aqbi, Lamees Abdalhasan Salman

Library Philosophy and Practice (e-journal)

Data mining is characterized as searching for useful information through very large data sets. Some of the key and most common techniques for data mining are association rules, classification, clustering, prediction, and sequential models. For a wide range of applications, data mining techniques are used. Data mining plays a significant role in disease detection in the health care industry. The patient should be needed to detect a number of tests for the disease. However, the number of tests should be reduced by using data mining techniques. In time and performance, this reduced test plays an important role. Heart disease is …

Machine Learning Approaches For Fracture Risk Assessment: A Comparative Analysis Of Genomic And Phenotypic Data In 5130 Older Men, Qing Wu, Fatma Nasoz, Jongyun Jung, Bibek Bhattarai, Mira V. Han

Public Health Faculty Publications

The study aims were to develop fracture prediction models by using machine learning approaches and genomic data, as well as to identify the best modeling approach for fracture prediction. The genomic data of Osteoporotic Fractures in Men, cohort Study (n = 5130), were analyzed. After a comprehensive genotype imputation, genetic risk score (GRS) was calculated from 1103 associated Single Nucleotide Polymorphisms for each participant. Data were normalized and split into a training set (80%) and a validation set (20%) for analysis. Random forest, gradient boosting, neural network, and logistic regression were used to develop prediction models for major osteoporotic fractures …

Analysis Of Information Security Methods In Biosystems And Application Of Intelligent Tools In Information Security Systems, Sherzod Sayfullaev

Chemical Technology, Control and Management

In this paper, the methods of information protection in bio systems are studied. The paper considers the use of intelligent tools in information security systems and the use of adaptive information security systems. Several articles on the field of information protection in bio systems are analyzed. Disadvantages and advantages of neural network technologies in modern information security systems are described. The characteristics of bio systems and the specificity of DNA, the main features of the DNA code that provide information security and functional stability of bio systems data protection structure. Application of intelligent tools to create a comprehensive adaptive protection …

Reading Robot, Gillian Watts, Andrew Myers, Sabrinna Tan, Taylor Klein, Omeed Djassemi

General Engineering

Presently, there is an insufficient availability of human experts to assist students in reading competency and comprehension. Our team’s goal was to create an improved socially assistive robot for use by therapists, teachers, and parents to help children and adults develop reading skills while they do not have access to specialists. HAPI is a socially assistive robot that we created with the goal of helping students practice their reading comprehension skills. HAPI enables a student to improve their reading skills without an educator present, while enabling educators to review the student's performance remotely. Design constraints included: physical size, weight, duration …

Edge-Cloud Iot Data Analytics: Intelligence At The Edge With Deep Learning, Ananda Mohon M. Ghosh

Electronic Thesis and Dissertation Repository

Rapid growth in numbers of connected devices, including sensors, mobile, wearable, and other Internet of Things (IoT) devices, is creating an explosion of data that are moving across the network. To carry out machine learning (ML), IoT data are typically transferred to the cloud or another centralized system for storage and processing; however, this causes latencies and increases network traffic. Edge computing has the potential to remedy those issues by moving computation closer to the network edge and data sources. On the other hand, edge computing is limited in terms of computational power and thus is not well suited for …

Ml-Medic: A Preliminary Study Of An Interactive Visual Analysis Tool Facilitating Clinical Applications Of Machine Learning For Precision Medicine, Laura Stevens, David Kao, Jennifer Hall, Carsten Görg, Kaitlyn Abdo, Erik Linstead

Engineering Faculty Articles and Research

Accessible interactive tools that integrate machine learning methods with clinical research and reduce the programming experience required are needed to move science forward. Here, we present Machine Learning for Medical Exploration and Data-Inspired Care (ML-MEDIC), a point-and-click, interactive tool with a visual interface for facilitating machine learning and statistical analyses in clinical research. We deployed ML-MEDIC in the American Heart Association (AHA) Precision Medicine Platform to provide secure internet access and facilitate collaboration. ML-MEDIC’s efficacy for facilitating the adoption of machine learning was evaluated through two case studies in collaboration with clinical domain experts. A domain expert review was also …

Implementation Of A Computer-Vision System As A Supportive Diagnostic Tool For Parkinson’S Disease, Diego Machado Reyes

Honors Theses

Parkinson’s disease is the second most common neurodegenerative disorder, affecting nearly 1 million people in the US and it is predicted that the number will keep increasing. Parkinson’s disease is difficult to diagnose due to its similarity with other diseases that share the parkinsonian symptoms and the subjectivity of its assessment, thus increasing the probabilities of misdiagnosis. Therefore, it is relevant to develop diagnostic tools that are quantitatively based and monitoring tools to improve the patient’s quality of life. Computer-based assessment systems have shown to be successful in this field through diverse approaches that can be classified into two main …

Deep Cellular Recurrent Neural Architecture For Efficient Multidimensional Time-Series Data Processing, Lasitha S. Vidyaratne

Electrical & Computer Engineering Theses & Dissertations

Efficient processing of time series data is a fundamental yet challenging problem in pattern recognition. Though recent developments in machine learning and deep learning have enabled remarkable improvements in processing large scale datasets in many application domains, most are designed and regulated to handle inputs that are static in time. Many real-world data, such as in biomedical, surveillance and security, financial, manufacturing and engineering applications, are rarely static in time, and demand models able to recognize patterns in both space and time. Current machine learning (ML) and deep learning (DL) models adapted for time series processing tend to grow in …

Scalable Profiling And Visualization For Characterizing Microbiomes, Camilo Valdes

FIU Electronic Theses and Dissertations

Metagenomics is the study of the combined genetic material found in microbiome samples, and it serves as an instrument for studying microbial communities, their biodiversities, and the relationships to their host environments. Creating, interpreting, and understanding microbial community profiles produced from microbiome samples is a challenging task as it requires large computational resources along with innovative techniques to process and analyze datasets that can contain terabytes of information.

The community profiles are critical because they provide information about what microorganisms are present in the sample, and in what proportions. This is particularly important as many human diseases and environmental disasters …

Load-Balancing Rendezvous Approach For Mobility-Enabled Adaptive Energy-Efficient Data Collection In Wsns, Jian Zhang, Jian Tang, Zhonghui Wang, Feng Wang, Gang Yu

Faculty and Student Publications

Copyright © 2020 KSII The tradeoff between energy conservation and traffic balancing is a dilemma problem in Wireless Sensor Networks (WSNs). By analyzing the intrinsic relationship between cluster properties and long distance transmission energy consumption, we characterize three node sets of the cluster as a theoretical foundation to enhance high performance of WSNs, and propose optimal solutions by introducing rendezvous and Mobile Elements (MEs) to optimize energy consumption for prolonging the lifetime of WSNs. First, we exploit an approximate method based on the transmission distance from the different node to an ME to select suboptimal Rendezvous Point (RP) on the …

Revisiting Lightweight Encryption For Iot Applications: Error Performance And Throughput In Wireless Fading Channels With And Without Coding, Yazid M. Khattabi, Mustafa M. Matalgah, Mohammed M. Olama

Faculty and Student Publications

© 2013 IEEE. Employing heavy conventional encryption algorithms in communications suffers from added overhead and processing time delay; and in wireless communications, in particular, suffers from severe performance deterioration (avalanche effect) due to fading. Consequently, a tremendous reduction in data throughput and increase in complexity and time delay may occur especially when information traverse resource-limited devices as in Internet-of-Things (IoT) applications. To overcome these drawbacks, efficient lightweight encryption algorithms have been recently proposed in literature. One of those, that is of particular interest, requires using conventional encryption only for the first block of data in a given frame being transmitted. …

Cooperative Relay Selection For Load Balancing With Mobility In Hierarchical Wsns: A Multi-Armed Bandit Approach, Jian Zhang, Jian Tang, Feng Wang

Faculty and Student Publications

© 2013 IEEE. Energy efficiency is the major concern in hierarchical wireless sensor networks(WSNs), where the major energy consumption originates from radios for communication. Due to notable energy expenditure of long-range transmission for cluster members and data aggregation for Cluster Head (CH), saving and balancing energy consumption is a tricky challenge in WSNs. In this paper, we design a CH selection mechanism with a mobile sink (MS) while proposing relay selection algorithms with multi-user multi-armed bandit (UM-MAB) to solve the problem of energy efficiency. According to the definition of node density and residual energy, we propose a conception referred to …

Timcc: On Data Freshness In Privacy-Preserving Incentive Mechanism Design For Continuous Crowdsensing Using Reverse Auction, Xiaoqiang Ma, Weiwei Deng, Feng Wang, Menglan Hu, Fei Chen, Mohammad Mehedi Hassan

Faculty and Student Publications

© 2013 IEEE. As an emerging paradigm that leverages the wisdom and efforts of the crowd, mobile crowdsensing has shown its great potential to collect distributed data. The crowd may incur such costs and risks as energy consumption, memory consumption, and privacy leakage when performing various tasks, so they may not be willing to participate in crowdsensing tasks unless they are well-paid. Hence, a proper privacy-preserving incentive mechanism is of great significance to motivate users to join, which has attracted a lot of research efforts. Most of the existing works regard tasks as one-shot tasks, which may not work very …

Increasing Performance Of Classifiers For Ssvep-Based Brain-Computer Interfaces Using Extension Methods, Ethan Douglas Webster

Legacy Theses & Dissertations (2009 - 2024)

Brain-computer interfaces (BCI) provide an alternative communication method that does not require standard physical mediums (speech, typing, etc.). These systems have been implemented to provide additional communication and control options for people with certain motor disabilities. Classification is an important part of BCI systems and consists of inferring user commands from brain activity. Supervised classification methods often achieve higher accuracy, but unsupervised classification methods are useful when training is not practical for the user. This thesis focuses on unsupervised classification algorithms used for a BCI speller application and presents extensions for two existing classifiers that improve classification accuracy and thus …

Deep Learning For Digitized Histology Image Analysis, Sudhir Sornapudi

Doctoral Dissertations

“Cervical cancer is the fourth most frequent cancer that affects women worldwide. Assessment of cervical intraepithelial neoplasia (CIN) through histopathology remains as the standard for absolute determination of cancer. The examination of tissue samples under a microscope requires considerable time and effort from expert pathologists. There is a need to design an automated tool to assist pathologists for digitized histology slide analysis. Pre-cervical cancer is generally determined by examining the CIN which is the growth of atypical cells from the basement membrane (bottom) to the top of the epithelium. It has four grades, including: Normal, CIN1, CIN2, and CIN3. In …