Computer Engineering Commons^™

An Optimized Deep Learning-Based Framework For Predicting Diabetes Mellitus Using Ffnn, Norhan S. Elmongy, Sally M. Elghamrawy, Amr M. T. Ali-Eldin, Ali I. Eldesouky

Mansoura Engineering Journal

Diabetes mellitus (DM) is a major public health problem in Egypt, and the illness is regarded as a contemporary epidemic across the world. Diabetes is becoming more common, which is a cause for serious concern. As a result, precise and timely identification of the illness is critical. Health and research institutions have also recently expressed a serious interest in developing and implementing cutting-edge healthcare systems. Therefore, it is necessary to accurately and quickly identify the condition. To solve this issue, scientific research has been carried out, but the outcomes have fallen short. Four layers make up the proposed Diabetes mellitus …

Automation, Ai, And Future Skills Needs: An Irish Perspective, Raimunda Bukartaite, Daire Hooper

Articles

This study explores insights from key stakeholders into the skills they believe will be necessary for the future of work as we become more reliant on artificial intelligence (AI) and technology. The study also seeks to understand what human resource policies and educational interventions are needed to support and take advantage of these changes.

Machine Learning And Deep Learning Approaches For Gene Regulatory Network Inference In Plant Species, Sai Teja Mummadi

Dissertations, Master's Theses and Master's Reports

The construction of gene regulatory networks (GRNs) is vital for understanding the regulation of metabolic pathways, biological processes, and complex traits during plant growth and responses to environmental cues and stresses. The increasing availability of public databases has facilitated the development of numerous methods for inferring gene regulatory relationships between transcription factors and their targets. However, there is limited research on supervised learning techniques that utilize available regulatory relationships of plant species in public databases.

This study investigates the potential of machine learning (ML), deep learning (DL), and hybrid approaches for constructing GRNs in plant species, specifically Arabidopsis thaliana, …

A Platform For In-Situ Creation Of Markerless, Location-Based Augmented Reality Content, Brett Kidman

Dartmouth College Master’s Theses

Augmented reality (AR) renders virtual objects over a real-world physical environment. Currently, the majority of the digital content for AR is created by professional developers with knowledge of AR frameworks such as ARKit and ARCore. User-Generated Content (UGC) is critical for the future of AR, as it will not only increase the number of AR experiences to match the projected rapid growth in the user base, but also democratize content creation. However, there is a current lack of UGC authoring tools for Augmented Reality (AR) to enable users to create, save, and share location-based, markerless AR content. Location-based AR persistently …

Energy-Efficient Hmac For Wireless Communications, Cesar Enrique Castellon Escobar

UNF Graduate Theses and Dissertations

This thesis introduces the Farming Lightweight Protocol (FLP) optimized for energy-restricted environments that depend upon secure communication, such as multi-robot information gathering systems within the vision of ``smart'' agriculture. FLP uses a hash-based message authentication code (HMAC) to achieve data integrity. HMAC implementations, resting upon repeated use of the SHA256 hashing operator, impose additional resource requirements and thus also impact system availability. We address this particular integrity/availability trade-off by proposing an energy-saving algorithmic engineering method on the internal SHA256 hashing operator. The energy-efficient hash is designed to maintain the original security benefits yet reduce the negative effects on system availability. …

Efficient Gpu Implementation Of Automatic Differentiation For Computational Fluid Dynamics, Mohammad Zubair, Desh Ranjan, Aaron Walden, Gabriel Nastac, Eric Nielsen, Boris Diskin, Marc Paterno, Samuel Jung, Joshua Hoke Davis

Computer Science Faculty Publications

Many scientific and engineering applications require repeated calculations of derivatives of output functions with respect to input parameters. Automatic Differentiation (AD) is a method that automates derivative calculations and can significantly speed up code development. In Computational Fluid Dynamics (CFD), derivatives of flux functions with respect to state variables (Jacobian) are needed for efficient solutions of the nonlinear governing equations. AD of flux functions on graphics processing units (GPUs) is challenging as flux computations involve many intermediate variables that create high register pressure and require significant memory traffic because of the need to store the derivatives. This paper presents a …

Image-Based Malware Detection Using Convolutional Neural Network Techniques, Brandon Palomino

Master's Projects

In this study, we delve into the realm of malware detection and classification, leveraging the capabilities of different Convolutional Neural Networks (CNNs). Our approach involves transforming executable files into image formats and subsequently applying advanced CNN techniques for image recognition. The study emphasizes the use of two distinct CNN architectures: a traditional CNN model and a modified CNN variant known as a convolutional recurrent neural network (CRNN), each with unique structural and functional attributes. To effectively train these models, we adopt a transfer learning strategy, utilizing pre-existing CNN models that have been extensively trained on large-scale image datasets. This methodology …

Identification Of Copy Number Variations (Cnvs) Of Epigenetic Factors Related To The Progression Of Pancreatic Ductal Adenocarcinoma (Pdac), Pavithra Raju

Master's Projects

Pancreatic ductal adenocarcinoma (PDAC) is a formidable challenge in oncology due to its aggressive form and late-stage detection. PDAC is known to be influenced by various epigenetic factors like DNA methylation and histone modifications. This study focuses on copy number variations (CNVs) within epigenetic factors which for their role in early diagnosis. Thus, paving the way for identification of potential biomarkers. The epigenetic pipeline was extended based on CNVs and the CNV modified sequences extracted were compared with the wild type sequences of epigenetic PDAC genes. The epigenetic gene KCNJ11 with copy number gain of CNV id 46771406 was used …

Serverless Architecture For Machine Learning, Ikshaku Goswami

Master's Projects

Serverless computing is an area under cloud computing which does not require individual management of cloud infrastructure and services. It is the groundwork behind Function as a Service or FaaS cloud computing technique. FaaS provides a stateless event-driven orchestration of functions and services for applications deployed in the cloud, without having to manage the servers and other infrastructure resources. This event driven architecture is being well utilized to manage different web-applications and services. Machine learning can bring a unique challenge to serverless computing, as it involves high-intensive tasks which requires voluminous data. In such a scenario it becomes essential to …

Face Image And Video Analysis In Biometrics And Health Applications, Na Zhang

Graduate Theses, Dissertations, and Problem Reports (ETD)

Computer Vision (CV) enables computers and systems to derive meaningful information from acquired visual inputs, such as images and videos, and make decisions based on the extracted information. Its goal is to acquire, process, analyze, and understand the information by developing a theoretical and algorithmic model. Biometrics are distinctive and measurable human characteristics used to label or describe individuals by combining computer vision with knowledge of human physiology (e.g., face, iris, fingerprint) and behavior (e.g., gait, gaze, voice). Face is one of the most informative biometric traits. Many studies have investigated the human face from the perspectives of various different …

Identifying Potential Alzheimer’S Disease Biomarkers Beyond Amyloid-Beta And Tau, Frank Cai

Master's Projects

Alzheimer's Disease (AD) and other forms of Mild Cognitive Impairment (MCI) affect millions of people around the world. The buildup of Amyloid-Beta (Aβ) and Tau proteins in the brain produced by amyloid precursor protein (APP) has been identified as an important cofactor in the onset and progression of AD. However, although patients diagnosed with AD exhibit Aβ and Tau buildup, about 40% of the subjects with Aβ and Tau buildup are not diagnosed with AD. In this project, we hypothesize the involvement of other epigenetic interactions between APP and related genes in addition to the buildup of Aβ and Tau …

Enhancing The Queueing Process For Yioop's Scheduler, Gargi Sheguri

Master's Projects

Indexing in search engines is the process of storing information related to crawled pages to facilitate searches. A crucial determinant of the success of a search engine is the efficiency of the indexing process utilized, which greatly affects both the speed and relevancy of search results. Yioop is an open-source web search engine that employs an inverted index strategy, wherein each term is mapped to a list of the documents it appeared in while crawling.

The primary aim of this project is to better the indexing system used by Yioop, and thus improve the quality of the Search Engine Results …

Image-Based Malware Classification On Noise Extraction, Venkata Sai Sathwik Nadella

Master's Projects

Any malicious software designed to cause harm or damage to a computer system can be termed as malware. One common form of malware is as executable files. Such files are often used as a delivery mechanism for malware since they can be easily disguised as legitimate software and can be executed without raising suspicion. They are often used to exploit vulnerabilities in software, allowing malware to bypass security measures and gain access to sensitive information.

There are several methods used to detect malware in executable files, including Signaturebased detection, Behavioral-based detection, Heuristic-based detection, Sandboxing, Machine Learning and Artificial Intelligence (AI). …

30 Años De Transformación Regional. Chía Y Zipaquirá, José Zacarías Mayorga Sánchez, Luz Deicy Flórez Espinal, Viviana Andrea Caballero Moreno, Luz Mireya Rincón Mora, John Jairo Zambrano Rocha, Eduard Neftali Gutiérrez, Carlos Augusto Trujillo Padilla, Carlos Germán González Pedraza, Juan Manuel Urrego Laurin, León Fabio Salcedo Ortiz, Ana Betina Morgante Combariza, Eduardo Ernesto Castro Neira

Institucional

En el trigésimo aniversario de vida universitaria de nuestro claustro académico, la Extensión Chía, recordamos con agrado el proceso desarrollado en esta corta pero muy exitosa actividad universitaria. Conocida como la ciudad de La Luna, el municipio de Chía es un importante centro empresarial e industrial del departamento de Cundinamarca, sede de varias instituciones de educación superior. Hoy es un referente de primer orden el proceso de desarrollo económico y social que ha atravesado la provincia Sabana Centro, en especial, debido a la cercanía a la capital del país. Es un centro cultural y turístico por excelencia con una rica …

The Evolution Of Gendered Software: Products, Scientific Reasoning, Criticism, And Tools, Victoria A. E. Kratel

Human-Machine Communication

Over the past 7 decades, gendered software has become globally established. In this theoretical distribution, I outline the evolution of gendered software. The journey of gendered software started with the raw idea fueled by Alan Turing’s imitation game in the 1950s. And only shortly thereafter, in the 1960s and 1970s, the first gendered software products like Joseph Weizenbaum’s ELIZA were developed. Thus, academia took its time to not only explore technological aspects, but to further investigate the matter of gender in the 1990s CASA-paradigm (Nass et al., 1994) and Media Equation (Reeves & Nass, 1996). As these theories reasoned the …

Do People Perceive Alexa As Gendered? A Cross-Cultural Study Of People’S Perceptions, Expectations, And Desires Of Alexa, Leopoldina Fortunati, Autumn P. Edwards, Anna Maria Manganelli, Chad Edwards, Federico De Luca

Human-Machine Communication

Mainly, the scholarly debate on Alexa has focused on sexist/anti-woman gender representations in the everyday life of many families, on a cluster of themes such as privacy, insecurity, and trust, and on the world of education and health. This paper takes another stance and explores via online survey methodology how university student respondents in two countries (the United States, n = 333; and Italy, n = 322) perceive Alexa’s image and gender, what they expect from this voice-based assistant, and how they would like Alexa to be. Results of a free association exercise showed that Alexa’s image was scarcely embodied …

Towards Multipronged On-Chip Memory And Data Protection From Verification To Design And Test, Senwen Kan, Jennifer Dworak

Computer Science and Engineering Theses and Dissertations

Modern System on Chips (SoCs) generally include embedded memories, and these memories may be vulnerable to malicious attacks such as hardware trojan horses (HTHs), test access port exploitation, and malicious software. This dissertation contributes verification as well as design obfuscation solutions aimed at design level detection of memory HTH circuits as well as obfuscation to prevent HTH triggering for embedded memory during functional operation. For malicious attack vectors stemming from test/debug interfaces, this dissertation presents novel solutions that enhance design verification and securitization of an IJTAG based test access interface. Such solutions can enhance SoC protection by preventing memory test …

A Comparative Study On Blockchain-Based Electronic Health Record Systems: Performance, Privacy, And Security Between Hyperledger Fabric And Ethereum Frameworks, Md Jobair Hossain Faruk, Hossain Shahriar, Maria Valero, Xia Li

Master of Science in Software Engineering Theses

Traditional data collection, storage, and processing of Electronic Health Records (EHR) utilize centralized techniques that pose several risks of single point of failure and lean the systems to a number of internal and external data breaches that compromise their reliability and availability. Addressing the challenges of conventional database techniques and improving the overall aspects of EHR application, blockchain technology is being evaluated to find a possible solution. Blockchain refers to an emerging distributed technology and incorruptible database of records or digital events which execute, validate, and maintain by a ledger technology to provide an immutable architecture and prevent records manipulation …

Comparing Importance Of Knowledge And Professional Skill Areas For Engineering Programming Utilizing A Two Group Delphi Survey, John F. Hutton

Theses and Dissertations

All engineering careers require some level of programming proficiency. However, beginning programming classes are challenging for many students. Difficulties have been well-documented and contribute to high drop-out rates which prevent students from pursuing engineering. While many approaches have been tried to improve the performance of students and reduce the dropout rate, continued work is needed. This research seeks to re-examine what items are critical for programming education and how those might inform what is taught in introductory programming classes (CS1). Following trends coming from accreditation and academic boards on the importance of professional skills, we desire to rank knowledge and …

A Study Of Heart Disease Diagnosis Using Machine Learning And Data Mining, Intisar Ahmed

Electronic Theses, Projects, and Dissertations

Heart disease is the leading cause of death for people around the world today. Diagnosis for various forms of heart disease can be detected with numerous medical tests, however, predicting heart disease without such tests is very difficult. Machine learning can help process medical big data and provide hidden knowledge which otherwise would not be possible with the naked eye. The aim of this project is to explore how machine learning algorithms can be used in predicting heart disease by building an optimized model. The research questions are; 1) What Machine learning algorithms are used in the diagnosis of heart …

The Effects Of Virtual Reality On Mental Health Software User Satisfaction And Retention, William Hooten

Honors Theses

Mental health issues have become increasingly important in today's society. With that being said, researchers and consumers are looking for new ways to manage and treat mental health using new technologies in labs and the consumer space. This innovation has led to the presence of mobile self-help mental health applications, applications for peoples’ phones that are used to manage symptoms of mental health problems, such as depression and anxiety, track goals, meditate, and more. However, mobile mental health applications, and mobile applications in general, have a problem concerning user satisfaction and overall user retention – studies have shown that 95% …

Lung Cancer Type Classification, Mohit Ramajibhai Ankoliya

Electronic Theses, Projects, and Dissertations

Lung cancer is the third most common cancer in the U.S. This research focuses on classifying lung cancer cells based on their tumor cell, shape, and biological traits in images automatically obtained by passing through the

convolutional layers. Additionally, I classify whether the lung cell is adenocarcinoma, large cell carcinoma, squamous cell carcinoma, or normal cell carcinoma. The benefit of this classification is an accurate prognosis, leading to patients receiving proper therapy. The Lung Cancer CT(Computed Tomography) image dataset from Kaggle has been drawn with 1000 CT images of various types of lung cancer. Two state-of-the-art convolutional neural networks (CNNs) …

Large Genomes Assembly Using Mapreduce Framework, Yuehua Zhang

All Dissertations

Knowing the genome sequence of an organism is the essential step toward understanding its genomic and genetic characteristics. Currently, whole genome shotgun (WGS) sequencing is the most widely used genome sequencing technique to determine the entire DNA sequence of an organism. Recent advances in next-generation sequencing (NGS) techniques have enabled biologists to generate large DNA sequences in a high-throughput and low-cost way. However, the assembly of NGS reads faces significant challenges due to short reads and an enormously high volume of data. Despite recent progress in genome assembly, current NGS assemblers cannot generate high-quality results or efficiently handle large genomes …

Stuck-At-Fault Immunity Enhancement Of Memristor-Based Edge Ai Systems, Md. Oli-Uz-Zaman

Graduate Theses and Dissertations (2019 - present)

Deep Neural Networks (DNN) are widely used in edge AI. But the complex perception and decision-making demand the overlarge computation and make the DNN architecture very sophisticated. Memristors have multilevel resistance property that enables faster in-memory DNN computation to remove the bottleneck caused by the von Neumann architecture and CMOS technology. However, the Stuck-At-Fault (SAF) defect of memristor generated from immature fabrication and heavy device utilization makes the memristor-based edge AI commercially unavailable. To mitigate this problem, an Adaptive Mapping Method (AMM) is proposed in this project. Based on the analysis for the VGG8 model with CIFAR10 dataset, the experiment …

Effects Of Surface Noise On Printing Artifacts: An Artistic Approach To Hiding Print Artifacts, Samuel New

All Theses

This research focuses on improving the quality of Fused Filament Fabrication (FFF) 3D printing by using fractal noise to mask certain print artifacts (e.g. layer lines and stair-stepping). The use of textures is quite common in digital sculpting for aesthetic reasons. This study focuses on finding specific textures that minimize visible 3D print artifacts.

Iot In Smart Communities, Technologies And Applications., Muhammad Zaigham Abbas Shah Syed

Electronic Theses and Dissertations

Internet of Things is a system that integrates different devices and technologies, removing the necessity of human intervention. This enables the capacity of having smart (or smarter) cities around the world. By hosting different technologies and allowing interactions between them, the internet of things has spearheaded the development of smart city systems for sustainable living, increased comfort and productivity for citizens. The Internet of Things (IoT) for Smart Cities has many different domains and draws upon various underlying systems for its operation, in this work, we provide a holistic coverage of the Internet of Things in Smart Cities by discussing …

Digital Forensics For Investigating Control-Logic Attacks In Industrial Control Systems, Nauman Zubair

LSU New Orleans Theses and Dissertations

Programmable logic controllers (PLC) are required to handle physical processes and thus crucial in critical infrastructures like power grids, nuclear facilities, and gas pipelines. Attacks on PLCs can have disastrous consequences, considering attacks like Stuxnet and TRISIS. Those attacks are examples of exploits where the attacker aims to inject into a target PLC malicious control logic, which engineering software compiles as a reliable code. When investigating a security incident, acquiring memory can provide valuable insight such as runtime system activities and memory-based artifacts which may contain the attacker's footprints. The existing memory acquisition tools for PLCs require a hardware-level debugging …

Securing The Nation’S Critical Infrastructures 1st Edition - Chapter 8, Stanley Mierzwa, Lauren Spath

Center for Cybersecurity

When you consider industries that were reinvented in recent history, you will find companies that continued to morph and remain relevant and viable and those who started off valid and in favor, but alas, perhaps didn’t make the sustainable cut. Consider, for example, Blockbuster Video, they were at the forefront of providing the legal rental of movies using physical media, such as a tape or DVD. They had the opportunity to transition from physical movie rentals to online—they had a customer base and name recognition. Do you see any Blockbuster Video stores around? Chances are you do not, and this …

Enabling The Human Perception Of A Working Camera In Web Conferences Via Its Movement, Anish Shrestha

LSU Master's Theses

In recent years, video conferencing has seen a significant increase in its usage due to the COVID-19 pandemic. When casting user’s video to other participants, the videoconference applications (e.g. Zoom, FaceTime, Skype, etc.) mainly leverage 1) webcam’s LED-light indicator, 2) user’s video feedback in the software and 3) the software’s video on/off icons to remind the user whether the camera is being used. However, these methods all impose the responsibility on the user itself to check the camera status, and there have been numerous cases reported when users expose their privacy inadvertently due to not realizing that their camera is …

Device Free Indoor Localization Of Human Target Using Wifi Fingerprinting, Prasanga Neupane

LSU Master's Theses

Indoor localization of human objects has many important applications nowadays. Proposed here is a new device free approach where all the transceiver devices are fixed in an indoor environment so that the human target doesn't need to carry any transceiver device with them. This work proposes radio-frequency fingerprinting for the localization of human targets which makes this even more convenient as radio-frequency wireless signals can be easily acquired using an existing wireless network in an indoor environment. This work explores different avenues for optimal and effective placement of transmitter devices for better localization. In this work, an experimental environment is …