Computer Engineering Commons^™

Crime Data Prediction Based On Geographical Location Using Machine Learning, Sai Bharath Yarlagadda

Electronic Theses, Projects, and Dissertations

This project employs machine learning methods like K Nearest Neighbors (KNN), Random Forest, Logistic Regression, and Decision Tree algorithms to monitor crime data based on location and pinpoint areas with risks. The project implements and tunes the four models to improve the precision of predicting crime levels. These models collaborate to offer a trustworthy evaluation of crime patterns. K Nearest Neighbors (KNN) categorizes locations by examining the proximity of data points considering coordinates and other factors to identify trends linked to increased crime data. Logistic Regression gauges the likelihood of crime incidents by studying the connection, between factors (like location …

Development Of An Efficient Multi-Objective Approach For Secure Live Virtual Machine Migration, Venkata Subramanian N

Theses and Dissertations

Cloud computing offers organizations flexibility and cost-efficiency through pay-asyou- go services, allowing them to scale resources according to their needs and reduce expenditures. Cloud as a Service (CaaS) offloads IT management complexities, while Cloud Data Center (CDC) provides infrastructure for on-demand, scalable, and flexible services over the Internet. Virtualization improves operational efficiency by providing simultaneous access to multiple virtual machines, while Live Virtual Machine Migration enhances agility, resilience, resource allocation, and fault tolerance.

However, achieving effective VMM requires forecasting cloud resource utilization, selecting the right target host, and ensuring security. Live VM migration is inevitable for optimizing CDC resource utilization. …

Industria 4.0. Internet De Las Cosas: Ciberseguridad Y Aplicaciones, Jairo Eduardo Márquez Díaz, Arles Prieto Moreno, Luz Jaddy Castañeda Rodríguez, Luis Gonzalo Benavides Ramírez

Ingeniería

Este libro explora cómo la Cuarta Revolución Industrial, también conocida como Industria 4.0, está transformando sectores industriales mediante tecnologías disruptivas como el internet de las cosas (IoT), la inteligencia artificial, el big data y la realidad aumentada, lo que ofrece una mayor eficiencia, productividad y seguridad. Aborda los riesgos de ciberseguridad asociados y propone soluciones basadas en la inteligencia artificial y el blockchain. Además, analiza el uso de drones y tecnologías como el LiDAR en la minería, lo que mejora la exploración, la seguridad y la sostenibilidad de operaciones complejas. Con un enfoque en la minería del carbón en Colombia, …

Criminal Confrontation Of The Crime Committed Via An Automated Robot In Libyan And Emirati Law, . Mashaallah Alzwae

Journal of Police and Legal Sciences

Today's world is witnessing an important development in telecommunications, information technology and computers that has resulted in what are known as automated robots as one of the most important applications of artificial intelligence and has increased reliance on them in various areas of life for the importance of the services they provide to humanity. However, such robots may be used to commit an offence and the study therefore aims to determine the effectiveness of legal texts in the face of the offence from which they may occur. The study required an analytical and comparative approach by analysing and comparing the …

“The Role Of Modern Technologies In Effective Management Of Crowds”, Ayman El-Din

Journal of Police and Legal Sciences

Scientists started to think about the importance of developing crowd management methods, because of human gatherings - in different countries - have been accompanied by lots of disasters that have killed many and resulted in many injuries. Gathering crowd data is vital in the process of managing crowds, as crowd information contributes significantly to planning and decision-making processes and increasing communication between parties interacting with them, and data collected includes "the number of crowd members, crowd density, and problems that may occur”. Technology and technical awareness play an important role in “collecting, organizing, analyzing” real-time collected data of crowd. This …

Authenticated Diagnosing Of Covid-19 Using Deep Learning-Based Ct Image Encryption Approach, Mohamed Attia Abdelgwad, Amira Hassan Abed, Mahmoud Bahloul

Future Computing and Informatics Journal

Researchers are motivated to use artificial intelligence in biometrics, medical imaging encryption, as well as cybersecurity due to its rapid progress. An encryption method for CT scans—which are used to diagnose COVID-19 disease—is proposed in this study. The suggested encryption method creates a connection among an individual's face picture and CT image to increase confidentiality. The simple CT picture is first enhanced with a host image. An encryption key is multiplied by the final result. This key is produced by applying a Convolutional Neural Network (CNN) to recognize characteristics from people's face photographs. Additionally, a straightforward CNN with three convolutional …

Experimentation With Speech Recognition And Word Error Rates, Sarah Zelikovitz, Orit D. Gruber

Open Educational Resources

In this lab you will be using the speech recognition program in Windows. After the initial training session, you will proceed to conduct experiments to evaluate the accuracy of the speech program.

At the end of this lab, you will be able to answer the following questions:

What is the purpose of speech recognition programs ?

• What are some applications of speech recognition programs ?

• What is the metric to evaluate speech recognition programs?

• What parameters are used to test speech recognition programs?

Problem Solving / Javascript Programming, Sarah Zelikovitz, Orit D. Gruber

Open Educational Resources

This Lab Experiment focuses on JavaScript Programming. Upon completing the lab, you will be able to understand the following:

· The definition of Algorithmic Problem Solving.

· The role of JavaScript in web pages.

· The concept of Iteration in computer programming.

Digimindready: Enhancing Military Readiness Through Edge Ai-Driven Wellness, Education, And Digital Discipline Via Privacy-First Mhealth Innovation, Md Mehedi Hasan

Master's Theses

Military personnel often find themselves in intense situations that require high focus. Successfully engaging in these dangerous missions means they must efficiently control cognitive load, manage overwhelming stress, and stay focused through distractions to perform at their best. Military training significantly focuses on human performance, which benefits military readiness. The 21st century has introduced unanticipated challenges to all, such as the adverse effects of excessive screen time, external distractions, and over-reliance on technology without being aware of digital discipline. Militaries are no exception. These challenges have become an emerging threat to military personnel's cognitive, emotional, and physical well-being. On top …

A Comparative Study Of The Npm, Pypi, Maven, And Rubygems Open-Source Communities, Saurav Gupta

Master's Theses

Open-source software (OSS) ecosystems, defined as environments composed of package managers and programming languages (e.g., NPM for JavaScript), are essential for software development and foster collaboration and innovation. Although their significance is acknowledged, understanding what makes OSS communities healthy and sustainable requires further exploration. This thesis quantitatively assesses the health of OSS projects and communities within the NPM, PyPI, Maven, and RubyGems ecosystems. We explore five research questions addressing project standards, community responsiveness, contribution distribution, contributor retention, and newcomer integration strategies. Our analysis shows varied documentation practices, insider engagement levels, and contribution patterns. Our findings highlight both strengths and different …

Sequential Memory Generation For Cognitive Models, Eben Miles Sherwood

Master's Theses

Understanding the process of memory formation in neural systems is of great interest in the field of neuroscience. Valiant’s Neuroidal Model poses a plausible theory for how memories are created within a computational context. Previously, the algorithm JOIN has been used to show how the brain could perform conjunctive and disjunctive coding to store memories. A limitation of JOIN is that it does not consider the coding of temporal information in a meaningful manner. We propose SeqMem, a similar algorithmic primitive that is designed to encode a series of items within a random graph model. We investigate the feasibility of …

Optimal False Data Injection (Fdi) In Simulated Cooperative Adaptive Cruise Control (Cacc) Systems, Lovro Dukic

Master's Theses

In the rapidly advancing field of autonomous vehicles, ensuring the security and reliability of self-driving systems is crucial. Autonomous vehicle systems, such as cooperative adaptive cruise control (CACC), must undergo significant research and testing before their integration into commercial intelligent transportation systems. CACC considers multiple vehicles in close proximity as a single entity, or platoon, with each vehicle equipped with a controller that uses sensor-based measurements and vehicle-to-vehicle (V2V) communication to control inter-vehicle spacing. While this system offers numerous potential benefits for traffic safety and efficiency, it is also susceptible to False Data Injection (FDI) attacks, which can cause the …

Communication Challenges In Underwater Wireless Networks: Mac Protocols And Software Solutions, Dmitrii Dugaev

Dissertations, Theses, and Capstone Projects

Underwater wireless networks (UWNs) represent a diverse and intriguing research domain, encompassing a wide array of scientific and industrial applications. This dissertation delves into the communication challenges at the Medium Access Control (MAC) layer within UWNs, stemming from the distinctive signal propagation conditions and the harshness of the deployment environment. The manuscript provides comprehensive coverage of key aspects of UWNs, including potential applications, communication protocols, methodologies employed in such networks, and existing software solutions that facilitate simulation, emulation, and real testbed scenarios for underwater research endeavors. Furthermore, this research introduces innovative software and communication solutions designed to facilitate the seamless …

Design Of Multi-Objective Optimization Algorithms For Vlsi Floor Planning, Srinivasan B

Theses and Dissertations

VLSI floorplanning is a key design step that determines the optimal placement of circuit modules to minimize chip area, wire length, and heat generation. Existing swarm intelligence–based metaheuristics improve area and wire length but often ignore thermal effects.

To address this, the Multi-Objective Firefly Optimization–based Floorplanning (MOFO-FP) technique is introduced, using a Heat-Aware Firefly Optimization (HAFO) algorithm that minimizes heat, space, and wire length under fixed outline constraints. Each firefly represents a floorplan, with brightness indicating solution quality; dimmer fireflies move toward brighter ones to find optimal placements.

A second method, the Hybridized Multicriteria Ant Colony and …

Toward Intuitive 3d Interactions In Virtual Reality: A Deep Learning- Based Dual-Hand Gesture Recognition Approach, Trudi Di Qi, Franceli L. Cibrian, Meghna Raswan, Tyler Kay, Hector M. Camarillo-Abad, Yuxin Wen

Engineering Faculty Articles and Research

Dual-hand gesture recognition is crucial for intuitive 3D interactions in virtual reality (VR), allowing the user to interact with virtual objects naturally through gestures using both handheld controllers. While deep learning and sensor-based technology have proven effective in recognizing single-hand gestures for 3D interactions, research on dual-hand gesture recognition for VR interactions is still underexplored. In this work, we introduce CWT-CNN-TCN, a novel deep learning model that combines a 2D Convolution Neural Network (CNN) with Continuous Wavelet Transformation (CWT) and a Temporal Convolution Network (TCN). This model can simultaneously extract features from the time-frequency domain and capture long-term dependencies using …

Beyond The Horizon: Exploring Anomaly Detection Potentials With Federated Learning And Hybrid Transformers In Spacecraft Telemetry, Juan Rodriguez

Computer Science and Engineering Theses and Dissertations

Telemetry sensors play a crucial role in spacecraft operations, providing essential data on efficiency, sustainability, and safety. However, identifying irregularities in telemetry data can be a time-consuming process that risks the success of missions. With the rise of CubeSats and smallsats, telemetry data has become more abundant, but concerns about privacy and scalability have resulted in untapped data potential. To address these issues, we propose a new approach to anomaly detection that utilizes machine learning models at data sources. These models solely transmit weights to a centralized server for aggregation, resulting in improved dataset performance with a single global model. …

Development Of Hybrid Multi Criteria Decision Making Techniques For Efficient Cloud Service Selection, Obulaporam Gireesha

Theses and Dissertations

During the past few decades, cloud computing became a primary driver for the next generation of digital technology due to the increase in organizational performance and profitability based on a ‘pay-as-you-use’ fashion at anytime and anywhere across the globe. Cloud computing enables various enterprises to access pooled resources (like storage, network bandwidth, software applications, processing power, etc.) over the Internet with minimal Information Technology (IT) infrastructure and capital expenditure.

Indeed, the enormous popularity of cloud computing in both academia & industry over the decade has resulted in a wide range of similar cloud services offered by numerous service providers. Even …

Neuro-Symbolic Commonsense Reasoning With Resistance To Data Poisoning: A First-Order Logic And Sub-Symbolic Embeddings Framework, Bryce Shurts, King-Ip Lin

Computer Science and Engineering Theses and Dissertations

Commonsense reasoning has long presented a hurdle between conversational agents and their ability to naturally engage with humans in conversation, as the infinitely dimensional nature of a dialogue’s topics presents a significant reasoning challenge in the study of Natural Language Understanding (NLU). Such a system must conceivably be able to act as a generalizable system for evaluating and reasoning about commonsense statements, problems, and queries: in this way, the agent can attempt to quantify the reasonability of a given input. We attempt to address this through the integration of an explainable neuro-symbolic system that leverages Logical Tensor Networks (LTNs) and …

Generalized Model To Enable Zero-Shot Imitation Learning For Versatile Robots, Yongshuai Wu

Master's Theses

The rapid advancement in Deep Learning (DL), especially in Reinforcement Learning (RL) and Imitation Learning (IL), has positioned it as a promising approach for a multitude of autonomous robotic systems. However, the current methodologies are predominantly constrained to singular setups, necessitating substantial data and extensive training periods. Moreover, these methods have exhibited suboptimal performance in tasks requiring long-horizontal maneuvers, such as Radio Frequency Identification (RFID) inventory, where a robot requires thousands of steps to complete.

In this thesis, we address the aforementioned challenges by presenting the Cross-modal Reasoning Model (CMRM), a novel zero-shot Imitation Learning policy, to tackle long-horizontal robotic …

Brain Computer Interface-Based Drone Control Using Gyroscopic Data From Head Movements, Ikaia Cacha Melton

Honors College Theses

This research explores the potential of using gyroscopic data from a person’s head movement to control a DJI Tello quadcopter via a Brain-Computer Interface (BCI). In this study, over 100 gyroscopic recordings capturing the X, Y and Z columns (formally known as GyroX, GyroY, GyroZ) between 4 volunteers with the Emotiv Epoc X headset were collected. The Emotiv Epoc X data captured (left, right, still, and forward) head movements of each participant associated with the DJI Tello quadcopter navigation. The data underwent thorough processing and analysis, revealing distinctive patterns in charts using Microsoft Excel. A Python condition algorithm was then …

Practical Static Binary Instrumentation Attacks Against Binary Stylometry, Justin Lee Carpenter

Masters Theses, 2020-current

Binary stylometry aims to find the features in a binary computer program and use them to identify the developers of the corresponding source code. Despite the noises in the code compilation process from the compiler, assembler, linker, and library functions, two existing studies based on machine learning for binary stylometry have reported high success rates (Alrabaee, Shirani, Wang, Debbabi, and Hanna 2018; Caliskan, Yamaguchi, Dauber, Harang, Rieck, Greenstadt, and Narayanan 2018). In this thesis, we first observe that both existing studies are based on a largely benign security model and assume that the binaries used in testing and prediction are …

Improving Ethics Surrounding Collegiate-Level Hacking Education: Recommended Implementation Plan & Affiliation With Peer-Led Initiatives, Shannon Morgan, Dr. Sanjay Goel

Military Cyber Affairs

Cybersecurity has become a pertinent concern, as novel technological innovations create opportunities for threat actors to exfiltrate sensitive data. To meet the demand for professionals in the workforce, universities have ramped up their academic offerings to provide a broad range of cyber-related programs (e.g., cybersecurity, informatics, information technology, digital forensics, computer science, & engineering). As the tactics, techniques, and procedures (TTPs) of hackers evolve, the knowledge and skillset required to be an effective cybersecurity professional have escalated accordingly. Therefore, it is critical to train cyber students both technically and theoretically to actively combat cyber criminals and protect the confidentiality, integrity, …

Using Digital Twins To Protect Biomanufacturing From Cyberattacks, Brenden Fraser-Hevlin, Alec W. Schuler, B. Arda Gozen, Bernard J. Van Wie

Military Cyber Affairs

Understanding of the intersection of cyber vulnerabilities and bioprocess regulation is critical with the rise of artificial intelligence and machine learning in manufacturing. We detail a case study in which we model cyberattacks on network-mediated signals from a novel bioreactor, where it is important to control medium feed rates to maintain cell proliferation. We use a digital twin counterpart reactor to compare glucose and oxygen sensor signals from the bioreactor to predictions from a kinetic growth model, allowing discernment of faulty sensors from hacked signals. Our results demonstrate a successful biomanufacturing cyberattack detection system based on fundamental process control principles.

Characterizing Advanced Persistent Threats Through The Lens Of Cyber Attack Flows, Logan Zeien, Caleb Chang, Ltc Ekzhin Ear, Dr. Shouhuai Xu

Military Cyber Affairs

Effective cyber defense must build upon a deep understanding of real-world cyberattacks to guide the design and deployment of appropriate defensive measures against current and future attacks. In this abridged paper (of which the full paper is available online), we present important concepts for understanding Advanced Persistent Threats (APTs), our methodology to characterize APTs through the lens of attack flows, and a detailed case study of APT28 that demonstrates our method’s viability to draw useful insights. This paper makes three technical contributions. First, we propose a novel method of constructing attack flows to describe APTs. This abstraction allows technical audiences, …

Machine Learning Security For Tactical Operations, Dr. Denaria Fields, Shakiya A. Friend, Andrew Hermansen, Dr. Tugba Erpek, Dr. Yalin E. Sagduyu

Military Cyber Affairs

Deep learning finds rich applications in the tactical domain by learning from diverse data sources and performing difficult tasks to support mission-critical applications. However, deep learning models are susceptible to various attacks and exploits. In this paper, we first discuss application areas of deep learning in the tactical domain. Next, we present adversarial machine learning as an emerging attack vector and discuss the impact of adversarial attacks on the deep learning performance. Finally, we discuss potential defense methods that can be applied against these attacks.

Securing The Void: Assessing The Dynamic Threat Landscape Of Space, Brianna Bace, Dr. Unal Tatar

Military Cyber Affairs

Outer space is a strategic and multifaceted domain that is a crossroads for political, military, and economic interests. From a defense perspective, the U.S. military and intelligence community rely heavily on satellite networks to meet national security objectives and execute military operations and intelligence gathering. This paper examines the evolving threat landscape of the space sector, encompassing natural and man-made perils, emphasizing the rise of cyber threats and the complexity introduced by dual-use technology and commercialization. It also explores the implications for security and resilience, advocating for collaborative efforts among international organizations, governments, and industry to safeguard the space sector.

Commercial Enablers Of China’S Cyber-Intelligence And Information Operations, Ethan Mansour, Victor Mukora

Military Cyber Affairs

In a globally commercialized information environment, China uses evolving commercial enabler networks to position and project its goals. They do this through cyber, intelligence, and information operations. This paper breaks down the types of commercial enablers and how they are used operationally. It will also address the CCP's strategy to gather and influence foreign and domestic populations throughout cyberspace. Finally, we conclude with recommendations for mitigating the influence of PRC commercial enablers.

Analysis And Evaluation Of Different Transformer Architectures For The Protein Sequence Representation And The Corresponding Hypothetical Applications, Mary Mao

Theses and Dissertations

This project is to investigate and assess several Transformer topologies for the modeling of protein sequences and their corresponding uses. Each major model for the protein sequence representations is inspected with its mathematical theory and analyzed for the different performance of the models with various validation repositories.

Analyzing Information Cascades Through Machine Learning And Data Analytics, Betul Agirman

Honors Scholar Theses

In today's digital age, social media platforms have become pivotal in influencing public opinion and behavior, with information spreading being both beneficial and detrimental. This rapid spread is typically called an information cascade, and they are important in further understanding social influence, managing misinformation, and even predicting potential trends of public responses. With social media, people are connected so easily to one another like a network, wherein it becomes possible for them to influence each other’s behavior and decisions. Utilizing a dataset from Weibo that spans critical periods of the COVID-19 outbreak, this study integrates machine learning and data analytics …

Adaptive Solutions For Improving The Quality Of Mobile User Experiences, Meena V

Theses and Dissertations

Nowadays, mobile applications have drawn a lot of attention as they bring computational and storage resources close to consumers globally through high-speed networks. Applications such as the medical microscope, 2D barcode reader, environmental sensor(s), mobile security and authenticator, vehicle remote controller, and IoT-based synchronizer come pre- applications are resource-intensive, as it performs computation(s) by utilizing diverse services like location, app-tracking, networking, camera, calendar, contacts, Bluetooth, etc. for each user activity. Parallel execution of such intense services might utilize the utmost memory and CPU of the mobile device which in turn degrades the overall Quality of Experience (QoE). This ultimately ends …