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Articles 1 - 20 of 20
Full-Text Articles in Physical Sciences and Mathematics
Sc-Matrl: Semi-Centralized Multi-Agent Transfer Reinforcement Learning, Ayesha Siddika Nipu
Sc-Matrl: Semi-Centralized Multi-Agent Transfer Reinforcement Learning, Ayesha Siddika Nipu
MSU Graduate Theses
Distributed decision-making in multi-agent systems (MAS) poses significant challenges for interactive behavior learning in both cooperative and competitive environments. While reinforcement learning (RL) has shown great success in single-agent domains like Checkers, Chess and Go, researchers are motivated to extend RL to MAS. However, as the number of agents increases, effectively dealing with each agent becomes increasingly complex. To mitigate the resulting complexity, a semi-centralized Multi-Agent Influence Dense Reinforcement Learning (MAIDRL) algorithm was previously developed, enhancing agent influence maps to facilitate effective multi-agent control in StarCraft Multi-Agent Challenge (SMAC) scenarios. While MAIDRL shows improved performance in homogeneous multi-agent scenarios, it …
An Explainable Deep Learning Prediction Model For Severity Of Alzheimer's Disease From Brain Images, Godwin O. Ekuma
An Explainable Deep Learning Prediction Model For Severity Of Alzheimer's Disease From Brain Images, Godwin O. Ekuma
MSU Graduate Theses
Deep Convolutional Neural Networks (CNNs) have become the go-to method for medical imaging classification on various imaging modalities for binary and multiclass problems. Deep CNNs extract spatial features from image data hierarchically, with deeper layers learning more relevant features for the classification application. The effectiveness of deep learning models are hampered by limited data sets, skewed class distributions, and the undesirable "black box" of neural networks, which decreases their understandability and usability in precision medicine applications. This thesis addresses the challenge of building an explainable deep learning model for a clinical application: predicting the severity of Alzheimer's disease (AD). AD …
Bimee: Blockchain Based Incentive Mechanism Considering Endowment Effect, Jayanth Madupalli
Bimee: Blockchain Based Incentive Mechanism Considering Endowment Effect, Jayanth Madupalli
MSU Graduate Theses
Crowdsensing, a paradigm in modern data collection, harnesses the collective power of mobile users equipped with sensory devices to contribute valuable data based on task-specific criteria. The efficacy of crowdsensing relies on sustained engagement from proficient users over extended periods. Incentivizing long-term participation is crucial, and blockchain technology emerges as a promising framework, providing a decentralized and immutable ledger. However, existing blockchain-based incentive mechanisms for crowdsensing encounter challenges. Firstly, they often overlook users' inherent bias towards loss aversion, a psychological phenomenon where individuals prioritize avoiding losses over acquiring equivalent rewards. Secondly, fairness issues arise, especially concerning newly participating users in …
Machine Learning Strategies For Potential Development In High-Entropy Driven Nickel-Based Superalloys, Marium Mostafiz Mou
Machine Learning Strategies For Potential Development In High-Entropy Driven Nickel-Based Superalloys, Marium Mostafiz Mou
MSU Graduate Theses
In this study, I developed Deep Learning interatomic potentials to model a multi-phase and multi-component system of Ni-based Superalloys. The system has up to three major phase constituents, namely Gamma, Gamma Prime, and Transition-metal rich Carbide. I utilized invariant scalar-based and/or equivariant, tensor-based neural network (NN) approach as implemented in DEEPMD, NEQUIP/ALLEGRO codes, respectively, and Moment Tensor Potential (MTP). For the training and validation sets, I employed the ab-initio molecular dynamics (AIMD) trajectory results and ground state DFT calculations, including the energy, force, and virial database from highly diverse compositions, temperatures, and pressures following a “High Entropy Strategy.” The Deep …
Sensor Relationship Inference In Single Resident Smart Homes Using Time Series, Samuel Nack
Sensor Relationship Inference In Single Resident Smart Homes Using Time Series, Samuel Nack
MSU Graduate Theses
Determining sensor relationships in smart environments is complex due to the variety and volume of time series information they provide. Moreover, identifying sensor relationships to connect them with actuators is difficult for smart home users who may not have technical experience. Yet, gathering information on sensor relationships is a crucial intermediate step towards more advanced smart home applications such as advanced policy generation or automatic sensor configuration. Therefore, in this thesis, I propose a novel unsupervised learning approach, named SeReIn, to automatically group sensors by their inherent relationships solely using time series data for single resident smart homes. SeReIn extracts …
Video Error Concealment Using Convolutional Neural Network, Shashi Khanal
Video Error Concealment Using Convolutional Neural Network, Shashi Khanal
MSU Graduate Theses
Missing information in the video frames is estimated as close as possible to the actual data during video error concealment process. Blocks or slices of information in the video frames can be missing in the decoder due to various reasons like corrupt media drives, network congestion, etc. which reduces the quality of experience for the viewers. One approach to deal with missing information in the video decoder is to use error concealment techniques to fill the missing information. Until now many of these error concealment techniques were based on conventional methods such as block copy, motion vector prediction, and interpolation. …
Modeling Of Argon Bombardment And Densification Of Low Temperature Organic Precursors Using Reactive Md Simulations And Machine Learning, Kwabena Asante-Boahen
Modeling Of Argon Bombardment And Densification Of Low Temperature Organic Precursors Using Reactive Md Simulations And Machine Learning, Kwabena Asante-Boahen
MSU Graduate Theses
In this study, an important aspect of the synthesis process for a-BxC:Hy was systematically modeled by utilizing the Reactive Molecular Dynamics (MD) in modeling the argon bombardment from the orthocarborane molecules as the precursor. The MD simulations are used to assess the dynamics associated with the free radicals that result from the ion bombardment. By applying the Data Mining/Machine Learning analysis into the datasets generated from the large reactive MD simulations, I was able to identify and quality the kinetics of these radicals. Overall, this approach allows for a better understanding of the overall mechanism at the atomistic level of …
Identification Of Chemical Structures And Substructures Via Deep Q-Learning And Supervised Learning Of Ftir Spectra, Joshua D. Ellis
Identification Of Chemical Structures And Substructures Via Deep Q-Learning And Supervised Learning Of Ftir Spectra, Joshua D. Ellis
MSU Graduate Theses
Fourier-transform infrared (FTIR) spectra of organic compounds can be used to compare and identify compounds. A mid-FTIR spectrum gives absorbance values of a compound over the 400-4000 cm-1 range. Spectral matching is the process of comparing the spectral signature of two or more compounds and returning a value for the similarity of the compounds based on how closely their spectra match. This process is commonly used to identify an unknown compound by searching for its spectrum’s closes match in a database of known spectra. A major limitation of this process is that it can only be used to identify …
Predicting Severity Of Traumatic Brain Injury: A Residual Learning Model From Magnetic Resonance Images, Dacosta Yeboah
Predicting Severity Of Traumatic Brain Injury: A Residual Learning Model From Magnetic Resonance Images, Dacosta Yeboah
MSU Graduate Theses
One of the most significant frontiers for computational scientists is the engineering of human healthcare delivery based on intelligent analysis of health data. In a variety of neurological disorders such as Traumatic Brain Injury (TBI), neuro-imaging information plays a crucial role in the decision-making regarding patient care and as a potential prognostic marker for outcome. TBI is a heterogeneous neurological disorder. Due to the economic burdens of the disorder, sorting out this heterogeneity could provide more insights and better understanding of TBI recovery trajectories, thus improving overall diagnosis and treatment options. Magnetic Resonance Imaging (MRI) is a non-invasive technique that …
Maidrl: Semi-Centralized Multi-Agent Reinforcement Learning Using Agent Influence, Anthony Lee Harris
Maidrl: Semi-Centralized Multi-Agent Reinforcement Learning Using Agent Influence, Anthony Lee Harris
MSU Graduate Theses
In recent years, reinforcement learning algorithms, a subset of machine learning that focuses on solving problems through trial-and-error learning, have been used in the field of multi-agent systems to help the agents with interactions and cooperation on a variety of tasks. Given the enormous success of reinforcement learning in single-agent systems like Chess, Shogi, and Go, it is natural for the next step to be the expansion into multi-agent systems. However, controlling multiple agents simultaneously is extremely challenging, as the complexity increases tremendously with the number of agents in the system. Existing approaches in this regard use a wide range …
Lightweight Deep Learning For Botnet Ddos Detection On Iot Access Networks, Eric A. Mccullough
Lightweight Deep Learning For Botnet Ddos Detection On Iot Access Networks, Eric A. Mccullough
MSU Graduate Theses
With the proliferation of the Internet of Things (IoT), computer networks have rapidly expanded in size. While Internet of Things Devices (IoTDs) benefit many aspects of life, these devices also introduce security risks in the form of vulnerabilities which give hackers billions of promising new targets. For example, botnets have exploited the security flaws common with IoTDs to gain unauthorized control of hundreds of thousands of hosts, which they then utilize to carry out massively disruptive distributed denial of service (DDoS) attacks. Traditional DDoS defense mechanisms rely on detecting attacks at their target and deploying mitigation strategies toward the attacker …
Cloud Resource Prediction Using Explainable And Cooperative Artificial Neural Networks, Nathan R. Nelson
Cloud Resource Prediction Using Explainable And Cooperative Artificial Neural Networks, Nathan R. Nelson
MSU Graduate Theses
This work proposes a system for predicting cloud resource utilization by using runtime assembled cooperative artificial neural networks (RACANN). RACANN breaks up the problem into smaller contexts, each represented by a small-scale artificial neural network (ANN). The relevant ANNs are joined together at runtime when the context is present in the data for training and predictions. By analyzing the structure of a complete ANN, the influence of inputs is calculated and used to create linguistic descriptions (LD) of model behavior, so RACANN becomes explainable (eRACANN). The predictive results of eRACANN are compared against its prototype and a single deep ANN …
Applications Of Artificial Intelligence And Graphy Theory To Cyberbullying, Jesse D. Simpson
Applications Of Artificial Intelligence And Graphy Theory To Cyberbullying, Jesse D. Simpson
MSU Graduate Theses
Cyberbullying is an ongoing and devastating issue in today's online social media. Abusive users engage in cyber-harassment by utilizing social media to send posts, private messages, tweets, or pictures to innocent social media users. Detecting and preventing cases of cyberbullying is crucial. In this work, I analyze multiple machine learning, deep learning, and graph analysis algorithms and explore their applicability and performance in pursuit of a robust system for detecting cyberbullying. First, I evaluate the performance of the machine learning algorithms Support Vector Machine, Naïve Bayes, Random Forest, Decision Tree, and Logistic Regression. This yielded positive results and obtained upwards …
A Software Source Code Recommendation System For Code Reuse From Private Repositories, Md Mazharul Islam
A Software Source Code Recommendation System For Code Reuse From Private Repositories, Md Mazharul Islam
MSU Graduate Theses
Motivated by the idea of reusing existing source code from previous projects within a software company, in this thesis, I present a new source code recommendation technique to help programmers find relevant implementations or sample code based on software requirement specifications. My proposed technique assists programmers to search existing code repositories using natural language query. My approach summarizes the uploaded code into sentences or phrases to match them against user queries. This version of my proposed technique extracts and analyzes the content of Python code (such as variables, functions, docstrings, and comments) to generate code summary for each function which …
The Generation Of Operational Policy For Cyber-Physical Systems In Smart Homes, Jared Wayne Hall
The Generation Of Operational Policy For Cyber-Physical Systems In Smart Homes, Jared Wayne Hall
MSU Graduate Theses
The term “Cyber-Physical Systems” (CPS) refers to those systems which seamlessly integrate sensing, computation, control, and networking into physical objects and infrastructure [1]. In these systems, computers and networks of physical entities interact with each other to bring new capabilities to traditional physical systems. Since its introduction, the field of Cyber-Physical Systems (CPS) has evolved with new and interesting advancements concerning its capability, adaptability, scalability, and usability [1]. One such advancement is the unification of the Internet of Things (IoT), a concept that enables real-world everyday objects to connect to the internet and interact with each other, with CPS [1]. …
A Multimodal Approach To Sarcasm Detection On Social Media, Dipto Das
A Multimodal Approach To Sarcasm Detection On Social Media, Dipto Das
MSU Graduate Theses
In recent times, a major share of human communication takes place online. The main reason being the ease of communication on social networking sites (SNSs). Due to the variety and large number of users, SNSs have drawn the attention of the computer science (CS) community, particularly the affective computing (also known as emotional AI), information retrieval, natural language processing, and data mining groups. Researchers are trying to make computers understand the nuances of human communication including sentiment and sarcasm. Emotion or sentiment detection requires more insights about the communication than it does for factual information retrieval. Sarcasm detection is particularly …
Cybersecurity For Critical Infrastructure: Addressing Threats And Vulnerabilities In Canada, Samuel A. Cohen
Cybersecurity For Critical Infrastructure: Addressing Threats And Vulnerabilities In Canada, Samuel A. Cohen
MSU Graduate Theses
The aim of this thesis is to assess the unique technical and policy-based cybersecurity challenges facing Canada’s critical infrastructure environment and to analyze how current government and industry practices are not equipped to remediate or offset associated strategic risks to the country. Further, the thesis also provides cases and evidence demonstrating that Canada’s critical infrastructure has been specifically targeted by foreign and domestic cyber threat actors to pressure the country’s economic, safety and national security interests. Essential services that Canadians and Canadian businesses rely on daily are intricately linked to the availability and integrity of vital infrastructure sectors, such as …
3d Canopy Model Reconstruction From Unmanned Aerial System And Automated Single Tree Extraction, Hai Ha Duong
3d Canopy Model Reconstruction From Unmanned Aerial System And Automated Single Tree Extraction, Hai Ha Duong
MSU Graduate Theses
This project aims to develop and assess methodology for spatial modeling and extracting individual trees from high spatial resolution Digital Surface Model (DSMs) derived from unmanned aerial system (UAS) or drone-based aerial photos. Those results could be used for monitoring of vegetative response of forests, grasslands and vineyards to regional and localized fluctuations in climate and seasonality. The primary objective of this research is to extract 3D spatial information using drone-based aerial imagery through photogrammetric methods. UAS flights were taken place at phenologically critical times over several locations owned and managed by Missouri State University (MSU). The 3D DSM can …
An Adaptive Memory-Based Reinforcement Learning Controller, Keith August Cissell
An Adaptive Memory-Based Reinforcement Learning Controller, Keith August Cissell
MSU Graduate Theses
Recently, the use of autonomous robots for exploration has drastically expanded--largely due to innovations in both hardware technology and the development of new artificial intelligence methods. The wide variety of robotic agents and operating environments has led to the creation of many unique control strategies that cater to each specific agent and their goal within an environment. Most control strategies are single purpose, meaning they are built from the ground up for each given operation. Here we present a single, reinforcement learning control solution for autonomous exploration intended to work across multiple agent types, goals, and environments. The solution presented …
Using Virtual Reality To Improve Sitting Balance, Alice Kay Barnes
Using Virtual Reality To Improve Sitting Balance, Alice Kay Barnes
MSU Graduate Theses
This thesis focuses on using virtual reality (VR) to enhance sitting balance and core strength. It is a study in how to create a VR exercise program which is interesting enough to keep players/patients motivated, but comfortable to play and not overwhelming to the senses. The software used for this study was written with the hope that a later version of it might be used with occupational/physical therapy patients one day. For this master’s thesis, the initial testing has been done with healthy volunteers. The software incorporates what developers know thus far about designing for VR, and it is hoped …