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Articles 1 - 8 of 8
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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 …
Evaluation Of Different Machine Learning, Deep Learning And Text Processing Techniques For Hate Speech Detection, Nabil Shawkat
Evaluation Of Different Machine Learning, Deep Learning And Text Processing Techniques For Hate Speech Detection, Nabil Shawkat
MSU Graduate Theses
Social media has become a domain that involves a lot of hate speech. Some users feel entitled to engage in abusive conversations by sending abusive messages, tweets, or photos to other users. It is critical to detect hate speech and prevent innocent users from becoming victims. In this study, I explore the effectiveness and performance of various machine learning methods employing text processing techniques to create a robust system for hate speech identification. I assess the performance of Naïve Bayes, Support Vector Machines, Decision Trees, Random Forests, Logistic Regression, and K Nearest Neighbors using three distinct datasets sourced from social …
Detecting User Emotions From Audio Conversations With The Smart Assistants, Sunanda Guha
Detecting User Emotions From Audio Conversations With The Smart Assistants, Sunanda Guha
MSU Graduate Theses
With the proliferation of smart home devices like Google Home or Amazon Alexa, significant research endeavors are being carried out to improve the user experience while interacting with these smart assistants. One such dimension in this endeavor is ongoing research on successful emotion detection from short voice commands used in smart home environment. Besides facial expression and body language, etc., speech plays a pivotal role in the classification of emotions when it comes to smart home application. Upon successful implementation of accurate emotion recognition, the smart devices will be able to intelligently and empathetically suggest appropriate actions based on the …
Applications Of A Combined Approach Of Kinetic Monte Carlo Simulations And Machine Learning To Model Atomic Layer Deposition (Ald) Of Metal Oxides, Emily Justus
MSU Graduate Theses
Metal-oxides such as ZnO or Al2O3 synthesized through Atomic Layer Deposition (ALD) have been of great research interest as the candidate materials for ultra-thin tunnel barriers. In this study, I have applied a 3D on-lattice Kinetic Monte Carlo (kMC) code developed by Timo Weckman’s group to simulate the growth mechanisms of the tunnel barrier layer and to evaluate the role of various experimentally relevant factors in the ALD processes. I have systematically studied the effect of parameters such as the chamber pressure temperature, pulse, and purge times. The database generated from the kMC simulations was subsequently used …
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 …
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 …
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 …
Construction Ergonomic Risk And Productivity Assessment Using Mobile Technology And Machine Learning, Nipun Deb Nath
Construction Ergonomic Risk And Productivity Assessment Using Mobile Technology And Machine Learning, Nipun Deb Nath
MSU Graduate Theses
The construction industry has one of the lowest productivity rates of all industries. To remedy this problem, project managers tend to increase personnel's workload (growing output), or assign more (often insufficiently trained) workers to certain tasks (reducing time). This, however, can expose personnel to work-related musculoskeletal disorders which if sustained over time, lead to health problems and financial loss. This Thesis presents a scientific methodology for collecting time-motion data via smartphone sensors, and analyzing the data for rigorous health and productivity assessment, thus creating new opportunities in research and development within the architecture, engineering, and construction (AEC) domain. In particular, …