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Articles 1 - 12 of 12
Full-Text Articles in Engineering
Visual Speech Recognition Using A 3d Convolutional Neural Network, Matthew Rochford
Visual Speech Recognition Using A 3d Convolutional Neural Network, Matthew Rochford
Master's Theses
Main stream automatic speech recognition (ASR) makes use of audio data to identify spoken words, however visual speech recognition (VSR) has recently been of increased interest to researchers. VSR is used when audio data is corrupted or missing entirely and also to further enhance the accuracy of audio-based ASR systems. In this research, we present both a framework for building 3D feature cubes of lip data from videos and a 3D convolutional neural network (CNN) architecture for performing classification on a dataset of 100 spoken words, recorded in an uncontrolled envi- ronment. Our 3D-CNN architecture achieves a testing accuracy of …
Artificial Intelligence Empowered Uavs Data Offloading In Mobile Edge Computing, Nicholas Alexander Kemp
Artificial Intelligence Empowered Uavs Data Offloading In Mobile Edge Computing, Nicholas Alexander Kemp
Electrical and Computer Engineering ETDs
The advances introduced by Unmanned Aerial Vehicles (UAVs) are manifold and have paved the path for the full integration of UAVs, as intelligent objects, into the Internet of Things (IoT). This paper brings artificial intelligence into the UAVs data offloading process in a multi-server Mobile Edge Computing (MEC) environment, by adopting principles and concepts from game theory and reinforcement learning. Initially, the autonomous MEC server selection for partial data offloading is performed by the UAVs, based on the theory of the stochastic learning automata. A non-cooperative game among the UAVs is then formulated to determine the UAVs' data to be …
Empathy Based Reinforcement Learning, Dhwani Himanshu Patel
Empathy Based Reinforcement Learning, Dhwani Himanshu Patel
Theses - ALL
Among the different emotional functions, empathy is difficult to model but is important for a robot to have so that they can socially interact with society. Perceived empathy has positive consequences on attitude and the social behaviors of an individual. The project aims to model empathic behavior and perceptive capabilities in a robot in such a way that they can engage in empathic interactions with other agents in a shared physical space. In this study, a small grid environment world was designed and developed with multiple agents. Also, The Markov Decision Process and a Hand Coded algorithm were implemented and …
An Explainable Recommender System Based On Semantically-Aware Matrix Factorization., Mohammed Sanad Alshammari
An Explainable Recommender System Based On Semantically-Aware Matrix Factorization., Mohammed Sanad Alshammari
Electronic Theses and Dissertations
Collaborative Filtering techniques provide the ability to handle big and sparse data to predict the ratings for unseen items with high accuracy. Matrix factorization is an accurate collaborative filtering method used to predict user preferences. However, it is a black box system that recommends items to users without being able to explain why. This is due to the type of information these systems use to build models. Although rich in information, user ratings do not adequately satisfy the need for explanation in certain domains. White box systems, in contrast, can, by nature, easily generate explanations. However, their predictions are less …
Computer Vision Machine Learning And Future-Oriented Ethics, Abagayle Lee Blank
Computer Vision Machine Learning And Future-Oriented Ethics, Abagayle Lee Blank
Honors Projects
Computer Vision Machine Learning (CVML) in the application of facial recognition is currently being researched, developed, and deployed across the world. It is of interest to governments, technology companies, and consumers. However, fundamental issues remain related to human rights, error rates, and bias. These issues have the potential to create societal backlash towards the technology which could limit its benefits as well as harm people in the process. To develop facial recognition technology that will be beneficial to society in and beyond the next decade, society must put ethics at the forefront. Drawing on AI4People’s adaption of bioethics for AI, …
Artificial Intelligence In The Assessment Of Transmission And Distribution Systems Under Natural Disasters Using Machine Learning And Deep Learning Techniques In A Knowledge Discovery Framework, Rossana Villegas
Open Access Theses & Dissertations
Warming trends and increasing temperatures have been observed and reported by federal agencies, such as the National Oceanic and Atmospheric Administration (NOAA). Extreme-weather events, especially hurricanes, tornadoes and winter storms, are among the highly devastating natural disasters responsible for massive and prolonged power outages in Electrical Transmission and Distribution Systems (ETDS). Moreover, the failure rate probability of any system component under extreme-weather tends to increase in the impacted geographic area. This Dissertation proposes an Artificial Intelligence (AI) Decision Support System that can predict damage in the ETDS and allow operators to mitigate disastrous extreme weather events. The document reports the …
Exploring Cyber-Physical Systems, Misbah Uddin Mohammed
Exploring Cyber-Physical Systems, Misbah Uddin Mohammed
Graduate Research Theses & Dissertations
The advances in IOT, Computer Vision, AI and Machine Learning have made these technologies ubiquitous to our daily lives. From Smart Phones to Connected Vehicles, Cyber Physical systems have been interspersed into everything we interact in today’s world. The aim or this thesis was to explore these advances in Cyber Physical Systems and analyze the different sectors they were affecting. We then hand-picked certain domains and explored further by carrying out practical projects using some of the latest software and hardware resources available. Technologies like Amazon Alexa services, NVIDIA Jetson boards, TensorFlow, OpenCV, NodeJS were heavily employed in our various …
Artificial Intelligence Of Stormwater Operations, Eric Bednar
Artificial Intelligence Of Stormwater Operations, Eric Bednar
Williams Honors College, Honors Research Projects
The current infrastructure in our country will not be able to adequately support the growing demands of an exponentially increasing population. A rise in population contributes to a greater service demand necessary to treat sanitary sewer waste which in many cases contributes to the flow of combined sewers. When it comes to managing these combined sewers, rain and the snowmelt caused from climate changes are major factors that need to be addressed. Some water treatment facilities do not have the ability to treat the capacities or peak flows that a system experiences, resulting in combined sewer overflows which are both …
Efficient Detection Of Diseases By Feature Engineering Approach From Chest Radiograph, Avishek Mukherjee
Efficient Detection Of Diseases By Feature Engineering Approach From Chest Radiograph, Avishek Mukherjee
Legacy Theses & Dissertations (2009 - 2024)
Deep Learning is the new state-of-the-art technology in Image Processing. We applied Deep Learning techniques for identification of diseases from Radiographs made publicly available by NIH. We applied some Feature Engineering approach to augment the data from Anterior-Posterior position to Posterior-Anterior position and vice-versa for all the diseases, at the same point we suppressed ‘No Finding’ radiographs which contributed to more than 50% (approximately 60,000) of the dataset to top 1000 images. We also prepared a model by adding a huge amount of noise to the augmented data, which if need be can be deployed at rural locations which lack …
Knowledge-Enabled Entity Extraction, Hussein S. Al-Olimat
Knowledge-Enabled Entity Extraction, Hussein S. Al-Olimat
Browse all Theses and Dissertations
Information Extraction (IE) techniques are developed to extract entities, relationships, and other detailed information from unstructured text. The majority of the methods in the literature focus on designing supervised machine learning techniques, which are not very practical due to the high cost of obtaining annotations and the difficulty in creating high quality (in terms of reliability and coverage) gold standard. Therefore, semi-supervised and distantly-supervised techniques are getting more traction lately to overcome some of the challenges, such as bootstrapping the learning quickly. This dissertation focuses on information extraction, and in particular entities, i.e., Named Entity Recognition (NER), from multiple domains, …
Agent-Based Simulation Of Artificial-Intelligence-Assisted Transfer Of Care, Paul B. Stone
Agent-Based Simulation Of Artificial-Intelligence-Assisted Transfer Of Care, Paul B. Stone
Browse all Theses and Dissertations
This study demonstrates the application of Agent-Based Simulation as a potential training aid for Transfer of Care (ToC) between EMS and a hospital triage department. The specific aim was to develop a simulation to increase the efficiency and accountability of information communication during ToC to test the suitability of Agent-Based Simulation to address training requirements in complex, health provision settings. This paper focuses on the design of the training simulation, including the development of individual agents within the simulation through the user interface elements and the evaluation and verification of the prototype simulator. The primary objective is for the simulation …
Intelligent Data-Driven Decision-Making To Mitigate Or Stop Lost Circulation, Husam Hasan Alkinani
Intelligent Data-Driven Decision-Making To Mitigate Or Stop Lost Circulation, Husam Hasan Alkinani
Doctoral Dissertations
”Lost circulation is a challenging problem in the oil and gas industry. Each year, millions of dollars are spent to mitigate or stop this problem. The aim of this work is to utilize machine learning and other intelligent solutions to help to make better decision to mitigate or stop lost circulation. A detailed literature review on the applications of decision tree analysis, expected monetary value, and artificial neural networks in the oil and gas industry was provided. Data for more than 3000 wells were gathered from many sources around the world. Detailed economics and probability analyses for lost circulation treatments’ …