Global ETD Search

1	Information Retrieval using Markov random Fields and Restricted Boltzmann Machines Monika Kamma (10276277) 06 April 2021 (has links) <div>When a user types in a search query in an Information Retrieval system, a list of top ‘n’ ranked documents relevant to the query are returned by the system. Relevant means not just returning documents that belong to the same category as that of the search query, but also returning documents that provide a concise answer to the search query. Determining the relevance of the documents is a significant challenge as the classic indexing techniques that use term/word frequencies do not consider the term (word) dependencies or the impact of previous terms on the current words or the meaning of the words in the document. There is a need to model the dependencies of the terms in the text data and learn the underlying statistical patterns to find the similarity between the user query and the documents to determine the relevancy.</div><div><br></div><div>This research proposes a solution based on Markov Random Fields (MRF) and Restricted Boltzmann Machines (RBM) to solve the problem of term dependencies and learn the underlying patterns to return documents that are very similar to the user query.</div> Information Retrieval and Web Search Natural Language Processing Restricted Boltzmann Machines Markov Random Fields
2	CyberWater: An open framework for data and model integration Ranran Chen (18423792) 03 June 2024 (has links) <p dir="ltr">Workflow management systems (WMSs) are commonly used to organize/automate sequences of tasks as workflows to accelerate scientific discoveries. During complex workflow modeling, a local interactive workflow environment is desirable, as users usually rely on their rich, local environments for fast prototyping and refinements before they consider using more powerful computing resources.</p><p dir="ltr">This dissertation delves into the innovative development of the CyberWater framework based on Workflow Management Systems (WMSs). Against the backdrop of data-intensive and complex models, CyberWater exemplifies the transition of intricate data into insightful and actionable knowledge and introduces the nuanced architecture of CyberWater, particularly focusing on its adaptation and enhancement from the VisTrails system. It highlights the significance of control and data flow mechanisms and the introduction of new data formats for effective data processing within the CyberWater framework.</p><p dir="ltr">This study presents an in-depth analysis of the design and implementation of Generic Model Agent Toolkits. The discussion centers on template-based component mechanisms and the integration with popular platforms, while emphasizing the toolkit’s ability to facilitate on-demand access to High-Performance Computing resources for large-scale data handling. Besides, the development of an asynchronously controlled workflow within CyberWater is also explored. This innovative approach enhances computational performance by optimizing pipeline-level parallelism and allows for on-demand submissions of HPC jobs, significantly improving the efficiency of data processing.</p><p dir="ltr">A comprehensive methodology for model-driven development and Python code integration within the CyberWater framework and innovative applications of GPT models for automated data retrieval are introduced in this research as well. It examines the implementation of Git Actions for system automation in data retrieval processes and discusses the transformation of raw data into a compatible format, enhancing the adaptability and reliability of the data retrieval component in the adaptive generic model agent toolkit component.</p><p dir="ltr">For the development and maintenance of software within the CyberWater framework, the use of tools like GitHub for version control and outlining automated processes has been applied for software updates and error reporting. Except that, the user data collection also emphasizes the role of the CyberWater Server in these processes.</p><p dir="ltr">In conclusion, this dissertation presents our comprehensive work on the CyberWater framework's advancements, setting new standards in scientific workflow management and demonstrating how technological innovation can significantly elevate the process of scientific discovery.</p> Data engineering and data science Database systems Information retrieval and web search Automated software engineering Programming languages Requirements engineering Software architecture Software quality, processes and metrics Concurrency theory High performance computing techniques Data Integration Open Framework Workflow Management System Model Integration Software Maintenance Management
3	PLANT LEVEL IIOT BASED ENERGY MANAGEMENT FRAMEWORK Liya Elizabeth Koshy (14700307) 31 May 2023 (has links) <p><strong>The Energy Monitoring Framework</strong>, designed and developed by IAC, IUPUI, aims to provide a cloud-based solution that combines business analytics with sensors for real-time energy management at the plant level using wireless sensor network technology.</p> <p>The project provides a platform where users can analyze the functioning of a plant using sensor data. The data would also help users to explore the energy usage trends and identify any energy leaks due to malfunctions or other environmental factors in their plant. Additionally, the users could check the machinery status in their plant and have the capability to control the equipment remotely.</p> <p>The main objectives of the project include the following:</p> <ul> <li>Set up a wireless network using sensors and smart implants with a base station/ controller.</li> <li>Deploy and connect the smart implants and sensors with the equipment in the plant that needs to be analyzed or controlled to improve their energy efficiency.</li> <li>Set up a generalized interface to collect and process the sensor data values and store the data in a database.</li> <li>Design and develop a generic database compatible with various companies irrespective of the type and size.</li> <li> Design and develop a web application with a generalized structure. Hence the database can be deployed at multiple companies with minimum customization. The web app should provide the users with a platform to interact with the data to analyze the sensor data and initiate commands to control the equipment.</li> </ul> <p>The General Structure of the project constitutes the following components:</p> <ul> <li>A wireless sensor network with a base station.</li> <li>An Edge PC, that interfaces with the sensor network to collect the sensor data and sends it out to the cloud server. The system also interfaces with the sensor network to send out command signals to control the switches/ actuators.</li> <li>A cloud that hosts a database and an API to collect and store information.</li> <li>A web application hosted in the cloud to provide an interactive platform for users to analyze the data.</li> </ul> <p>The project was demonstrated in:</p> <ul> <li>Lecture Hall (https://iac-lecture-hall.engr.iupui.edu/LectureHallFlask/).</li> <li>Test Bed (https://iac-testbed.engr.iupui.edu/testbedflask/).</li> <li>A company in Indiana.</li> </ul> <p>The above examples used sensors such as current sensors, temperature sensors, carbon dioxide sensors, and pressure sensors to set up the sensor network. The equipment was controlled using compactable switch nodes with the chosen sensor network protocol. The energy consumption details of each piece of equipment were measured over a few days. The data was validated, and the system worked as expected and helped the user to monitor, analyze and control the connected equipment remotely.</p> <p><br></p> Electronic sensors Systems engineering Database systems Information extraction and fusion Information retrieval and web search Query processing and optimisation Stream and sensor data Cloud computing Networking and communications Knowledge and information management Software architecture Data structures and algorithms IIoT architectures IIoT IIOT SENSOR APPLICATIONS software architecting generic application design Energy Monitoring Z- Wave OpenHAB Real time Energy Monitoring System Sensors and actuators Wireless Sensor Network (WSN) web server Web Application Generic web application Database design Generic database Architecture edge computing based Industrial Application edge computing strategy Python Flask Framework API sensor interfaces cloud storage data interactively remote Control
4	EXPLORING GRAPH NEURAL NETWORKS FOR CLUSTERING AND CLASSIFICATION Fattah Muhammad Tahabi (14160375) 03 February 2023 (has links) <p><strong>Graph Neural Networks</strong> (GNNs) have become excessively popular and prominent deep learning techniques to analyze structural graph data for their ability to solve complex real-world problems. Because graphs provide an efficient approach to contriving abstract hypothetical concepts, modern research overcomes the limitations of classical graph theory, requiring prior knowledge of the graph structure before employing traditional algorithms. GNNs, an impressive framework for representation learning of graphs, have already produced many state-of-the-art techniques to solve node classification, link prediction, and graph classification tasks. GNNs can learn meaningful representations of graphs incorporating topological structure, node attributes, and neighborhood aggregation to solve supervised, semi-supervised, and unsupervised graph-based problems. In this study, the usefulness of GNNs has been analyzed primarily from two aspects - <strong>clustering and classification</strong>. We focus on these two techniques, as they are the most popular strategies in data mining to discern collected data and employ predictive analysis.</p> Biomechanical engineering Neural engineering Health promotion Preventative health care Applications in health Spatial data and applications Evolutionary computation Natural language processing Planning and decision making Data engineering and data science Data mining and knowledge discovery Graph, social and multimedia data Information retrieval and web search Knowledge and information management Context learning Deep learning Neural networks Semi- and unsupervised learning Data structures and algorithms Graph neural network Node classification Graph clustering Temporal graphs dynamic graphs NODE2VEC Graph Attention Mechanism Hunting BiLSTM model EHR data colorectal Cancer Cancers Cancer symptoms symptom Symptom cluster studies Coauthorship networks network analysis Word2vec Hierarchical Clustering method Dunn index semantic analysis text mining Natural Language Processing Tool UMLS identifiers umls Clinical Data Management

1

Page generated in 0.1263 seconds