Дисертації з теми "Machine learning not elsewhere classified"
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Abo, Al Ahad George, and Abbas Salami. "Machine Learning for Market Prediction : Soft Margin Classifiers for Predicting the Sign of Return on Financial Assets." Thesis, Linköpings universitet, Produktionsekonomi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-151459.
Повний текст джерела(10506350), Amogh Agrawal. "Compute-in-Memory Primitives for Energy-Efficient Machine Learning." Thesis, 2021.
Знайти повний текст джерела(9189263), Shuo Han. "FLUORESCENCE MICROSCOPY IMAGES SEGMENTATION AND ANALYSIS USING MACHINE LEARNING." Thesis, 2020.
Знайти повний текст джерелаMicroscopy image analysis can provide substantial information for clinical study and understanding of the biological structure. Two-photon microscopy is a type of fluorescence microscopy that can visualize deep into tissue with near-infrared excitation light. Large 3D image volumes of complex subcellular are often produced, which calls for automatic image analysis techniques. Automatic methods that can obtain nuclei quantity in microscopy image volumes are needed for biomedical research and clinical diagnosis. In general, several challenges exist for counting nuclei in 3D image volumes. These include “crowding” and touching of nuclei, overlapping of two or morenuclei, and shape and size variances of the nuclei. In this thesis, a 3D nuclei counterusing two different generative adversarial networks (GAN) is proposed and evaluated.Synthetic data that resembles real microscopy image is generated with a GAN. The synthetic data is used to train another 3D GAN network that counts the number o fnuclei. Our approach is evaluated with respect to the number of ground truth nuclei and compared with common ways of counting used in the biological research.Fluorescence microscopy 3D image volumes of rat kidneys are used to test our 3D nuclei counter. The evaluation of both networks shows that the proposed technique is successful for counting nuclei in 3D. Then, a 3D segmentation and classification method to segment and identify individual nuclei in fluorescence microscopy volumes without having ground truth volumes is introduced. Three dimensional synthetic data is generated using the Recycle-GAN with the Hausdorff distance loss introduced into preserve the shape of individual nuclei. Realistic microscopy image volumes with nuclei segmentation mask and nucleus boundary ground truth volumes are generated.A subsequent 3D CNN with a regularization term that discourage detection out of nucleus boundary is used to detect and segment nuclei. Nuclei boundary refinement is then performed to enhance nuclei segmentation. Experimental results on our rat kidney dataset show the proposed method is competitive with respect to several state-of-the-art methods. A Distributed and Networked Analysis of Volumetric Image Data(DINAVID) system is developed to enable remote analysis of microscopy images for biologists. There are two main functions integrated in the system, a 3D visualization tool and a remote computing tool for nuclei segmentation. The 3D visualization enables real-time rendering of large volumes of microscopy data. The segmentation tool provides fast inferencing of pre-trained deep learning models trained with 5 different types of microscopy data.
(5930189), Javier Ribera Prat. "Image-based Plant Phenotyping Using Machine Learning." Thesis, 2019.
Знайти повний текст джерелаThis thesis also examines the use of crowdsourcing information in video analytics. The large number of cameras deployed for public safety surveillance systems requires intelligent processing capable of automatically analyzing video in real time. We incorporate crowdsourcing in an online basis to improve a crowdflow estimation method. We present various approaches to characterize this uncertainty and to aggregate crowdsourcing results. Our techniques are evaluated using publicly available datasets.
(7534550), David Güera. "Media Forensics Using Machine Learning Approaches." Thesis, 2019.
Знайти повний текст джерела(9136835), Sungbum Jun. "SCHEDULING AND CONTROL WITH MACHINE LEARNING IN MANUFACTURING SYSTEMS." Thesis, 2020.
Знайти повний текст джерела(8812160), Alex Joseph Raynor. "DEVELOPMENT OF MACHINE LEARNING TECHNIQUES FOR APPLICATIONS IN THE STEEL INDUSTRY." Thesis, 2020.
Знайти повний текст джерела(9811085), Anand Koirala. "Precision agriculture: Exploration of machine learning approaches for assessing mango crop quantity." Thesis, 2020. https://figshare.com/articles/thesis/Precision_agriculture_Exploration_of_machine_learning_approaches_for_assessing_mango_crop_quantity/13411625.
Повний текст джерела(11173365), Youlin Liu. "MACHINE LEARNING METHODS FOR SPECTRAL ANALYSIS." Thesis, 2021.
Знайти повний текст джерелаmaking in measurement sciences, and this process is automated for the liberation of labor. In light of the adversarial approaches shown in digital image processing, Chapter 2 demonstrate how the same attack is possible with spectroscopic data. Chapter 3 takes the question presented in Chapter 2 and optimized the classifier through an iterative approach. The optimized LDA was cross-validated and compared with other standard chemometrics methods, the application was extended to bi-distribution mineral Raman data. Chapter 4 focused on a novel Artificial Neural Network structure design with diffusion measurements; the architecture was tested both with simulated dataset and experimental dataset. Chapter 5 presents the construction of a novel infrared hyperspectral microscope for complex chemical compound classification, with detailed discussion in the segmentation of the images and choice of a classifier to choose.
(10713342), Judy Yang. "Development of a warehouse model using machine learning technologies with application in receiving management." Thesis, 2021. https://figshare.com/articles/thesis/Development_of_a_warehouse_model_using_machine_learning_technologies_with_application_in_receiving_management/14499078.
Повний текст джерела(12214559), Sonal Chawda. "Determination of distance relay characteristics using an inductive learning system." Thesis, 1993. https://figshare.com/articles/thesis/Determination_of_distance_relay_characteristics_using_an_inductive_learning_system/19326599.
Повний текст джерела(11180610), Indranil Chakraborty. "Toward Energy-Efficient Machine Learning: Algorithms and Analog Compute-In-Memory Hardware." Thesis, 2021.
Знайти повний текст джерела(7027766), Jonathan A. Fine. "Proton to proteome, a multi-scale investigation of drug discovery." Thesis, 2020.
Знайти повний текст джерела(8788244), Rohan Kumar Manna. "Leakage Conversion For Training Machine Learning Side Channel Attack Models Faster." Thesis, 2020.
Знайти повний текст джерела(9868160), Wan-Eih Huang. "Image Processing, Image Analysis, and Data Science Applied to Problems in Printing and Semantic Understanding of Images Containing Fashion Items." Thesis, 2020.
Знайти повний текст джерела(11181642), Deboleena Roy. "Exploring Methods for Efficient Learning in Neural Networks." Thesis, 2021.
Знайти повний текст джерела(8067608), Zhi Li. "COPING WITH LIMITED DATA: MACHINE-LEARNING-BASED IMAGE UNDERSTANDING APPLICATIONS TO FASHION AND INKJET IMAGERY." Thesis, 2019.
Знайти повний текст джерела(8802593), Eric D. Katz. "Differentiating Users Based on Changes in the Underlying Block Space of Their Smartphones." Thesis, 2020.
Знайти повний текст джерела(7011098), Mian Yang. "Quantitative-Scientific Company and Product Scorecard Considerations and Modeling." Thesis, 2019.
Знайти повний текст джерелаFDA has long served as the front safeguard to the U.S. citizen public health, is also perceived as one of the world-leading drug regulators. Despite the tremendous efforts and progress have been made to promote the public health, FDA was criticized for putting the agency’s trust icon at stake and was questioned of its ability to serve the agency’s ultimate mission to protect the public. In the wake of the arousing concerns, FDA sought the transformation the oversight model of the medicinal products. One of the actions is to launch quality metrics program. However, this program has been unanimously opposed by the industry. Instead of the current conventional approach, which is constrained by the high dependence on industry cooperation, we try to explore
the measurement of company and product quality risk with public domain data, try to help in visualizing quality and risk. To that end, we develop conceptual frameworks for both company and product quality, examine some of the factors (education, local authority intensity, historical inspection results, physiochemical, physiological, formulation factors, etc.), further developed a warning letter and product recall prediction model with machine learning method referenced to the data analysis outcome.
(6634508), Amruthavarshini Talikoti. "ESTIMATING PHENYLALANINE OF COMMERCIAL FOODS : A COMPARISON BETWEEN A MATHEMATICAL APPROACH AND A MACHINE LEARNING APPROACH." Thesis, 2019.
Знайти повний текст джерелаPhenylketonuria (PKU) is an inherited metabolic disorder affecting 1 in every 10,000 to 15,000 newborns in the United States every year. Caused by a genetic mutation, PKU results in an excessive build up of the amino acid Phenylalanine (Phe) in the body leading to symptoms including but not limited to intellectual disability, hyperactivity, psychiatric disorders and seizures. Most PKU patients must follow a strict diet limited in Phe. The aim of this research study is to formulate, implement and compare techniques for Phe estimation in commercial foods using the information on the food label (Nutritional Fact Label and ordered ingredient list). Ideally, the techniques should be both accurate and amenable to a user friendly implementation as a Phe calculator that would aid PKU patients monitor their dietary Phe intake.
The first approach to solve the above problem is a mathematical one that comprises three steps. The three steps were separately proposed as methods by Jieun Kim in her dissertation. It was assumed that the third method, which is more computationally expensive, was the most accurate one. However, by performing the three methods subsequently in three different steps and combining the results, we actually obtained better results than by merely using the third method.
The first step makes use of the protein content in the foods and Phe:protein multipliers. The second step enumerates all the ingredients in the food and uses the minimum and maximum Phe:protein multipliers of the ingredients along with the protein content. The third step lists the ingredients in decreasing order of their weights, which gives rise to inequality constraints. These constraints hold assuming that there is no loss in the preparation process. The inequality constraints are optimized numerically in two phases. The first involves nutrient content estimation by approximating the ingredient amounts. The second phase is a refinement of the above estimates using the Simplex algorithm. The final Phe range is obtained by performing an interval intersection of the results of the three steps. We implemented all three steps as web applications. Our proposed three-step method yields a high accuracy of Phe estimation (error <= +/- 13.04mg Phe per serving for 90% of foods).
The above mathematical procedure is contrasted against a machine learning approach that uses the data in an existing database as training data to infer the Phe in any given food. Specifically, we use the K-Nearest Neighbors (K-NN) classification method using a feature vector containing the (rounded) nutrient data. In other words, the Phe content of the test food is a weighted average of the Phe values of the neighbors closest to it using the nutrient values as attributes. A four-fold cross validation is carried out to determine the hyper-parameters and the training is performed using the United States Department of Agriculture (USDA) food nutrient database. Our tests indicate that this approach is not very accurate for general foods (error <= +/- 50mg Phe per 100g in about 38% of the foods tested). However, for low-protein foods which are typically consumed by PKU patients, the accuracy increases significantly (error <= +/- 50mg Phe per 100g in over 77% foods).
The machine learning approach is more user-friendly than the mathematical approach. It is convenient, fast and easy to use as it takes into account just the nutrient information. In contrast, the mathematical method additionally takes as input a detailed ingredient list, which is cumbersome to be located in a food database and entered as input. However, the Mathematical method has the added advantage of providing error bounds for the Phe estimate. It is also more accurate than the ML method. This may be due to the fact that for the ML method, the nutrition facts alone are not sufficient to estimate Phe and that additional information like the ingredients list is required.
(11167785), Nicolae Christophe Iovanac. "GENERATIVE, PREDICTIVE, AND REACTIVE MODELS FOR DATA SCARCE PROBLEMS IN CHEMICAL ENGINEERING." Thesis, 2021.
Знайти повний текст джерела(5930033), Curtis P. Martin. "RATIONAL DESIGN OF TYPE II KINASE INHIBITORS VIA NOVEL MULTISCALE VIRTUAL SCREENING APPROACH." Thesis, 2019.
Знайти повний текст джерела(7036478), Aiyshwariya Paulvannan Kanmani. "A data-centric framework for assessing environmental sustainability." Thesis, 2019.
Знайти повний текст джерела(6861362), Yang Yan. "Image-Based Non-Contact Conductivity Prediction for Inkjet Printed Electrodes and Follow-Up Work of Toner Usage Prediction for Laser Electro-Phorographic Printers." Thesis, 2019.
Знайти повний текст джерела(10695907), Wo Jae Lee. "AI-DRIVEN PREDICTIVE WELLNESS OF MECHANICAL SYSTEMS: ASSESSMENT OF TECHNICAL, ENVIRONMENTAL, AND ECONOMIC PERFORMANCE." Thesis, 2021.
Знайти повний текст джерелаOne way to reduce the lifecycle cost and environmental impact of a product in a circular economy is to extend its lifespan by either creating longer-lasting products or managing the product properly during its use stage. Life extension of a product is envisioned to help better utilize raw materials efficiently and slow the rate of resource depletion. In the case of manufacturing equipment (e.g., an electric motor on a machine tool), securing reliable service life as well as the life extension are important for consistent production and operational excellence in a factory. However, manufacturing equipment is often utilized without a planned maintenance approach. Such a strategy frequently results in unplanned downtime, owing to unexpected failures. Scheduled maintenance replaces components frequently to avoid unexpected equipment stoppages, but increases the time associated with machine non-operation and maintenance cost.
Recently, the emergence of Industry 4.0 and smart systems is leading to increasing attention to predictive maintenance (PdM) strategies that can decrease the cost of downtime and increase the availability (utilization rate) of manufacturing equipment. PdM also has the potential to foster sustainable practices in manufacturing by maximizing the useful lives of components. In addition, advances in sensor technology (e.g., lower fabrication cost) enable greater use of sensors in a factory, which in turn is producing greater and more diverse sets of data. Widespread use of wireless sensor networks (WSNs) and plug-and-play interfaces for the data collection on product/equipment states are allowing predictive maintenance on a much greater scale. Through advances in computing, big data analysis is faster/improved and has allowed maintenance to transition from run-to-failure to statistical inference-based or machine learning prediction methods.
Moreover, maintenance practice in a factory is evolving from equipment “health management” to equipment “wellness” by establishing an integrated and collaborative manufacturing system that responds in real-time to changing conditions in a factory. The equipment wellness is an active process of becoming aware of the health condition and of making choices that achieve the full potential of the equipment. In order to enable this, a large amount of machine condition data obtained from sensors needs to be analyzed to diagnose the current health condition and predict future behavior (e.g., remaining useful life). If a fault is detected during this diagnosis, a root cause of a fault must be identified to extend equipment life and prevent problem reoccurrence.
However, it is challenging to build a model capturing a relationship between multi-sensor signals and mechanical failures, considering the dynamic manufacturing environment and the complex mechanical system in equipment. Another key challenge is to obtain usable machine condition data to validate a method.
A goal of the proposed work is to develop a systematic tool for maintenance in manufacturing plants using emerging technologies (e.g., AI, Smart Sensor, and IoT). The proposed method will facilitate decision-making that supports equipment maintenance by rapidly detecting a worn component and estimating remaining useful life. In order to diagnose and prognose a health condition of equipment, several data-driven models that describe the relationships between proxy measures (i.e., sensor signals) and machine health conditions are developed and validated through the experiment for several different manufacturing-oriented cases (e.g., cutting tool, gear, and bearing). To enhance the robustness and the prediction capability of the data-driven models, signal processing is conducted to preprocess the raw signals using domain knowledge. Through this process, useful features from the large dataset are extracted and selected, thus increasing computational efficiency in model training. To make a decision using the processed signals, a customized deep learning architecture for each case is designed to effectively and efficiently learn the relationship between the processed signals and the model’s outputs (e.g., health indicators). Ultimately, the method developed through this research helps to avoid catastrophic mechanical failures, products with unacceptable quality, defective products in the manufacturing process as well as to extend equipment service life.
To summarize, in this dissertation, the assessment of technical, environmental and economic performance of the AI-driven method for the wellness of mechanical systems is conducted. The proposed methods are applied to (1) quantify the level of tool wear in a machining process, (2) detect different faults from a power transmission mini-motor testbed (CNN), (3) detect a fault in a motor operated under various rotation speeds, and (4) to predict the time to failure of rotating machinery. Also, the effectiveness of maintenance in the use stage is examined from an environmental and economic perspective using a power efficiency loss as a metric for decision making between repair and replacement.