Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
Results
Showing 1 to 20 of 21 for “"Classical Machine Learning"”.
-
Large-scale programmable silicon photonics for quantum and classical machine learning
… platform for applications in quantum and classical machine learning and information processing. Using our developed processor architecture, we then present a nanophotonic Ising sampler for noise-assisted combinatorial optimization. Subsequently, we present a novel, foundry-compatible …
-
A Comparative Analysis of Classical Machine Learning Methods and Variational Quantum Methods for Breast Tumor Classification
Ο καρκίνος του μαστού αντιπροσωπεύει το 36% των γυναικείων ογκολογικών περιστατικών, καθιστώντας αναγκαία τη χρήση διαγνωστικών εργαλείων υψηλής ακρίβειας. Ενώ η παραδοσιακή μηχανική μάθηση αποτελεί το καθιερωμένο πρότυπο, η παρούσα μελέτη διερευνά την αποτελεσματικότητα των Μεταβλητών Κβαντικών …
-
Horizons of Artificial Intelligence in Quantum Computation
… by the success of deep neural networks in classical machine learning, a prevailing hope is that such success will translate to so-called quantum variational algorithms or quantum neural networks inspired by their classical counterparts. Contemporary deep learning algorithms are primarily …
-
Deep CNN-Based Automated Optical Inspection for Aerospace Components
… materials image dataset (ACMID). First, classical machine learning models, namely, Support Vector Machine and Random Forest, are employed for both datasets. Second, deep CNN-based models, such as improved ResNet50 and MobileNetV2 architectures are trained on ACMID datasets. Third, an …
-
TOWARDS ACCELERATION OF QUANTUM-CLASSICAL ORCHESTRATION
… future computing infrastructures, complementing classical processors in the same way GPUs accelerate specific workloads today. While algorithms such as Shor’s and Grover’s suggest potential advantages, the practical use of quantum devices will occur within hybrid architectures where classical and …
-
Leveraging Unlabeled Data in Supervised Learning to Objectively Assess Depression
… from a number of participants, and used to train classical machine learning models aimed at assessing depression. In this thesis, those methods are expanded upon with the intent of improving them, with varied success. Investigations conducted include training a small neural network on the same …
-
Approximating a wavelet kernel using a quantum computer
Machine learning and quantum computing are both fields which have gained a significant amount of popularity and attention in recent years. The intersection of these two fields, quantum machine learning, looks at whether quantum computers can aid or improve classical machine learning methods, or …
-
Characterizing human vision through large-scale brain imaging and computational models
… In the first chapter, I use a combination of classical machine learning, artificial neural networks, and a joint MEG/fMRI neuroimaging dataset to reveal that the human visual system extensively processes highly memorable images in regions distributed throughout visual cortex late in time. In …
-
Quantum Machine Learning Applied to Astronomical Datasets
… investigates the application of quantum machine learning techniques in the field of astronomy. The focus is on a variety of supervised and unsupervised tasks, including classification, clustering, and anomaly detection. Quantum kernel methods, such as quantum-enhanced support vector …
-
Bridging adaptive estimation and control with modern machine learning : a quorum sensing inspired algorithm for dynamic clustering
… both adaptive estimation and control, and modern machine learning, we propose an algorithm to estimate an "influence radius" for each cell that represents a single data, which is similar to a kernel tuning process in classical machine learning. Then we utilize the knowledge of local connectivity …
-
Artificial Neural Networks for Programming Quantum Annealers
Quantum machine learning is an emerging field of research at the intersection of quantum computing and machine learning. It has the potential to enable advances in artificial intelligence, such as solving problems intractable on classical computers. Some of the fundamental ideas behind quantum …
-
Causal Inference and Machine Learning Methods in Parkinson's Disease Data Analysis
… investigation into Parkinson’s Disease utilizing machine learning and causal inference methods. I will cover a descriptive analysis of Parkinson’s Disease (PD) in a vast, high-quality database and present costs associated with Parkinson’s Disease medications. I also researched a causal inference …
-
Problem-solving recognition in scientific text
… is automated in two stages using a blend of classical machine learning and state-of-the-art deep learning methods. The first stage involves the implementation of problem and solution recognisers which operate at the sentence level. The second stage is more complex in that it recognises …
-
DeepGeoMap
In recent years, deep learning improved the way remote sensing data is processed. The classification of hyperspectral data is no exception. 2D or 3D convolutional neural networks have outperformed classical algorithms on hyperspectral image classification in many cases. However, geological …
-
Human-centered Ambient Artificial Intelligence In Smart Buildings
… levels. A recommender system employing machine learning models was studied and achieved 92% accuracy and F1 score in predicting users' preferred configurations and control modes, demonstrating that user persona-driven modeling can inform adaptive configuration of smart buildings. Second, …
-
Knowledge Augmentation in Language Models to Overcome Domain Adaptation and Scarce Data Challenges in Clinical Domain
… fairness and bias assessments of the employed Classical Machine Learning (CML) and Deep Learning (DL) approach to develop reliable classification models. Finally, the sixth chapter provides the conclusion and outcomes of all the work presented in this thesis. The scientific contributions of …
-
Estimation, Prediction and Counterfactual Inference with Dependent Observations
… to large-scale, high dimensional data. Much of classical machine learning has been developed under the assumption that this data is generated independently from some distribution. However, this assumption is often violated when data exhibit complex dependencies across a spatial or temporal …
-
An integrated model of flight and passenger delay for policy analysis in the National Air Transportation System
… (FAA) reports or constructed by using classical machine learning algorithms on airport-level data. We validate our estimation of flight delays by using data of January, 2008, showing that both the capacity profiles and the RAND perform well in terms of replicating the actual monthly …
-
Harnessing Deep Learning with Protein Language Models to Unveil Microbial Enzyme Function in Health and Disease
… query and target sequences. Advancements in deep learning have significantly aided the functional annotation of proteins and comprehension of their diverse functions. Protein language models (pLMs), such as those used for structural prediction and other tasks, demonstrate remarkable capabilities …
-
Correlations Between Song Popularity and Their Audio Features Using Machine Learning
… features and song popularity on Spotify using machine learning techniques. Through the collection of nearly half a million songs and implementation of seven different machine learning models, including Linear Regression, Random Forest, Decision Trees, and Gradient Boosting, I investigated how …
Page 1 of 2