Tag: machine learning

Machine-Learning-Based Approaches for Multi-Level Sentiment Analysis of Romanian Reviews (2024)

Mathematics Authors Anamaria Briciu, A. Călin, Diana-Lucia Miholca, Cristiana Moroz-Dubenco, Vladiela Petrașcu, George Dascălu Abstract Sentiment analysis has increasingly gained significance in commercial settings, driven by the rising impact of reviews on purchase decision-making in recent years. This research conducts a thorough examination of the suitability of machine learning and deep learning approaches for…

ContRail: A Framework for Realistic Railway Image Synthesis using ControlNet (2024)

arXiv.org Authors Andrei-Robert Alexandrescu, Răzvan-Gabriel Petec, Alexandru Manole, Laura Diosan Abstract Our research focuses on creating a framework for extracting 3D deformable objects from 2D scenes. We research the possibility of using multiple graph convolutional operators and depth estimators to extract the object, while also using predefined segmentation masks for the objects in the…

DeGraRec: 3D Deformable Object Reconstruction Using Graph Neural Networks and Depth Estimation (2024)

Computer Graphics International Conference Authors Mihai-Adrian Loghin, A. Andreica Abstract Obtaining 3D representation of objects from scenes composed out of multiple images or frames is a task that often reacquires advanced hardware and knowledge in 3D rendering. With the rise of machine learning applications, the task became easier to solve for static objects, but…

Machine Learning Model Predicts Postoperative Outcomes in Chronic Rhinosinusitis With Nasal Polyps (2024)

Clinical Otolaryngology Authors Anda Gâta, L. Raduly, Liviuța Budișan, Adél Bajcsi, Teodora-Maria Ursu, C. Chira, Laura-Silvia Dioşan, I. Berindan‐Neagoe, S. Albu Abstract Objective: Evaluating the possibility of predicting chronic rhinosinusitis with nasal polyps (CRSwNP) disease course using Artificial Intelligence. Methods: We prospectively included patients undergoing first endoscopic sinus surgery (ESS) for nasal polyposis. Preoperative…

The Impact of Data Annotations on the Performance of Object Detection Models in Icon Detection for GUI Images (2024)

Hybrid Artificial Intelligence Systems Authors Madalina Dicu, Enol García González, Camelia Chira, J. Villar Abstract Detecting icons in Graphical User Interfaces (GUIs) is essential for effective application automation. This study examines the impact of different annotation methods on the performance of object detection models for icon detection in GUIs. We compared manual, automated, and hybrid annotations…

A Nash equilibria decision tree for binary classification (2024)

Applied intelligence (Boston) Authors M. Suciu, R. Lung Abstract Decision trees rank among the most popular and efficient classification methods. They are used to represent rules for recursively partitioning the data space into regions from which reliable predictions regarding classes can be made. These regions are usually delimited by axis-parallel or oblique hyperplanes. Axis-parallel…

A game theoretic decision forest for feature selection and classification (2024)

Logic Journal of the IGPL Authors M. Suciu, R. Lung Abstract Classification and feature selection are two of the most intertwined problems in machine learning. Decision trees (DTs) are straightforward models that address these problems offering also the advantage of explainability. However, solutions that are based on them are either tailored for the problem…

Improving Unsupervised Graph-Based Skull Stripping: Enhancements and Comparative Analysis With State-Of-The-Art Methods (2024)

International Workshop on Informatics & Data-Driven Medicine Authors Maria Popa, A. Andreica Abstract Brain disorders are increasingly prevalent today, making accurate brain segmentation essential for effective treatment andrecovery. This paperintroducesanenhancedunsupervisedgraph-basedbrainsegmentationmethod that employs an ellipsoid to select the nodes forming the graph. The method was rigorously evaluated on T1 and T2 modalities using four diverse…

Teeth segmentation and carious lesions segmentation in panoramic X-ray images using CariSeg, a networks’ ensemble (2024)

Heliyon Authors Andra Carmen Marginean, Sorana Mureșanu, M. Hedeșiu, L. Dioşan Abstract Background: Dental cavities are common oral diseases that can lead to pain, discomfort, and eventually, tooth loss. Early detection and treatment of cavities can prevent these negative consequences. We propose CariSeg, an intelligent system composed of four neural networks that result in…