Search results for "Machine"

Literāru tekstu mašīntulkojumu un cilvēka tulkojumu no franču un angļu valodas latviešu valodā salīdzinošā analīze

2018

Mašīntulkošanas ietekme tulkošanas jomā ir krietni pieaugusi, taču literāru tekstu tulkošanā tā tiek izmantota reti to sarežģītības dēļ. Šī maģistra darba mērķis ir salīdzināt literāru tekstu mašīntulkojumus ar cilvēka tulkojumiem un analizēt, kāda varētu būt tās iespējamā loma šajā tulkošanas apakšnozarē. Šī darba praktiskajā daļā romānu fragmentu mašīntulkojumi tika salīdzināti ar cilvēku tulkotiem tekstiem divos valodu pāros. Tekstu kvalitāte tika novērtēta, izmantojot manuālu lingvistisko, kā arī automātisko metodi. Kaut gan morfoloģiskās tipoloģijas ziņā angļu valoda un latviešu valoda ir savstarpēji attālinātākas, iegūtie rezultāti liecināja, ka sniegums bija labāks, nekā tulkojot no …

PREDICTIVE MODELS BASED ON RADIOMICS AND MACHINE LEARNING FOR LUNG CANCER RADIOTHERAPY DATA ANALYSIS

2020

Updating strategies for distance based classification model with recursive least squares

2022

Abstract. The idea is to create a self-learning Minimal Learning Machine (MLM) model that is computationally efficient, easy to implement and performs with high accuracy. The study has two hypotheses. Experiment A examines the possibilities of introducing new classes with Recursive Least Squares (RLS) updates for the pre-trained self learning-MLM model. The idea of experiment B is to simulate the push broom spectral imagers working principles, update and test the model based on a stream of pixel spectrum lines on a continuous scanning process. Experiment B aims to train the model with a significantly small amount of labelled reference points and update it continuously with (RLS) to reach ma…

Investigating Novice Developers’ Code Commenting Trends Using Machine Learning Techniques

2023

Code comments are considered an efficient way to document the functionality of a particular block of code. Code commenting is a common practice among developers to explain the purpose of the code in order to improve code comprehension and readability. Researchers investigated the effect of code comments on software development tasks and demonstrated the use of comments in several ways, including maintenance, reusability, bug detection, etc. Given the importance of code comments, it becomes vital for novice developers to brush up on their code commenting skills. In this study, we initially investigated what types of comments novice students document in their source code and further categoriz…

What makes segmentation good? A case study in boreal forest habitat mapping

2013

Segmentation goodness evaluation is a set of approaches meant for deciding which segmentation is good. In this study, we tested different supervised segmentation evaluation measures and visual interpretation in the case of boreal forest habitat mapping in Southern Finland. The data used were WorldView-2 satellite imagery, a lidar digital elevation model (DEM), and a canopy height model (CHM) in 2 m resolution. The segmentation methods tested were the fractal net evolution approach (FNEA) and IDRISI watershed segmentation. Overall, 252 different segmentation methods, layers, and parameter combinations were tested. We also used eight different habitat delineations as reference polygons agains…

Automatic image‐based identification and biomass estimation of invertebrates

2020

Understanding how biological communities respond to environmental changes is a key challenge in ecology and ecosystem management. The apparent decline of insect populations necessitates more biomonitoring but the time-consuming sorting and expert-based identification of taxa pose strong limitations on how many insect samples can be processed. In turn, this affects the scale of efforts to map and monitor invertebrate diversity altogether. Given recent advances in computer vision, we propose to enhance the standard human expert-based identification approach involving manual sorting and identification with an automatic image-based technology. We describe a robot-enabled image-based identificat…

The Truth is Out There : Focusing on Smaller to Guess Bigger in Image Classification

2023

In Artificial Intelligence (AI) in general and in Machine Learning (ML) in particular, which are important and integral components of modern Industry 4.0, we often deal with uncertainty, e.g., lack of complete information about the objects we are classifying, recognizing, diagnosing, etc. Traditionally, uncertainty is considered to be a problem especially in the responsible use of AI and ML tools in the smart manufacturing domain. However, in this study, we aim not to fight with but rather to benefit from the uncertainty to improve the classification performance in supervised ML. Our objective is a kind of uncertainty-driven technique to improve the performance of Convolutional Neural Netwo…

Machine Learning for Predicting Chemical Potentials of Multifunctional Organic Compounds in Atmospherically Relevant Solutions

2022

We have trained the Extreme Minimum Learning Machine (EMLM) machine learning model to predict chemical potentials of individual conformers of multifunctional organic compounds containing carbon, hydrogen, and oxygen. The model is able to predict chemical potentials of molecules that are in the size range of the training data with a root-mean-square error (RMSE) of 0.5 kcal/mol. There is also a linear correlation between calculated and predicted chemical potentials of molecules that are larger than those included in the training set. Finding the lowest chemical potential conformers is useful in condensed phase thermodynamic property calculations, in order to reduce the number of computationa…

Computational complexity of prediction strategies

1977

The value f(m+1) is predicted from given f(1), ..., f(m). For every enumeration T(n, x) there is a strategy that predicts the n-th function of T making no more than log2(n) errors (Barzdins-Freivalds). It is proved in the paper that such "optimal" strategies require 2^2^cm time to compute the m-th prediction (^ stands for expoentiation).

Machine Learning for Early Diagnosis of ATTRv Amyloidosis in Non-Endemic Areas: A Multicenter Study from Italy

2023

Background: Hereditary transthyretin amyloidosis with polyneuropathy (ATTRv) is an adult-onset multisystemic disease, affecting the peripheral nerves, heart, gastrointestinal tract, eyes, and kidneys. Nowadays, several treatment options are available; thus, avoiding misdiagnosis is crucial to starting therapy in early disease stages. However, clinical diagnosis may be difficult, as the disease may present with unspecific symptoms and signs. We hypothesize that the diagnostic process may benefit from the use of machine learning (ML). Methods: 397 patients referring to neuromuscular clinics in 4 centers from the south of Italy with neuropathy and at least 1 more red flag, as well as undergoin…