Search results for "LEARNING"
showing 10 items of 6669 documents
Multitask deep learning for native language identification
2020
Identifying the native language of a person by their text written in English (L1 identification) plays an important role in such tasks as authorship profiling and identification. With the current proliferation of misinformation in social media, these methods are especially topical. Most studies in this field have focused on the development of supervised classification algorithms, that are trained on a single L1 dataset. Although multiple labeled datasets are available for L1 identification, they contain texts authored by speakers of different languages and do not completely overlap. Current approaches achieve high accuracy on available datasets, but this is attained by training an individua…
Pre-service teachers and guided inquiry-based science teaching with simulations
2017
The aim of this dissertation was explore the beliefs and practices of pre-service primary teachers on using simulations as a part of guided inquiry-based lessons. Even though research has shown that using simulations to learn science offers certain learning benefits compared to other forms of instruction, their use in Finnish schools is still rare compared to the international average. Teacher training has the potential to promote the use of simulations in primary classrooms. Internationally, research has been called for the role of teachers in learning and teaching with simulations. As a part of this dissertation, an intervention was designed to accustom a group of pre-service teachers to …
Tutkiva oppiminen evoluution opetuksessa lukiossa
2017
Uusi opetussuunnitelma velvoittaa opettajia kokeellisuuden ja tutkimuksellisuuden hyödyntämiseen opetuksessa, ja eräs keino tähän on tutkiva oppiminen. Tutkivassa oppimisessa tietoa ei omaksuta suoraan oppikirjasta tai opettajalta, vaan sitä hankitaan tieteelle tuttujen toimintamallien, kuten kysymysten, hypoteesien, selitysten ja kokeiden avulla. Tutkivan oppimisen vaiheet muodostavat yhdessä tutkivan oppimisen polun, jossa tieto tarkentuu vaihe vaiheelta. Opiskelijoita voidaan haastaa tutkivassa oppimisessa eri tavalla riippuen ovatko tutkimuskysymykset, menetelmät tai ratkaisut annettu heille etukäteen. Tutkivaa oppimista voidaan hyödyntää erilaisten aihepiirien opetuksessa kaikenikäiste…
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…
Efficient contour-based annotation by iterative deep learning for organ segmentation from volumetric medical images
2022
Abstract Purpose Training deep neural networks usually require a large number of human-annotated data. For organ segmentation from volumetric medical images, human annotation is tedious and inefficient. To save human labour and to accelerate the training process, the strategy of annotation by iterative deep learning recently becomes popular in the research community. However, due to the lack of domain knowledge or efficient human-interaction tools, the current AID methods still suffer from long training time and high annotation burden. Methods We develop a contour-based annotation by iterative deep learning (AID) algorithm which uses boundary representation instead of voxel labels to incorp…
Mobile learning for instructional purpose in Nigeria : an exploratory analysis
2013
The main purpose of this research is to explore the use of M - learning for instructional purpose in Nigeria with a view to uncovering the degree to which it is in use in the institutions of learning. The issue was addressed from the view point of students and also from the theoretical point drawn from relevant extant literatures of other students and some learning theories, frameworks as well as relevant literatures were examined. This research is empirical in nature and as such employs a cross sectional approach which involved the use of survey design in collecting data from students of different departments from two highly rated and recognized universities in Nigeria. Finally, the result…
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…
Glottal Source Features for Automatic Speech-Based Depression Assessment
2017
Depression is one of the most prominent mental disorders, with an increasing rate that makes it the fourth cause of disability worldwide. The field of automated depression assessment has emerged to aid clinicians in the form of a decision support system. Such a system could assist as a pre-screening tool, or even for monitoring high risk populations. Related work most commonly involves multimodal approaches, typically combining audio and visual signals to identify depression presence and/or severity. The current study explores categorical assessment of depression using audio features alone. Specifically, since depression-related vocal characteristics impact the glottal source signal, we exa…
Estimating Information in Earth System Data with Machine Learning
2021
El aprendizaje automático ha hecho grandes avances en la ciencia e ingeniería actuales en general y en las ciencias de la Tierra en particular. Sin embargo, los datos de la Tierra plantean problemas particularmente difíciles para el aprendizaje automático debido no sólo al volumen de datos implicado, sino también por la presencia de correlaciones no lineales tanto espaciales como temporales, por una gran diversidad de fuentes de ruido y de incertidumbre, así como por la heterogeneidad de las fuentes de información involucradas. Más datos no implica necesariamente más información. Por lo tanto, extraer conocimiento y contenido informativo mediante el análisis y el modelado de datos resulta c…