Search results for "Machine learning"
showing 10 items of 1464 documents
Recognition of Falls and Daily Living Activities Using Machine Learning
2018
A robust fall detection system is essential to support the independent living of elderlies. In this context, we develop a machine learning framework for fall detection and daily living activity recognition. Using acceleration data from public databases, we test the performance of two algorithms to classify seven different activities including falls and activities of daily living. We extract new features from the acceleration signal and demonstrate their effect on improving the accuracy and the precision of the classifier. Our analysis reveals that the quadratic support vector machine classifier achieves an overall accuracy of 93.2% and outperforms the artificial neural network algorithm. Re…
Combining Benford's Law and machine learning to detect money laundering. An actual Spanish court case.
2017
Abstract Objectives This paper is based on the analysis of the database of operations from a macro-case on money laundering orchestrated between a core company and a group of its suppliers, 26 of which had already been identified by the police as fraudulent companies. In the face of a well-founded suspicion that more companies have perpetrated criminal acts and in order to make better use of what are very limited police resources, we aim to construct a tool to detect money laundering criminals. Methods We combine Benford’s Law and machine learning algorithms (logistic regression, decision trees, neural networks, and random forests) to find patterns of money laundering criminals in the conte…
Machine learning of reverse transcription signatures of variegated polymerases allows mapping and discrimination of methylated purines in limited tra…
2020
AbstractReverse transcription (RT) of RNA templates containing RNA modifications leads to synthesis of cDNA containing information on the modification in the form of misincorporation, arrest, or nucleotide skipping events. A compilation of such events from multiple cDNAs represents an RT-signature that is typical for a given modification, but, as we show here, depends also on the reverse transcriptase enzyme. A comparison of 13 different enzymes revealed a range of RT-signatures, with individual enzymes exhibiting average arrest rates between 20 and 75%, as well as average misincorporation rates between 30 and 75% in the read-through cDNA. Using RT-signatures from individual enzymes to trai…
The reverse transcription signature of N-1-methyladenosine in RNA-Seq is sequence dependent
2015
The combination of Reverse Transcription (RT) and high-throughput sequencing has emerged as a powerful combination to detect modified nucleotides in RNA via analysis of either abortive RT-products or of the incorporation of mismatched dNTPs into cDNA. Here we simultaneously analyze both parameters in detail with respect to the occurrence of N-1-methyladenosine (m1A) in the template RNA. This naturally occurring modification is associated with structural effects, but it is also known as a mediator of antibiotic resistance in ribosomal RNA. In structural probing experiments with dimethylsulfate, m1A is routinely detected by RT-arrest. A specifically developed RNA-Seq protocol was tailored to …
Dosage individualization of erythropoietin using a profile-dependent support vector regression
2003
The external administration of recombinant human erythropoietin is the chosen treatment for those patients with secondary anemia due to chronic renal failure in periodic hemodialysis. The objective of this paper is to carry out an individualized prediction of the EPO dosage to be administered to those patients. The high cost of this medication, its side-effects and the phenomenon of potential resistance which some individuals suffer all justify the need for a model which is capable of optimizing dosage individualization. A group of 110 patients and several patient factors were used to develop the models. The support vector regressor (SVR) is benchmarked with the classical multilayer percept…
A finite element-based machine learning approach for modeling the mechanical behavior of the breast tissues under compression in real-time
2017
[EN] This work presents a data-driven method to simulate, in real-time, the biomechanical behavior of the breast tissues in some image-guided interventions such as biopsies or radiotherapy dose delivery as well as to speed up multimodal registration algorithms. Ten real breasts were used for this work. Their deformation due to the displacement of two compression plates was simulated off-line using the finite element (FE) method. Three machine learning models were trained with the data from those simulations. Then, they were used to predict in real-time the deformation of the breast tissues during the compression. The models were a decision tree and two tree-based ensemble methods (extremely…
A deep learning framework for automatic diagnosis of unipolar depression.
2019
Abstract Background and purpose In recent years, the development of machine learning (ML) frameworks for automatic diagnosis of unipolar depression has escalated to a next level of deep learning frameworks. However, this idea needs further validation. Therefore, this paper has proposed an electroencephalographic (EEG)-based deep learning framework that automatically discriminated depressed and healthy controls and provided the diagnosis. Basic procedures In this paper, two different deep learning architectures were proposed that utilized one dimensional convolutional neural network (1DCNN) and 1DCNN with long short-term memory (LSTM) architecture. The proposed deep learning architectures au…
Prediction of the hemoglobin level in hemodialysis patients using machine learning techniques
2013
HighlightsDifferent prediction algorithms were used to predict Hb levels in CRF patients.Prediction errors in the validation cohorts of patients were around 0.6g/dl.Difficulty to obtain lower errors due to the measuring machine precision (0.2g/dl).Relevance analysis of features have been applied for each predictor. Patients who suffer from chronic renal failure (CRF) tend to suffer from an associated anemia as well. Therefore, it is essential to know the hemoglobin (Hb) levels in these patients. The aim of this paper is to predict the hemoglobin (Hb) value using a database of European hemodialysis patients provided by Fresenius Medical Care (FMC) for improving the treatment of this kind of …
Shape analysis of the cingulum, uncinate and arcuate fasciculi in patients with bipolar disorder
2016
Background: Abnormal maturation of brain connectivity is supposed to underlie the dysfunctional emotion regulation in patients with bipolar disorder (BD). To test this hypothesis, white matter integrity is usually investigated using measures of water diffusivity provided by MRI. Here we consider a more intuitive aspect of the morphometry of the white matter tracts: the shape of the fibre bundles, which is associated with neurodevelopment. We analyzed the shape of 3 tracts involved in BD: the cingulum (CG), uncinate fasciculus (UF) and arcuate fasciculus (AF). Methods: We analyzed diffusion MRI data in patients with BD and healthy controls. The fibre bundles were reconstructed using Q-ball–b…
LOW-RANK APPROXIMATION BASED NON-NEGATIVE MULTI-WAY ARRAY DECOMPOSITION ON EVENT-RELATED POTENTIALS
2014
Non-negative tensor factorization (NTF) has been successfully applied to analyze event-related potentials (ERPs), and shown superiority in terms of capturing multi-domain features. However, the time-frequency representation of ERPs by higher-order tensors are usually large-scale, which prevents the popularity of most tensor factorization algorithms. To overcome this issue, we introduce a non-negative canonical polyadic decomposition (NCPD) based on low-rank approximation (LRA) and hierarchical alternating least square (HALS) techniques. We applied NCPD (LRAHALS and benchmark HALS) and CPD to extract multi-domain features of a visual ERP. The features and components extracted by LRAHALS NCP…