503 research outputs found

    Нейросетевые алгоритмы распознавания эмоций на основе сигналов ЭЭГ

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    Объектом исследования являются Обнаружение эмоций с помощью электроэнцефалограмм и машинного обучения.. Цель работы: Использование нейронных сетей для распознавания эмоций на основе сигналов ЭЭГ. Задачами исследование является анализ базы данных, использование вейвлет-преобразования в процессе извлечения признаков, построение модели для классификации эмоций и анализ результатов. В соответствии с поставленными задачами база данных была проанализирована на наличие сигналов ЭЭГ, а затем было использовано вейвлет-преобразование для извлечения важных признаков и информации в этих сигналах с целью получения признаков, дающих нам наилучшие результаты. Для построения модели использовались нейронная сеть GRU и полносвязная нейронная сеть.The object of research is the Detection of emotions using electroencephalograms and machine learning. Purpose of the work: Using neural networks to recognize emotions based on EEG signals. The objectives of the study are to analyze the database, use the wavelet transform in the feature extraction process, build a model for classifying emotions, and analyze the results. In accordance with the tasks set, the database was analyzed for the presence of EEG signals, and then wavelet transform was used to extract important features and information in these signals in order to obtain features that give us the best results. The GRU neural network and a fully connected neural network were used to build the model

    Artificial Intelligence for Cognitive Health Assessment: State-of-the-Art, Open Challenges and Future Directions

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    The subjectivity and inaccuracy of in-clinic Cognitive Health Assessments (CHA) have led many researchers to explore ways to automate the process to make it more objective and to facilitate the needs of the healthcare industry. Artificial Intelligence (AI) and machine learning (ML) have emerged as the most promising approaches to automate the CHA process. In this paper, we explore the background of CHA and delve into the extensive research recently undertaken in this domain to provide a comprehensive survey of the state-of-the-art. In particular, a careful selection of significant works published in the literature is reviewed to elaborate a range of enabling technologies and AI/ML techniques used for CHA, including conventional supervised and unsupervised machine learning, deep learning, reinforcement learning, natural language processing, and image processing techniques. Furthermore, we provide an overview of various means of data acquisition and the benchmark datasets. Finally, we discuss open issues and challenges in using AI and ML for CHA along with some possible solutions. In summary, this paper presents CHA tools, lists various data acquisition methods for CHA, provides technological advancements, presents the usage of AI for CHA, and open issues, challenges in the CHA domain. We hope this first-of-its-kind survey paper will significantly contribute to identifying research gaps in the complex and rapidly evolving interdisciplinary mental health field

    Investigating the role of enhancer-mediated gene expression in the human brain and its potential contribution to psychiatric disorders

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    Autism spectrum disorder (ASD) and schizophrenia (SCZ) are two neuropsychiatric conditions with variable times of onset and are influenced by both genetic and environmental factors. Genome-wide association studies (GWASs) have led to the identification of numerous genetic loci common to both these disorders, however our understanding remains far from complete, with many clinical cases without a genetic cause. While increasing the statistical power of genome-wide association studies (GWASs) to find additional risk variants could rule-in or rule out rare cases of ASD and SCZ, this presently remains a difficult task. Furthermore, the biological functions for genetic susceptibility loci remains poorly understood, particularly for more-recent discoveries of loci devoid of gene bodies. On the other hand, recent biotechnological developments have made it possible to conduct high-resolution experimental measurements of the three-dimensional architecture of the genome, including enhancer-promoter interactions (EPIs). Such data have been used to connect GWAS risk variants to their potential target genes which, in turn, provide insights into underlying molecular mechanisms and cellular processes. The functions of enhancer-promoter interactions in controlling gene expression programmes is crucial to how implicated genes mediate neurological function and disease. Yet, knowledge on enhancer-promoter interactions remains to be used in conjunction with GWAS data, particularly on such data from specific brain cell types, which may be useful to uncover the biological underpinnings of psychiatric conditions. This thesis examines the role of enhancer-mediated gene expression in the human brain and its potential contribution to psychiatric conditions. In Chapter 2, I report on the identification of significant chromosomal interactions from studies of brain Hi-C data generated from neuronal and glial cells, with the goal to investigate the impact of EPIs genome-wide, as well as to provide a template for an in-depth understanding of how EPIs impact transcriptional regulation. In the Chapter 3, I discuss a novel approach integrating Activity by Contact (ABC) and gene set enrichment analyses of GWAS data in two steps. In the first step, ABC is used to predict enhancer-gene regulatory interactions in a given cell type (e.g., glial cells, neurons). Secondly, Hi-C coupled multi-marker analysis of genomic annotation (H-MAGMA) is used to assign the SNPs located in the regulatory regions identified by ABC to each gene and calculate gene-level association p-values. I applied this novel framework (ABC-HMAGMA) to GWAS data from SCZ and ASD, to identify novel SCZ and ASD trait-associated genes and molecular pathways. In Chapter 4, I have evaluated a potential novel mechanism for the regulation of enhancer activity within cells. I hypothesized that, in addition to its known roles in DNA replication and transcription, Topoisomerase I may regulate enhancer activity in brain cells. To test this hypothesis, I employed RNA-seq and transient transcriptome sequencing (TT-seq) data, a method that enriches for short-lived enhancer derived RNAs. These data showed that Topoisomerase I inhibition leads to significant changes in eRNA expression and offers evidence that such changes are relevant to the homeostatic functions for Top 1 in cellular gene expression regulation

    Modern automatic recognition technologies for visual communication tools

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    Общение представляет собой широкий спектр различных действий, связанных с приёмом и передачей информации. Процесс общения складывается из вербальных, паравербальных и невербальных компонентов, содержащих информационную часть передаваемого сообщения и его эмоциональную окраску соответственно. Комплексный анализ всех компонентов общения позволяет оценить не только содержательную составляющую, но и ситуативный контекст сказанного, а также выявлять дополнительные факторы, относящиеся к психическому и соматическому состоянию говорящего. Существует несколько методов передачи вербального сообщения, среди которых устная и жестовая речь. Речевые и околоречевые компоненты общения могут содержаться в различных каналах данных, таких как аудио- или видеоканалы. В данном обзоре рассматриваются системы анализа видеоданных ввиду того, что аудиоканал не способен передать ряд околоречевых компонентов общения, вносящих в передаваемое сообщение дополнительную информацию. Проводится анализ существующих баз данных статических и динамических образов и систем, разрабатываемых для распознавания вербальной составляющей в устной и жестовой речи, а также систем, оценивающих паравербальные и невербальные компоненты общения. Обозначены сложности, с которыми сталкиваются разработчики подобных баз данных и систем. Также сформулированы перспективные направления разработок, связанные в том числе с комплексным анализом всех компонентов общения с целью наиболее полной оценки передаваемого сообщения.Работа выполнена при поддержке Госпрограммы 47 ГП «Научно-технологическое развитие Российской Федерации» (2019-2030), тема 0134-2019-0006

    A kernel-free L1 norm regularized ν-support vector machine model with application

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    With a view to overcoming a few shortcomings resulting from the kernel-based SVM models, these kernel-free support vector machine (SVM) models are newly promoted and researched. With the aim of deeply enhancing the classification accuracy of present kernel-free quadratic surface support vector machine (QSSVM) models while avoiding computational complexity, an emerging kernel-free ν-fuzzy reduced QSSVM with L1 norm regularization model is proposed. The model has well-developed sparsity to avoid computational complexity and overfitting and has been simplified as these standard linear models on condition that the data points are (nearly) linearly separable. Computational tests are implemented on several public benchmark datasets for the purpose of showing the better performance of the presented model compared with a few known binary classification models. Similarly, the numerical consequences support the more elevated training effectiveness of the presented model in comparison with those of other kernel-free SVM models. What`s more, the presented model is smoothly employed in lung cancer subtype diagnosis with good performance, by using the gene expression RNAseq-based lung cancer subtype (LUAD/LUSC) dataset in the TCGA database

    An overview of data integration in neuroscience with focus on Alzheimer's Disease

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    : This work represents the first attempt to provide an overview of how to face data integration as the result of a dialogue between neuroscientists and computer scientists. Indeed, data integration is fundamental for studying complex multifactorial diseases, such as the neurodegenerative diseases. This work aims at warning the readers of common pitfalls and critical issues in both medical and data science fields. In this context, we define a road map for data scientists when they first approach the issue of data integration in the biomedical domain, highlighting the challenges that inevitably emerge when dealing with heterogeneous, large-scale and noisy data and proposing possible solutions. Here, we discuss data collection and statistical analysis usually seen as parallel and independent processes, as cross-disciplinary activities. Finally, we provide an exemplary application of data integration to address Alzheimer's Disease (AD), which is the most common multifactorial form of dementia worldwide. We critically discuss the largest and most widely used datasets in AD, and demonstrate how the emergence of machine learning and deep learning methods has had a significant impact on disease's knowledge particularly in the perspective of an early AD diagnosis

    A comprehensive review for machine learning on neuroimaging in obsessive-compulsive disorder

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    Obsessive-compulsive disorder (OCD) is a common mental disease, which can exist as a separate disease or become one of the symptoms of other mental diseases. With the development of society, statistically, the incidence rate of obsessive-compulsive disorder has been increasing year by year. At present, in the diagnosis and treatment of OCD, The clinical performance of patients measured by scales is no longer the only quantitative indicator. Clinical workers and researchers are committed to using neuroimaging to explore the relationship between changes in patient neurological function and obsessive-compulsive disorder. Through machine learning and artificial learning, medical information in neuroimaging can be better displayed. In this article, we discuss recent advancements in artificial intelligence related to neuroimaging in the context of Obsessive-Compulsive Disorder
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