Relevant bibliographies by topics / Computer network disorders

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Computer network disorders'

Author: Grafiati
Published: 4 June 2021
Last updated: 2 February 2022

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Journal articles on the topic "Computer network disorders"

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Shankar Beriha, Siba. "Computer Aided Diagnosis System To Distinguish Adhd From Similar Behavioral Disorders." Biomedical and Pharmacology Journal 11, no. 2 (June 12, 2018): 1135–41. http://dx.doi.org/10.13005/bpj/1474.

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ADHD is one of the most prevalent psychiatric disorder of childhood, characterized by inattention and distractibility, with or without accompanying hyperactivity. The main aim of this research work is to develop a Computer Aided Diagnosis (CAD) technique with minimal steps that can differentiate the ADHD children from the other similar children behavioral disorders such as anxiety, depression and conduct disorder based on the Electroencephalogram (EEG) signal features and symptoms. The proposed technique is based on soft computing and bio inspired computing algorithms. Four non-linear features are extracted from the EEG such as Higuchi fractal dimension, Katz fractal dimension, Sevick fractal dimension and Lyapunov exponent and 14 symptoms which are most important in differentiation are extracted by experts in the field of psychiatry. Particle Swarm Optimization (PSO) tuned Back Propagation Neural Network (BPNN) and PSO tuned Radial Basis Function (RBF) employed as a classifier. By investigating these integrated features, we obtained good classification accuracy. Simulation results suggest that the proposed technique offer high potential in the diagnosis of ADHD and may be a good preliminary assistant for psychiatrists in diagnosing high risk behavioral disorders of children.
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Kawakubo, Hideko, Yusuke Matsui, Itaru Kushima, Norio Ozaki, and Teppei Shimamura. "A network of networks approach for modeling interconnected brain tissue-specific networks." Bioinformatics 35, no. 17 (January 15, 2019): 3092–101. http://dx.doi.org/10.1093/bioinformatics/btz032.

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Abstract Motivation Recent sequence-based analyses have identified a lot of gene variants that may contribute to neurogenetic disorders such as autism spectrum disorder and schizophrenia. Several state-of-the-art network-based analyses have been proposed for mechanical understanding of genetic variants in neurogenetic disorders. However, these methods were mainly designed for modeling and analyzing single networks that do not interact with or depend on other networks, and thus cannot capture the properties between interdependent systems in brain-specific tissues, circuits and regions which are connected each other and affect behavior and cognitive processes. Results We introduce a novel and efficient framework, called a ‘Network of Networks’ approach, to infer the interconnectivity structure between multiple networks where the response and the predictor variables are topological information matrices of given networks. We also propose Graph-Oriented SParsE Learning, a new sparse structural learning algorithm for network data to identify a subset of the topological information matrices of the predictors related to the response. We demonstrate on simulated data that propose Graph-Oriented SParsE Learning outperforms existing kernel-based algorithms in terms of F-measure. On real data from human brain region-specific functional networks associated with the autism risk genes, we show that the ‘Network of Networks’ model provides insights on the autism-associated interconnectivity structure between functional interaction networks and a comprehensive understanding of the genetic basis of autism across diverse regions of the brain. Availability and implementation Our software is available from https://github.com/infinite-point/GOSPEL. Supplementary information Supplementary data are available at Bioinformatics online.
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Verma, Archana, Shweta Singh Chauhan, Vaishali Pankaj, Neha Srivastva, and Prachi Srivastava. "Network Biology Approaches to Identify the Drug Lead Molecule for Neurodevelopmental Disorders in Human." Open Bioinformatics Journal 13, no. 1 (March 20, 2020): 15–24. http://dx.doi.org/10.2174/1875036202013010015.

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Aims: To identify most novel drug target and lead molecule for neurodevelopmental disorder Autism, Intellectual Disability (ID) and Attention Deficit Hyperactivity Disorder (ADHD) diseases through system biology approaches Background: Neurodevelopmental disorders (NNDs) are disabilities associated chiefly with the functioning of the neurological system and brain. Children with neurodevelopmental disorders have difficulties with speech, behaviour, learning and other neurological functions. Systems biology is a holistic approach to enciphering the complexity of biological systems and their interactions. It opens the way to a more successful discovery of novel therapeutics. Objective: To identify most novel drug target and lead molecule for neurodevelopmental disorder Autism, Intellectual Disability (ID) and Attention Deficit Hyperactivity Disorder (ADHD) diseases through system biology approaches. Methods: A list of genes was collected from NCBI database for Autism, Intellectual Disability (ID) and Attention Deficit Hyperactivity Disorder (ADHD) diseases. STRING database and Cytoscape software was used for construction and interpreting molecular interaction in the network. 3D structure of target protein, was build and validated.The phytochemicals were identified through various research articles and filtered out by virtual screening through Molinspiration. Molecular docking analyses of known phytochemical with target proteins were performed usingAutoDock tool. Result: AKT1 for Autism, SNAP25 for Intellectual Disability (ID) and DRD4 for Attention Deficit Hyperactivity Disorder (ADHD) were identified as most potential drug target through network study. further the modelled structure of obtained target were undergo molecular docking study with kown phytochemicals. Based on lowest binding energy, Huperzine A for Autism and ID, Valerenic acid for ADHD found to be the most potential therapeutic molecules. Conclusion: Huperzine A against Autism and ID, Valerenic acid against ADHD found to be the most potential therapeutic molecules and expected to be effective in the treatment of NNDs. Phytochemicals do not have side effects so extract of these can be taken in preventive form too as these disorders occur during developmental stages of the child. Further the obtained molecule if experimentally validated would play promising role for the treatment of NDDs in human.
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Basha, Omer, Chanan M. Argov, Raviv Artzy, Yazeed Zoabi, Idan Hekselman, Liad Alfandari, Vered Chalifa-Caspi, and Esti Yeger-Lotem. "Differential network analysis of multiple human tissue interactomes highlights tissue-selective processes and genetic disorder genes." Bioinformatics 36, no. 9 (January 21, 2020): 2821–28. http://dx.doi.org/10.1093/bioinformatics/btaa034.

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Abstract Motivation Differential network analysis, designed to highlight network changes between conditions, is an important paradigm in network biology. However, differential network analysis methods have been typically designed to compare between two conditions and were rarely applied to multiple protein interaction networks (interactomes). Importantly, large-scale benchmarks for their evaluation have been lacking. Results Here, we present a framework for assessing the ability of differential network analysis of multiple human tissue interactomes to highlight tissue-selective processes and disorders. For this, we created a benchmark of 6499 curated tissue-specific Gene Ontology biological processes. We applied five methods, including four differential network analysis methods, to construct weighted interactomes for 34 tissues. Rigorous assessment of this benchmark revealed that differential analysis methods perform well in revealing tissue-selective processes (AUCs of 0.82–0.9). Next, we applied differential network analysis to illuminate the genes underlying tissue-selective hereditary disorders. For this, we curated a dataset of 1305 tissue-specific hereditary disorders and their manifesting tissues. Focusing on subnetworks containing the top 1% differential interactions in disease-relevant tissue interactomes revealed significant enrichment for disorder-causing genes in 18.6% of the cases, with a significantly high success rate for blood, nerve, muscle and heart diseases. Summary Altogether, we offer a framework that includes expansive manually curated datasets of tissue-selective processes and disorders to be used as benchmarks or to illuminate tissue-selective processes and genes. Our results demonstrate that differential analysis of multiple human tissue interactomes is a powerful tool for highlighting processes and genes with tissue-selective functionality and clinical impact. Availability and implementation Datasets are available as part of the Supplementary data. Supplementary information Supplementary data are available at Bioinformatics online.
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Chien, Chung-Yao, Szu-Wei Hsu, Tsung-Lin Lee, Pi-Shan Sung, and Chou-Ching Lin. "Using Artificial Neural Network to Discriminate Parkinson’s Disease from Other Parkinsonisms by Focusing on Putamen of Dopamine Transporter SPECT Images." Biomedicines 9, no. 1 (December 24, 2020): 12. http://dx.doi.org/10.3390/biomedicines9010012.

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Background: The challenge of differentiating, at an early stage, Parkinson’s disease from parkinsonism caused by other disorders remains unsolved. We proposed using an artificial neural network (ANN) to process images of dopamine transporter single-photon emission computed tomography (DAT-SPECT). Methods: Abnormal DAT-SPECT images of subjects with Parkinson’s disease and parkinsonism caused by other disorders were divided into training and test sets. Striatal regions of the images were segmented by using an active contour model and were used as the data to perform transfer learning on a pre-trained ANN to discriminate Parkinson’s disease from parkinsonism caused by other disorders. A support vector machine trained using parameters of semi-quantitative measurements including specific binding ratio and asymmetry index was used for comparison. Results: The predictive accuracy of the ANN classifier (86%) was higher than that of the support vector machine classifier (68%). The sensitivity and specificity of the ANN classifier in predicting Parkinson’s disease were 81.8% and 88.6%, respectively. Conclusions: The ANN classifier outperformed classical biomarkers in differentiating Parkinson’s disease from parkinsonism caused by other disorders. This classifier can be readily included into standalone computer software for clinical application.
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De Silva, Senuri, Sanuwani Udara Dayarathna, Gangani Ariyarathne, Dulani Meedeniya, and Sampath Jayarathna. "fMRI Feature Extraction Model for ADHD Classification Using Convolutional Neural Network." International Journal of E-Health and Medical Communications 12, no. 1 (January 2021): 81–105. http://dx.doi.org/10.4018/ijehmc.2021010106.

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Biomedical intelligence provides a predictive mechanism for the automatic diagnosis of diseases and disorders. With the advancements of computational biology, neuroimaging techniques have been used extensively in clinical data analysis. Attention deficit hyperactivity disorder (ADHD) is a psychiatric disorder, with the symptomology of inattention, impulsivity, and hyperactivity, in which early diagnosis is crucial to prevent unwelcome outcomes. This study addresses ADHD identification using functional magnetic resonance imaging (fMRI) data for the resting state brain by evaluating multiple feature extraction methods. The features of seed-based correlation (SBC), fractional amplitude of low-frequency fluctuation (fALFF), and regional homogeneity (ReHo) are comparatively applied to obtain the specificity and sensitivity. This helps to determine the best features for ADHD classification using convolutional neural networks (CNN). The methodology using fALFF and ReHo resulted in an accuracy of 67%, while SBC gained an accuracy between 84% and 86% and sensitivity between 65% and 75%.
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Morabito, Francesco Carlo, Domenico Ursino, Nadia Mammone, Francesco Cauteruccio, Paolo Lo Giudice, and Giorgio Terracina. "A new network-based approach to investigating neurological disorders." International Journal of Data Mining, Modelling and Management 11, no. 4 (2019): 315. http://dx.doi.org/10.1504/ijdmmm.2019.10023732.

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Cauteruccio, Francesco, Paolo Lo Giudice, Giorgio Terracina, Domenico Ursino, Nadia Mammone, and Francesco Carlo Morabito. "A new network-based approach to investigating neurological disorders." International Journal of Data Mining, Modelling and Management 11, no. 4 (2019): 315. http://dx.doi.org/10.1504/ijdmmm.2019.102730.

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Gavrilescu, Mihai, and Nicolae Vizireanu. "Feedforward Neural Network-Based Architecture for Predicting Emotions from Speech." Data 4, no. 3 (July 15, 2019): 101. http://dx.doi.org/10.3390/data4030101.

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We propose a novel feedforward neural network (FFNN)-based speech emotion recognition system built on three layers: A base layer where a set of speech features are evaluated and classified; a middle layer where a speech matrix is built based on the classification scores computed in the base layer; a top layer where an FFNN- and a rule-based classifier are used to analyze the speech matrix and output the predicted emotion. The system offers 80.75% accuracy for predicting the six basic emotions and surpasses other state-of-the-art methods when tested on emotion-stimulated utterances. The method is robust and the fastest in the literature, computing a stable prediction in less than 78 s and proving attractive for replacing questionnaire-based methods and for real-time use. A set of correlations between several speech features (intensity contour, speech rate, pause rate, and short-time energy) and the evaluated emotions is determined, which enhances previous similar studies that have not analyzed these speech features. Using these correlations to improve the system leads to a 6% increase in accuracy. The proposed system can be used to improve human–computer interfaces, in computer-mediated education systems, for accident prevention, and for predicting mental disorders and physical diseases.
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Jalata, Ibsa K., Thanh-Dat Truong, Jessica L. Allen, Han-Seok Seo, and Khoa Luu. "Movement Analysis for Neurological and Musculoskeletal Disorders Using Graph Convolutional Neural Network." Future Internet 13, no. 8 (July 28, 2021): 194. http://dx.doi.org/10.3390/fi13080194.

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Using optical motion capture and wearable sensors is a common way to analyze impaired movement in individuals with neurological and musculoskeletal disorders. However, using optical motion sensors and wearable sensors is expensive and often requires highly trained professionals to identify specific impairments. In this work, we proposed a graph convolutional neural network that mimics the intuition of physical therapists to identify patient-specific impairments based on video of a patient. In addition, two modeling approaches are compared: a graph convolutional network applied solely on skeleton input data and a graph convolutional network accompanied with a 1-dimensional convolutional neural network (1D-CNN). Experiments on the dataset showed that the proposed method not only improves the correlation of the predicted gait measure with the ground truth value (speed = 0.791, gait deviation index (GDI) = 0.792) but also enables faster training with fewer parameters. In conclusion, the proposed method shows that the possibility of using video-based data to treat neurological and musculoskeletal disorders with acceptable accuracy instead of depending on the expensive and labor-intensive optical motion capture systems.
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Dissertations / Theses on the topic "Computer network disorders"

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Patel, Avani Rajnikant. "Cognitive Rehab Solutions: A computer-assisted cognitive training program." CSUSB ScholarWorks, 2002. https://scholarworks.lib.csusb.edu/etd-project/2321.

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The purpose of this project is to offer a functionally comprehensive application, Cognitive Rehab Solutions (CRS), that is designed for neuropsychologists to deliver restorative cognitive training in areas of attention and memory of persons with brain impairment.
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Pllashniku, Edlir, and Zolal Stanikzai. "Normalization of Deep and Shallow CNNs tasked with Medical 3D PET-scans : Analysis of technique applicability." Thesis, Högskolan i Halmstad, Akademin för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-45521.

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There has in recent years been interdisciplinary research on utilizing machine learning for detecting and classifying neurodegenerative disorders with the sole goal of outperforming state-of-the-art models in terms of metrics such as accuracy, specificity, and sensitivity. Specifically, these studies have been conducted using existing networks on ”novel” methods of pre-processing data or by developing new convolutional neural networks. As of now, no work has looked into how different normalization techniques affect a deep or shallow convolutional neural network in terms of numerical stability, its performance, explainability, and interpretability. This work delves into what normalization technique is most suitable for deep and shallow convolutional neural networks. Two baselines were created, one shallow and one deep, and applied eight different normalization techniques to these model architectures. Conclusions were drawn based on our analysis of numerical stability, performance (metrics), and methods of Explainable Artificial Intelligence. Our findings indicate that normalization techniques affect models differently regarding the mentioned aspects of our analysis, especially numerical stability and explainability. Moreover, we show that there should indeed be a preference to select one method over the other in future studies of this interdisciplinary field.
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Laughton, Stephen Nicholas. "Dynamics of neural networks and disordered spin systems." Thesis, University of Oxford, 1995. http://ora.ox.ac.uk/objects/uuid:5531cef6-4682-4750-9c5c-cb69e5e72d64.

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I obtain a number of results for the dynamics of several disordered spin systems, of successively greater complexity. I commence with the generalised Hopfield model trained with an intensive number of patterns, where in the thermodynamic limit macroscopic, deterministic equations of motion can be derived exactly for both the synchronous discrete time and asynchronous continuous time dynamics. I show that for symmetric embedding matrices Lyapunov functions exist at the macroscopic level of description in terms of pattern overlaps. I then show that for asymmetric embedding matrices several types of bifurcation phenomena to complex non-transient dynamics occur, even in this simplest model. Extending a recent result of Coolen and Sherrington, I show how the dynamics of the generalised Hopfield model trained with extensively many patterns and non-trivial embedding matrix can be described by the evolution of a small number of overlaps and the disordered contribution to the 'energy', upon calculation of a noise distribution by the replica method. The evaluation of the noise distribution requires two key assumptions: that the flow equations are self averaging, and that equipartitioning of probability occurs within the macroscopic sub-shells of the ensemble. This method is inexact on intermediate time scales, due to the microscopic information integrated out in order to derive a closed set of equations. I then show how this theory can be improved in a systematic manner by introducing an order parameter function - the joint distribution of spins and local alignment fields, which evolves in time deterministically, according to a driven diffusion type equation. I show how the coefficients in this equation can be evaluated for the generalised Sherrington-Kirkpatrick model, both within the replica symmetric ansatz, and using Parisi's ultrametric ansatz for the replica matrices, upon making once again the two key assumptions (self averaging and equipartitioning). Since the order parameter is now a continuous function, however, the assumption of equipartitioning within the macroscopic sub-shells is much less restricting.
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De, Waal Rouviere. "Objective prediction of pure tone thresholds in normal and hearing-impaired ears with distortion product otoacoustic emissions and artificial neural networks." Pretoria : [s.n.], 2000. http://upetd.up.ac.za/thesis/available/etd-07142006-112943.

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Nguyen, Anh Dung. "Contributions to Modeling, Structural Analysis, and Routing Performance in Dynamic Networks." Phd thesis, Institut National Polytechnique de Toulouse - INPT, 2013. http://tel.archives-ouvertes.fr/tel-00908502.

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Cette thèse apporte des contributions à la modélisation, compréhension ainsi qu'à la communication efficace d'information dans les réseaux dynamiques peuplant la périphérie de l'Internet. Par réseaux dynamiques, nous signifions les réseaux pouvant être modélisés par des graphes dynamiques dans lesquels noeuds et liens évoluent temporellement. Dans la première partie de la thèse, nous proposons un nouveau modèle de mobilité - STEPS - qui permet de capturer un large spectre de comportement de mobilité humains. STEPS mets en oeuvre deux principes fondamentaux de la mobilité humaine : l'attachement préférentiel à une zone de prédilection et l'attraction vers une zone de prédilection. Nous proposons une modélisation markovienne de ce modèle de mobilité. Nous montrons que ce simple modèle paramétrique est capable de capturer les caractéristiques statistiques saillantes de la mobilité humaine comme la distribution des temps d'inter-contacts et de contacts. Dans la deuxième partie, en utilisant STEPS, nous analysons les propriétés comportementales et structurelles fondamentales des réseaux opportunistes. Nous redéfinissons dans le contexte des réseaux dynamiques la notion de structure petit monde et montrons comment une telle structure peut émerger. En particulier, nous montrons que les noeuds fortement dynamiques peuvent jouer le rôle de ponts entre les composants déconnectés, aident à réduire significativement la longueur du chemin caractéristique du réseau et contribuent à l'émergence du phénomène petit-monde dans les réseaux dynamiques. Nous proposons une façon de modéliser ce phénomène sous STEPS. À partir d'un réseau dynamique régulier dans lequel les noeuds limitent leur mobilité à leurs zones préférentielles respectives. Nous recablons ce réseau en injectant progressivement des noeuds nomades se déplaçant entre plusieurs zones. Nous montrons que le pourcentage de tels nœuds nomades est de 10%, le réseau possède une structure petit monde avec un fort taux de clusterisation et un faible longueur du chemin caractéristique. La troisième contribution de cette thèse porte sur l'étude de l'impact du désordre et de l'irrégularité des contacts sur la capacité de communication d'un réseau dynamique. Nous analysons le degré de désordre de réseaux opportunistes réels et montrons que si exploité correctement, celui-ci peut améliorer significativement les performances du routage. Nous introduisons ensuite un modèle permettant de capturer le niveau de désordre d'un réseau dynamique. Nous proposons deux algorithmes simples et efficaces qui exploitent la structure temporelle d'un réseau dynamique pour délivrer les messages avec un bon compromis entre l'usage des ressources et les performances. Les résultats de simulations et analytiques montrent que ce type d'algorithme est plus performant que les approches classiques. Nous mettons également en évidence aussi la structure de réseau pour laquelle ce type d'algorithme atteint ses performances optimum. Basé sur ce résultat théorique nous proposons un nouveau protocole de routage efficace pour les réseaux opportunistes centré sur le contenu. Dans ce protocole, les noeuds maintiennent, via leurs contacts opportunistes, une fonction d'utilité qui résume leur proximité spatio-temporelle par rapport aux autres noeuds. En conséquence, router dans un tel contexte se résume à suivre le gradient de plus grande pente conduisant vers le noeud destination. Cette propriété induit un algorithme de routage simple et efficace qui peut être utilisé aussi bien dans un contexte d'adressage IP que de réseau centré sur les contenus. Les résultats de simulation montrent que ce protocole superforme les protocoles de routage classiques déjà définis pour les réseaux opportunistes. La dernière contribution de cette thèse consiste à mettre en évidence une application potentielle des réseaux dynamiques dans le contexte du " mobile cloud computing ". En utilisant les techniques d'optimisation particulaires, nous montrons que la mobilité peut augmenter considérablement la capacité de calcul des réseaux dynamiques. De plus, nous montrons que la structure dynamique du réseau a un fort impact sur sa capacité de calcul.
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Pérez, Ramírez María Úrsula. "Characterizing functional and structural brain alterations driven by chronic alcohol drinking: a resting-state fMRI connectivity and voxel-based morphometry analysis." Doctoral thesis, Universitat Politècnica de València, 2018. http://hdl.handle.net/10251/113164.

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El balance del cerebro se altera a nivel estructural y funcional por el consumo de alcohol y puede causar trastornos por consumo de alcohol (TCA). El objetivo de esta Tesis Doctoral fue investigar los efectos del consumo crónico y excesivo de alcohol en el cerebro desde una perspectiva funcional y estructural, mediante análisis de imágenes multimodales de resonancia magnética (RM). Realizamos tres estudios con objetivos específicos: i) Para entender cómo las neuroadaptaciones desencadenadas por el consumo de alcohol se ven reflejadas en la conectividad cerebral funcional entre redes cerebrales, así como en la actividad cerebral, realizamos estudios en ratas msP en condiciones de control y tras un mes con acceso a alcohol. Para cada sujeto se obtuvieron las señales específicas de sus redes cerebrales tras aplicar análisis probabilístico de componentes independientes y regresión espacial a las imágenes funcionales de RM en estado de reposo (RMf-er). Después, estimamos la conectividad cerebral en estado de reposo mediante correlación parcial regularizada. Para una lectura de la actividad neuronal realizamos un experimento con imágenes de RM realzadas con manganeso. En la condición de alcohol encontramos hipoconectividades entre la red visual y las redes estriatal y sensorial; todas con incrementos en actividad. Por el contrario, hubo hiperconectividades entre tres pares de redes cerebrales: 1) red prefrontal cingulada media y red estriatal, 2) red sensorial y red parietal de asociación y 3) red motora-retroesplenial y red sensorial, siendo la red parietal de asociación la única red sin incremento de actividad. Estos resultados indican que las redes cerebrales ya se alteran desde una fase temprana de consumo continuo y prolongado de alcohol, disminuyendo el control ejecutivo y la flexibilidad comportamental. ii) Para comparar el volumen de materia gris (MG) cortical entre 34 controles sanos y 35 pacientes con dependencia al alcohol, desintoxicados y en abstinencia de 1 a 5 semanas, realizamos un análisis de morfometría basado en vóxel. Las principales estructuras cuyo volumen de MG disminuyó en los sujetos en abstinencia fueron el giro precentral (GPreC), el giro postcentral (GPostC), la corteza motora suplementaria (CMS), el giro frontal medio (GFM), el precúneo (PCUN) y el lóbulo parietal superior (LPS). Disminuciones de MG en el volumen de esas áreas pueden dar lugar a cambios en el control de los movimientos (GPreC y CMS), en el procesamiento de información táctil y propioceptiva (GPostC), personalidad, previsión (GFM), reconocimiento sensorial, entendimiento del lenguaje, orientación (PCUN) y reconocimiento de objetos a través de su forma (LPS). iii) Caracterizar estados cerebrales dinámicos en señales de RMf mediante una metodología basada en un modelo oculto de Markov (HMM en inglés)-Gaussiano en un paradigma con diseño de bloques, junto con distintas señales temporales de múltiples redes: componentes independientes y modos funcionales probabilísticos (PFMs en inglés) en 14 sujetos sanos. Cuatro condiciones experimentales formaron el paradigma de bloques: reposo, visual, motora y visual-motora. Mediante la aplicación de HMM-Gaussiano a los PFMs pudimos caracterizar cuatro estados cerebrales a partir de la actividad media de cada PFM. Los cuatro mapas espaciales obtenidos fueron llamados HMM-reposo, HMM-visual, HMM-motor y HMM-RND (red neuronal por defecto). HMM-RND apareció una vez el estado de tarea se había estabilizado. En un futuro cercano se espera obtener estados cerebrales en nuestros datos de RMf-er en ratas, para comparar dinámicamente el comportamiento de las redes cerebrales como un biomarcador de TCA. En conclusión, las técnicas de neuroimagen aplicadas en imagen de RM multimodal para estimar la conectividad cerebral en estado de reposo, la actividad cerebral y el volumen de materia gris han permitido avanzar en el entendimiento de los mecanismos homeostático
La ingesta d'alcohol altera el balanç del cervell a nivell estructural i funcional i pot causar trastorns per consum d' alcohol (TCA). L'objectiu d'aquesta Tesi Doctoral fou estudiar els efectes en el cervell del consum crònic i excessiu d'alcohol, des d'un punt de vista funcional i estructural i per mitjà d'anàlisi d'imatges de ressonància magnètica (RM). Vam realitzar tres anàlisis amb objectius específics: i) Per a entendre com les neuroadaptacions desencadenades pel consum d'alcohol es veuen reflectides en la connectivitat cerebral funcional entre xarxes cerebrals, així com en l'activitat cerebral, vam realitzar estudis en rates msP en les condicions de control i després d'un mes amb accés a alcohol. Per a cada subjecte vam obtindre els senyals de les xarxes cerebrals tras aplicar a les imatges funcionals de RM en estat de repòs una anàlisi probabilística de components independents i regressió espacial. Després, estimàrem la connectivitat cerebral en estat de repòs per mitjà de correlació parcial regularitzada. Per a una lectura de l'activitat cerebral vam adquirir imatges de RM realçades amb manganés. En la condició d'alcohol vam trobar hipoconnectivitats entre la xarxa visual i les xarxes estriatal i sensorial, totes amb increments en activitat. Al contrari, va haver-hi hiperconnectivitats entre tres parells de xarxes cerebrals: 1) xarxa prefrontal cingulada mitja i xarxa estriatal, 2) xarxa sensorial i xarxa parietal d'associació i 3) xarxa motora-retroesplenial i xarxa sensorial, sent la xarxa parietal d'associació l'única xarxa sense increment d'activitat. Aquests resultats indiquen que les xarxes cerebrals ja s'alteren des d'una fase primerenca caracteritzada per consum continu i prolongat d'alcohol, disminuint el control executiu i la flexibilitat comportamental. ii) Per a comparar el volum de MG cortical entre 34 controls sans i 35 pacients amb dependència a l'alcohol, desintoxicats i en abstinència de 1 a 5 setmanes vam emprar anàlisi de morfometria basada en vòxel. Les principals estructures on el volum de MG va disminuir en els subjectes en abstinència van ser el gir precentral (GPreC), el gir postcentral (GPostC), la corteça motora suplementària (CMS), el gir frontal mig (GFM), el precuni (PCUN) i el lòbul parietal superior (LPS). Les disminucions de MG en eixes àrees poden donar lloc a canvis en el control dels moviments (GPreC i CMS), en el processament d'informació tàctil i propioceptiva (GPostC), personalitat, previsió (GFM), reconeixement sensorial, enteniment del llenguatge, orientació (PCUN) i reconeixement d'objectes a través de la seua forma (LPS). iii) Caracterització de les dinàmiques temporals del cervell com a diferents estats cerebrals, en senyals de RMf mitjançant una metodologia basada en un model ocult de Markov (HMM en anglès)-Gaussià en imatges de RMf, junt amb dos tipus de senyals temporals de múltiples xarxes cerebrals: components independents i modes funcionals probabilístics (PFMs en anglès) en 14 subjectes sans. Quatre condicions experimentals van formar el paradigma de blocs: repòs, visual, motora i visual-motora. HMM-Gaussià aplicat als PFMs (senyals de RM funcional de xarxes cerebrals) va permetre la millor caracterització dels quatre estats cerebrals a partir de l'activitat mitjana de cada PFM. Els quatre mapes espacials obtinguts van ser anomenats HMM-repòs, HMM-visual, HMM-motor i HMM-XND (xarxa neuronal per defecte). HMM-XND va aparèixer una vegada una tasca estava estabilitzada. En un futur pròxim s'espera obtindre estats cerebrals en les nostres dades de RMf-er en rates, per a comparar dinàmicament el comportament de les xarxes cerebrals com a biomarcador de TCA. En conclusió, s'han aplicat tècniques de neuroimatge per a estimar la connectivitat cerebral en estat de repòs, l'activitat cerebral i el volum de MG, aplicades a imatges multimodals de RM i s'han obtés resultats que han permés avançar en l'enteniment dels m
Alcohol intake alters brain balance, affecting its structure and function, and it may cause Alcohol Use Disorders (AUDs). We aimed to study the effects of chronic, excessive alcohol consumption on the brain from a functional and structural point of view, via analysis of multimodal magnetic resonance (MR) images. We conducted three studies with specific aims: i) To understand how the neuroadaptations triggered by alcohol intake are reflected in between-network resting-state functional connectivity (rs-FC) and brain activity in the onset of alcohol dependence, we performed studies in msP rats in control and alcohol conditions. Group probabilistic independent component analysis (group-PICA) and spatial regression were applied to resting-state functional magnetic resonance imaging (rs-fMRI) images to obtain subject-specific time courses of seven resting-state networks (RSNs). Then, we estimated rs-FC via L2-regularized partial correlation. We performed a manganese-enhanced (MEMRI) experiment as a readout of neuronal activity. In alcohol condition, we found hypoconnectivities between the visual network (VN), and striatal (StrN) and sensory-cortex (SCN) networks, all with increased brain activity. On the contrary, hyperconnectivities were found between three pairs of RSNs: 1) medial prefrontal-cingulate (mPRN) and StrN, 2) SCN and parietal association (PAN) and 3) motor-retrosplenial (MRN) and SCN networks, being PAN the only network without brain activity rise. Interestingly, the hypoconnectivities could be explained as control to alcohol transitions from direct to indirect connectivity, whereas the hyperconnectivities reflected an indirect to an even more indirect connection. These findings indicate that RSNs are early altered by prolonged and moderate alcohol exposure, diminishing the executive control and behavioral flexibility. ii) To compare cortical gray matter (GM) volume between 34 healthy controls and 35 alcohol-dependent patients who were detoxified and remained abstinent for 1-5 weeks before MRI acquisition, we performed a voxel-based morphometry analysis. The main structures whose GM volume decreased in abstinent subjects compared to controls were precentral gyrus (PreCG), postcentral gyrus (PostCG), supplementary motor cortex (SMC), middle frontal gyrus (MFG), precuneus (PCUN) and superior parietal lobule (SPL). Decreases in GM volume in these areas may lead to changes in control of movement (PreCG and SMC), in processing tactile and proprioceptive information (PostCG), personality, insight, prevision (MFG), sensory appreciation, language understanding, orientation (PCUN) and the recognition of objects by touch and shapes (SPL). iii) To characterize dynamic brain states in functional MRI (fMRI) signals by means of an approach based on the Hidden Markov model (HMM). Several parameter configurations of HMM-Gaussian in a block-design paradigm were considered, together with different time series: independent components (ICs) and probabilistic functional modes (PFMs) on 14 healthy subjects. The block-design fMRI paradigm consisted of four experimental conditions: rest, visual, motor and visual-motor. Characterizing brain states' dynamics in fMRI data was possible applying the HMM-Gaussian approach to PFMs, with mean activity driving the states. The four spatial maps obtained were named HMM-rest, HMM-visual, HMM-motor and HMM-DMN (default mode network). HMM-DMN appeared once a task state had stabilized. The ultimate goal will be to obtain brain states in our rs-fMRI rat data, to dynamically compare the behavior of brain RSNs as a biomarker of AUD. In conclusion, neuroimaging techniques to estimate rs-FC, brain activity and GM volume can be successfully applied to multimodal MRI in the advance of the understanding of brain homeostasis in AUDs. These functional and structural alterations are a biomarker of chronic alcoholism to explain impairments in executive control, reward evaluation and visuospatial processing.
Pérez Ramírez, MÚ. (2018). Characterizing functional and structural brain alterations driven by chronic alcohol drinking: a resting-state fMRI connectivity and voxel-based morphometry analysis [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/113164
TESIS
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Ramraj, Varun. "Exploiting whole-PDB analysis in novel bioinformatics applications." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:6c59c813-2a4c-440c-940b-d334c02dd075.

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The Protein Data Bank (PDB) is the definitive electronic repository for experimentally-derived protein structures, composed mainly of those determined by X-ray crystallography. Approximately 200 new structures are added weekly to the PDB, and at the time of writing, it contains approximately 97,000 structures. This represents an expanding wealth of high-quality information but there seem to be few bioinformatics tools that consider and analyse these data as an ensemble. This thesis explores the development of three efficient, fast algorithms and software implementations to study protein structure using the entire PDB. The first project is a crystal-form matching tool that takes a unit cell and quickly (< 1 second) retrieves the most related matches from the PDB. The unit cell matches are combined with sequence alignments using a novel Family Clustering Algorithm to display the results in a user-friendly way. The software tool, Nearest-cell, has been incorporated into the X-ray data collection pipeline at the Diamond Light Source, and is also available as a public web service. The bulk of the thesis is devoted to the study and prediction of protein disorder. Initially, trying to update and extend an existing predictor, RONN, the limitations of the method were exposed and a novel predictor (called MoreRONN) was developed that incorporates a novel sequence-based clustering approach to disorder data inferred from the PDB and DisProt. MoreRONN is now clearly the best-in-class disorder predictor and will soon be offered as a public web service. The third project explores the development of a clustering algorithm for protein structural fragments that can work on the scale of the whole PDB. While protein structures have long been clustered into loose families, there has to date been no comprehensive analytical clustering of short (~6 residue) fragments. A novel fragment clustering tool was built that is now leading to a public database of fragment families and representative structural fragments that should prove extremely helpful for both basic understanding and experimentation. Together, these three projects exemplify how cutting-edge computational approaches applied to extensive protein structure libraries can provide user-friendly tools that address critical everyday issues for structural biologists.
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Kitaygorodsky, Alexander. "Post-transcriptional gene expression regulation in developmental disorders." Thesis, 2021. https://doi.org/10.7916/d8-qejw-xf90.

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Gene expression regulation is a set of critical biological processes that give rise to the diversity of cell types across tissues and development stages. Noncoding regions of the genome (intergenic + intronic, >98% of genome) play an important role in these processes, with noncoding genetic variation quantitatively affecting transcriptional activity, splicing of pre-mRNA, and localization, stability, and translational control of mRNA transcripts. Previous genetic studies of human disease have implicated numerous common noncoding loci with small but significant effect in common conditions. Recently, we and others have reported evidence supporting a role of rare noncoding variants with larger effect in early onset conditions such as birth defects and neurodevelopmental disorders. These early onset conditions are quite common in aggregate, affecting over 3% of young children. A better understanding of the functional impact of rare regulatory noncoding variants will enable novel genetic discovery, give insights of disease mechanisms, and ultimately improve diagnosis, treatment, and clinical care. In this thesis dissertation, I describe three related projects. First, we used a combinatorial multi-testing framework to find excess burden of noncoding de novo mutations in congenital heart disease (impacting both transcriptional and post-transcriptional regulatory stages). This finding was central to the rest of my work, motivating the development of new computational approaches to predict genetic effect of noncoding variants through the lens of post-transcriptional regulation. Second, we used convolutional neural networks to model and understand sequence specific RBP binding processes. Finally, we designed a graphical neural network model capable of integrating cause and consequence to predict genetic effect of rare noncoding variants. In summary, we developed new machine learning methods to analyze multimodal human genome sequencing data, uncover deeper insights into post-transcriptional gene regulatory processes, and advance genomic medicine.
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"Applying a Novel Integrated Persistent Feature to Understand Topographical Network Connectivity in Older Adults with Autism Spectrum Disorder." Master's thesis, 2019. http://hdl.handle.net/2286/R.I.53896.

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abstract: Autism spectrum disorder (ASD) is a developmental neuropsychiatric condition with early childhood onset, thus most research has focused on characterizing brain function in young individuals. Little is understood about brain function differences in middle age and older adults with ASD, despite evidence of persistent and worsening cognitive symptoms. Functional Magnetic Resonance Imaging (MRI) in younger persons with ASD demonstrate that large-scale brain networks containing the prefrontal cortex are affected. A novel, threshold-selection-free graph theory metric is proposed as a more robust and sensitive method for tracking brain aging in ASD and is compared against five well-accepted graph theoretical analysis methods in older men with ASD and matched neurotypical (NT) participants. Participants were 27 men with ASD (52 +/- 8.4 years) and 21 NT men (49.7 +/- 6.5 years). Resting-state functional MRI (rs-fMRI) scans were collected for six minutes (repetition time=3s) with eyes closed. Data was preprocessed in SPM12, and Data Processing Assistant for Resting-State fMRI (DPARSF) was used to extract 116 regions-of-interest defined by the automated anatomical labeling (AAL) atlas. AAL regions were separated into six large-scale brain networks. This proposed metric is the slope of a monotonically decreasing convergence function (Integrated Persistent Feature, IPF; Slope of the IPF, SIP). Results were analyzed in SPSS using ANCOVA, with IQ as a covariate. A reduced SIP was in older men with ASD, compared to NT men, in the Default Mode Network [F(1,47)=6.48; p=0.02; 2=0.13] and Executive Network [F(1,47)=4.40; p=0.04; 2=0.09], a trend in the Fronto-Parietal Network [F(1,47)=3.36; p=0.07; 2=0.07]. There were no differences in the non-prefrontal networks (Sensory motor network, auditory network, and medial visual network). The only other graph theory metric to reach significance was network diameter in the Default Mode Network [F(1,47)=4.31; p=0.04; 2=0.09]; however, the effect size for the SIP was stronger. Modularity, Betti number, characteristic path length, and eigenvalue centrality were all non-significant. These results provide empirical evidence of decreased functional network integration in pre-frontal networks of older adults with ASD and propose a useful biomarker for tracking prognosis of aging adults with ASD to enable more informed treatment, support, and care methods for this growing population.
Dissertation/Thesis
Masters Thesis Biomedical Engineering 2019
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De, Waal Rouviere. "Objective prediction of pure tone thresholds in normal and hearing-impaired ears with distortion product otoacoustic emissions and artificial neural networks." Thesis, 2001. http://hdl.handle.net/2263/26276.

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In the evaluation of special populations, such as neonates, infants and malingerers, audiologists have to rely heavily on objective measurements to assess hearing ability. Current objective audiological procedures such as tympanometry, the acoustic reflex, auditory brainstem response and transient evoked otoacoustic emissions, however, have certain limitations, contributing to the need of an objective, non-invasive, rapid, economic test of hearing that evaluate hearing ability in a wide range of frequencies. The purpose of this study was to investigate distortion product otoacoustic emissions (DPOAEs) as an objective test of hearing. The main aim was to improve prediction of pure tone thresholds at 500 Hz, 1000 Hz, 2000 Hz and 4000 Hz with DPOAEs and artificial neural networks (ANNs) in normal and hearing-impaired ears. Other studies that attempted to predict hearing ability with DPOAEs and conventional statistical methods were only able to distinguish between normal and impaired hearing. Back propagation neural networks were trained with the pattern of all present and absent DPOAE responses of 11 DPOAE frequencies of eight DP Grams and pure tone thresholds at 500 Hz, 1000 Hz, 2000 Hz and 4000 Hz. The neural network used the learned correlation between these two data sets to predict hearing ability at 500 Hz, 1000 Hz, 2000 Hz and 4000 Hz. Hearing ability was not predicted as a decibel value, but into one of several categories spanning 1 OdB. Results for prediction accuracy of normal hearing improved from 92% to 94% at 500 Hz, 87% to 88% at 1000 Hz, 84% to 88% at 2000 Hz and 91% to 93% at 4000 Hz from the De Waal (1998) study to the present study. The improvement of prediction of normal hearing can be attributed to extensive experimentation with neural network topology and manipulation of input data to present information to the network optimally. The prediction of hearing-impaired categories was less satisfactory, due to insufficient data for the ANNs to train on. A prediction versus ear count correlation strongly suggested that the inaccurate predictions of hearing-impaired categories is not a result of an inability of DPOAEs to predict pure tone thresholds in hearing impaired ears, but a result of insufficient data for the neural network to train on. This research concluded that DPOAEs and ANNs can be used to accurately predict hearing ability within 10dB in normal and hearing-impaired ears from 500 Hz to 4000 Hz for hearing losses of up to 65dB HL.
Thesis (DPhil (Communication Pathology))--University of Pretoria, 2007.
Speech-Language Pathology and Audiology
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Books on the topic "Computer network disorders"

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Parker, James N., and Philip M. Parker. Antisocial personality disorder: A medical dictionary, bibliography, and annotated research guide to Internet references. San Diego, CA: ICON Health Publications, 2003.

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Parker, James N., and Philip M. Parker. Personality disorders: A medical dictionary, bibliography, and annotated research guide to Internet references. San Diego, CA: ICON Health Publications, 2004.

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Inc, ebrary, ed. Entering an online support group on eating disorders: A discourse analysis. Amsterdam: Rodopi, 2009.

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Leeper, Linda H. Quick guide to the Internet for speech-language pathology and audiology. Boston, Mass: Allyn and Bacon, 1999.

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Slavney, Phillip R. Psychiatry September 2001--August 2002: An Internet resource guide. Edited by eMedguides com Inc. 2nd ed. Princeton, N.J: EMedguides.com, 2001.

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Parker, Philip M., and James N. Parker. Multiple personality disorders: A medical dictionary, bibliography, and annotated research guide to Internet references. San Diego, CA: ICON Health Publications, 2004.

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Parker, Philip M., and James N. Parker. Insomnia: A medical dictionary, bibliography and annotated research guide to Internet references. San Diego, CA: ICON Health Publications, 2004.

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Parker, Philip M., and James N. Parker. Affective disorders: A medical dictionary, bibliography, and annotated research guide to internet references. San Diego, CA: ICON Health Publications, 2004.

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Parker, Philip M., and James N. Parker. Bipolar affective disorder: A medical dictionary, bibliography and annotated research guide to Internet references. San Diego, CA: ICON Health Publications, 2004.

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Parker, James N., and Philip M. Parker. Dysphagia: A medical dictionary, bibliography, and annotated research guide to internet references. San Diego, CA: ICON Health Publications, 2004.

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Book chapters on the topic "Computer network disorders"

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Rudas, Jorge, Darwin Martínez, Athena Demertzi, Carol Di Perri, Lizette Heine, Luaba Tshibanda, Andrea Soddu, Steven Laureys, and Francisco Gómez. "Multivariate Functional Network Connectivity for Disorders of Consciousness." In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 434–42. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-52277-7_53.

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Ma, Xin, Guorong Wu, Seong Jae Hwang, and Won Hwa Kim. "Learning Multi-resolution Graph Edge Embedding for Discovering Brain Network Dysfunction in Neurological Disorders." In Lecture Notes in Computer Science, 253–66. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-78191-0_20.

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Kulanthaivel, Anand, Robert P. Light, Katy Börner, Chin Hua Kong, and Josette F. Jones. "Neurological Disorders and Publication Abstracts Follow Elements of Social Network Patterns when Indexed Using Ontology Tree-Based Key Term Search." In Universal Access in Human-Computer Interaction. Aging and Assistive Environments, 278–88. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07446-7_27.

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Cao, Huan, Lili Wu, Yue Chen, Yongtao Su, Zhengchao Lei, and Chunping Zhao. "Analysis on the Security of Satellite Internet." In Communications in Computer and Information Science, 193–205. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-33-4922-3_14.

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AbstractSatellite Internet (SI) is a new way to provide internet access all over the world. It will bring great convenience to international communication. Compared with the traditional communication networks, SI has a significant change in network architecture and communication model, which will have an important impact on national information network security. For example, the global interconnected SI consists of a large number of small satellites and each satellite has multi-beams to cover a vast area, which leads to the disorderly flow of information across the border, and greatly increases the difficulty of network protection. Therefore, it is necessary to closely track the development of SI and analyze security problems brought by SI. In this paper, we analyze the security risks of SI from the perspective of national security, network security and equipment security, and thirteen security issues have been summarized to provide reference for the healthy development of SI industry.
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Dutta, Sarmistha, and Munmun De Choudhury. "Characterizing Anxiety Disorders with Online Social and Interactional Networks." In Lecture Notes in Computer Science, 249–64. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60152-2_20.

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Yang, Huzheng, Xiaoxiao Li, Yifan Wu, Siyi Li, Su Lu, James S. Duncan, James C. Gee, and Shi Gu. "Interpretable Multimodality Embedding of Cerebral Cortex Using Attention Graph Network for Identifying Bipolar Disorder." In Lecture Notes in Computer Science, 799–807. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32248-9_89.

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Ballester, Pedro, Ulisses B. Correa, Marco Birck, and Ricardo Araujo. "Assessing the Performance of Convolutional Neural Networks on Classifying Disorders in Apple Tree Leaves." In Communications in Computer and Information Science, 31–38. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-71011-2_3.

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Thomson, Rebecca, and Robert Esnouf. "Prediction of Natively Disordered Regions in Proteins Using a Bio-basis Function Neural Network." In Lecture Notes in Computer Science, 108–16. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-28651-6_16.

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Zhao, Yu, Fangfei Ge, Shu Zhang, and Tianming Liu. "3D Deep Convolutional Neural Network Revealed the Value of Brain Network Overlap in Differentiating Autism Spectrum Disorder from Healthy Controls." In Medical Image Computing and Computer Assisted Intervention – MICCAI 2018, 172–80. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00931-1_20.

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Oeda, Shinichi, Takumi Ichimura, Toshiyuki Yamashita, and Katsumi Yoshida. "A Proposal of Immune Multi-agent Neural Networks and Its Application to Medical Diagnostic System for Hepatobiliary Disorders." In Lecture Notes in Computer Science, 526–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45226-3_72.

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Conference papers on the topic "Computer network disorders"

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Mohamed, Nebras A., Maison Yaseen, Weam Hateem, Thuria Musa, and Safaa Ismail. "Diagnosis of Cardiac Disorders in ECG Signal using Artificial Neural Network Algorithm." In 2019 International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE). IEEE, 2019. http://dx.doi.org/10.1109/iccceee46830.2019.9071371.

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Garg, Vijay Kumar, and R. K. Bansal. "Comparison of neural network back propagation algorithms for early detection of sleep disorders." In 2015 International Conference on Advances in Computer Engineering and Applications (ICACEA). IEEE, 2015. http://dx.doi.org/10.1109/icacea.2015.7164648.

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RajaRajan, A. "Brain disorder detection using artificial neural network." In 2011 3rd International Conference on Electronics Computer Technology (ICECT). IEEE, 2011. http://dx.doi.org/10.1109/icectech.2011.5941901.

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Saboksayr, Seyed Saman, John J. Foxe, and Axel Wismüller. "Attention-deficit/hyperactivity disorder prediction using graph convolutional networks." In Computer-Aided Diagnosis, edited by Horst K. Hahn and Maciej A. Mazurowski. SPIE, 2020. http://dx.doi.org/10.1117/12.2551364.

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Altieri, Alex, Silvia Ceccacci, Abudukaiyoumu Talipu, and Maura Mengoni. "A Low Cost Motion Analysis System Based on RGB Cameras to Support Ergonomic Risk Assessment in Real Workplaces." In ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/detc2020-22308.

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Abstract This paper introduces a motion analysis system based on a network of common RGB cameras, which provides the measurement of various angles considered for postural assessment, in order to facilitate the evaluation of the ergonomic indices commonly used for the determination of risk of musculoskeletal disorders of operators in manufacturing workplaces. To enable the tracking of operator postures during the performed tasks, the system exploits the multi person keypoints detection library “OpenPose”. The proposed system has been validated with a real industrial case study regarding a washing machine assembly line. Results suggest how the proposed system supports ergonomists in risk assessment of musculoskeletal disorders through the OCRA index.
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Anh-Dung Nguyen, Patrick Senac, and Michel Diaz. "On the impact of disorder on dynamic network navigation." In 2013 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). IEEE, 2013. http://dx.doi.org/10.1109/infcomw.2013.6562854.

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Chen, Hao, Fuzhen Zhuang, Li Xiao, Ling Ma, Haiyan Liu, Ruifang Zhang, Huiqin Jiang, and Qing He. "AMA-GCN: Adaptive Multi-layer Aggregation Graph Convolutional Network for Disease Prediction." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/308.

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Recently, Graph Convolutional Networks (GCNs) have proven to be a powerful mean for Computer Aided Diagnosis (CADx). This approach requires building a population graph to aggregate structural information, where the graph adjacency matrix represents the relationship between nodes. Until now, this adjacency matrix is usually defined manually based on phenotypic information. In this paper, we propose an encoder that automatically selects the appropriate phenotypic measures according to their spatial distribution, and uses the text similarity awareness mechanism to calculate the edge weights between nodes. The encoder can automatically construct the population graph using phenotypic measures which have a positive impact on the final results, and further realizes the fusion of multimodal information. In addition, a novel graph convolution network architecture using multi-layer aggregation mechanism is proposed. The structure can obtain deep structure information while suppressing over-smooth, and increase the similarity between the same type of nodes. Experimental results on two databases show that our method can significantly improve the diagnostic accuracy for Autism spectrum disorder and breast cancer, indicating its universality in leveraging multimodal data for disease prediction.
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Choi, Jungkyu, Hee-Sang Lee, and Jung-Ja Kim. "Analysis of Pediatric Foot Disorders Using Decision Tree and Neural Networks." In 2017 European Conference on Electrical Engineering and Computer Science (EECS). IEEE, 2017. http://dx.doi.org/10.1109/eecs.2017.17.

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Yang, Xin, Ning Zhang, and Donglin Wang. "Deriving Autism Spectrum Disorder Functional Networks from RS-FMRI Data using Group ICA and Dictionary Learning." In 11th International Conference on Computer Science and Information Technology (CCSIT 2021). AIRCC Publishing Corporation, 2021. http://dx.doi.org/10.5121/csit.2021.110714.

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The objective of this study is to derive functional networks for the autism spectrum disorder (ASD) population using the group ICA and dictionary learning model together and to classify ASD and typically developing (TD) participants using the functional connectivity calculated from the derived functional networks. In our experiments, the ASD functional networks were derived from resting-state functional magnetic resonance imaging (rs-fMRI) data. We downloaded a total of 120 training samples, including 58 ASD and 62 TD participants, which were obtained from the public repository: Autism Brain Imaging Data Exchange I (ABIDE I). Our methodology and results have five main parts. First, we utilize a group ICA model to extract functional networks from the ASD group and rank the top 20 regions of interest (ROIs). Second, we utilize a dictionary learning model to extract functional networks from the ASD group and rank the top 20 ROIs. Third, we merged the 40 selected ROIs from the two models together as the ASD functional networks. Fourth, we generate three corresponding masks based on the 20 selected ROIs from group ICA, the 20 ROIs selected from dictionary learning, and the 40 combined ROIs selected from both. Finally, we extract ROIs for all training samples using the above three masks, and the calculated functional connectivity was used as features for ASD and TD classification. The classification results showed that the functional networks derived from ICA and dictionary learning together outperform those derived from a single ICA model or a single dictionary learning model.
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Lytridis, Chris, Cristina I. Papadopoulou, George A. Papakostas, Vassilis G. Kaburlasos, Vasiliki Aliki Nikopoulou, Maria Dialechti Kerasidou, and Nikolaos Dalivigkas. "Robot-Assisted Autism Spectrum Disorder (ASD) Interventions: A Multi-Robot Approach." In 2020 International Conference on Software, Telecommunications and Computer Networks (SoftCOM). IEEE, 2020. http://dx.doi.org/10.23919/softcom50211.2020.9238273.

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