Autistic Language Processing by Patterns Detection

2018 ◽  
Vol 8 (1) ◽  
pp. 36-61
Author(s):  
Daniela Lopez De Luise ◽  
Ben Raul Saad ◽  
Pablo D Pescio ◽  
Christian Martin Saliwonczyk
Keyword(s):  
Machine Learning ◽  
Performance Evaluation ◽  
Verbal Behavior ◽  
Language Processing ◽  
Video Recording ◽  
Individual Performance ◽  
Neural Nets ◽  
Autistic Spectrum ◽  
Spectrum Disorders ◽  
Leo Kanner

The main goal of this article is to present an approach that allows the automatic management of autistic communication patterns by processing audio and video from the therapy session of individuals suffering autistic spectrum disorders (ASD). Such patients usually have social and communication alterations that make it difficult to evaluate the meaning of those expressions. As their communicational skills may have different degrees of variation, it is very hard to understand the semantics behind the verbal behavior. The current work is based on previous work on machine learning for individual performance evaluation. Statistics show that autistic verbal behavior are physically expressed by repetitive sounds and related movements that are evident and stereotyped. The works of Leo Kanner and Ángel Riviere are also considered here. Using machine learning and neural nets with certain set of parameters, it is possible to automatically detect patterns in audio and video recording of patient's performance, which is an interesting opportunity to communicate with ASD patients.

scholarly journals Analysis of comparative performance of deep learning models for sentiment analysis

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Mirza Murtaza
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Short Term Memory ◽  
Neural Nets ◽  
Test Accuracy ◽  
Comparative Performance ◽  
Text Filtering ◽  
Percent Accuracy

Abstract Sentiment analysis of text can be performed using machine learning and natural language processing methods. However, there is no single tool or method that is effective in all cases. The objective of this research project is to determine the effectiveness of neural network-based architecture to perform sentiment analysis of customer comments and reviews, such as the ones on Amazon site. A typical sentiment analysis process involves text preparation (of acquired content), sentiment detection, sentiment classification and analysis of results. In this research, the objective is to a) identify the best approach for text preparation in a given application (text filtering approach to remove errors in data), and, most importantly, b) what is the best machine learning (feed forward neural nets, convolutional neural nets, Long Short-Term Memory networks) approach that provides best classification accuracy. In this research, a set of three thousand two hundred reviews of food related products were used to train and experiment with a neural network-based sentiment analysis system. The neural network implementation of six different models provided close to one-hundred percent accuracy of test data, and a decent test accuracy in mid-80%. The results of the research would be useful to businesses in evaluating customer preferences for products or services.  

scholarly journals Precision Assessment of COVID-19 Phenotypes Using Large-Scale Clinic Visit Audio Recordings: Harnessing the Power of the Patient Voice (Preprint)

2020 ◽  
Author(s):  
Paul J Barr ◽  
James Ryan ◽  
Nicholas C Jacobson
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Large Scale ◽  
Clinical Phenotype ◽  
Low Cost ◽  
Video Recording ◽  
Severe Infection ◽  
Patient Characteristics ◽  
Clinic Visit ◽  
Clinic Visits

UNSTRUCTURED The novel coronavirus (SARS-CoV-2) and its related disease, COVID-19, are exponentially increasing across the world, yet there is still uncertainty about the clinical phenotype. Natural Language Processing (NLP) and machine learning may hold one key to quickly identify individuals at high risk for COVID-19 and understand key symptoms in its clinical manifestation and presentation. In healthcare, such data often come the medical record, yet when overburdened, clinicians may focus on documenting widely reported symptoms that appear to confirm the diagnosis of COVID-19, at the expense of infrequently reported symptoms. A comprehensive record of the clinic visit is required—an audio recording may be the answer. If done at scale, a combination of data from the EHR and recordings of clinic visits can be used to power NLP and machine learning models, quickly creating a clinical phenotype of COVID-19. We propose the creation of a pipeline from the audio/video recording of clinic visits to the clinical symptomatology model and prediction of COVID-19 infection. With vast amounts of data available, we believe a prediction model can be quickly developed that could promote the accurate screening of individuals at risk of COVID-19 and identify patient characteristics predicting a greater risk of a more severe infection. If clinical encounters are recorded and our NLP is adequately refined, then benchtop-virology will be better informed and risk of spread reduced. While recordings of clinic visits are not the panacea to this pandemic, they are a low cost option with many potential benefits that have only just begun to be explored.

Prefrontal Cortex Hypothesis and Autistic Spectrum Disorders

2006 ◽  
Author(s):  
Robert E. Nida ◽  
Christopher Lopata ◽  
Martin A. Volker ◽  
Marcus L. Thomeer ◽  
Gloria K. Lee ◽  
...  
Keyword(s):  
Prefrontal Cortex ◽  
Autistic Spectrum Disorders ◽  
Autistic Spectrum ◽  
Spectrum Disorders

Deep Learning Based High-Resolution Remote Sensing Image classification

2017 ◽  
Vol 7 (10) ◽  
pp. 22
Author(s):  
Sumit Kaur
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Deep Learning ◽  
Image Classification ◽  
Language Processing ◽  
Object Perception ◽  
Remote Sensing Image ◽  
Research Area ◽  
Remote Sensing Image Classification ◽  
Unsupervised Algorithms

Abstract- Deep learning is an emerging research area in machine learning and pattern recognition field which has been presented with the goal of drawing Machine Learning nearer to one of its unique objectives, Artificial Intelligence. It tries to mimic the human brain, which is capable of processing and learning from the complex input data and solving different kinds of complicated tasks well. Deep learning (DL) basically based on a set of supervised and unsupervised algorithms that attempt to model higher level abstractions in data and make it self-learning for hierarchical representation for classification. In the recent years, it has attracted much attention due to its state-of-the-art performance in diverse areas like object perception, speech recognition, computer vision, collaborative filtering and natural language processing. This paper will present a survey on different deep learning techniques for remote sensing image classification. 

Genetic testing for autistic spectrum disorders

2020 ◽  
pp. 78-79
Author(s):  
Е.А. Померанцева ◽  
А.А. Исаев ◽  
А.П. Есакова ◽  
И.В. Поволоцкая ◽  
Е.В. Денисенкова ◽  
...  
Keyword(s):  
Genetic Testing ◽  
American Academy ◽  
Autistic Spectrum Disorder ◽  
Genetic Factors ◽  
Molecular Genetic ◽  
Autistic Spectrum Disorders ◽  
Autistic Spectrum ◽  
American Academy Of Pediatrics ◽  
Spectrum Disorders ◽  
Testing Algorithm

Согласно рекомендациям Американской академии педиатрии при постановке диагноза аутизм, следует направить семью на консультацию генетика и генетическое обследование. Однако оптимальный подход к алгоритму генетического обследования при выявлении расстройства аутистического спектра еще предстоит разработать. В рамках исследования было проведено сравнение выявляемости генетических факторов аутизма различными молекулярно-генетическими тестами. According to American Academy of Pediatrics recent guidelines, each family with a child diagnosed with autistic spectrum disorder should be reffered to a medical geneticist and offered genetic tests. However, an optimal genetic testing algorithm has yet to be developed. This study was conducted to compare abilities of different molecular-genetic methods to detect genetic factors of autistic spectrum disorders.

Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition

2017 ◽  
Author(s):  
Sabrina Jaeger ◽  
Simone Fulle ◽  
Samo Turk
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Supervised Machine Learning ◽  
Learning Approach ◽  
Learning Approaches ◽  
Unsupervised Machine Learning ◽  
Feature Representations ◽  
Machine Learning Approach ◽  
The Individual ◽  
Vector Representations

Inspired by natural language processing techniques we here introduce Mol2vec which is an unsupervised machine learning approach to learn vector representations of molecular substructures. Similarly, to the Word2vec models where vectors of closely related words are in close proximity in the vector space, Mol2vec learns vector representations of molecular substructures that are pointing in similar directions for chemically related substructures. Compounds can finally be encoded as vectors by summing up vectors of the individual substructures and, for instance, feed into supervised machine learning approaches to predict compound properties. The underlying substructure vector embeddings are obtained by training an unsupervised machine learning approach on a so-called corpus of compounds that consists of all available chemical matter. The resulting Mol2vec model is pre-trained once, yields dense vector representations and overcomes drawbacks of common compound feature representations such as sparseness and bit collisions. The prediction capabilities are demonstrated on several compound property and bioactivity data sets and compared with results obtained for Morgan fingerprints as reference compound representation. Mol2vec can be easily combined with ProtVec, which employs the same Word2vec concept on protein sequences, resulting in a proteochemometric approach that is alignment independent and can be thus also easily used for proteins with low sequence similarities.

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