Machine Learning Based Assistive Speech Technology for People with Neurological Disorders

2019 ◽  
pp. 143-163
Author(s):  
Shanmuganathan Chandrakala
Keyword(s):  
Machine Learning ◽  
Neurological Disorders ◽  
Speech Technology

Intelligent Big Data Analytics in Health

2022 ◽  
pp. 1663-1702
Author(s):  
Ebru Aydindag Bayrak ◽  
Pinar Kirci
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Early Diagnosis ◽  
Neurological Disorders ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Healthcare Systems ◽  
Learning Systems ◽  
Learning System ◽  
Definition Of

Intelligent big data analytics and machine learning systems have been introduced to explain for the early diagnosis of neurological disorders. A number of scholarly researches about intelligent big data analytics in healthcare and machine learning system used in the healthcare system have been mentioned. The authors have explained the definition of big data, big data samples, and big data analytics. But the main goal is helping researchers or specialists in providing opinion about diagnosing or predicting neurological disorders using intelligent big data analytics and machine learning. Therefore, they focused on the healthcare systems using these innovative ways in particular. The information of platform and tools about big data analytics in healthcare is investigated. Numerous academic studies based on the detection of neurological disorders using both machine learning methods and big data analytics have been reviewed.

scholarly journals Mining Big Neuron Morphological Data

2018 ◽  
Vol 2018 ◽  
pp. 1-13
Author(s):  
Maryamossadat Aghili ◽  
Ruogu Fang
Keyword(s):  
Machine Learning ◽  
3D Reconstruction ◽  
Neurological Disorders ◽  
Morphological Data ◽  
Future Directions ◽  
Ongoing Research ◽  
Systematic Data ◽  
Survey Paper ◽  
Processing Pipeline ◽  
Data Capturing

The advent of automatic tracing and reconstruction technology has led to a surge in the number of neurons 3D reconstruction data and consequently the neuromorphology research. However, the lack of machine-driven annotation schema to automatically detect the types of the neurons based on their morphology still hinders the development of this branch of science. Neuromorphology is important because of the interplay between the shape and functionality of neurons and the far-reaching impact on the diagnostics and therapeutics in neurological disorders. This survey paper provides a comprehensive research in the field of automatic neurons classification and presents the existing challenges, methods, tools, and future directions for automatic neuromorphology analytics. We summarize the major automatic techniques applicable in the field and propose a systematic data processing pipeline for automatic neuron classification, covering data capturing, preprocessing, analyzing, classification, and retrieval. Various techniques and algorithms in machine learning are illustrated and compared to the same dataset to facilitate ongoing research in the field.

scholarly journals Speech Technology Progress Based on New Machine Learning Paradigm

2019 ◽  
Vol 2019 ◽  
pp. 1-19 ◽  
Author(s):  
Vlado Delić ◽  
Zoran Perić ◽  
Milan Sečujski ◽  
Nikša Jakovljević ◽  
Jelena Nikolić ◽  
...  
Keyword(s):  
Machine Learning ◽  
Signal Processing ◽  
Speech Signal ◽  
Speech Synthesis ◽  
Machine Learning Algorithms ◽  
Speech Communication ◽  
Speech Signal Processing ◽  
Speech Technology ◽  
Spoken Dialogue Systems ◽  
New Machine

Speech technologies have been developed for decades as a typical signal processing area, while the last decade has brought a huge progress based on new machine learning paradigms. Owing not only to their intrinsic complexity but also to their relation with cognitive sciences, speech technologies are now viewed as a prime example of interdisciplinary knowledge area. This review article on speech signal analysis and processing, corresponding machine learning algorithms, and applied computational intelligence aims to give an insight into several fields, covering speech production and auditory perception, cognitive aspects of speech communication and language understanding, both speech recognition and text-to-speech synthesis in more details, and consequently the main directions in development of spoken dialogue systems. Additionally, the article discusses the concepts and recent advances in speech signal compression, coding, and transmission, including cognitive speech coding. To conclude, the main intention of this article is to highlight recent achievements and challenges based on new machine learning paradigms that, over the last decade, had an immense impact in the field of speech signal processing.

scholarly journals An Intelligent Multicriteria Model for Diagnosing Dementia in People Infected with Human Immunodeficiency Virus

2021 ◽  
Vol 11 (21) ◽  
pp. 10457
Author(s):  
Luana I. C. C. Pinheiro ◽  
Maria Lúcia D. Pereira ◽  
Evandro C. de Andrade ◽  
Luciano C. Nunes ◽  
Wilson C. de Abreu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Human Immunodeficiency Virus ◽  
Mental Disorders ◽  
Neurological Disorders ◽  
Hybrid Approach ◽  
International Classification Of Diseases ◽  
Machine Learning Algorithms ◽  
World Health ◽  
Data Set ◽  
Immunodeficiency Virus

Hybrid models to detect dementia based on Machine Learning can provide accurate diagnoses in individuals with neurological disorders and cognitive complications caused by Human Immunodeficiency Virus (HIV) infection. This study proposes a hybrid approach, using Machine Learning algorithms associated with the multicriteria method of Verbal Decision Analysis (VDA). Dementia, which affects many HIV-infected individuals, refers to neurodevelopmental and mental disorders. Some manuals standardize the information used in the correct detection of neurological disorders with cognitive complications. Among the most common manuals used are the DSM-5 (Diagnostic and Statistical Manual of Mental Disorders, 5th edition) of the American Psychiatric Association and the International Classification of Diseases, 10th edition (ICD-10)—both published by World Health Organization (WHO). The model is designed to explore the predictive of specific data. Furthermore, a well-defined database data set improves and optimizes the diagnostic models sought in the research.

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