Artificial Intelligence, Machine Learning, and Signal Processing: Researchers are using artificial intelligence, machine learning, and signal processing to build powerful three-level platforms to help meet project goals [Special Reports]

2021 ◽  
Vol 38 (6) ◽  
pp. 6-145
Author(s):  
John Edwards
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Signal Processing

scholarly journals The Collaboration between Hearing Aid Users and Artificial Intelligence to Optimize Sound

2021 ◽  
Vol 42 (03) ◽  
pp. 282-294
Author(s):  
Laura Winther Balling ◽  
Lasse Lohilahti Mølgaard ◽  
Oliver Townend ◽  
Jens Brehm Bagger Nielsen
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Signal Processing ◽  
Hearing Aids ◽  
Hearing Aid ◽  
Bayesian Optimization ◽  
Listening Environments ◽  
User Data ◽  
The Individual ◽  
Average User

AbstractHearing aid gain and signal processing are based on assumptions about the average user in the average listening environment, but problems may arise when the individual hearing aid user differs from these assumptions in general or specific ways. This article describes how an artificial intelligence (AI) mechanism that operates continuously on input from the user may alleviate such problems by using a type of machine learning known as Bayesian optimization. The basic AI mechanism is described, and studies showing its effects both in the laboratory and in the field are summarized. A crucial fact about the use of this AI is that it generates large amounts of user data that serve as input for scientific understanding as well as for the development of hearing aids and hearing care. Analyses of users' listening environments based on these data show the distribution of activities and intentions in situations where hearing is challenging. Finally, this article demonstrates how further AI-based analyses of the data can drive development.

Design of Assistive Speller Machine Based on Brain Computer Interfacing

2017 ◽  
pp. 385-418
Author(s):  
Suryoday Basak
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Signal Processing ◽  
Data Analysis ◽  
Brain Computer Interface ◽  
Classification Methods ◽  
Eeg Signals ◽  
Signal Processing Methods ◽  
Brain Computer Interfacing

Machine Learning (ML) has assumed a central role in data assimilation and data analysis in the last decade. Many methods exist that cater to the different kinds of data centric applications in terms of complexity and domain. Machine Learning methods have been derived from classical Artificial Intelligence (AI) models but are a lot more reliant on statistical methods. However, ML is a lot broader than inferential statistics. Recent advances in computational neuroscience has identified Electroencephalography (EEG) based Brain Computer Interface (BCI) as one of the key agents for a variety of medical and nonmedical applications. However, efficiency in analysing EEG signals is tremendously difficult to achieve because of three reasons: size of data, extent of computation and poor spatial resolution. The book chapter discusses the Machine Learning based methods employed by the author to classify EEG signals for potentials observed based on varying levels of a subject's attention, measured using a NeuroSky Mindwave Mobile. It reports challenges faced in developing BCIs based on available hardware, signal processing methods and classification methods.

Machine Learning in Agriculture

2018 ◽  
Vol 8 (6) ◽  
pp. 26 ◽  
Author(s):  
Matthew N. O. Sadiku ◽  
Chandra M. M Kotteti ◽  
Sarhan M. Musa
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Disease Diagnosis ◽  
Automated Detection ◽  
Disease Prediction ◽  
Paper Briefly ◽  
Agriculture Sector ◽  
Modern Agriculture ◽  
Crop Disease

Machine learning is an emerging field of artificial intelligence which can be applied to the agriculture sector. It refers to the automated detection of meaningful patterns in a given data.  Modern agriculture seeks ways to conserve water, use nutrients and energy more efficiently, and adapt to climate change.  Machine learning in agriculture allows for more accurate disease diagnosis and crop disease prediction. This paper briefly introduces what machine learning can do in the agriculture sector.

Development of innovative hygienic technologies for preservation of the workers’ reproductive health

2020 ◽  
pp. 792-792
Author(s):  
M. A. Fesenko ◽  
G. V. Golovaneva ◽  
A. V. Miskevich
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Reproductive Health ◽  
Reproductive System ◽  
Preventive Measures ◽  
Learning Algorithms ◽  
Reproductive Function ◽  
Machine Learning Algorithms ◽  
New Model

The new model «Prognosis of men’ reproductive function disorders» was developed. The machine learning algorithms (artificial intelligence) was used for this purpose, the model has high prognosis accuracy. The aim of the model applying is prioritize diagnostic and preventive measures to minimize reproductive system diseases complications and preserve workers’ health and efficiency.

Virtual Screening Meets Deep Learning

2018 ◽  
Vol 15 (1) ◽  
pp. 6-28 ◽  
Author(s):  
Javier Pérez-Sianes ◽  
Horacio Pérez-Sánchez ◽  
Fernando Díaz
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Virtual Screening ◽  
Great Increase ◽  
New Drugs ◽  
Learning Approach ◽  
Screening Strategies ◽  
Computer Aided ◽  
Recent Developments

Background: Automated compound testing is currently the de facto standard method for drug screening, but it has not brought the great increase in the number of new drugs that was expected. Computer- aided compounds search, known as Virtual Screening, has shown the benefits to this field as a complement or even alternative to the robotic drug discovery. There are different methods and approaches to address this problem and most of them are often included in one of the main screening strategies. Machine learning, however, has established itself as a virtual screening methodology in its own right and it may grow in popularity with the new trends on artificial intelligence. Objective: This paper will attempt to provide a comprehensive and structured review that collects the most important proposals made so far in this area of research. Particular attention is given to some recent developments carried out in the machine learning field: the deep learning approach, which is pointed out as a future key player in the virtual screening landscape.

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