Artificial Neural Network Market Size 2020, Industry Growth, Size, Company Profiles

Global Artificial Neural Network Market Research Report: By Type (Feedback Artificial Neural Network, Feedforward Artificial Neural Network, Other), by Component (Software, Services, Other), by Application (Drug Development, Others) – Forecast Till 2023

Leading Market Players

Key players profiled in the artificial neural network market include Oracle Corporation, IBM Corporation, Microsoft Corporation, Intel Corporation, Qualcomm Technologies, Inc., Neuralware, Google Inc., Alyuda Research, LLC., NeuroDimension, Inc., SAP SE, Ward Systems Group, Inc., Afiniti, SwiftKey, Neural Technologies Limited, and Starmind International AG.

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Market Overview

The global artificial neural network market is expected to exhibit a stellar growth rate from 2018 to 2023 (forecast period). Emergence of ANN in healthcare and finance sectors are predicted to drive the market growth. Increasing prevalence of chronic diseases, growing number of admissions in hospitals, and inflated healthcare expenditure of nations are other drivers supporting the adoption of ANN in healthcare. Development of software to support the large patient volume can drive the market growth. For instance, the MAQ software by Microsoft Corporation contains an ANN to predict future trends.

Segmentation

The artificial neural network market is segmented by type, component, and application.

By type, it is segmented into feedback artificial neural network, feedforward artificial neural network, and others. The feedforward artificial neural network segment is expected to reign supreme over the forecast period due to immense demand for advanced analytical systems.

By component, it is segmented into software, services, and platform.

By application, it is segmented into drug development, image analysis and interpretation, bioelectric signal analysis and interpretation, clinical diagnosis and prognostics, and others. The image analysis application is projected to possess future potential due to the use of deep learning algorithms for identifying faces in massive crowds. This is highly useful for law enforcement agencies in thwarting potential attacks.

Regional Analysis

Regionally, the artificial neural network market covers the Americas, Asia Pacific (APAC), Europe, and the Middle East & Africa.

The Americas is anticipated to be the dominant region in the global market due to large number of enterprises for development of neural network technologies and well-developed healthcare sector. The large healthcare expenditure for the region is likely to bode well for the market growth. Emergence of self-driving or autonomous driving has gained the interest of various manufacturers in the technology. Artificial neurons can be used in analyzing driver behavior on the roads. This has become useful for Tesla, one of the biggest advocates of self-driving cars, in developing its Self Driving Computer.

Europe is assumed to be in the second position in the global market due to inflated healthcare expenditure and adoption of AI among healthcare providers. The ballooned defense expenditure of nations is likely to fuel the adoption of ANN and deep learning to further the development of autonomous systems to strengthen existing defenses. On the other hand, the APAC region is presumed to exhibit the fastest growth rate during the forecast period due to need to control healthcare costs and rising economic growth of countries. Digitization of patient records is expected to influence the market growth.

Table Of Contents:

Chapter 1. Report Prologue

Chapter 2. Market Introduction

2.1 Definition

2.2 Scope Of The Study

2.2.1 Research Objective

2.2.2 Assumptions

2.2.3 Limitations

Chapter 3. Research Methodology

3.1 Introduction

3.2 Primary Research

3.3 Secondary Research

3.4 Market Size Estimation

Chapter 4. Market Dynamics

4.1 Drivers

4.2 Restraints

4.3 Opportunities

4.4 Challenges

4.5 Macroeconomic Indicators

4.6 Technology Trends & Assessment

Continued…

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In August 2019, Google researchers launched the new Weight Agnostic Neural Networks that would perform without assessing the weight first. This would help in devising programs that would require no training.

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