Artificial Neural Network Market is expected to accumulate a value of US$ 389 Billion by 2030 registering a CAGR of 20.5% – Report by Market Research Future (MRFR)

Market Overview

According to a Comprehensive Research Report by Market Research Future (MRFR), Artificial Neural Network Market Information by Type, Application, Component, and Region – Forecast till 2030”, Artificial Neural Network Market could thrive at a rate of 20.5% between 2022 and 2030. The market size will be reaching around USD 389 Billion by the end of the year 2030

Market Synopsis

The Artificial Neural Network Market is experiencing rapid growth as industries across various sectors recognize the power of intelligent algorithms to solve complex problems and make accurate predictions. Artificial neural networks (ANNs) are a type of machine learning model inspired by the structure and functioning of the human brain. ANNs consist of interconnected nodes, or artificial neurons, that process and transmit information, enabling the network to learn and make decisions.

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One of the key drivers propelling the growth of the Artificial Neural Network Market is the increasing demand for advanced data analytics. Industries generate vast amounts of data, and organizations are seeking ways to extract valuable insights from this data to drive decision-making and improve operational efficiency. Artificial neural networks excel at analyzing complex and unstructured data, enabling businesses to uncover patterns, trends, and correlations that might not be apparent through traditional analytical methods.

Market Competitive Landscape:

The affluent companies in the Artificial Neural Network industry include

• Neural Technologies Limited
• IBM Corporation
• Neuro Dimension, Inc.
• Alyuda Research
• Microsoft Corporation
• SAP SE
• Swift Key
• Starmind International AGA
• finiti
• Ward Systems Group, Inc
• LLC,
• Google Inc.
• Neuralware
• Qualcomm Technologies, Inc.
• Intel Corporation
• Oracle Corporation

June 2021

Google has been actively involved in artificial neural networks and has released various products and services in the AI domain. Google’s TensorFlow, an open-source software library for machine learning, including neural networks, is widely used by developers. TensorFlow has seen multiple updates and releases, and the specific dates of these launches can vary.

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Market USP Covered:

Market Drivers:

One of the driving factors contributing to the growth of the Artificial Neural Network Market is the advancements in deep learning algorithms. Deep learning is a subfield of machine learning that focuses on training artificial neural networks with multiple layers to automatically learn hierarchical representations of data. Deep learning algorithms have shown exceptional performance in various domains, including image and speech recognition, natural language processing, and autonomous vehicles. The continuous advancements in deep learning techniques and the availability of large datasets have expanded the applications of artificial neural networks across industries.

Market Restraints:

The adoption of artificial neural networks also faces certain restraining factors. One such factor is the need for high computational power and resources. Training complex neural networks requires significant computational resources, including powerful hardware such as graphics processing units (GPUs) and access to large-scale computing infrastructure. The cost and availability of these resources can pose challenges, particularly for smaller organizations with limited budgets and resources.

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COVID 19 Analysis

The COVID-19 pandemic has had a mixed impact on the Artificial Neural Network Market. On one hand, the pandemic has accelerated the adoption of artificial neural networks in certain industries. For example, in healthcare, artificial neural networks have been used for COVID-19 diagnosis, drug discovery, and predicting the spread of the virus. In finance, artificial neural networks have been deployed for risk assessment and fraud detection in the context of rapidly changing economic conditions.

On the other hand, the pandemic has also posed challenges to the market. The disruptions caused by lockdowns and restrictions have affected industries’ ability to invest in new technologies and implementation of artificial neural network solutions. Additionally, the unpredictability of the pandemic has made it difficult to train neural networks on historical data, as the patterns and trends have significantly deviated from pre-pandemic times.

Market Segmentation

By Type

The Type in the market includes Feedback Artificial Neural Network, Feedforward Artificial Neural Network, Other

By Application

By Application, the segment includes Drug Development, Others

By Component

By Component, the segment includes Software, Services, Other

Regional Insights

North America dominates the market due to the presence of a large number of key players, technological advancements, and substantial investments in research and development activities. The United States, in particular, holds a major share in the market, driven by the adoption of ANN technologies across industries such as healthcare, finance, and automotive.

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In Europe, countries like the United Kingdom, Germany, and France are experiencing substantial growth in the ANN market. The increasing focus on automation, machine learning, and AI-driven solutions is propelling the demand for artificial neural networks in the region. Additionally, the growing adoption of ANN technologies in sectors like manufacturing, energy, and retail is further fueling market growth.

The Asia-Pacific region is also witnessing significant market expansion, primarily driven by emerging economies such as China, India, and Japan. The increasing digitalization, rapid technological advancements, and rising investments in AI and machine learning are contributing to the growth of the ANN market in the region. Moreover, the presence of a large population and the growing demand for advanced analytics and predictive modeling solutions are bolstering market growth.

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