Neural Network Software to Gain Higher Traction with Rise in Applications

global neural network market

Neural network software is witnessing robust adoption, with technological advancements in data analysis, benefitting several enterprises in terms of time & cost reduction. Neural network software enables enterprises to assess risks and detect fraud associated with their business applications. Neural network software have been witnessing deployment across various domains including financial operations, trading, product maintenance, and business analytics.

Neural Network Software enables End-Users in Organising Huge Amount of Unorganised Data Created

Major factor contributing to growth of global neural network software market is soaring data achieving tools adoption by diverse end-users, according to a report by Future Market Insights (FMI). This enables end-users to organise substantial amount of unorganised data created. Surging demand for predicting solutions and growing application of digital technologies are further expected to propel market growth in the near future. FMI’s report projects significant growth for the global neural network software market. However sluggish digitisation rate among evolving economies, operational challenges and lack of skilled technical professionals are some factors estimated to impede growth of the global neural network software market in the upcoming years.

Neural Network Software for Enabling Autonomous Operations of Non-satellite Missions

In recent past, researchers from Italy focused on CubeSats- new category of space systems for missions related to low-Earth orbit. Various technical challenges were incurred on numerous fronts. These researchers focused on event detection capabilities, coupled with the intention of enabling autonomous operations associated with non-satellite missions via presentation of neural network technology-based artificial intelligence algorithm. These algorithms were then applied to future missions and used as case studies. This involves a dense paper, complex particularly, with various unknowns, analysing neural network considerations for solving optimisation as well as other issues.

Neural Network Software for Detection of Cancer Types

Researchers from University of Michigan worked on advanced image recognition for detection of aggressive types of cancer, capable of being cured in early stages. Melanoma is not only deadly but also challenging to screen accurately. Researchers trained a neural network to isolate features including texture and structure of suspicious lesions & moles to recognise them in a better way. Researchers have stated the experimental results from quantitative as well as qualitative evaluations to demonstrate this method’s outperformance as compared to various other state-of-art algorithms utilised for detecting melanoma.

Neural Network Software for Futures Exchange

Futures exchange has witnessed a phenomenal success from the time of their introduction across both developed as well as developing regions over the previous four decades. A strategic trading benefits from leveraging cost-of-carry relationship, and CAPM- Capital Asset Pricing Model. Developed via spot market prices, rules for technical trading were applied to prices of futures market using CAPM-based hedge ratio. Daily historical prices of 20 stocks among ten markets had been used for analysis by researchers. Popular technical indicators, along with techniques of AI- artificial intelligence including neural networks were used for producing selling & buying signals based on every stock and stock portfolios. Two specific areas to successfully develop neural networks are trading and risk management. Neural networks also witnessed deployment for determination of large-scale banking & financial health, and for prediction of corporate bankruptcies.

Large-scale supercomputers have become a traditional area for developing neural networks, particularly while considering detection of weather events. In one specific utilisation case, computational fluid dynamic codes were correlated with neural networks as well as other approaches of genetic algorithms for detection of cyclone activity.

About the Author

Abhishek Budholiya

Abhishek Budholiya is a tech blogger, digital marketing pro, and has contributed to numerous tech magazines. Currently, as a technology and digital branding consultant, he offers his analysis on the tech market research landscape. His forte is analysing the commercial viability of a new breakthrough, a trait you can see in his writing. When he is not ruminating about the tech world, he can be found playing table tennis or hanging out with his friends.

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