Neural Network Software to Gain Higher Traction with Rise in Applications
Neural network software with technological advancements in data analysis is witnessing robust adoption, 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.
According to a report by Future Market Insights (FMI), soaring data achieving tools adoption by diverse end-users i s the Major factor contributing to growth of global neural network software market.This enables end-users to organise substantial amount of unorganised data created.
Growing application of digital technologies and Surging demand for predicting solutions are further expected to propel market growth in the near future. FMI’s report projects significant growth for the global neural network software market.
The 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.
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.
The 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.
This involves a complex particularly, with various unknowns, analysing neural network considerations for solving optimisation as well as other issues.
Researchers from University of Michigan worked on advanced image recognition for detection of aggressive types of cancer, capable of being cured in early stages.
Researchers trained a neural network to isolate features including texture and structure of suspicious lesions & moles to recognise them in a better way. Melanoma is not only deadly but also challenging to screen accurately.
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 networks also witnessed deployment for determination of large-scale banking & financial health, and for prediction of corporate bankruptcies. Two specific areas to successfully develop neural networks are trading and risk management.
A strategic trading benefits from leveraging cost-of-carry relationship, and CAPM- Capital Asset Pricing Model. 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.
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.
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.