NNs was incorporated in 2021 as a free learning platform.
NNs is free learning platform that is created by LA. Our learning platform offers an integrated set of interactive online services that provide our visitors with information, tools and resources to support and enhance educational delivery and management.
We are specialized in the development and application of Artificial Neural Networks. Today, neural networks are revolutionizing business and everyday life, bringing us to the next level in artificial intelligence.
Our goal is to show real examples how Artificial Neural Networks can solve a lot of complex issues. Below you can find more information about spheres of applications of Artificial Neural Networks that we develop:
Neural Networks are widely used in Banking and Finance fields. They can detect fraud for credit card transactions, use street view images to verify the existence of money laundering, predict overdraft based on the customer’s transaction history, extract information regarding profit or losses from financial reports to aid investment decision-making, retrieve information from invoices to substantiate a transaction on business accounts, help traders decide what price to quote when buying or selling bonds for their clients, summarize legal documents and automate due diligence checks.
Neural Networks in Health Care are of great importance. Neural Networks can be used in diagnostic systems to detect heart diseases, in biochemical analysis to analyze urine and blood samples, tracking glucose levels in diabetics, determining ion levels in fluids, and detecting various pathological conditions, in image recognition to analyze medical images from various areas of healthcare, including tumor detection, x-ray classifications, and MRIs. in the development of drugs for various conditions – working by using large amounts of data to come to conclusions about treatment options.
Neural Networks have recently been widely used to model some of the human activities in many areas of science and engineering. Engineers often deal with incomplete and noisy data, which is one area where NN are most applicable. This is particularly the case at the conceptual stage of the design process. Presents the current trends for the solution of complex engineering problems that cannot be solved through conventional methods. Includes real-life scenarios where a wide range of artificial neural network architectures can be used to solve the problems encountered in engineering.
From a marketing perspective, neural networks are a form of the software tool used to assist in decision making. Classification of customers can be facilitated through the neural network approach allowing companies to make informed marketing decisions. Sales forecasting is the process of estimating future events with the goal of providing benchmarks for monitoring actual performance. Artificial intelligence techniques have emerged to facilitate the process of forecasting through increasing accuracy in the areas of demand for products, distribution and inventory control.
Customer databases are becoming increasingly larger and more complex, and may tax the capabilities and exacerbate the shortcomings of the techniques currently used to analyze them. To address this challenge, we examine the use of artificial neural networks as an alternative means of segmenting retail databases. Retail sales usually exhibit strong trend and seasonal patterns. Neural Networks improve retail sales prediction. Sales forecasting allows firms to plan their production outputs, which contributes to optimizing firms' inventory management via a cost reduction.
Artificial neural networks are used in the field of solar energy for the estimation of a parabolic trough collector intercept factor and local concentration ratio, for modelling and design of a solar steam generating plant and for the modelling and performance prediction of solar water heating systems. They provide information about the issues relating to energy utilization, production, infrastructure and efficiency of energy resources. They can help in the estimation of heating loads of buildings, the prediction of the energy consumption and in in the prediction of air flow in a naturally ventilated room.