Prediction of well‐grounded market information, particularly short‐term forecast of prices of
agricultural commodities, is the essential requirement for the sustainable development of the …

This paper investigates whether a specific type of a recurrent neural network, in particular
Jordan neural network (JNN), captures the expected inflation better than commonly used …

We present RHODE, a novel system that enables privacy-preserving training of and
prediction on Recurrent Neural Networks (RNNs) in a cross-silo federated learning setting …

The development of a Time series Forecasting System is a major concern for Artificial
Intelligence researchers. Commonly, existing systems only assess temporal features and …

Inflation is one of the critical parameters that indicate a country's economic position.
Therefore, maintaining it at a stable level is one of the objectives of any country's financial …

The agriculture sector is India's primary source of national income and occupation. Today
India is considered a global agricultural powerhouse, but the agriculture sector suffers from …

Training accurate and robust machine learning models requires a large amount of data that
is usually scattered across data silos. Sharing, transferring, and centralizing the data from …

Há situações no cotidiano em que nos deparamos com análises de dados obtidos ao longo
do tempo, de forma sequencial, sendo estas informações coletadas em intervalos de horas …

Prediction of well-grounded market information, particularly short-term forecast of prices of
agricultural commodities, is the essential requirement for the sustainable development of the …

A newly introduced method called Taylor-based Optimized Recursive Extended Exponential
Smoothed Neural Networks Forecasting method is applied and extended in this study to …