Related papers: A Deep Learning Model for Heterogeneous Dataset An…

Crop production needs to increase in a sustainable manner to meet the growing global demand for food. To identify crop varieties with high yield potential, plant scientists and breeders evaluate the performance of hundreds of lines in…

Financial time series forecasting is, without a doubt, the top choice of computational intelligence for finance researchers from both academia and financial industry due to its broad implementation areas and substantial impact. Machine…

Accurate electrical load forecasting is crucial for optimizing power system operations, planning, and management. As power systems become increasingly complex, traditional forecasting methods may fail to capture the intricate patterns and…

Crop yield forecasting plays a significant role in addressing growing concerns about food security and guiding decision-making for policymakers and farmers. When deep learning is employed, understanding the learning and decision-making…

Accurate forecasts of photovoltaic power generation (PVPG) are essential to optimize operations between energy supply and demand. Recently, the propagation of sensors and smart meters has produced an enormous volume of data, which supports…

Climate change is posing new challenges to crop-related concerns including food insecurity, supply stability and economic planning. As one of the central challenges, crop yield prediction has become a pressing task in the machine learning…

Maize, a crucial crop globally cultivated across vast regions, especially in sub-Saharan Africa, Asia, and Latin America, occupies 197 million hectares as of 2021. Various statistical and machine learning models, including mixed-effect…

Drought is a serious natural disaster that has a long duration and a wide range of influence. To decrease the drought-caused losses, drought prediction is the basis of making the corresponding drought prevention and disaster reduction…

Currently, the issue that concerns the world leaders most is climate change for its effect on agriculture, environment and economies of daily life. So, to combat this, temperature prediction with strong accuracy is vital. So far, the most…

This paper presents an interpretable review of various machine learning and deep learning models to predict the maintenance of aircraft engine to avoid any kind of disaster. One of the advantages of the strategy is that it can work with…

Accurate prediction of crop yield before harvest is of great importance for crop logistics, market planning, and food distribution around the world. Yield prediction requires monitoring of phenological and climatic characteristics over…

Deep learning has made great strides lately with the availability of powerful computing machines and the advent of user-friendly programming environments. It is anticipated that the deep learning algorithms will entirely provision the…

Short-term rainfall forecasting, also known as precipitation nowcasting has become a potentially fundamental technology impacting significant real-world applications ranging from flight safety, rainstorm alerts to farm irrigation timings.…

Hybrid methods have been shown to outperform pure statistical and pure deep learning methods at both forecasting tasks, and at quantifying the uncertainty associated with those forecasts (prediction intervals). One example is Multivariate…

Accurate short range weather forecasting has significant implications for various sectors. Machine learning based approaches, e.g., deep learning, have gained popularity in this domain where the existing numerical weather prediction (NWP)…

UAV remote sensing technology has become a key technology in crop breeding, which can achieve high-throughput and non-destructive collection of crop phenotyping data. However, the multidisciplinary nature of breeding has brought technical…

This paper explores the application of Hidden Markov Models (HMM) and Long Short-Term Memory (LSTM) neural networks for economic forecasting, focusing on predicting CPI inflation rates. The study explores a new approach that integrates…

Eradicating hunger and malnutrition is a key development goal of the 21st century. We address the problem of optimally identifying seed varieties to reliably increase crop yield within a risk-sensitive decision-making framework.…

Multivariate time series forecasting is extensively studied throughout the years with ubiquitous applications in areas such as finance, traffic, environment, etc. Still, concerns have been raised on traditional methods for incapable of…

Childhood obesity is a major public health challenge. Early prediction and identification of the children at a high risk of developing childhood obesity may help in engaging earlier and more effective interventions to prevent and manage…