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Ive Botunac, Jurica Bosna and Maja Matetic
Investment decision-makers increasingly rely on modern digital technologies to enhance their strategies in today?s rapidly changing and complex market environment. This paper examines the impact of incorporating Long Short-term Memory (LSTM) models into ...
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Shifeng Chen, Jialin Wang and Ketai He
The popularization of the internet and the widespread use of smartphones have led to a rapid growth in the number of social media users. While information technology has brought convenience to people, it has also given rise to cyberbullying, which has a ...
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Konstantinos Dolaptsis, Xanthoula Eirini Pantazi, Charalampos Paraskevas, Selçuk Arslan, Yücel Tekin, Bere Benjamin Bantchina, Yahya Ulusoy, Kemal Sulhi Gündogdu, Muhammad Qaswar, Danyal Bustan and Abdul Mounem Mouazen
Irrigation plays a crucial role in maize cultivation, as watering is essential for optimizing crop yield and quality, particularly given maize?s sensitivity to soil moisture variations. In the current study, a hybrid Long Short-Term Memory (LSTM) approac...
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Yoga Sasmita, Heri Kuswanto and Dedy Dwi Prastyo
Standard time-series modeling requires the stability of model parameters over time. The instability of model parameters is often caused by structural breaks, leading to the formation of nonlinear models. A state-dependent model (SDM) is a more general an...
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Aymane Ahajjam, Jaakko Putkonen, Emmanuel Chukwuemeka, Robert Chance and Timothy J. Pasch
Local weather forecasts in the Arctic outside of settlements are challenging due to the dearth of ground-level observation stations and high computational costs. During winter, these forecasts are critical to help prepare for potentially hazardous weathe...
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Kevin Mero, Nelson Salgado, Jaime Meza, Janeth Pacheco-Delgado and Sebastián Ventura
Unemployment, a significant economic and social challenge, triggers repercussions that affect individual workers and companies, generating a national economic impact. Forecasting the unemployment rate becomes essential for policymakers, allowing them to ...
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Nosa Aikodon, Sandra Ortega-Martorell and Ivan Olier
Patients in Intensive Care Units (ICU) face the threat of decompensation, a rapid decline in health associated with a high risk of death. This study focuses on creating and evaluating machine learning (ML) models to predict decompensation risk in ICU pat...
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Anik Baul, Gobinda Chandra Sarker, Prokash Sikder, Utpal Mozumder and Ahmed Abdelgawad
Short-term load forecasting (STLF) plays a crucial role in the planning, management, and stability of a country?s power system operation. In this study, we have developed a novel approach that can simultaneously predict the load demand of different regio...
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Shuai Ma, Yafeng Wu, Hua Zheng and Linfeng Gou
Aiming at engine health management, a novel hybrid prediction method is proposed for exhaust gas temperature (EGT) prediction of gas turbine engines. This hybrid model combines a nonlinear autoregressive with exogenous input (NARX) model and a moving ave...
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Xianlei Fu, Maozhi Wu, Sasthikapreeya Ponnarasu and Limao Zhang
This research introduces a hybrid deep learning approach to perform real-time forecasting of passenger traffic flow for the metro railway system (MRS). By integrating long short-term memory (LSTM) and the graph convolutional network (GCN), a hybrid deep ...
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