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INTRODUCTION: With the deepening of China's "Dual Carbon" goals, the scale of photovoltaic (PV) installations continues to expand, placing stringent requirements on the accuracy, scenario adaptability, and robustness of ...

To overcome the limitations of traditional models to simulate discrete environmental variables, this study introduces a novel, integrated simulation framework: the Spatio-Temporal Integrated Geographic Generative Adversa...

Options trading is a type of financial trading that can offer significant profit opportunities depending on market conditions, but its profit and loss structure is complex. Traditional trading decisions are often made ba...

IntroductionStudent engagement is a pivotal element in educational psychology, significantly impacting learning outcomes and academic achievement. Traditional methods for analyzing student engagement often rely on static...

Forest growth and yield projection is essential for sustainable forest management planning. In Brazil, the Clutter model is widely applied for yield forecasting and commonly achieves prediction errors below 10%. However,...

The purpose of the article is to identify and establish regular climate changes in Kharkiv Oblast, as well as to highlight individual variability in certain settlements. The main material. In general, in Ukraine, the ...

ABSTRACT This study was carried out to statistically evaluate the performance of the PHYGROW model in simulating the growth of corn and sorghum plants in different locations in the semi-arid region of Ceará State, Brazi...

Abstract In the world of the Internet of Things (IoT), wireless sensor networks (WSNs) play a vital role in enabling seamless communication between connected devices. In a WSN data prediction system based, clustering and...

The Global Flood Awareness System (GloFAS) is a promising flood-modelling tool available for most river basins worldwide. However, it contains inherent biases, notably linked to its spatial resolution. This study propose...

The management and operation of the power system would be significantly enhanced if accurate prior knowledge of power exchanges were provided, which could provide valuable insights into the system and help predict the re...

ABSTRACT The objective of this study was to assess behavior patterns in Brazilian farm milk prices. We employed a structural time series techniques model, the Unobserved Component Model (UCM), which is part of the family...

ObjectiveA hybrid model based on bidirectional short term memory （BiLSTM） and Transformer was constructed to predict the population behavior of Listeria monocytogenes under different environmental conditions.MethodsI...

Abstract The precise forecasting of the Remaining Useful Life (RUL, expressed in hours) of aircraft engine-like systems using a synthetic dataset designed to mimic real engine degradation patterns is a key factor in sett...

Artificial Intelligence (AI) refers to systems designed to perform tasks requiring human-like intelligence, such as learning, reasoning, decision-making, and language processing. Its integration into Human Resource Man...

This paper explores the application of convolutional neural networks (CNNs) in predictive modelling for seismic events and flood risks, with a particular focus on forecasting extreme quantile events that exceed historica...

Abstract Traffic flow prediction is a fundamental building block for Intelligent Transportation Systems (ITS), enabling proactive congestion mitigation, adaptive traffic signal control, and reliable route guidance. Howev...

Pharmaceutical technology has increasingly integrated artificial intelligence (AI) in recent years. This technology offers significant benefits, including saving time and money while enhancing the understanding of the re...

Drought is a slow-onset natural disaster that has far-reaching effects on agriculture, water security, and socio-economic systems, especially in climate-vulnerable countries such as Malaysia. It is imperative to predict ...

Abstract This research proposes an innovative Arabic financial forecasting model that integrates linguistic relation extraction with advanced deep learning and machine learning techniques. The framework combines Arabic O...

Artificial intelligence (AI), including machine learning, deep learning, reinforcement learning, and generative AI, has the potential to advance critical care nephrology (CCN) by enhancing prediction accuracy, improving ...

Abstract To address the limitations of existing oil-well production forecasting models, particularly suboptimal hyperparameter selection, difficulties in fine-tuning, insufficient global search capability, and slow conve...

Selected aspects of methodological developments in socio-economic research are addressed from a conceptual perspective, with outlining the areas of transformations in methodology, and illustrating the feasibilities of it...

When voyage report data is utilized as the main data source for ship fuel efficiency analysis, its information on weather and sea conditions is often regarded as unreliable. To solve this issue, this study approaches AIS...

Jaringan syaraf tiruan merupakan metode yang sangat sering digunakan untuk peramalan. Akurasi jaringan syaraf tiruan dipengaruhi oleh jumlah lapisan tersembunyi dan neuron didalamnya. Salah satu teori yang membahas tenta...