Electroencephalography (EEG) has evolved into an indispensable instrument for estimating cognitive workload in various domains. Machine Learning (ML) and deep learning (DL) techniques have been increasingly employed to develop accurate workload estim...
We propose TIC-FusionNet, a trend-aware multimodal deep learning framework for time series forecasting with integrated visual signal analysis, aimed at addressing the limitations of unimodal and short-range dependency models in noisy financial enviro...
Cost overruns are common on long-term construction projects. This is mostly because of inaccurate early estimates and unexpected changes in the economy and finances. In Egypt, the costs of materials like steel, cement, bricks, sand, and aggregates ma...
The open radio access network (O-RAN) architecture facilitates intelligent radio resource management via RAN intelligent controllers (RICs). Deep reinforcement learning (DRL) algorithms are integrated into RICs to address dynamic O-RAN slicing challe...
Automated cause classification of fire accident reports (FIREAR) is crucial for enhancing public safety and developing data-driven prevention strategies. However, existing deep learning models often struggle with the unique challenges these documents...
In the energy sector, accurate forecasting of natural gas production and liquid level detection is crucial for efficient resource management and operational planning. This study proposes an integrated deep learning model by incorporating bidirectiona...
The issue of regional haze pollution has become increasingly prominent. However, early warning models for regional haze pollution are significantly lacking. To accurately predict regional PM2.5 pollution, hourly average concentration data of pollutan...
Much machine learning research progress is based on developing models and evaluating them on a benchmark dataset (e.g., ImageNet for images). However, applying such benchmark-successful methods to real-world data often does not work as expected. This...
Inspection and diagnosis of construction projects involves health monitoring of buildings and related facilities, and the utilization of renewable energy sources, such as solar energy, is critical to the smooth operation of modern construction projec...
This research explores the novel application of aromatic odors, specifically rosemary, in reducing mental workload, employing deep learning methods to analyze electroencephalogram (EEG) signals without feature extraction. Thirty volunteers participat...
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