[HTML][HTML] Machine learning and deep learning for safety applications: Investigating the intellectual structure and the temporal evolution
Over the last decades, safety requirements have become of primary concern. In the context
of safety, several strategies could be pursued in many engineering fields. Moreover, many …
of safety, several strategies could be pursued in many engineering fields. Moreover, many …
The impact of artificial intelligence on language translation: a review
In the context of a more linked and globalized society, the significance of proficient cross-
cultural communication has been increasing to a position of utmost importance. Language …
cultural communication has been increasing to a position of utmost importance. Language …
Enhancing heart disease prediction using a self-attention-based transformer model
Cardiovascular diseases (CVDs) continue to be the leading cause of more than 17 million
mortalities worldwide. The early detection of heart failure with high accuracy is crucial for …
mortalities worldwide. The early detection of heart failure with high accuracy is crucial for …
[HTML][HTML] Comparison of accuracy and reliability of random forest, support vector machine, artificial neural network and maximum likelihood method in land use/cover …
MS Chowdhury - Environmental Challenges, 2024 - Elsevier
Accurate land use and land cover (LULC) is crucial for sustainable urban planning and for
many scientific researches. However, the demand for accurate LULC maps is increasing; it …
many scientific researches. However, the demand for accurate LULC maps is increasing; it …
An improved deep learning procedure for statistical downscaling of climate data
Recent climate change (CC) scenarios from the Coupled Model Intercomparison Project
Phase 6 (CMIP6) have just been released in coarse resolution. Deep learning (DL) based …
Phase 6 (CMIP6) have just been released in coarse resolution. Deep learning (DL) based …
Time series forecasting and anomaly detection using deep learning
Recent advances in time series forecasting and anomaly detection have been attributed to
the growing popularity of deep learning approaches. Traditional methods, such as rule …
the growing popularity of deep learning approaches. Traditional methods, such as rule …
ChurnNet: Deep Learning Enhanced Customer Churn Prediction in Telecommunication Industry
In the Telecommunication Industry (TCI) customer churn is a significant issue because the
revenue of the service provider is highly dependent on the retention of existing customers. In …
revenue of the service provider is highly dependent on the retention of existing customers. In …
Hybrid WT–CNN–GRU-based model for the estimation of reservoir water quality variables considering spatio-temporal features
Water quality indicators (WQIs), such as chlorophyll-a (Chl-a) and dissolved oxygen (DO),
are crucial for understanding and assessing the health of aquatic ecosystems. Precise …
are crucial for understanding and assessing the health of aquatic ecosystems. Precise …
Photocatalytic degradation of drugs and dyes using a maching learning approach
G Anandhi, M Iyapparaja - RSC advances, 2024 - pubs.rsc.org
The waste management industry uses an increasing number of mathematical prediction
models to accurately forecast the behavior of organic pollutants during catalytic degradation …
models to accurately forecast the behavior of organic pollutants during catalytic degradation …
[HTML][HTML] Citrus yield prediction using deep learning techniques: A combination of field and satellite data
The goal of this paper is to develop a deep learning model for predicting citrus yield. The
data used consists of two sources:(1) field data that includes information on fertilization and …
data used consists of two sources:(1) field data that includes information on fertilization and …