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Predicting drug solubility in organic solvents mixtures: A machine-learning approach supported by high-throughput experimentation.

International journal of pharmaceutics
A novel approach based on supervised machine-learning is proposed to predict the solubility of drugs and drug-like molecules in mixtures of organic solvents. Similar to quantitative structure-property relationship (QSPR) models, different solvent typ...

The Rohingya refugee crisis in Bangladesh: assessing the impact on land use patterns and land surface temperature using machine learning.

Environmental monitoring and assessment
Bangladesh, a third-world country with the seventh highest population density in the world, has always struggled to ensure its residents' basic needs. But in recent years, the country is going through a serious humanitarian and financial crisis that ...

Machine learning to predict the relationship between Vibrio spp. concentrations in seawater and oysters and prevalent environmental conditions.

Food research international (Ottawa, Ont.)
Vibrio parahaemolyticus and Vibrio vulnificus are bacteria with a significant public health impact. Identifying factors impacting their presence and concentrations in food sources could enable the identification of significant risk factors and preven...

Deep Survival Analysis With Latent Clustering and Contrastive Learning.

IEEE journal of biomedical and health informatics
Survival analysis is employed to analyze the time before the event of interest occurs, which is broadly applied in many fields. The existence of censored data with incomplete supervision information about survival outcomes is one key challenge in sur...

Time series (2003-15) analysis of selected physicochemical parameters in Indian Ocean: Cumulative impacts prediction on coral bleaching using machine learning.

The Science of the total environment
Coral bleaching is an important ecological threat worldwide, as the coral ecosystem supports a rich marine biodiversity to survive. Sea surface temperature was considered a major culprit; however, later it was observed that other water parameters lik...

Development of new materials for electrothermal metals using data driven and machine learning.

PloS one
After adopting a combined approach of data-driven methods and machine learning, the prediction of material performance and the optimization of composition design can significantly reduce the development time of materials at a lower cost. In this rese...

Development of machine learning-based shelf-life prediction models for multiple marine fish species and construction of a real-time prediction platform.

Food chemistry
At least 10 million tons of seafood products are spoiled or damaged during transportation or storage every year worldwide. Monitoring the freshness of seafood in real time has become especially important. In this study, four machine learning algorith...

Investigation of direct contact membrane distillation (DCMD) performance using CFD and machine learning approaches.

Chemosphere
Direct Contact Membrane Distillation (DCMD) is emerging as an effective method for water desalination, known for its efficiency and adaptability. This study delves into the performance of DCMD by integrating two powerful analytical tools: Computation...

Integrated AI-driven optimization of Fenton process for the treatment of antibiotic sulfamethoxazole: Insights into mechanistic approach.

Chemosphere
Antibiotics, as a class of environmental pollutants, pose a significant challenge due to their persistent nature and resistance to easy degradation. This study delves into modeling and optimizing conventional Fenton degradation of antibiotic sulfamet...

Predicting maturity and identifying key factors in organic waste composting using machine learning models.

Bioresource technology
The measurement of germination index (GI) in composting is a time-consuming and laborious process. This study employed four machine learning (ML) models, namely Random Forest (RF), Artificial Neural Network (ANN), Support Vector Regression (SVR), and...