Algorithms
Researchers at Sant Gadge Baba Amravati University have made a breakthrough in soil data analysis using machine learning algorithms, achieving an accuracy of 93.82% in predicting soil characteristics. The study aims to improve agricultural productivity and environmental sustainability by leveraging the potential of machine learning in transforming traditional soil
Machine learning
Mansoura University researchers have utilized machine learning algorithms to analyze and predict annual carbon emissions from 1990 to 2023 in the top ten high-carbon emission source countries. The study employed various algorithms, including random forest and support vector machines, to forecast CO emissions based on selected features such as primary
Algorithms
Researchers from the University of Rwanda have developed a novel onboard measurement system for monitoring wheel flange wear depth in railway systems. The system utilizes displacement and temperature sensors to accurately measure wear depth, while a dynamic machine learning algorithm compensates for sensor noise and temperature effects. Laboratory experiments demonstrated
Algorithms
Researchers at Khon Kaen University in Thailand have published a new study on artificial intelligence, highlighting the significance of imbalanced and overlapping data in customer churn prediction. The study reveals that various sampling methods, including hybrid sampling, can address these issues but struggle with classical machine learning algorithms. To optimize
Algorithms
Researchers at Sidi Mohamed Ben Abdellah University in Fez, Morocco have published a study on the use of machine learning algorithms to predict student engagement in online learning environments. The study, published in the journal Informatics, used a dataset from a Kaggle database to develop predictive models that can identify
Machine learning
Researchers at the Universidad de Caldas have published a study on the use of machine learning algorithms to differentiate between cross-linked and non-cross-linked gelatin samples using Fourier transform infrared spectroscopy (FTIR). The study found that machine learning classification models developed in the Python language were effective in distinguishing between cross-linked
Machine learning
The increasing presence of artificial intelligence (AI) in our daily lives raises important questions about data privacy. AI tools and technologies can make life easier, but they often collect large amounts of data, sometimes without people even realizing their data is being collected. This information can be used to identify
Machine learning
Researchers from Hubei University of Arts & Science have conducted new research on machine learning, with a focus on the state of health estimation of lithium-ion batteries. According to the study, the battery data from the existing publicly available dataset was measured under uninterrupted charge/discharge experiments. However, the aging
Machine learning
Researchers from the Islamic Azad University and University of Zanjan have made significant advancements in using Machine Learning (ML) and Deep Learning (DL) algorithms to improve computational efficiency in predicting the parameters of a 2D permanent magnetic lattice. By applying these algorithms, the team was able to achieve high accuracy
Machine learning
Research conducted by the University of Toledo has demonstrated the efficacy of machine learning algorithms in predicting the compressive strength of sustainable concrete incorporating waste glass powder. The study employed six machine learning algorithms, including linear regression, elastic net regression, and support vector regressor, to analyze 187 sets of experimental
Machine learning
Researchers at Clemson University have conducted an in-depth review of benchmark and test functions used in global optimization algorithms and metaheuristics, highlighting the importance of selecting appropriate benchmarks for various algorithmic challenges. The study categorizes and analyzes 25 commonly used functions, proposes two new functions, and identifies gaps in current
Machine learning
AI has transformed the landscape of Non-Small Cell Lung Cancer (NSCLC) diagnosis, treatment, and management by providing more accurate diagnoses, predicting treatment responses, and guiding personalized therapeutic strategies. Recent studies showcase AI's potential in identifying genetic mutations, predicting clinical outcomes, and aiding surgical resection. AI-driven genetic profiling and
