Natural language processing
Researchers at Novacene Ai Corp. have submitted a patent application for a system and method for data mining and exploration, tackling several challenges in the field. The proposed system would allow users to easily explore and analyze large datasets, providing valuable insights in a user-friendly interface. Key innovations include the
Machine learning
Researchers in Hangzhou, People's Republic of China, have announced a major breakthrough in the application of machine learning (ML) to catalysis research. According to a recent study, ML has emerged as a powerful engine transforming the landscape of catalysis, enabling data mining, performance prediction, and mechanistic analysis. Funded
Machine learning
Researchers at Yaba College of Technology have introduced a novel approach for proactive road repair that combines machine learning algorithms with Internet of Things (IoT) devices. The study investigates the effectiveness of this method in scheduling and prioritizing maintenance tasks, reducing expenses, and improving efficiency. By utilizing sensors and machine
Machine learning
A study published in the Journal of Surgical Research has demonstrated the efficacy of a machine learning algorithm-based tumor-stroma ratio in stratifying the prognosis of intrahepatic cholangiocarcinoma (iCCA) patients after radical surgery. The research, conducted by a team of scientists from Sun Yat-Sen University, aimed to quantify tumor components and
Machine learning
Researchers from Capital Medical University have made a significant breakthrough in the diagnosis of asymptomatic tuberculosis (TB) by identifying plasma biomarkers that can accurately discriminate between asymptomatic TB, latent TB infection, and healthy controls. The study, published in the Journal of Proteome Research, employed the Olink Proximity Extension Assay to
Data mining
Researchers from Shahrood University of Technology have utilized machine learning methods to predict the compressive and flexural strengths of ultra-high-performance concrete (UHPC) with various fiber types. The study employed six machine learning algorithms, including gradient boosting, light gradient-boosting machine, and deep neural networks, to analyze a dataset of 321 and
Data mining
Researchers at Hamadan University of Medical Sciences conducted a study to evaluate the effectiveness of Synthetic Minority Over-sampling Technique (SMOTE)-based machine learning algorithms in predicting musculoskeletal disorders (MSDs) in elementary school students. The study aimed to improve the accuracy of MSD prediction in children using SMOTE-based techniques, which address
Data mining
A new study from Edith Cowan University evaluates the effectiveness of machine learning models in predicting Biochemical Oxygen Demand (BOD) and Ammonia Nitrogen (NH4+-N) in raw wastewater. The research involves training seven machine learning algorithms on standard wastewater indicators, including organic load, nutrient levels, and pH, with hyperparameters tuned
Data mining
Research from Xiamen University has presented a hybrid model that combines convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and traditional machine learning (ML) methods to predict pancreatic ductal adenocarcinoma (PDAC) staging based on metabolic characteristics. The model achieved an optimal classification accuracy of 90.00%, surpassing traditional classification
Data mining
Researchers from Jawaharlal Nehru Technological University Kakinada have designed a new clustering-based unstructured data analysis model using a deep learning algorithm. The model, which incorporates a Layer Improved Transformer Network (LITN) and Deep Learning-based Adaptive Clustering (DLAC), has achieved impressive results in terms of clustering performance. The researchers' approach
Data mining
In a study published in the journal Pervasive and Mobile Computing, researchers from Hohai University have proposed a federated blockchain-based incentive and selection mechanism for a federated learning system. The new mechanism aims to improve data sharing and analysis by selecting clients using support vector machines (SVM) and providing a
Data mining
A team of researchers at Zhejiang University has published a new study proposing a novel approach that integrates machine learning algorithms with computational fluid dynamics (CFD) simulations to efficiently predict the aerodynamic performance of supersonic aircraft under cruising flight conditions. This innovative method has been shown to be more efficient
