Machine learning
A recent patent application filed by four inventors has described a system and method for providing alarm risk score analysis and intelligence in controlled environments. The system uses a combination of sensor information and video frames to determine the risk value of an event and generate an alarm if it
Machine learning
Researchers from the University of Texas Austin have proposed a new model-constrained Tikhonov autoencoder neural network framework, called TAEN, capable of learning both forward and inverse surrogate models using a single arbitrary observational sample. This framework, TAEN, is designed to address the challenge of addressing scarce data regimes and overfitting
Machine learning
Researchers from the University of Texas Austin have proposed a new model-constrained Tikhonov autoencoder neural network framework, called TAEN, capable of learning both forward and inverse surrogate models using a single arbitrary observational sample. This framework, TAEN, is designed to address the challenge of addressing scarce data regimes and overfitting
Information management
Researchers at Shanghai University have proposed a domain knowledge-assisted data anomaly detection (DKA-DAD) workflow to improve the accuracy of machine learning models in materials design and discovery. The study, funded by the National Natural Science Foundation of China and the National Key Research & Development Program of China, found that
Exports
A patent application by Jonathan Edward Hensley has outlined a method for the controlled release of information in data export. This approach aims to balance the need for access to sensitive information with the requirement for protection against unauthorized access. The proposal involves exporting data in a format with unmasked,
Machine learning
A recent study from the New Jersey Institute of Technology has developed a competitive bidding framework to help contractors prepare for bundled contracts in transportation infrastructure. The framework involves machine-learning models that predict the level of competition on bundled contracts based on input variables such as contract characteristics. This research
Machine learning
A study conducted by researchers from National Yang Ming Chiao Tung University in Taiwan has developed an advanced machine learning framework for predicting building information modeling (BIM) user satisfaction. The framework integrates the forensic-based investigation (FBI) algorithm with gradient boosting machine, light gradient boosting machine, adaptive boosting (AdaBoost), extreme gradient
Iowa State University
Scientists at Iowa State University have developed a novel generative deep learning framework that can design printable, multifunctional microstructural materials. This breakthrough has significant implications for advancing material design technologies. The framework combines a custom-developed voxelized microstructure generator, HetMiGen, with a new machine learning model, TransVNet, to rapidly and accurately
Eye diseases
Research from Nanyang Technological University has detailed the application of machine learning approaches to predict treatment failure in ocular tuberculosis. This study, published in Translational Vision Science & Technology, utilized the Collaborative Ocular Tuberculosis Study (COTS) dataset of 836 patients with tubercular uveitis across 27 international eye care centers. The
Machine learning
Google LLC has been granted a patent for an innovative method of generating synthetic data using augmented reality (AR) techniques, which can improve the performance of machine learning models. The patent, filed on November 19, 2019, and published online on October 14, 2025, reveals a system and method for generating
Machine learning
Indoor localization has gained significant attention in recent years due to its applications across various sectors. However, despite significant research progress, indoor localization remains challenging. A new study conducted by researchers at the University of Salamanca highlights the need for more robust and accurate localization solutions, citing the scarcity of
Machine learning
Research from the University of Tennessee has introduced a novel method for capturing dynamic electron correlation using machine learning. The study, published in the Journal of Chemical Theory and Computation, presents a data-driven approach, dubbed DDCASPT2, which leverages features generated from lower-level electronic structure methods to recover missing electron correlation.
