Machine learning
The clinical decision support systems market is expected to experience significant growth, driven by the rising adoption of healthcare IT solutions, increasing demand for better clinical outcomes, and concerns over medication errors and patient safety. The global market, valued at $2.25 billion in 2024, is forecasted to grow at
Machine learning
The decision intelligence market is expected to experience significant growth, driven by the increasing demand for data-driven decision-making solutions. The market is anticipated to expand at a compound annual growth rate (CAGR) of 24.7% from USD 13.3 billion in 2024 to USD 50.1 billion in 2030. Enhancements
Machine learning
A study published on biorxiv.org has demonstrated the potential of using sex-dependent gene expression patterns to deconvolute pooled single-nucleus RNA sequencing (snRNA-seq) data. This approach enables the incorporation of pooled barcoding and sequencing protocols, increasing data throughput and analytical sample size without requiring increases in experimental sample size and
Machine learning
Researchers from the Department of Computer Science have made significant contributions to the field of applied computational intelligence and soft computing. Their study presents four ensemble machine learning models for predicting residential energy consumption in South Africa, leveraging historical energy consumption patterns and enhancing predictive abilities through feature engineering methodologies.
Machine learning
Researchers at the University of Bristol have developed a soft transduction mechanism called Coil-Tac, which utilizes a liquid metal core and a conductive helical coil to measure changes in capacitance. The mechanism is capable of oscillatory tactile sensing and touch location estimation when coupled with a soft interface. According to
Machine learning
A team of researchers at the University of Bonn has made a breakthrough in robotic perception, developing a novel method for semantic occupancy prediction using only vision data. This approach, called Sfmocc, does not require manual or LiDAR-based labels, achieving state-of-the-art results for 3D occupancy prediction among methods trained without
Machine learning
A new report from Cedars Sinai Medical Center discusses the use of deep learning models to improve the accuracy of myocardial perfusion imaging for detecting obstructive coronary artery disease. Researchers aimed to develop an artificial intelligence (AI) approach that could be integrated into clinical practice to enhance diagnostic performance. The
Machine learning
Research conducted by the Department of Mechanical Engineering has evaluated the performance of various Particle Swarm Optimization (PSO) variants for tracking the trajectory of a cable-driven continuum robot. The study used descriptive statistics, parametric, and non-parametric methods to assess the performance of five PSO variants: Standard PSO, Weighted PSO, Quantum
Machine learning
Researchers from Makerere University in Kampala, Uganda, have developed two deep learning models, YOLOv5-PBB and YOLOv8-PBB, for accurate B-line detection and localization in lung ultrasound imaging. These models are designed to address the challenges of interpreting lung ultrasound images, which are often subject to observer variability and require significant expertise.
Machine learning
Researchers at Nanyang Technological University in Singapore have developed a novel approach to brain tumor classification using machine learning algorithms. The study introduces a combination of pre-trained convolutional neural network (CNN) models and machine learning algorithms to achieve high accuracy in detecting brain tumors. The results show that the proposed
Machine learning
Researchers from the University of Edinburgh have made a significant breakthrough in power electronics, proposing a novel solution to the long-standing issues of calculation burden and latency in conventional model predictive control (MPC) systems. By leveraging machine learning models, the researchers have developed an artificial neural network (ANN) that can
Machine learning
Researchers at Middle Tennessee State University have developed a novel approach to detect honey adulteration using differential scanning calorimetry (DSC) and machine learning classification (MLC) techniques. The study demonstrates a significant improvement in accuracy when integrating convolutional neural networks (CNN) with the Synthetic Minority Over-sampling TEchnique (SMOTE) for data augmentation.
