Machine learning
Researchers at Taiyuan University of Science & Technology have investigated the isothermal transformation process of TC17 alloy under different hydrogen contents using machine learning models and microstructure analysis. The study found that the transformation microstructure changes as the hydrogen content increases. A team of experts employed artificial neural networks, Gaussian
Machine learning
Researchers at Kunming University in China have developed a new method for detecting and classifying the ripeness of litchi bunches, a crucial task for robotic harvesting. The method combines deep learning, image processing, and clustering algorithms to achieve high accuracy and efficiency. The study's findings have significant implications
Machine learning
Researchers from the University of Tokyo have developed a novel electromagnetic tomographic approach for simultaneously imaging contact pressure and temperature using a single sensing material. This innovative approach features adjustable detection ranges, a concise, scalable, and easily fabricated structure. The sensor can be used for multi-modal tactile imaging and has
Machine learning
Recent research by Poznan University of Technology's Institute of Robotics and Machine Intelligence has introduced a framework for simultaneous optimization of sensor poses and 3D maps, represented as surfels, in robotics and automation. The study, funded by the Polish National Agency for Academic Exchange and others, proposes a
Machine learning
Researchers at Ulsan National Institute of Science and Technology (UNIST) have emphasized the importance of accurate time synchronization between heterogeneous sensors in multi-sensor fusion systems. Sensor delays can cause discrepancies between actual and measured times, leading to temporal misalignment and inaccurate state estimation. To address this issue, the researchers proposed
Machine learning
Researchers from Fudan University have developed an artificial neural network (ANN) model to predict the depth, width, and volume of ablation thermal lesions during radiofrequency cardiac ablation (RFCA). According to the study, the two-branch ANN model was able to predict thermal lesion size with errors of 0.1986 mm, 0.
Machine learning
Researchers at Ateneo de Manila University have developed a Machine Vision System (MVS) that can automate the inspection of microcontroller units (MCU) and detect empty trays, addressing a serious issue in the manufacturing environment. The MVS uses a Convolutional Neural Network (CNN) deployed in OpenMV H7 Plus, a low-power, memory-constrained
Machine learning
Research conducted by the Defence Institute of Advanced Technology has found that a novel approach using Artificial Neural Networks (ANNs) can optimize the decolorization of Rhodamine B dye from effluents. The ANNs were used to model the adsorption capacity of sodium hydroxide-functionalized sugarcane bagasse biochar (NaOH-SBB), which demonstrated a strong
Machine learning
A team of researchers from Fujian, People's Republic of China, has developed a novel prognostic signature for breast cancer patients by integrating multi-omics data and machine learning algorithms. The study, published in Scientific Reports, used transcriptomic and gene expression data from TCGA and GEO databases to identify 72
Machine learning
Recent research from the Punjab Remote Sensing Centre, supported by the University of Pecs, has demonstrated the effectiveness of machine learning algorithms in assessing wheat lodging. The study utilized multi-temporal Sentinel-2 data to analyze the temporal characteristics of crop phenology and detect lodging during the end of March and April
Machine learning
Artificial intelligence (AI) is rapidly transforming the healthcare sector by enhancing medical diagnosis, informing treatment strategies, and supporting patient care. However, its widespread implementation faces institutional challenges, including limited infrastructure, lack of integration with existing health records, financial constraints, inadequate training opportunities, and regulatory ambiguities. A scoping review of medical
Machine learning
Researchers have made significant strides in the development of artificial intelligence for natural language processing and computer vision. This progress has been driven by large-scale datasets like OpenWebText and ImageNet. Inspired by this, a new research effort presents MassNet, a groundbreaking resource for proteomics designed to accelerate deep learning applications.
