Machine learning
Scientists at the Water Research and Technology Center in Tunisia have proposed an integrated approach combining convolutional neural networks (CNNs) with a support vector machine (SVM) classifier to enhance the automatic detection of geological lineaments from satellite imagery. This research study reveals that Sentinel-1, with its 10-meter radar resolution, detects
Machine learning
Researchers from King Saud University have developed an innovative solution to accurately assess the extent and quality of green spaces in urban areas. The team, led by Meshal Alfarhood, used live satellite imagery and advanced deep learning techniques to identify trees and measure green density in vegetated areas. Their methodology
Machine learning
Researchers at Federal University Paraiba have developed a machine learning model that estimates daily precipitation in Brazil with high accuracy, without relying on ground-based data. The IMERG BraMaL-D model uses satellite-based precipitation data and 53 re-analysis variables from MERRA-2 to produce accurate daily precipitation estimations. The model outperformed other global
Machine learning
Exosuit adaptation, a crucial aspect of human-machine interaction, has been investigated in a recent study by Chinese researchers. The findings highlight the importance of bi-directional adaptation, where both the exosuit and the wearer adapt to each other's needs. The study reveals that precise gait phase prediction and dynamic
Machine learning
Investigations into machine learning have uncovered a proposed framework that enables efficient and private data analysis on IoT networks. Conducted by researchers at the University of Ha'il in Saudi Arabia, the study highlights the importance of protecting sensitive applications and combining machine learning models to analyze data locally
Machine learning
Researchers from Delhi Technological University have made a breakthrough in enhancing cybersecurity in the Internet of Things (IoT) by developing advanced deep learning-based intrusion detection frameworks. The study, published in the journal Applied Soft Computing, presents novel models, including Embed-Net, ConvNet-SVM, and DeepSVM-Net, designed to detect network-based attacks on the
Machine learning
Recent research from Shenyang Normal University has shed light on the potential of artificial intelligence (AI) in transforming industrial environments. The study highlights the benefits of digital technologies, such as increased efficiency and dependability of systems, but also mentions the drawbacks, including cybercrime and the need for robust security measures.
Machine learning
Researchers at Bar-Ilan University, led by Dr. Itay Koren and Prof. Yuval Feldman, have been awarded Proof of Concept (PoC) Grants from the European Research Council (ERC). These grants will support the development of groundbreaking projects in biomedical science and AI-driven public policy, marking a significant achievement in the university&
Machine learning
A team of researchers at the University of Iowa has published a new study on global aerosol climatology derived from six years of Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) observations. The study utilizes a machine learning algorithm to analyze the data and provide valuable insights into the global aerosol
Machine learning
A new study conducted by researchers at United Arab Emirates University sheds light on the application of machine learning algorithms for land use and land cover (LULC) mapping in Al Ain city, UAE. The study utilizes a range of machine learning classifiers, including Gradient Tree Boosting (GTB), Random Forest (RF)
Machine learning
Researchers have made significant breakthroughs in understanding Alzheimer's disease (AD) through the development of PRISM-ML, an open-source integrated analysis pipeline. By combining interpretable machine learning with systems biology and electronic health record data mining, the study has shed light on the molecular diversity of AD and identified promising
Machine learning
Researchers at Jiangxi Agricultural University have used machine learning algorithms to simulate soil organic matter (SOM) in agricultural soils, a critical parameter in soil nutrient cycling, aggregate stability, and water retention. The study, supported by the National Key Research & Development Program of China, explored the effectiveness of different machine
