Machine learning
A team of researchers at the Wroclaw University of Science & Technology in Poland has developed a machine vision-based garbage sorting system that shows promise in efficiently and accurately sorting waste. The system, which integrates a robotic arm with advanced machine vision technologies, has been tested in various scenarios, including
Machine learning
Italian researchers have made significant strides in robotic grippers, tackling the limitations of their performance in various industrial scenarios. With financial support from Camozzi Automation S.p.A., a team from the Italian Institute of Technology proposed novel algorithms that aim to enhance gripper functionality. The algorithms enable safe gripping
Machine learning
Research conducted at Chiang Mai University has revealed weaknesses in the security of dimensionality reduction methods, a crucial aspect of information technology and information security. The study focused on machine learning-based reconstruction attacks, which can potentially breach user privacy. Key findings indicate that certain dimensionality reduction techniques, such as principal
Machine learning
Scientists at Beijing Key Laboratory have developed a method to accurately estimate nitrogen use efficiency in maize using a combination of multi-spectral and LiDAR data acquired by a multi-sensor UAV platform. The technique, which utilizes machine learning algorithms to analyze the data, has shown significant improvements in accuracy compared to
Machine learning
Research conducted at the Beijing University of Posts and Telecommunications has led to the development of an anti-degeneracy system for Lidar SLAM (Simultaneous Localization and Mapping) in geometry feature-less environments. The system utilizes a scale-invariant linear mapping, data augmentation based on a Gaussian model, and a degeneracy detection model to
Machine learning
Scientists at the Julius Kuehn-Institute have successfully integrated machine learning and remote sensing techniques to enhance spatial predictions of wheat yield and quality. The study, published in the journal Computers and Electronics In Agriculture, aimed to improve wheat yield prediction by combining process-based models, machine learning, and remote sensing. The
Machine learning
In a breakthrough study published in the Journal of the American Chemical Society, researchers from PSL University have explored the potential of large language models (LLMs) in accelerating and automating materials development research. The research, led by Francois-Xavier Coudert, focuses on the application of LLMs in metal-organic framework (MOF) research,
Machine learning
Researchers from K.N. Toosi University of Technology in Tehran, Iran, have developed an automated system using machine learning and computer vision to detect cellphone use during driving. The system, which uses Wavelet Scattering Networks and Convolutional Neural Networks, achieved a test accuracy of 91% for a new dataset and
Machine learning
Researchers have filed a patent application for a groundbreaking system and method that enables the transfer of semantic segmentation via learnable image prompting of foundation models. The innovative approach aims to overcome the challenges of conventional methods, which often rely on pseudo-labeling and suffer from confidence bias and high computational
Machine learning
Researchers at Victoria University have proposed a new framework, the Top-K Similarity Graph Framework (TKSGF), for detecting intrusions in the Internet of Things (IoT) ecosystem. The framework uses a graph neural network, GraphSAGE, to capture node representations and maintain scalability. Experiments using the NF-ToN IoT and NF-BoT IoT datasets showed
Machine learning
Researchers from Polytechnic University Milan have investigated the potential impact of quantum machine learning (QML) on cybersecurity applications of traditional machine learning. Their findings suggest that QML could revolutionize the field of cybersecurity, but they also highlight the need for future quantum hardware and software advancements. The study focused on
Machine learning
Cybersecurity experts are sounding the alarm as machine learning-based Intrusion Detection Systems (IDS) have significantly improved operational efficiency in Industrial Control Systems (ICS), but are increasingly vulnerable to Adversarial Machine Learning (AML) attacks. Researchers from Texas A&M University have introduced Reactive Autoencoder Defense for Industrial Adversarial Network Threats
