Machine learning
Seiko Epson Corporation has filed a patent application for an advanced calculation and projection method that uses convolutional neural networks (CNNs) to enhance image recognition and projection accuracy. The method, disclosed by inventor Takumi Aragaki, extracts feature points from material and object images, and derives a correspondence relationship between the
Machine learning
Researchers from Xiamen University have proposed a new intelligent music score generation method that combines short-time Fourier transform and improved convolutional neural network. The method aims to address the limitations of existing music score generation methods, which are limited by scene limitations and the quality of their generated scores. The
Machine learning
Researchers at the University of Mosul have found that a machine learning-based approach can effectively identify and mitigate real-time threats in Internet of Things (IoT) systems. The study, published in the Journal of Engineering, suggests that the use of ensemble algorithms, particularly Random Forest, can significantly improve the accuracy and
Machine learning
Recent research published in IET Circuits, Devices and Systems has shed light on the performance and reliability of various D flip-flop (DFF) architectures, with significant implications for the development of internet of things (IoT) devices and artificial intelligence (AI) systems. The study, conducted by researchers at the School of Engineering,
Machine learning
Researchers at Xidian University in Guangzhou, China, have developed a novel DDoS defense mechanism, CRH4DDoS, which employs conflict resolution strategies for the integration of heterogeneous data. This approach addresses the challenges associated with existing machine learning-based approaches, including the need for pre-existing databases of DDoS incidents and performance limitations due
Machine learning
Samsung Electronics Co. Ltd. has been awarded a patent for its novel robot control method, which enhances location recognition and navigation capabilities in robots. This achievement demonstrates the company's commitment to advancing robotics and artificial intelligence. The proposed method involves a robot with a spherical shape, equipped with
Machine learning
MIT researchers have created an artificial intelligence-powered tool that enables medical researchers to rapidly segment new biomedical imaging datasets by clicking, scribbling, and drawing boxes on the images. This new AI model, called MultiverSeg, uses user interactions to predict segmentation and can accurately segment each new image without user input.
Machine learning
Researchers at the American University of Beirut have explored the feasibility of using machine learning on speech transcripts to detect Autism Spectrum Disorder (ASD) in children, aiming to improve developmental outcomes through early detection. The study's findings emphasize the potential of computational linguistics in advancing non-invasive, ethical approaches
Machine learning
A new study presented at the 129th annual meeting of the American Academy of Ophthalmology has shown that a machine learning algorithm significantly outperformed human graders at diagnosing glaucoma, with an accuracy rate of 88-90% compared to 79-81% for human graders. The study suggests that AI-powered screening could be a
Machine learning
Researchers from Anhui University of Chinese Medicine have developed a novel AI-powered prediction model for osteoporosis, a metabolic bone disease affecting millions worldwide. The model, constructed using machine learning and SHAP techniques, demonstrates potential clinical utility in improving the accuracy of clinical diagnosis and treatment experience for older adults. The
Machine learning
Researchers at Guangxi Medical University Cancer Hospital in Nanning, People's Republic of China, have developed a new machine learning-based predictive model for rapidly progressive nasopharyngeal carcinoma (RP-NPC). According to the study, the model demonstrated superior predictive capability and enhanced generalizability over conventional TNM staging in identifying RP-NPC. The
Machine learning
Researchers at the University of Girona, Spain have developed an innovative approach to enhance microbial water quality forecasting by combining accurate gradient-boosted decision trees (GBDT) predictions with well-calibrated uncertainty estimates. This study, funded by UdG, Spanish Government, and the Ministry of Science & Innovation, benchmarked state-of-the-art regression algorithms and uncertainty
