Deep Learning is a subset of machine learning that uses artificial neural networks with multiple layers (hence "deep") to model and understand complex patterns in datasets. It's particularly effective for tasks like image and speech recognition, natural language processing, and translation, and it's the technology behind many advanced AI systems.
This article discusses the need for regulatory oversight of large language models (LLMs)/generative artificial intelligence (AI) in healthcare. LLMs can be implemented in healthcare settings to summarize research papers, obtain insurance pre-authorization, and facilitate clinical documentation. LLMs can also improve research equity and scientific writing, improve personalized learning in medical education, streamline the healthcare workflow, work as a chatbot to answer patient queries and address their concerns, and assist physicians to diagnose conditions based on laboratory results and medical records.
This article reviews the transformative impact of artificial intelligence (AI) techniques such as deep learning and machine learning in the field of superconductivity. From condition monitoring and design optimization to intelligent modeling and estimation, AI offers innovative solutions to overcome challenges, accelerate commercialization, and unlock new opportunities in the realm of superconducting technologies and materials.
Researchers propose DLIPHE, a novel algorithm that combines deep learning and image processing, to estimate building heights using static Google Street View images. The algorithm employs semantic segmentation and advanced techniques to identify buildings and extract their contours, enabling real-time and automatic height estimation for aerial devices. The study demonstrates promising results, highlighting the potential for DLIPHE to enhance communication paths for unmanned aerial vehicles (UAVs) and electric vertical take-off and landing aircraft (eVTOLs) in future urban networks.
Researchers present a thorough analysis of machine learning (ML) methods for detecting Android malware, highlighting the escalating threat to mobile device security. This review article explores the effectiveness of diverse ML algorithms, emphasizing the importance of dataset selection and evaluation metrics, while also identifying limitations and proposing avenues for future research in this critical domain.
Researchers from CASUS and Sandia National Laboratories have introduced Materials Learning Algorithms (MALA), a groundbreaking software stack that employs machine learning to simulate electronic structures of materials. MALA surpasses traditional methods, providing high fidelity and scalability across various length scales, opening doors to advancements in drug design, energy storage, and more.
Researchers from New York University, Columbia Engineering, and the New York Genome Center have developed an artificial intelligence model, called TIGER, that combines deep learning with CRISPR screens to predict the on- and off-target activity of RNA-targeting CRISPR tools.
This groundbreaking study explores the transformative potential of artificial intelligence, machine learning, deep learning, and big data in revolutionizing the field of superconductivity. The integration of these cutting-edge technologies promises to enhance the development, production, operation, fault identification, and condition monitoring of superconducting devices and systems.
The study proposes a smart system for monitoring and detecting anomalies in IoT devices by leveraging federated learning and machine learning techniques. The system analyzes system call traces to detect intrusions, achieving high accuracy in classifying benign and malicious samples while ensuring data privacy. Future research directions include incorporating deep learning techniques, implementing multi-class classification, and adapting the system to handle the scale and complexity of IoT deployments.
The study explores the use of large language models (LLMs), specifically ChatGPT, to generate important questions in plant science. ChatGPT successfully generated relevant questions, highlighting the importance of sustainable products, plant-environment interactions, plant mechanisms, and enhanced plant traits. While ChatGPT overlooked certain aspects emphasized by researchers, it demonstrated its potential as a supportive tool in plant science research.
Researchers introduce a speech emotion recognition (SER) system that accurately predicts a speaker's emotional state using audio signals. By employing convolutional neural networks (CNN) and Mel-frequency cepstral coefficients (MFCC) for feature extraction, the proposed system outperforms existing approaches, showcasing its potential in various applications such as human-computer interaction and emotion-aware technologies.
Researchers have developed the PETAL sensor patch, a paper-like wearable device that incorporates five colorimetric sensors for comprehensive wound monitoring. With the aid of artificial intelligence and deep learning algorithms, the patch accurately classifies wound healing status, providing early warning for timely intervention and enhancing wound care management.
Engineers at Rice University and the University of Maryland have developed NeuWS, a full-motion video technology capable of seeing through scattering media like fog, smoke, and even body tissues. The technology uses a combination of neural networks and complex wavefront shaping techniques to rapidly measure and correct for light scattering, overcoming a significant challenge in optical imaging.
Large-scale multi-label dataset, Incidents1M, is introduced for incident detection and image-filtering experiments on social media. It enables timely understanding of natural disaster progression and aftermath using automated methods.
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