Machine learning is a subfield of artificial intelligence that focuses on developing algorithms and models capable of automatically learning and making predictions or decisions from data without being explicitly programmed. It involves training models on labeled datasets to recognize patterns and make accurate predictions or classifications in new, unseen data.
Researchers introduce the A-Lab, an autonomous laboratory integrating AI, robotics, and historical data to synthesize 41 new compounds from 58 targets over 17 days. With a 71% success rate, the study underscores the impact of active learning, computational insights, and refined synthesis strategies in advancing materials discovery. The A-Lab's innovative approach advocates for the fusion of technology and experimental endeavors, marking a significant step towards autonomous materials research and development.
Researchers, leveraging DeepMind's GNoME, showcase AI's potential in accelerating the discovery of functional materials. The synergy of advanced graph networks and autonomous lab robots, exemplified at Lawrence Berkeley National Lab, yields 381,000 viable materials for energy solutions. The paradigm shift combines AI's scalability with adaptive experimentation, promising groundbreaking advances in materials science, energy, and sustainability.
Researchers unveil a pioneering method for accurately estimating food weight using advanced boosting regression algorithms trained on a vast Mediterranean cuisine image dataset. Achieving remarkable accuracy with a mean weight absolute error of 3.93 g, this innovative approach addresses challenges in dietary monitoring and offers a promising solution for diverse food types and shapes.
A groundbreaking study from Kyoto Prefectural University of Medicine introduces an advanced AI system leveraging deep neural networks and CT scans to objectively and accurately determine the biological sex of deceased individuals based on skull morphology. Outperforming human experts, this innovative approach promises to enhance forensic identification accuracy, addressing challenges in reliability and objectivity within traditional methods.
This paper demonstrates the efficacy of advanced machine learning techniques in accurately estimating crucial water distribution uniformity metrics for efficient sprinkler system analysis, design, and evaluation. The study explores the intersection of hydraulic parameters, meteorological influences, and machine learning models to optimize sprinkler uniformity, providing valuable insights for precision irrigation management.
Researchers propose leveraging a Quality Management System (QMS) tailored to healthcare AI as a systematic solution to bridge the translation gap from research to clinical application. The QMS, aligned with ISO 13485 and risk-based approaches, addresses key components enabling healthcare organizations to navigate regulatory complexities, minimize redundancy, and optimize the ethical deployment of AI in patient care.
Researchers propose a groundbreaking framework, PGL, for autonomous and programmable graph representation learning (PGL) in heterogeneous computing systems. Focused on optimizing program execution, especially in applications like autonomous vehicles and machine vision, PGL leverages machine learning to dynamically map software computations onto CPUs and GPUs.
This pioneering study investigated the accuracy of smartphone-based estimation of body composition in youth soccer players, utilizing a novel app (Mobile Fit) for digital anthropometric assessments. Researchers evaluated its validity against dual-energy X-ray absorptiometry (DXA) and developed population-specific equations for appendicular lean mass and body fat percentage estimation.
Researchers emphasize the growing significance of radar-based human activity recognition (HAR) in safety and surveillance, highlighting its advantages over vision-based sensing in challenging conditions. The study reviews classical Machine Learning (ML) and Deep Learning (DL) approaches, with DL's advantage in avoiding manual feature extraction and ML's robust empirical basis. A comparative study on benchmark datasets evaluates performance and computational efficiency, aiming to establish a standardized assessment framework for radar-based HAR techniques.
Researchers present an intelligent framework, integrating a Group Method of Data Handling (GMDH) neural network and Shapley Additive Explanations (SHAP) analysis, to predict free atmospheric corrosion in marine steel structures. Leveraging historical sensor data, the framework demonstrates high forecasting accuracy, with optimal parameter selection enhancing performance. The SHAP analysis reveals the impact of environmental factors on corrosion, providing valuable insights into the dynamics of atmospheric corrosion in marine settings.
This study unveils a groundbreaking dataset of over 1.3 million solar magnetogram images paired with solar flare records. Spanning two solar cycles, the dataset from NASA's Solar Dynamics Observatory facilitates advanced studies in solar physics and space weather prediction. The innovative approach, integrating multi-source information and applying machine learning models, showcases the dataset's potential for improving our understanding of solar phenomena and paving the way for highly accurate automated solar flare forecasting systems.
This research delves into the realm of electronic board manufacturing, aiming to enhance reliability and lifespan through the automated detection of solder splashes using cutting-edge machine learning algorithms. The study meticulously compares object detection models, emphasizing the efficacy of the custom-trained YOLOv8n model with 1.9 million parameters, showcasing a rapid 90 ms detection speed and an impressive mean average precision of 96.6%. The findings underscore the potential for increased efficiency and cost savings in electronic board manufacturing, marking a significant shift from manual inspection to advanced machine learning techniques.
Researchers introduce artificial neural networks (ANNs) as a powerful tool for forecasting the generation of ten common types of demolition waste from buildings. Analyzing data from 150 buildings in South Korean redevelopment zones, the research develops specialized ANN prediction models for each waste category, achieving significant performance gains over past studies.
This article introduces a novel machine learning approach for non-invasive broiler weight estimation in large-scale production. Utilizing Gaussian mixture models, Isolation Forest, and OPTICS algorithm in a two-stage clustering process, the researchers achieved accurate predictions of individual broiler weights. The comprehensive methodology, combining polynomial fitting, gray models, and adaptive forecasting, offers a promising and cost-effective solution for precise broiler weight monitoring in large-scale farming setups, as evidenced by considerable accuracy in evaluations across 111 datasets.
This article explores the algorithmic foundations and applications of autoencoders in molecular informatics and drug discovery, with a focus on their role in data-driven molecular representation and constructive molecular design. The study highlights the versatility of autoencoders, especially variational autoencoders (VAEs), in handling diverse molecular data types and their applications in tasks such as dimensionality reduction, preprocessing, and generative molecular design.
Researchers introduce machine learning (ML) models for predicting the bulk modulus in High Entropy Alloys (HEA), a crucial property for aerospace and high-pressure applications. The Gradient Boosting Classifier (GBC) excels in HEA classification, while the LASSO Regression model predicts bulk modulus values, accelerating the discovery and design of HEAs with superior mechanical traits. This pioneering study addresses a significant gap in HEA research and offers a pathway for optimized alloy compositions in diverse applications.
Researchers introduce an innovative approach for speech-emotion analysis employing a multi-stage process involving spectro-temporal modulation, entropy features, convolutional neural networks, and a combined GC-ECOC classification model. Evaluating against Berlin and ShEMO datasets, the method showcases remarkable performance, achieving average accuracies of 93.33% and 85.73%, respectively, surpassing existing methods by at least 2.1% in accuracy and showing significant potential for improved emotion recognition in speech across various applications.
Researchers propose a novel deep learning (DL) method utilizing convolutional neural networks (CNNs) for automatic sediment core analysis. The DL-based approach employs semantic segmentation on digital images of sediment cores, demonstrating high accuracy in interpreting sedimentary facies, offering a precise, efficient tool for subsurface stratigraphic modeling in geoscience applications.
Researchers introduced and evaluated four metaheuristic algorithms—teaching–learning-based optimization, sine cosine algorithm, water cycle algorithm, and electromagnetic field optimization—integrated with a multi-layer perceptron neural network for predicting dissolved oxygen concentration in the Klamath River. These algorithms optimized computational variables, improving DO prediction accuracy in water quality assessment.
This study conducts a systematic literature review to categorize critiques and challenges of the proposed European Artificial Intelligence Act (AIA). As AI governance becomes crucial, the AIA aims to regulate AI development and deployment, considering potential harms. The interdisciplinary Information Systems (IS) field's attention to societal AI dimensions highlights the need for a thorough analysis of the AIA, guiding responsible innovation amidst rapid advancements.
Terms
While we only use edited and approved content for Azthena
answers, it may on occasions provide incorrect responses.
Please confirm any data provided with the related suppliers or
authors. We do not provide medical advice, if you search for
medical information you must always consult a medical
professional before acting on any information provided.
Your questions, but not your email details will be shared with
OpenAI and retained for 30 days in accordance with their
privacy principles.
Please do not ask questions that use sensitive or confidential
information.
Read the full Terms & Conditions.