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.
This research investigates the determinants of earthquake insurance uptake in Oklahoma post-2011 seismic events. Through supervised machine learning, it identifies influential factors including age, gender, ethnicity, political affiliation, tenure, housing status, education, income, earthquake experience, and environmental attitudes. The study emphasizes the significance of awareness and advanced machine learning tools for predictive modeling in managing environmental risks and advocates for informed disaster management strategies.
Utilizing machine learning, a PLOS One study delves into the correlation between Japanese TV drama success and various metadata, including facial features extracted from posters. Analyzing 800 dramas from 2003 to 2020, the study reveals the impact of factors like genre, cast, and broadcast details on ratings, emphasizing the unexpected significance of facial information in predicting success.
Employing AI and ML, this study analyzed elite junior female tennis players' game statistics to predict tournament outcomes and understand career trajectories. While accurately forecasting junior tournament results, predicting future careers faced challenges, emphasizing the role of non-game factors and junior tournament participation in shaping successful careers. The study recommends refining models, emphasizing serve improvement, and supporting young talents through international tournaments for a nuanced understanding of tennis dynamics and enhanced training programs.
Researchers present a novel approach combining hybrid feature selection and ensemble-based machine learning for robust botnet detection. Using diverse datasets and synthetic techniques, the model, particularly employing the extra-trees ensemble approach, showcases exceptional accuracy, precision, and recall, establishing its reliability and effectiveness in identifying botnets.
This research delves into the synergy of Artificial Intelligence (AI) and Internet of Things (IoT) security. The study evaluates and compares various AI algorithms, including machine learning (ML) and deep learning (DL), for classifying and detecting IoT attacks. It introduces a novel taxonomy of AI methodologies for IoT security and identifies LSTM as the top-performing algorithm, emphasizing its potential applications in diverse fields.
This paper introduces MLpronto, a user-friendly machine learning platform aimed at democratizing the field by providing accessibility without requiring programming skills. This web-based tool swiftly processes data, executes prevalent supervised machine learning algorithms, and generates corresponding programming code, catering to both novice users and those inclined towards programming.
Researchers pioneer individual welfare assessment for gestating sows using machine learning and behavioral data. Clustering behavioral patterns and employing a decision tree for classification, the study achieves an 80% accuracy in categorizing sows into welfare clusters, emphasizing the potential for automated decision support systems in livestock management. The innovative approach addresses gaps in individual welfare assessment, showcasing adaptability to real-time farm data for proactive animal welfare management.
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.
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