Big Data refers to extremely large datasets that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions. It's characterized by its high volume, velocity, and variety (the "3 Vs"), and requires specific tools and methods for storage, processing, and analysis.
In their study published in the journal Smart Cities, researchers employed smart sensing and predictive analytics to address challenges in Japan's urban development and infrastructure resilience. Focusing on Setagaya, Tokyo, the research produced predictive models accurately determining critical bearing layer depths, crucial for government plans and construction risk assessments.
Researchers explore the application of AI and ML in volatility forecasting, revealing their promise in improving accuracy and informing financial decisions. The review underscores the need for further exploration in explainable AI, uncertainty quantification, and alternative data sources to advance forecasting capabilities.
Exploring the financial challenges faced by expanding enterprises like the ZH group, researchers present a strategic financial control strategy incorporating intelligent algorithms. Through practical implementation and theoretical analysis, they highlight the efficacy of reverse neural networks and particle swarm optimization in enhancing decision-making and mitigating financial risks.
Researchers detail advancements in using Random Forest (RF) models to pinpoint tax risks within real estate, providing insights into discrepancies between declared values and model judgments. By meticulously selecting indicators and leveraging historical data, the RF model emerges as a robust tool for tax risk identification, offering decision support for tax authorities and promoting improved compliance levels within the real estate industry.
This study provides an in-depth exploration of the advancements, challenges, and future prospects of digital twins in various industrial applications. It covers the theoretical frameworks, technological implementations, and practical considerations essential for understanding and leveraging digital twins effectively across different sectors.
In a study published in Scientific Reports, advanced AI techniques dissected the social media activity of 1358 VK users, unveiling correlations between behavior and personality traits. Through meticulous analysis of 753,252 posts and reposts alongside Big Five traits and intelligence assessments, the research highlighted the influence of emotional tone and engagement metrics on psychological attributes, advocating for behavior-based diagnostic models in the digital realm.
Researchers demonstrate the transformative potential of agricultural digital twins (DTs) using mandarins as a model crop, showcasing how data-driven decisions at the individual plant level can enhance precision farming, optimize resource allocation, and improve fruit quality, ultimately leading to a paradigm shift in agriculture towards individualized farming practices.
Researchers from the University of Ostrava delve into the intricate landscape of AI's societal implications, emphasizing the need for ethical regulations and democratic values alignment. Through interdisciplinary analysis and policy evaluation, they advocate for transparent, participatory AI deployment, fostering societal welfare while addressing inequalities and safeguarding human rights.
Researchers developed a comprehensive system leveraging IoT and cloud computing to monitor and predict drinking water quality in real-time. The system integrates sensors, microcontrollers, web servers, and machine learning models to collect, transmit, analyze, and predict water quality parameters. Machine learning algorithms, particularly decision trees, achieved high accuracy in predicting drinkability, demonstrating the system's potential to enhance water safety and contribute to achieving Sustainable Development Goals.
Researchers propose a Correlated Optical Convolutional Neural Network (COCNN) inspired by quantum neural networks (QCNN), aiming to overcome the limitations of existing optical neural networks (ONNs) and achieve algorithmic speed-up. COCNN introduces optical correlation to mimic quantum states' symmetry identification, demonstrating faster convergence and higher learning accuracy compared to conventional CNN models. Experimental validation shows COCNN's capability to perform quantum-inspired tasks, indicating its potential to bridge the gap between quantum and classical computing paradigms in information processing.
The Mobilise-D consortium unveils a groundbreaking protocol using IMU-based wearables for real-world mobility monitoring across clinical cohorts. Despite achieving accurate walking speed estimates, the study emphasizes context-dependent variations and charts a visionary future, envisioning wearables as integral in ubiquitous remote patient monitoring and personalized interventions, revolutionizing healthcare.
Contrary to common concerns, a study published in Nature unveils that the introduction of artificial intelligence, particularly industrial robots, has positively impacted employment in China's manufacturing sector from 2006 to 2020. The research challenges pessimistic views, highlighting increased job creation, enhanced labor productivity, and refined division of labor, with a significant positive effect on female employment, offering valuable insights for global AI employment dynamics.
Researchers employ advanced intelligent systems to analyze extensive traffic data on northern Iranian suburban roads, revolutionizing traffic state prediction. By integrating principal component analysis, genetic algorithms, and cyclic features, coupled with machine learning models like LSTM and SVM, the study achieves a significant boost in prediction accuracy and efficiency, offering valuable insights for optimizing transportation management and paving the way for advancements in traffic prediction methodologies.
Researchers presented a groundbreaking method for predicting industrial product manufacturing quality. Leveraging Synthetic Minority Oversampling Technique (SMOTE), Extreme Gradient Boosting (XGBoost), and edge computing, the active control approach tackles imbalanced data challenges in quality prediction, introducing a novel framework for flexible industrial data handling. The study's application in brake disc production showcased superior performance, with the proposed SMOTE-XGboost_t method outperforming other classifiers, demonstrating its effectiveness in real-world industrial environments.
Published in Humanities and Social Sciences Communications, this paper explores the impact of language style congruity in AI voice assistants (VAs) on user experience. By aligning VAs with utilitarian or hedonic service contexts and adapting language styles accordingly, the study reveals a congruity effect that significantly influences users' evaluations, providing valuable insights for technology providers to enhance continuous usage intention.
This groundbreaking article presents a comprehensive three-tiered approach, utilizing machine learning to assess Division-1 Women's basketball performance at the player, team, and conference levels. Achieving over 90% accuracy, the predictive models offer nuanced insights, enabling coaches to optimize training strategies and enhance overall sports performance. This multi-level, data-driven methodology signifies a significant leap in the intersection of artificial intelligence and sports analytics, paving the way for dynamic athlete development and strategic team planning.
This research paper introduces an ensemble learning model, combining extreme gradient boosting (XGBoost) and random forest (RF) algorithms, to optimize bank marketing strategies. By leveraging financial datasets, the model demonstrates superior accuracy, achieving a 91% accuracy rate and outperforming other algorithms, leading to substantial sales growth (25.67%) and increased customer satisfaction (20.52%). The study provides valuable insights for banking decision-makers seeking to enhance marketing precision and customer relationships.
In this article, researchers unveil a cutting-edge gearbox fault diagnosis method. Leveraging transfer learning and a lightweight channel attention mechanism, the proposed EfficientNetV2-LECA model showcases superior accuracy, achieving over 99% classification accuracy in both gear and bearing samples. The study signifies a pivotal leap in intelligent fault diagnosis for mechanical equipment, addressing challenges posed by limited samples and varying working conditions.
Researchers address critical forest cover shortage, utilizing Sentinel-2 satellite imagery and sophisticated algorithms. Artificial Neural Networks (ANN) and Random Forest (RF) algorithms showcase exceptional accuracy, achieving 97.75% and 96.98% overall accuracy, respectively, highlighting their potential in precise land cover classification. The study's success recommends integrating hyperspectral satellite imagery for enhanced accuracy and explores the possibilities of deep learning algorithms for further advancements in forest cover assessment.
This study introduces innovative unsupervised machine-learning techniques to analyze and interpret high-resolution global storm-resolving models (GSRMs). By leveraging variational autoencoders and vector quantization, the researchers systematically break down massive datasets, uncover spatiotemporal patterns, identify inconsistencies among GSRMs, and even project the impact of climate change on storm dynamics.
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