AI is used in data analysis to extract insights, discover patterns, and make predictions from large and complex datasets. Machine learning algorithms and statistical techniques enable automated data processing, anomaly detection, and advanced analytics, facilitating data-driven decision-making in various industries and domains.
Researchers from the UK, Germany, USA, and Canada unveiled a groundbreaking quantum-enhanced cybersecurity analytics framework using hybrid quantum machine learning algorithms. The novel approach leverages quantum computing to efficiently detect malicious domain names generated by domain generation algorithms (DGAs), showcasing superior speed, accuracy, and stability compared to traditional methods, marking a significant advancement in proactive cybersecurity analytics.
Researchers conducted an omnibus survey with 1150 participants to delve into attitudes towards occupations based on their likelihood of automation, uncovering a general discomfort with AI management. The findings, emphasizing demographic influences and unexpected correlations, contribute to a nuanced understanding of public perceptions surrounding AI, shedding light on distinctive attitudes compared to other technological innovations and advocating for a thoughtful approach to AI integration in various occupational domains.
Researchers from Iran and Turkey showcase the power of machine learning, employing artificial neural networks (ANN) and support vector regression (SVR) to analyze the optical properties of zinc titanate nanocomposite. The study compares these machine learning techniques with the conventional nonlinear regression method, revealing superior accuracy and efficiency in assessing spectroscopic ellipsometry data, offering insights into the nanocomposite's potential applications in diverse fields.
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.
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.
Researchers from the Technical University of Darmstadt delve into the interplay between different datasets and machine learning models in the realm of human risky choices. Their analysis uncovers dataset bias, particularly between online and laboratory experiments, leading to the proposal of a hybrid model that addresses increased decision noise in online datasets, shedding light on the complexities of understanding human decision-making through the combination of machine learning and theoretical reasoning.
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.
This paper delves into the transformative role of attention-based models, including transformers, graph attention networks, and generative pre-trained transformers, in revolutionizing drug development. From molecular screening to property prediction and molecular generation, these models offer precision and interpretability, promising accelerated advancements in pharmaceutical research. Despite challenges in data quality and interpretability, attention-based models are poised to reshape drug discovery, fostering breakthroughs in human health and pharmaceutical science.
This article explores the integration of artificial intelligence (AI), blockchain, and the Internet of Things (IoT) to enhance the safety of power equipment. The innovative wireless temperature monitoring system, incorporating real-time monitoring and intelligent anomaly detection, showcases the potential for proactive preventive measures, minimizing the risk of fire hazards in electric power engineering.
This article explores the rising significance of Quantum Machine Learning (QML) in reshaping the scientific landscape. With attention from tech giants like IBM and Google, QML combines quantum computing and machine learning, holding promise despite challenges. The article highlights ongoing studies, the application landscape, challenges such as quantum-classical data fusion, and the potential of quantum sensing techniques, urging a balanced focus on experimentation over solely relying on theoretical quantum speed-up claims.
This study explores the synergies between artificial intelligence (AI) and electronic skin (e-skin) systems, envisioning a transformative impact on robotics and medicine. E-skins, equipped with diverse sensors, offer a wealth of health data, and the integration of advanced machine learning techniques promises to revolutionize data analysis, optimize hardware, and propel applications from prosthetics to personalized health diagnostics.
This study introduces a Digital Twin (DT)-centered Fire Safety Management (FSM) framework for smart buildings. Harnessing technologies like AI, IoT, AR, and BIM, the framework enhances decision-making, real-time information access, and FSM efficiency. Evaluation by Facility Management professionals affirms its effectiveness, with a majority expressing confidence in its clarity, data security, and utility for fire evacuation planning and Fire Safety Equipment (FSE) maintenance.
Researchers introduced a hybrid Ridge Generative Adversarial Network (RidgeGAN) model to predict road network density in small and medium-sized Indian cities under the Integrated Development of Small and Medium Towns (IDSMT) project. Integrating City Generative Adversarial Network (CityGAN) and Kernel Ridge Regression (KRR), the model successfully generated realistic urban patterns, aiding urban planners in optimizing layouts for efficient transportation infrastructure development.
Researchers propose an innovative fault monitoring approach for high-voltage circuit breakers, utilizing a specialized device and deep learning techniques. The unsupervised deep learning method showcases over 95% accuracy in fault diagnosis, outperforming traditional algorithms in feature extraction and computation speed. The study suggests a practical and efficient solution for real-time fault monitoring, holding promise for enhancing reliability in high-voltage systems.
Researchers investigate the feasibility of using action sport cameras (ASC) for motion analysis, comparing them with marker-based motion capture systems. The study assesses accuracy through different calibration methods, revealing slight differences in marker position and angular displacement between systems. Dynamic calibration for machine vision (MV) cameras and static calibration for ASC prove most accurate, showcasing the potential of consumer-grade setups for reliable motion analysis.
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 publication analyzes challenges in the European AI Act (AIA), offering insights applicable to subsequent versions. The research, focused on the April 2021 draft, categorizes critiques into regulation, compliance, and anticipated impact themes. Notable concerns include the AIA's broad scope, ambiguous wording, unrealistic provider requirements, and potential negative effects on innovation and industry, providing valuable guidance for further AI regulation research.
Researchers introduce a pioneering framework leveraging IoT and wearable technology to enhance the adaptability of AR glasses in the aviation industry. The multi-modal data processing system, employing kernel theory-based design and machine learning, classifies performance, offering a dynamic and adaptive approach for tailored AR information provision.
This study examines the impact of different peripheral vision multiplexing configurations on augmented information detection, focusing on head-mounted displays and smart glasses. The research, involving 19 participants across three experiments, reveals that bilateral see-through setups consistently outperform unilateral configurations, offering insights for improved design in vision multiplexing technologies, especially in real-world scenarios involving mobility.
This study introduces MetaQA, a groundbreaking data search model that combines artificial intelligence (AI) techniques with metadata search to enhance the discoverability and usability of scientific data, particularly in geospatial contexts. The MetaQA system, employing advanced natural language processing and spatial-temporal search logic, significantly outperforms traditional keyword-based approaches, offering a paradigm shift in scientific data search that can accelerate research across disciplines.
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