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 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.
This paper explores the profound impact of artificial intelligence (AI) on art history, showcasing how algorithms decode intricate details in art compositions. The study reveals AI's role in analyzing poses, color palettes, brushwork, and perspectives, contributing to the understanding of artists' use of optical science. Additionally, AI aids in art restoration, uncovering hidden layers, reconstructing missing elements, and disproving theories.
A recent article in Nature Machine Intelligence delves into the progress and challenges of Differentiable Visual Computing (DVC). The study proposes a unified DVC pipeline, integrating differentiable geometry, physics, and animation, enhancing data efficiency, accuracy, and speed in machine learning applications for real-world physical systems. The authors review key aspects, including rendering, animation, and geometry, highlighting the potential of DVC to bridge the gap between visual computing and deep learning.
Researchers delve into the challenges of lifelong learning in AI, proposing specialized hardware accelerators for edge platforms. The study explores intricacies in design, outlines crucial features, and suggests metrics for evaluating these accelerators, emphasizing the co-evolution of models and hardware. The future vision involves reconfigurable architectures, innovative memory designs, and advancements in on-chip communication, calling for a holistic hardware-software co-design approach to enable efficient, adaptable, and robust lifelong learning systems in edge AI.
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 article presents an ensemble learning approach utilizing convolutional neural networks (CNNs) for precise identification of medicinal plant species based solely on leaf images. The research addresses the challenges of manual identification by taxonomic experts and demonstrates how advanced AI techniques can significantly enhance the efficiency, reliability, and accessibility of plant recognition systems, showcasing potential applications in cataloging and utilizing medicinal plant biodiversity.
This study presents an innovative method for predicting individual Chinese cabbage harvest weight using unmanned aerial vehicles (UAVs) and multi-temporal features. By automating plant detection with an object detection algorithm and leveraging various UAV data sources, the study achieves accurate and early predictions, addressing limitations in existing methods and offering valuable insights for precision agriculture and crop management.
This article explores the expanding role of artificial intelligence (AI) in scientific research, focusing on its creative ability in hypothesis generation and collaborative efforts with human researchers. AI, particularly large language models (LLMs), aids in proposing hypotheses, identifying blind spots, and collaborating on broad hypotheses, showcasing its potential in various fields like chemistry, biology, and materials science.
DeepMind's GraphCast model, featured in Nature, emerges as a groundbreaking innovation in weather forecasting. Outperforming traditional and AI-based methods, GraphCast provides highly accurate global weather predictions within minutes, showcasing the potential of machine learning to transform and enhance the efficiency of this critical scientific field.
Researchers investigate the application of the deterministic quantum computing with one qubit (DQC1) model in supervised machine learning. By exploring quantum discord and coherence, the study on IBM hardware demonstrates DQC1's efficiency in estimating complex kernel functions, offering potential advancements in quantum machine learning despite challenges related to hardware noise and coherence consumption.
This paper presents a novel approach for automatically counting manatees within a region using deep learning, even when provided with low-quality images. Manatees, being slow-moving aquatic mammals often found in aggregations in shallow waters, pose challenges such as water surface reflections, occlusion, and camouflage.
This study delves into the discrepancies within Scope 3 emissions data reported by major providers like Bloomberg, Refinitiv Eikon, and ISS. Analyzing divergence, data composition, and predictive accuracy through statistical and machine learning techniques, the research unveils substantial inconsistencies, incomplete reporting, and predictive challenges, emphasizing the urgent need for standardized disclosure and awareness among investors.
A groundbreaking machine learning weather prediction (MLWP) approach revolutionizing global medium-range weather forecasting. Unlike traditional numerical weather prediction systems, GraphCast leverages machine learning directly from reanalysis data, achieving unparalleled speed and accuracy in 10-day forecasts. With superior performance in severe weather event prediction, GraphCast signifies a crucial stride in precise and efficient weather forecasting, showcasing the potential of machine learning in modeling intricate dynamical systems.
Researchers introduced a paradigm-shifting approach to neuromorphic computing by showcasing the reconfigurability of physical reservoir computers (PRC). Leveraging the unique properties of chiral magnets, particularly the controlled nucleation of metastable skyrmions through magnetic field manipulation, the research demonstrated on-demand reconfiguration of reservoir properties, paving the way for energy-efficient and task-adaptive computing systems.
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