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 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 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.
Study by Global Fishing Watch and partners, using machine learning and satellite imagery, reveals 75% of the world's industrial fishing vessels are untracked, highlighting extensive "dark" ocean activity, including in Africa and South Asia.
Researchers present ML-SEISMIC, a groundbreaking physics-informed neural network (PINN) named ML-SEISMIC, revolutionizing stress field estimation in Australia. The method autonomously integrates sparse stress orientation data with an elastic model, showcasing its potential for comprehensive stress and displacement field predictions, with implications for geological applications, including earthquake modeling, energy production, and environmental assessments.
Researchers focus on improving pedestrian safety within intelligent cities using AI, specifically support vector machine (SVM). Leveraging machine learning and authentic pedestrian behavior data, the SVM model outperforms others in predicting crossing probabilities and speeds, demonstrating its potential for enhancing road traffic safety and integrating with intelligent traffic simulations. The study emphasizes the significance of SVM in accurately predicting real-time pedestrian behaviors, contributing to refined decision models for safer road designs.
Researchers propose an AI-powered robotic crop farm, Agrorobotix, utilizing deep reinforcement learning (DRL) for enhanced urban agriculture. Tested in simulated conditions, Agrorobotix showcased a 16.3% increase in crop yield, 21.7% reduced water usage, and a 33% decline in chemical usage compared to conventional methods, highlighting its potential to transform urban farming, improve food security, and contribute to smart city development.
Researchers present an AI platform, Stochastic OnsagerNet (S-OnsagerNet), that autonomously learns clear thermodynamic descriptions of intricate non-equilibrium systems from microscopic trajectory observations. This innovative approach, rooted in the generalized Onsager principle, enables the interpretation of complex phenomena, showcasing its effectiveness in understanding polymer stretching dynamics and demonstrating potential applications in diverse dissipative processes like glassy systems and protein folding.
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 deep learning-based Motor Assessment Model (MAM) designed to automate General Movement Assessment (GMA) in infants, predicting the risk of cerebral palsy (CP). The MAM, utilizing 3D pose estimation and Transformer architecture, demonstrated high accuracy, sensitivity, and specificity in identifying fidgety movements, essential for CP risk assessment. With interpretability, the model aids GMA beginners and holds promise for streamlined, accessible, and early CP screening, potentially transforming video-based diagnostics for infant motor abnormalities.
This paper emphasizes the crucial role of machine learning (ML) in detecting and combating fake news amid the proliferation of misinformation on social media. The study reviews various ML techniques, including deep learning, natural language processing (NLP), ensemble learning, transfer learning, and graph-based approaches, highlighting their strengths and limitations in fake news detection. The researchers advocate for a multifaceted strategy, combining different techniques and optimizing computational strategies to address the complex challenges of identifying misinformation in the digital age.
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.
This article covers breakthroughs and innovations in natural language processing, computer vision, and data security. From addressing logical reasoning challenges with the discourse graph attention network to advancements in text classification using BERT models, lightweight mask detection in computer vision, sports analytics employing network graph theory, and data security through image steganography, the authors showcase the broad impact of AI across various domains.
Researchers advocate for employing artificial neural networks (ANNs) as "artificial physics engines" to compute complex inverse dynamics in human arm and hand movements. The study showcases ANNs' potential in enhancing assistive technologies, such as prosthetics and exoskeletons, offering a detailed, customizable, and reactive approach for more natural movement in individuals with impaired motor function.
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.
This research addresses the challenge of customer churn in the banking sector using Genetic Algorithm eXtreme Gradient Boosting (GA-XGBoost). The study emphasizes the significance of techniques like SMOTEENN in handling data imbalances and introduces the SHAP interpretation framework for model interpretability. The optimized GA-XGBoost model proves effective in predicting customer churn, offering valuable insights for proactive customer retention strategies in the dynamic banking landscape.
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 from Nanjing University of Science and Technology present a novel scheme, Spatial Variation-Dependent Verification (SVV), utilizing convolutional neural networks and textural features for handwriting identification and verification. The scheme outperforms existing methods, achieving 95.587% accuracy, providing a robust solution for secure handwriting recognition and authentication in diverse applications, including security, forensics, banking, education, and healthcare.
Researchers propose an Improved Deep Reinforcement Learning (IDRL) approach to optimize routing paths and reduce control overhead in Vehicular Ad-Hoc Networks (VANETs). The IDRL routing technique leverages deep reinforcement learning to dynamically determine routes based on transmission capacity and vehicular data, resulting in reduced latency, improved data reliability, and increased packet delivery ratio (PDR) compared to existing routing techniques.
Researchers employ deep neural networks and machine learning to predict facial landmarks and pain scores in cats using the Feline Grimace Scale. The study demonstrates advanced CNN models accurately predicting facial landmarks and an XGBoost model achieving high accuracy in discerning painful and non-painful cats. This breakthrough paves the way for an automated smartphone application, addressing the challenge of non-verbal pain assessment in felines and marking a significant advancement in veterinary care.
Researchers employed cutting-edge cloud computing and machine learning on Google Earth Engine to create a vast global land cover training dataset. This meticulous resource spans nearly four decades, encompassing diverse biogeographic regions and addressing challenges in existing global datasets. The GLanCE dataset's validation process, utilizing sophisticated machine learning techniques, ensures data accuracy while highlighting the complexities and challenges in distinguishing specific land cover categories even at a 30-meter spatial resolution.
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