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 explores the revolutionary impact of AI and ML in biomedical research and healthcare, emphasizing the need for responsible and equitable integration. Addressing challenges in governance, infrastructure, and international collaboration, it advocates for a holistic approach to harness AI's transformative potential while prioritizing inclusivity and ethical considerations in shaping the future of healthcare.
Researchers from the University of Tuscia, Italy, introduced a machine learning (ML)-based classification model to offer tailored support tools and learning strategies for university students with dyslexia. The model, trained on a self-evaluation questionnaire from over 1200 dyslexic students, demonstrated high accuracy in predicting effective methodologies, providing a personalized approach to enhance learning outcomes and well-being. The study emphasizes the potential applications in education, psychology, and tool/strategy development, encouraging future research directions and student involvement in the design process.
This paper explores the dynamic integration of artificial intelligence/machine learning (AI/ML) in biomedical research, emphasizing its pivotal role in predictive analysis across diverse domains. While acknowledging transformative potential, the paper highlights challenges such as inclusivity, synergy between computational models and human expertise, and standardization of clinical data, presenting them as opportunities for innovation in a transformative era for human health optimization through AI/ML in biomedical research.
This paper presents an extensive dataset of approximately 11,000 stomatal images from temperate hardwood trees, covering 17 common species and 55 genotypes. The dataset, validated for accuracy and machine learning model training, enables high-throughput analysis of stomatal characteristics, exploration of diversity among tree types, and the creation of new indices for stomatal measurements, offering valuable insights for ecologists, plant biologists, and ecophysiologists.
Researchers question the notion of artificial intelligence (AI) surpassing human thought. It critiques Max Tegmark's definition of intelligence, highlighting the differences in understanding, implementation of goals, and the crucial role of creativity. The discussion extends to philosophical implications, emphasizing the overlooked aspects of the body, brain lateralization, and the vital role of glia cells, ultimately contending that human thought's richness and complexity remain beyond current AI capabilities.
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.
Terms
While we only use edited and approved content for Azthena
answers, it may on occasions provide incorrect responses.
Please confirm any data provided with the related suppliers or
authors. We do not provide medical advice, if you search for
medical information you must always consult a medical
professional before acting on any information provided.
Your questions, but not your email details will be shared with
OpenAI and retained for 30 days in accordance with their
privacy principles.
Please do not ask questions that use sensitive or confidential
information.
Read the full Terms & Conditions.