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.
Chinese researchers propose a novel approach combining hierarchical reinforcement learning with experience decomposition to boost decision-making efficiency for multiple unmanned aerial vehicles (UAVs) in air combat. Tested and validated using simulation platforms, their method outperforms baseline algorithms, showcasing superior win rates, convergence speed, and stability, promising advancements in UAV-based combat decision-making technologies.
Researchers introduce V-JEPA, a novel family of vision models trained solely on feature prediction from video data, without any supervision. Leveraging modern tools and techniques, V-JEPA demonstrates superior performance on downstream tasks, showcasing the effectiveness of feature prediction as a standalone objective for unsupervised learning in visual representation.
Researchers introduce a hierarchical federated learning framework tailored for large-scale AIoT systems in smart cities. By integrating cloud, edge, and fog computing layers and leveraging the MQTT protocol, the framework addresses data privacy and communication latency challenges, demonstrating enhanced scalability and efficiency. Experimental validation in Docker environments confirms the framework's feasibility and performance improvements, laying the foundation for future optimizations.
Researchers investigate ChatGPT ADA, an extension of GPT-4, for developing ML models in clinical data analysis, showing comparable performance to manual methods. With transparent methodologies and robust performance across diverse clinical trials, ChatGPT ADA presents a promising tool for democratizing ML in medicine, emphasizing its potential alongside specialized training and resources.
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.
Scientists develop a reprogrammable light-based processor to advance quantum computing, promising faster computations, secure communications, and environmental and healthcare monitoring enhancements.
AI predicts energy expenses from passive design, offering a tool for reducing the energy burden on low-income households and advancing energy justice.
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 introduce programmable crack array within micro-crumples (PCAM) sensors, leveraging computational design for soft robots. These sensors exhibit robustness and tunability, enabling accurate trajectory prediction and terrain awareness in real-world scenarios, thus addressing key challenges in soft robotics automation. Integrated into an origami robot with machine intelligence, PCAM sensors signify a milestone in bridging predictive design complexities and practical implementation for enhanced soft robot performance in dynamic environments.
Researchers propose a novel approach combining web mining and machine learning (ML) techniques to classify learning objects effectively in e-learning systems, aiming to maximize their reusability. By employing advanced ML algorithms and web mining methods, the study demonstrates significant improvements in resource discovery and knowledge dissemination, ultimately enhancing the efficiency of e-learning environments.
Researchers present a hybrid recommendation system for virtual learning environments, employing bi-directional long short-term memory (BiLSTM) networks to capture users' evolving interests. Achieving remarkable accuracy and low loss, the system outperforms existing methods by integrating attention mechanisms and compression algorithms, offering personalized resource suggestions based on both short-term and long-term user behaviors.
Researchers propose an energy management scheme (EMS) using a rule-based grasshopper optimization algorithm (RB-GOA) to efficiently manage solar-powered battery-ultracapacitor (UC) hybrid systems, addressing output fluctuations and maximizing energy extraction. The RB-GOA EMS outperforms other swarm intelligence techniques in minimizing oscillations, reducing power surges, and ensuring stable output across varying load demands, demonstrating its effectiveness in optimizing solar energy utilization.
This study harnesses the CatBoost algorithm to predict transition temperatures (Tc) of superconducting materials, addressing challenges in dataset refinement and feature selection. Leveraging the Jabir and Soraya packages for generating atomic descriptors and selecting crucial features, the model achieved high accuracy with an R-squared (R2) of 0.952 and root mean square error (RMSE) of 6.45 K. Additionally, a novel web application for Tc prediction underscores the impactful synergy between AI and materials science.
Researchers investigated the feasibility of using machine learning (ML) models to predict the punching shear capacity of post-tensioned ultra-high-performance concrete (UHPC) flat slabs. By proposing correction factors based on finite element method-artificial intelligence (FEM-AI/ML) techniques, they extended the validity of punching shear capacity provisions in design codes like EC2 and ACI-318 to include PT-UHPC flat slabs.
The UK’s first master’s degree course focused on applying skills in AI to engineering and design is to begin this year at the University of Bath.
Researchers propose a novel approach for few-shot semantic segmentation, leveraging an ensemble of visual features learned from pre-trained classification and semantic segmentation networks. Their method utilizes a two-pass strategy, employing transductive meta-learning to improve prediction accuracy and mitigate false positives. Experimental results demonstrate significant performance improvements, achieving state-of-the-art results on benchmark datasets with minimal trainable parameters.
Researchers utilized various machine learning algorithms to develop predictive models for identifying students at risk of dropping out of secondary and higher education in Mexico. Leveraging demographic, socioeconomic, and educational data, the study demonstrated the effectiveness of artificial neural networks (ANN) in achieving high reliability (99%) in predicting school dropout, highlighting key variables such as school attendance, type, location, occupation, income, and marital status.
Researchers proposed a novel intrusion detection system (IDS) leveraging ensemble learning and deep neural networks (DNNs) to combat botnet attacks on Internet of Things (IoT) devices. By training device-specific DNN models on heterogeneous IoT data and aggregating predictions through ensemble averaging, the system achieved remarkable accuracy and effectively detected botnet activities. The study's structured methodology, comprehensive evaluation metrics, and ensemble approach offer promise in bolstering IoT security against evolving cyber threats.
This paper addresses the diagnostic challenges of distinguishing between Parkinson’s disease (PD) and essential tremor (ET) by proposing a Gaussian mixture models (GMMs) method for speech assessment. By adapting speech analysis technology to Czech and employing machine learning techniques, the study demonstrates promising accuracy in classifying PD and ET patients, highlighting the potential of automated speech analysis as a robust diagnostic tool for movement disorders.
The article discusses the application of autoencoder neural networks in archaeometry, specifically in reducing the dimensions of X-ray fluorescence spectra for analyzing cultural heritage objects. Researchers utilized autoencoders to compress data and extract essential features, facilitating efficient analysis of elemental composition in painted materials. Results demonstrated the effectiveness of this approach in attributing paintings to different creation periods based on pigment composition, highlighting its potential for automating and enhancing archaeological analyses.
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.