AI is employed in healthcare for various applications, including medical image analysis, disease diagnosis, personalized treatment planning, and patient monitoring. It utilizes machine learning, natural language processing, and data analytics to improve diagnostic accuracy, optimize treatment outcomes, and enhance healthcare delivery, leading to more efficient and effective patient care.
A study in Computers & Graphics examined model compression methods for computer vision tasks, enabling AI techniques on resource-limited embedded systems. Researchers compared various techniques, including knowledge distillation and network pruning, highlighting their effectiveness in reducing model size and complexity while maintaining performance, crucial for applications like robotics and medical imaging.
Researchers leverage AI to optimize the design, fabrication, and performance forecasting of diffractive optical elements (DOEs). This integration accelerates innovation in optical technology, enhancing applications in imaging, sensing, and telecommunications.
A study published in Future Internet explored the use of multimodal large language models (MLLMs) for emotion recognition from videos. The researchers combined visual and acoustic data to test MLLMs in a zero-shot learning setting, finding that MLLMs excelled in recognizing emotions with intensity deviations, though they did not outperform state-of-the-art models on the Hume-Reaction benchmark.
Researchers applied multiple machine learning techniques to predict the unconfined compressive strength (UCS) of cohesive soil reconstituted with cement and lime. Gradient boosting and k-nearest neighbor models demonstrated the highest accuracy, revealing maximum dry density, consistency limits, and cement content as key factors influencing UCS, providing reliable predictions for engineering applications.
Researchers in the Journal of the Air Transport Research Society evaluated 12 large language models (LLMs) across aviation tasks, revealing varied accuracy in fact retrieval and reasoning capabilities. A survey at Beihang University explored student usage patterns, highlighting optimism for LLMs' potential in aviation while emphasizing the need for improved reliability and safety standards.
A review in Materials & Design explores how big data, machine learning (ML), and digital twin technologies enhance additive manufacturing (AM). These technologies improve AM by optimizing design, material properties, and process efficiency, offering significant advancements in quality, efficiency, and sustainability.
A review in Data & Knowledge Engineering investigates how AI enhances digital twins, highlighting improved functionalities and key research gaps. The integration of these technologies shows promise across various sectors, from healthcare to smart cities.
A systematic review in the journal Sensors analyzed 77 studies on facial and pose emotion recognition using deep learning, highlighting methods like CNNs and Vision Transformers. The review examined trends, datasets, and applications, providing insights into state-of-the-art techniques and their effectiveness in psychology, healthcare, and entertainment.
A recent article in Education Sciences addresses the impact of generative AI on higher education assessments, highlighting academic integrity concerns. Researchers propose the "against, avoid, and adopt" (AAA) principle for assessment redesign to balance AI's potential with maintaining academic standards.
Integrating blockchain with the Internet of Drones (IoD) promises enhanced security, connectivity, and efficiency in drone applications like delivery, surveillance, and rescue operations.
Researchers highlight wearable optical sensors as an emerging technology for sweat monitoring. These sensors utilize advancements in materials and structural design to convert sweat chemical data into optical signals, employing methods like colorimetry and SERS to provide non-invasive, continuous health monitoring.
A study in Heliyon introduced a machine learning-based approach for predicting defects in BLDC motors used in UAVs. Researchers compared KNN, SVM, and Bayesian network models, with SVM demonstrating superior accuracy in fault classification, highlighting its potential for improving UAV operational safety and predictive maintenance.
This study in Nature Medicine introduces MEDIC, an AI system designed to mitigate medication direction errors in pharmacies. Trained on expert-annotated data, MEDIC prioritizes precise communication of essential clinical components, reducing near-miss events and highlighting the potential of AI in enhancing pharmacy operations' accuracy and efficiency.
Researchers introduce BS-SCRM, a novel method combining blockchain and swarm intelligence for secure clustering routing in WSNs, addressing energy efficiency and security challenges. Simulation results demonstrate superior performance in network lifetime, energy consumption, and security compared to existing methods, offering promise for diverse applications from IoT to healthcare.
Researchers utilized machine learning algorithms to predict anemia prevalence among young girls in Ethiopia, analyzing data from the 2016 Ethiopian Demographic and Health Survey. The study identified socioeconomic and demographic predictors of anemia and highlighted the efficacy of advanced ML techniques, such as random forest and support vector machine, in forecasting anemia status.
Researchers in Germany introduce a Word2vec-based NLP method to automatically infer ICD-10 codes from German ophthalmology records, offering a solution to the challenges of manual coding and variable natural language. Results show high accuracy, with potential for streamlining healthcare record analysis.
Researchers developed an explainable machine learning (ML) model using NHANES data to predict high-risk metabolic dysfunction-associated steatohepatitis (MASH). Their ensemble-based XGBoost model outperformed traditional biomarkers, offering a promising tool for early identification of high-risk MASH patients.
The article explores electrode design for wearable skin devices, crucial for health monitoring and human-machine interfaces. It discusses properties like flexibility and conductivity and proposes methods like structure modification and hybrid materials. Applications range from health monitoring to therapy and human-machine interfaces, emphasizing the need for innovative electrode design to enhance device performance and integration with AI for smarter functionalities.
The paper explores human action recognition (HAR) methods, emphasizing the transition to deep learning (DL) and computer vision (CV). It discusses the evolution of techniques, including the significance of large datasets and the emergence of HARNet, a DL architecture merging recurrent and convolutional neural networks (CNN).
Researchers propose the ISATR algorithm to optimize trajectory planning and resource allocation for UAV-assisted emergency communication. By employing cellular automata for user trajectory prediction and iterative scheduling, the algorithm effectively enhances communication quality in disaster scenarios like earthquakes.
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