Artificial Intelligence (AI) refers to the simulation of human intelligence processes by machines, especially computer systems. These processes include learning (the acquisition of information and rules for using the information), reasoning (using rules to reach approximate or definite conclusions), and self-correction.
Researchers integrated gradient quantization (GQ) into DenseNet architecture to improve image recognition (IR). By optimizing feature reuse and introducing GQ for parallel training, they achieved superior accuracy and accelerated training speed, overcoming communication bottlenecks.
Researchers conducted a noise audit on human-labeled benchmarks for machine commonsense reasoning (CSR), revealing significant levels of noise across different experimental conditions and datasets. The study emphasized the impact of noise on performance estimates of CSR systems, challenging the reliance on single ground truths in AI benchmarking practices and advocating for more nuanced evaluation methodologies.
This review explores the critical role of image-processing technologies in structural health monitoring (SHM) for civil infrastructures. It highlights the integration of artificial intelligence (AI) and machine learning (ML) to enhance SHM automation and accuracy. Various imaging modalities, including drones, thermography, LiDAR, and satellite imagery, are discussed for damage detection, crack identification, and deformation monitoring.
Researchers developed a hybrid classification model to autonomously extract crucial information from legal documents, achieving superior accuracy in comparison to baseline models. Leveraging GPT-3.5 and employing prompt strategies, the model demonstrated efficiency in extracting key information from murder verdicts, offering a promising tool to enhance investigative workflows and decision-making in criminal investigations.
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.
Researchers introduced OCTDL, an open-access dataset comprising over 2000 labeled OCT images of retinal diseases, including AMD, DME, and others. Utilizing high-resolution OCT scans obtained from an Optovue Avanti RTVue XR system, the dataset facilitated the development of deep learning models for disease classification. Validation with VGG16 and ResNet50 architectures demonstrated high performance, indicating OCTDL's potential for advancing automatic processing and early disease detection in ophthalmology.
Researchers proposed a novel approach utilizing a modified Manhattan grid topology to address challenges in vehicular communication technology, focusing on India's dynamic traffic conditions. Their methodology integrated the fuzzy c-means (FCM) algorithm for jamming attacker detection, the modified fisheye state routing (MFSR) algorithm for efficient data exchange, and the particle swarm optimization (PSO) algorithm for precise attacker localization.
Researchers developed a novel AI method, P-GAN, to improve the visualization of retinal pigment epithelial (RPE) cells using adaptive optics optical coherence tomography (AO-OCT). By transforming single noisy images into detailed representations of RPE cells, this approach enhances contrast and reduces imaging time, potentially revolutionizing ophthalmic diagnostics and personalized treatment strategies for retinal conditions.
Researchers leverage deep-learning AI models and Sentinel-2 satellite data to map and monitor threatened Posidonia oceanica (P. oceanica) meadows in the Mediterranean. Their operational pipeline, including data acquisition, DL neural networks, and post-processing, achieves high accuracy. This approach supports conservation efforts, aiding long-term monitoring and informing decisions for seagrass preservation amid climate change.
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 introduced the NDWPSO algorithm, enhancing PSO's performance for engineering optimization. Integrating various strategies like dynamic inertia weight, adaptive acceleration coefficients, and innovative initialization schemes, NDWPSO demonstrated superior convergence speed and solution accuracy.
Researchers evaluated 13 machine learning models to forecast compressive strength in preplaced aggregate concrete. Extreme gradient boosting (XGBoost) emerged as the most accurate, with sensitivity and SHAP analyses highlighting crucial factors like gravel and water-to-binder ratio.
Researchers introduced two novel predictive models employing metaheuristic algorithms, Backtracking Search Algorithm (BSA) and Equilibrium Optimizer (EO), combined with artificial neural networks (ANNs) to assess the bearing capacity of footings on two-layered soil masses. Both BSA-ANN and EO-ANN models demonstrated improved prediction accuracy over conventional ANN models, with EO exhibiting superior performance.
Researchers introduced an Improved Bacterial Foraging Optimization Algorithm (IBFO-A) to enhance Dynamic Bayesian Network (DBN) structure learning, addressing issues of search space complexity and reduced accuracy. The proposed IBFO-D method combined dynamic K2 scoring, V-structure orientation, and elimination-dispersal strategies, showcasing improved efficiency, accuracy, and stability in engineering applications.
Researchers explored the integration of artificial intelligence (AI) and machine learning (ML) in two-phase heat transfer research, focusing on boiling and condensation phenomena. AI was utilized for meta-analysis, physical feature extraction, and data stream analysis, offering new insights and solutions to predict multi-phase flow patterns. Interdisciplinary collaboration and sustainable cyberinfrastructures were emphasized for future advancements in thermal management systems and energy conversion devices.
Researchers present an autonomous electrochemical platform for investigating molecular electrochemistry mechanisms. Utilizing artificial intelligence, the platform autonomously identifies electrochemical mechanisms, designs experimental conditions, and extracts kinetic information.
Researchers employed AI techniques to analyze Reddit discussions on coronary artery calcium (CAC) testing, revealing diverse sentiments and concerns. The study identified 91 topics and 14 discussion clusters, indicating significant interest and engagement. While sentiment analysis showed predominantly neutral or slightly negative attitudes, there was a decline in sentiment over time.
Researchers propose an AI-driven approach for predicting and managing water quality, crucial for environmental sustainability. Utilizing explainable AI models, they showcase the significance of transparent decision-making in classifying drinkable water, emphasizing the potential of their methodology for real-time monitoring and proactive risk mitigation in water management practices.
Researchers leverage AI and earth observation techniques to predict citizen perceptions of deprivation in Nairobi's slums. Combining satellite imagery and citizen science, their methodology accurately forecasts deprivation, offering policymakers invaluable insights for targeted interventions aligned with Sustainable Development Goal 11, potentially benefiting millions worldwide.
Researchers advocate for a user-centric evaluation framework for healthcare chatbots, emphasizing trust-building, empathy, and language processing. Their proposed metrics aim to enhance patient care by assessing chatbots' performance comprehensively, addressing challenges and promoting reliability in healthcare AI systems.
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