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Advanced ML Approach for Extracting Pairing Glue Functions from Optical Spectra

Researchers introduce the regularized recurrent inference machine (rRIM), a novel ML method integrating physical principles for extracting pairing glue functions from optical spectra in superconductivity research. The rRIM offers robustness to noise, flexibility with out-of-distribution data, and reduced data requirements, bridging gaps in understanding complex physical phenomena.

29 Apr 2024

Real-Time Epileptic Activity Detection Using Neuromorphic Computing

Researchers developed a modular spiking neural network (SNN) on a mixed-signal neuromorphic device to process intraoperative electrocorticography (ECoG) in real time, efficiently detecting interictal epileptiform discharges (IED) and high-frequency oscillations (HFO). The system, integrated into the BCI2000 framework, accurately identified IED-HFO co-occurrences, showcasing potential for automated remote detection in clinical settings.

26 Apr 2024

Image Recognition with Gradient Quantization in Dense Convolutional Networks

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.

25 Apr 2024

Real-Time Safety Helmet Detection with Improved YOLOv5 Algorithm

Researchers introduced enhancements to the YOLOv5 algorithm for real-time safety helmet detection in industrial settings. Leveraging FasterNet, Wise-IoU loss function, and CBAM attention mechanism, the algorithm achieved higher precision and reduced computational complexity. Experimental results demonstrated superior performance compared to existing models, addressing critical safety concerns and paving the way for efficient safety management systems in construction environments.

25 Apr 2024

Predicting Complex Processes with Recurrent Neural Networks

Researchers investigated the performance of recurrent neural networks (RNNs) in predicting time-series data, employing complexity-calibrated datasets to evaluate various RNN architectures. Despite LSTM showing the best performance, none of the models achieved optimal accuracy on highly non-Markovian processes.

25 Apr 2024

SCB-YOLOv5: Lightweight Model for Gymnast Movement Detection

Researchers introduced SCB-YOLOv5, integrating ShuffleNet V2 and convolutional block attention modules (CBAM) into YOLOv5 for detecting standardized gymnast movements. SCB-YOLOv5 showed enhanced precision, recall, and mean average precision (mAP), making it effective for on-site athlete action detection. Extensive experiments validated its effectiveness, highlighting its potential for practical sports education in resource-limited settings.

24 Apr 2024

Explainable ML for High-Risk Non-Alcoholic Steatohepatitis Prediction

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.

24 Apr 2024

YOLOv8-PG: Lightweight and Efficient Model for Pigeon Egg Detection

Chinese researchers present YOLOv8-PG, a lightweight convolutional neural network tailored for accurate detection of real and fake pigeon eggs in challenging environments. By refining key model components and leveraging a novel loss function, YOLOv8-PG outperforms existing models in accuracy while maintaining efficiency, offering promising applications for automated egg collection in pigeon breeding.

23 Apr 2024

Automation of Structural Health Monitoring for Civil Infrastructures Using AI and ML

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.

23 Apr 2024

Carrot Quality Assessment Using Hyperspectral Imaging and Multivariate Analysis

Through hyperspectral imaging (HSI) and multivariate analysis, researchers accurately predicted pH and carotenoid content in carrots, crucial for nutritional assessment. Models utilizing partial least squares regression (PLSR) and least squares support vector machine (LS-SVM) showed LS-SVM's superiority in pH prediction and carotenoid content, offering a promising approach for internal quality evaluation in carrots.

22 Apr 2024

Automated Detection of Epiretinal Membranes in OCT Scans

Researchers developed a deep neural network (DNN) ensemble to automatically detect and classify epiretinal membranes (ERMs) in optical coherence tomography (OCT) scans of the macula. Leveraging over 11,000 images, the ensemble achieved high accuracy, particularly in identifying small ERMs, aided by techniques like mixup for data augmentation and t-stochastic neighborhood embeddings (t-SNE) for dimensional reduction.

20 Apr 2024

Machine Learning Analysis of Urban Residential Water Consumption in Developing Countries

Researchers employed machine learning techniques to analyze residential water consumption patterns in Adama City, Ethiopia, highlighting factors influencing water usage. Findings emphasized low per capita usage rates and the need for infrastructure improvements. ML models identified key determinants and suggested targeted interventions for promoting water conservation and sustainability.

16 Apr 2024

Integrated Machine Learning for Enhancing Footing Stability Prediction in Shallow Foundations

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.

16 Apr 2024

Dynamic Bayesian Network Structure Learning with Improved Bacterial Foraging Optimization Algorithm

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.

16 Apr 2024

Emergency Communication with UAVs: ISATR Algorithm Optimization

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.

15 Apr 2024

Remote Homologous Protein Detection Using Protein Language Model Search

Researchers introduced Protein Language Model Search (PLMSearch), a method designed to improve sensitivity and accuracy in detecting remote homologous proteins. Leveraging deep representations from a pre-trained protein language model, PLMSearch effectively identifies evolutionary relationships solely based on sequence information.

12 Apr 2024

Using AI and Satellite Imagery to Predict Citizen Deprivation Perception

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.

10 Apr 2024

Innovative Modeling and Navigation for Deformable-Wheeled Robots

Researchers introduce a groundbreaking parameterized water-bomb wheel modeling method and multi-link supporting structure, enhancing precision and reducing workload in rescue robotics. Their approach integrates IMU-fused odometry and parametric kinematic solutions, achieving high-precision navigation with minimal error rates, validated in both simulation and real environments.

9 Apr 2024

Optimizing Thermoelectric Properties with Bayesian Optimization

This study harnesses Bayesian optimization (BO) to enhance the thermoelectric properties of multicomponent III–V materials. Through iterative cycles of thin-film deposition, property measurements, and BO recommendations, the research achieves notable improvements in thermoelectric performance, underscoring the effectiveness of machine learning-driven optimization.

9 Apr 2024

Enhanced Vision SLAM for Wheeled Robots

Researchers unveil IMS-VSLAM, an advanced algorithm blending visual and inertial sensors to bolster wheeled robot navigation in low-texture environments. With superior accuracy and real-time performance, it marks a significant leap in robotic autonomy and efficiency.

7 Apr 2024