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ML Models Predict Gas Viscosity with High Accuracy

Researchers developed machine learning models, including ANN, RF, and GB, to accurately predict the viscosity of methane, nitrogen, and natural gas mixtures, achieving high precision (R² of 0.99) using over 4304 datasets. These models offer a cost-effective, efficient alternative to experimental methods, enhancing natural gas operations and providing valuable tools for research and industry.

15 Jul 2024

New Algorithm Enhances Heating and Cooling Load Predictions

Researchers introduced a semi-supervised concept bottleneck model (SSCBM) to improve the accuracy and interpretability of concept bottleneck models by generating pseudo labels and alignment loss with both labeled and unlabeled data. Experiments showed SSCBM achieved high prediction accuracy with only 20% labeled data, making it a promising solution for image analysis tasks requiring minimal annotation efforts.

3 Jul 2024

Advancing PD Localization in Power Transformers Using ML and DL

This article in Scientific Reports compares ML and DL methods for localizing PD sources within power transformer tanks using single-sensor electric field measurements. Various techniques including CNN, SVR, SVM, BPNN, KNN, MLP, and XGBoost were evaluated across multiple case studies, demonstrating the CNN model's superior accuracy and robustness.

30 May 2024

Ensemble Learning for Botnet Detection to Enhance IoT Security

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.

21 Feb 2024

Streamlining Coffee Grinder Maintenance with Remote Wear Classification

Researchers present a remote access server system leveraging image processing and deep learning to classify coffee grinder burr wear accurately. With over 96% accuracy, this mobile-friendly service streamlines assessment, benefiting both commercial coffee chains and enthusiasts, while its practicality and low cost suggest broader applications in machinery wear prediction.

12 Feb 2024

High-Resolution Aerial Dataset of Above-Ground Storage Tanks

Duke University researchers present a groundbreaking dataset of Above-Ground Storage Tanks (ASTs) using high-resolution aerial imagery from the USDA's National Agriculture Imagery Program. The dataset, with meticulous annotations and validation procedures, offers a valuable resource for diverse applications, including risk assessments, capacity estimations, and training object detection algorithms in the realm of remotely sensed imagery and ASTs.

18 Jan 2024

AI & Genetic Data Integration to Unlock Population Health Insights

In this study, researchers from Valley Children's Hospital leverage artificial intelligence and data visualization to harness clinical genetic data for estimating genetic disorder prevalence and mapping variants to local geographies. The groundbreaking methodology, exemplified in a five-year analysis, offers a blueprint for healthcare systems to translate genetic testing data into actionable insights for tailored population health management.

8 Jan 2024

MLpronto: Democratizing Machine Learning with a User-Friendly Interface

This paper introduces MLpronto, a user-friendly machine learning platform aimed at democratizing the field by providing accessibility without requiring programming skills. This web-based tool swiftly processes data, executes prevalent supervised machine learning algorithms, and generates corresponding programming code, catering to both novice users and those inclined towards programming.

5 Dec 2023

Boosting Functional Test Evaluation with Camera-Based System and Machine Learning

Researchers have explored the feasibility of using a camera-based system in combination with machine learning, specifically the AdaBoost classifier, to assess the quality of functional tests. Their study, focusing on the Single Leg Squat Test and Step Down Test, demonstrated that this approach, supported by expert physiotherapist input, offers an efficient and cost-effective method for evaluating functional tests, with the potential to enhance the diagnosis and treatment of movement disorders and improve evaluation accuracy and reliability.

8 Nov 2023

AI-Powered Equivalent Modeling of Mixed Wind Farms

Researchers have introduced an innovative approach for modeling mixed wind farms using artificial neural networks (ANNs) to capture complex relationships between variables. This method effectively represents the external characteristics of mixed wind farms in various wind conditions and voltage dip scenarios, addressing the challenges of power system stability in the presence of diverse wind turbine types.

26 Oct 2023

Machine Learning Algorithms for Predicting Water Quality Index

This research paper discusses the application of machine learning algorithms to predict the Water Quality Index (WQI) in groundwater in Sakrand, Pakistan. The study collected data samples, applied various classifiers, and found that the linear Support Vector Machine (SVM) model demonstrated the highest prediction accuracy for both raw and normalized data, with potential applications in assessing groundwater quality for various purposes, including drinking and irrigation.

13 Oct 2023

AI-Powered Groundwater Prediction for Sustainable Irrigation in Water-Scarce Regions

Researchers harnessed artificial intelligence to predict groundwater levels in Ethiopia's Bilate watershed, a water-scarce region. Their study revealed that Gradient Boosting Regression (GBR) performed exceptionally well, offering a valuable tool for sustainable borehole drilling decisions, particularly for irrigation, in water-scarce regions.

3 Oct 2023

Revolutionizing Food Safety with SUCOLA: A Data-Driven Approach

Researchers have introduced SUCOLA, a groundbreaking data-driven method, in their quest to enhance food safety. SUCOLA's innovative approach leverages self-supervised learning to provide early warnings for food safety risks, making significant advancements in the field of food safety risk assessment.

10 Sep 2023

Harnessing Soft Computing for Cardiovascular Disease Prediction and Diagnosis

This review explores how fuzzy logic, neural networks, and optimization algorithms hold immense promise in predicting, diagnosing, and detecting CVD. By handling complex medical uncertainties and delivering accurate and affordable insights, soft computing has the potential to transform cardiovascular care, especially in resource-limited settings, and significantly improve clinical outcomes.

27 Aug 2023

Top AI Algorithms for Sheep Weight Prediction: Revolutionizing Animal Husbandry

Researchers evaluated various machine learning algorithms to predict sheep body weights, highlighting the effectiveness of models like MARS, BRR, ridge regression, SVM, and gradient boosting for improving animal production decisions, ensuring economic growth and food security. Their study showcases the potential of AI in transforming animal husbandry practices.

17 Aug 2023

Intelligible Forecasting of Transportation Capacity Risk in Rail Transit Using Bayesian Networks

Researchers introduce a novel method for predicting transportation capacity risk in rail transit systems. By leveraging a linear Gaussian Bayesian network within a three-tier simulation model, the approach captures network, train flow, and passenger flow dynamics for accurate risk assessment. The proposed model, validated through a case study on Chongqing Rail Transit, demonstrates superior predictive capabilities compared to benchmark methods.

8 Aug 2023

Advancing Emotional Speech Recognition Using Deep Learning

Researchers introduce a speech emotion recognition (SER) system that accurately predicts a speaker's emotional state using audio signals. By employing convolutional neural networks (CNN) and Mel-frequency cepstral coefficients (MFCC) for feature extraction, the proposed system outperforms existing approaches, showcasing its potential in various applications such as human-computer interaction and emotion-aware technologies.

5 Jul 2023