Academic Articles & Research Papers

Machine Learning Model Coupled with Graphical User Interface for Predicting Mechanical Properties of Flax Fiber

Machine learning model coupled with graphical user interface was developed to predict mechanical properties of flax fiber. The experiment was conducted using test setup which applies constant rate of loading (CRL). Flax fiber was tested und...

Spatial analysis of smart tourism contrasts A comparison between tourist attractions in Cairo and Dubai

Technology has undergone rapid advancements, significantly impacting various sectors, with the tourism industry emerging as one of the most benefited. This research examines several key aspects, including: An overview of Cairo and Dubai as ...

Machine learning for neuroimaging with scikit-learn

Statistical machine learning methods are increasingly used for neuroimaging data analysis. Their main virtue is their ability to model high-dimensional datasets, e.g., multivariate analysis of activation images or resting-state time series....

Bayesian Reasoning and Machine Learning

Machine learning methods extract value from vast data sets quickly and with modest resources. They are established tools in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis, and robot ...

Machine learning for precision medicine

Precision medicine is an emerging approach to clinical research and patient care that focuses on understanding and treating disease by integrating multi-modal or multi-omics data from an individual to make patient-tailored decisions. With t...

From Causes to Forecasts: Granger Causality and Machine Learning Predictions of Housing Sales in Türkiye

This study aims to examine the impact of macro-financial indicators on the housing sales volume in Türkiye and evaluate the forecasting performance of traditional time-series models versus modern machine-learning algorithms. Using monthly d...

Interpretable Intersection Control by Reinforcement Learning Agent With Linear Function Approximator

ABSTRACT Reinforcement learning (RL) is a promising machine‐learning solution to traffic signal control problems, which have been extensively studied. However, variants of non‐linear, deep artificial neural network (ANN) function approximat...

Machine learning approach for the estimation of missing precipitation data: a case study of South Korea

Precipitation is one of the driving forces in water cycles, and it is vital for understanding the water cycle, such as surface runoff, soil moisture, and evapotranspiration. However, missing precipitation data at the observatory becomes an ...

Discovering subtypes with imaging signatures in the Motoric Cognitive Risk Syndrome Consortium using weakly supervised clustering

ABSTRACT INTRODUCTION Understanding the heterogeneity of brain structure in individuals with the Motoric Cognitive Risk Syndrome (MCR) may improve the current risk assessments of dementia. METHODS We used data from six cohorts from the MCR ...

Bioinformatics the machine learning approach

In this book Pierre Baldi and Soren Brunak present the key machine learning approaches and apply them to the computational problems encountered in the analysis of biological data. The book is aimed both at biologists and biochemists who nee...

Incidence and risk factors for rotator cuff retear after arthroscopic rotator cuff repair in middle-aged and older patients: a systematic review and meta-analysis.

Calibrated Machine Learning with Temporal Leakage Controls for Rare-Event Forecasting of Port Activity Anomalies: Evidence from Selected African Countries and Indonesia

Port activity anomalies are rare but may signal operational constraints relevant to supply chains. The aim of the study was to develop and empirically evaluate a calibrated machine learning framework for forecasting rare, algorithmically de...

Hybrid machine learning and 3D numerical modeling-based multi-objective optimization of geosynthetic-reinforced pile-supported embankments

Efficient and reliable design of Geosynthetic Reinforced Pile-Supported (GRPS) embankments on soft ground remains challenging due to the complex nonlinear soil–structure interaction and the high computational cost of repeated numerical anal...

Improved railway track faults detection using Mel-frequency cepstral coefficient and constant-Q transform features

Abstract Regular inspection of the health of railway tracks is crucial to maintaining reliable and safe train operations. Some factors including cracks, rail discontinuity, ballast issues, burn wheels, super-elevation, loose nuts and bolts,...

PCPAm - A dataset of histopathological images of penile cancer for classification tasksZenodo

Penile cancer has an incidence strongly linked to sociocultural factors, being more common in underdeveloped countries like Brazil, where it represents approximately 2% of cancers affecting men. This dataset was created to address the scarc...

Enhancing host-pathogen phenotyping dynamics: early detection of tomato bacterial diseases using hyperspectral point measurement and predictive modeling

Early diagnosis of plant diseases is needed to promote sustainable plant protection strategies. Applied predictive modeling over hyperspectral spectroscopy (HS) data can be an effective, fast, cost-effective approach for improving plant dis...

Surface circulation properties in the eastern Mediterranean emphasized using machine learning methods

The eastern Mediterranean surface circulation is highly energetic and composed of structures interacting stochastically. However, some main features are still debated, and the behavior of some fine-scale dynamics and their role in shaping t...

Machine Learning : A Probabilistic Perspective

Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict fu...

Machine learning in materials science

Abstract Traditional methods of discovering new materials, such as the empirical trial and error method and the density functional theory (DFT)‐based method, are unable to keep pace with the development of materials science today due to the...

Machine Learning

In machine learning, a computer first learns to perform a task by studying a training set of examples. The computer then performs the same task with data it hasn't encountered before. This article presents a brief overview of machine-learni...