Top Research Papers on ML

A comparative analysis of Machine Learning research methods allowing to assess the risk of mining damage occurring in traditional masonry buildings located in the mining area of Legnica-Głogów Copper District as a result of intense mining tremors confirms the thesis that the proposed methodology may allow to estimate the financial outlays that the mining plant should secure for the repair of the expected damage.

The results suggest that clinically relevant RCC subtypes may be extracted directly from H&E-stained WSI and may complement gene expression based patient stratification and selection strategies.

Pendant drops of oxide-coated high-surface tension fluids frequently produce perturbed shapes that impede interfacial studies. Eutectic gallium indium or Galinstan are high-surface tension fluids coated with a ∼5 nm gallium oxide (Ga2O3) film and falls under this fluid classification, also known as liquid metals (LMs). The recent emergence of LM-based applications often cannot proceed without analyzing interfacial energetics in different environments. While numerous techniques are available in the literature for interfacial studies- pendant droplet-based analyses are the simplest. However, the...

The abstract is not available

The MLR package provides a generic, object-oriented, and extensible framework for classification, regression, survival analysis and clustering for the R language and includes meta-algorithms and model selection techniques to improve and extend the functionality of basic learners with, e.g., hyperparameter tuning, feature selection, and ensemble construction.

DataView is introduced, the core data abstraction of ML.NET which allows it to capture full predictive pipelines efficiently and consistently across training and inference lifecycles.

This paper introduces ML.NET: a recently open-sourced machine learning framework allowing developers to author and deploy in their applications complex ML pipelines composed of data featurizers and state of the art machine learning models.

A data planing procedure for identifying combinations of variables -- aided by physical intuition -- that can discriminate signal from background that puts in context what it means for a machine to learn.

The study identified various challenges to implementation, such as resistance to change among employees, lack of technical expertise, integration issues with legacy systems, and incomplete data, along with best practices to overcome these hurdles including regular performance evaluations, robust security measures, and personalized customer experiences.

The results support the potential use of ML-based TIL scoring as a novel and independent biomarker to predict time to failure on ICI and demonstrate its utility as a biomarker in combination with tumor mutational burden.

A novel tool that simplifies machine learning (ML) for researchers by integrating Data Exploration, AutoML, CustomML, and Visualization functionalities, MLme improves efficiency and productivity by streamlining the ML workflow and eliminating the need for extensive coding efforts.

The elements that have an impact on Chinese EFL college students’ adoption and usage of mobile technology-integrated vocabulary acquisition are looked at as a means to promote more learner-centric education.

The problems MLC ++ aims to solve, the design of MLC++ , and the current functionality are discussed, as well as a list of classes and tools for supervised Machine Learning.

This work focuses on applying machine learning for personalized medicine, primarily oncology, dealing with attempts to generate as much as possible treatment response biomarkers from mediocre datasets.

A novel tool called Machine Learning Made Easy (MLme) is developed that streamlines the use of ML in research, specifically focusing on classification problems at present, and serves as a valuable resource for leveraging ML to facilitate insightful data analysis and enhance research outcomes.

The artificial neural network (ANN) is explored for real-time prediction of the time remaining to reach a target value of melt-fraction based on the prior history of the spatial distribution of the surface temperature transients and two different approaches were explored for training the ANN model.

Initialize G to the set of maximally general hypotheses in H Initialize S to theset of maximically specific hypotheses inH For each training example d, remove from G any hypothesis inconsistent with d.

The research paper, entitled " Machine Learning ", has been successfully published in the International Scientific Journal of Engineering and Management (ISJEM) on Volume 02 Issue 04 April 2023.

Artificial neural network (ANN) is explored for real-time prediction of the time remaining to reach a target value of melt-fraction based on the prior history of the spatial distribution of the surface temperature transients in this study.

The model reached an excellent accuracy in predicting SFS and complications in the paediatric population, leading the way to the validation of patient-specific predictive tools.