This dataset contains the data used in the paper "Hybrid Machine Learning Framework for Microstructure-Based Composition Reconstruction and Hardness Prediction of Al–Si Die-Casting Alloys". The dataset (approximately 3GB) is divided into two main parts: OMtoEDS: Contains the data used for the microstructure-based composition reconstruction. OMtoHV: Contains the data used for the hardness prediction.
This paper develops a machine learning framework for detecting and predicting liquidity sweep events in XAUUSD using event-based market microstructure analysis. Using 15-minute data from 2014–2024, the study formalizes liquidity sweeps as a binary classification problem evaluated…
Machine learning systems do not learn reality directly; they learn from the representations preserved in their datasets. This structured narrative review examines how dataset purpose, coverage, integrity, labeling, independence, reproducibility, governance, and continuity determi…
This study focuses on the analysis and comparison of machine learning classification algorithms and hybrid machine learning models for predicting student academic performance. Educational Data Mining techniques are used to extract meaningful insights from student datasets. Variou…
This dataset contains the supplementary numerical data generated and analysed in the study entitled: "Bearing Capacity and Safety Factors of Ring Foundations in Spatially Variable Soils: A Hybrid FELA-Machine Learning Approach." The dataset supports the investigation of ring foun…
Mobile Ad Hoc Networks (MANETs) operate without fixed infrastructure and are affected by node mobility, dynamic topology, unstable links, limited energy, and traffic congestion. This research introduces a Machine Learning-Based Performance Prediction Framework (ML-PPF) to predict…