Read more about AI and machine learning drive digital transformation across global mining operations on Devdiscourse ...

Incorporating marble dust and polypropylene fibers in concrete boosts strength and durability, highlighting the role of machine learning in mix optimization.

Support vector machines improve classification by mapping inseparable signals into higher-dimensional spaces. Random forest models, through ensemble decision trees, increase robustness against ...

Both approaches identified hemoglobin as one of the most significant predictors of CKD risk. Additional top-ranked features included blood urea, sodium levels, red blood cell count, potassium, and ...

Abstract: Modern fault diagnosis models that rely on machine learning and AI tools have been beset by three key challenges: first, highly uncertain training data due to sensor noise and potentially ...

Abstract: What if machine learning could predict inverter harmonics before prototyping? Conventional pulse width modulation (PWM) techniques in cascaded H-bridge (CHB) multilevel inverters (MLIs) ...

In today’s world dominated by artificial intelligence, terms like transformers, LLMs (large language models), and embeddings are everywhere. While many are familiar with models like ChatGPT, few truly ...

A project is trying to cut the cost of making machine learning applications for Nvidia hardware, by developing on an Apple Silicon Mac and exporting it to CUDA. Machine learning is costly to enter, in ...

ABSTRACT: In the field of machine learning, support vector machine (SVM) is popular for its powerful performance in classification tasks. However, this method could be adversely affected by data ...

In the era of big data and artificial intelligence, machine learning is one of the hot issues in the field of credit rating. On the basis of combing the literature on credit rating methods at home and ...