Supervised learning aims to build a function or model that seeks as many mappings possible between the training data and outputs, where each will predict label match its corresponding ground-truth value. Although supervised has achieved great success in tasks, sufficient supervision for labels is not accessible domains because accurate labelling costly laborious, particularly medical image anal...

Abstract The most promising standard machine learning methods can deliver highly accurate classification results, often outperforming white-box methods. However, it is hardly possible for humans to fully understand the rationale behind black-box and thus, these powerful hamper creation of new knowledge on part broader acceptance this technology. Explainable Artificial Intelligence attempts over...

Since its inception as a branch of Artificial Intelligence, Machine Learning (ML) has flourished in recent years [...]

The increasing volatility in pricing and growing potential for profit in digital currency have made predicting the price of cryptocurrency a very attractive research topic. Several studies have already been conducted using various machine-learning models to predict crypto currency prices. This study presented in this paper applied a classic Autoregressive Integrated Moving Average(ARIMA) model ...

The current processes for building machine learning systems require practitioners with deep knowledge of machine learning. This significantly limits the number of machine learning systems that can be created and has led to a mismatch between the demand for machine learning systems and the ability for organizations to build them. We believe that in order to meet this growing demand for machine l...

Abstract Learners’ affective states play a crucial role in learning evaluation, and the external expressions that can directly reflect affect are facial expressions. However, sample size of database for process learners is limited, most existing expression databases ordinary people, which difficult to support an in-depth study emotional scenarios. In order better influence learners’ emotion on ...

This review explores how Moodle's machine learning capabilities enhance adaptive learning. We analyze five studies using Moodle for predictive and prescriptive support in education. These cover topics like learner classification, early risk detection, predictor comparison, reliability analysis, custom indicators. extract key findings, address challenges while suggesting future research directio...