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...

Teaching of machine learning should aim at the readiness to understand and implement modern machine learning algorithms. Towards this goal, we often have course exercises involving the student to solve a practical machine learning problem involving a reallife data set. The students implement the programs of machine learning methods themselves and gain deep insight on the implementation details ...

1 Data Sources • Amazon abbreviates the AmazonBinary dataset in Chen’s collection. • Iris is from the UCI machine learning repository . • Votes is from the UCI machine learning repository . • ORL is from the Database of Faces of AT&T. • PIE is from CMU/VASC Image Database. • YaleB is from the Extended Yale Face Database B. • COIL20 is from Columbia University Image Library. • Isolet is from the...

Statistical machine learning continues to show promise as a tool for addressing complex problems in a variety of domains. An increasing number of developers are therefore looking to use statistical machine learning algorithms within applications. We have conducted two initial studies examining the difficulties that developers encounter when creating a statistical machine learning component of a...

This paper describes a method for incorporating canonical discriminant attributes in classification machine learning. Though decision trees and rules have semantic appeal when building expert systems, the merits of discriminant analysis are well documented. For data sets on which discriminant analysis obtains significantly better predictive accuracy than symbolic machine learning, the incorpora...