MAB is the only Dutch-language academic Open Access journal on new developments in accountancy, business economics and related areas. MAB was founded in 1924 and publishes articles on auditing, external reporting, accounting information systems, management accounting, finance and management and organization, always written by experts and always relevant for practitioners. The articles on this website are free for everyone to read, you can support MAB by taking a print subscription.
The largest abstract and citation database of research literature and quality web sources covering nearly 18,000 titles from more than 5,000 publishers.
Its main goal is to promote and disseminate knowledge about the author's work in its multiple aspects. Its main commitment is to academic excellence.
The International Journal of Automation and Computing (IJAC) publishes papers on original theoretical and experimental research and development in automation and computing. The scope of the journal is extensive. Topics include but are not limited to: Artificial intelligence, Automatic control, Bio-informatics, Computer science, Information technology, Modelling and simulation, Networks and communications, Optimization and decision, Pattern recognition, Robotics, Signal processing, Systems engineering.
Machine Learning is an international forum for research on computational approaches to learning. The journal publishes articles reporting substantive results on a wide range of learning methods applied to a variety of learning problems. The journal features papers that describe research on problems and methods, applications research, and issues of research methodology. Papers making claims about learning problems or methods provide solid support via empirical studies, theoretical analysis, or comparison to psychological phenomena. Applications papers show how to apply learning methods to solve important applications problems. Research methodology papers improve how machine learning research is conducted. All papers describe the supporting evidence in ways that can be verified or replicated by other researchers. The papers also detail the learning component clearly and discuss assumptions regarding knowledge representation and the performance task.
Machine Learning in Geotechnics aims to disseminate original contributions in the emerging themes of machine learning, artificial intelligence, and big data analysis that focus on addressing different geotechnical engineering problems.