Journal Guide

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.

Machine Learning: Earth is a multidisciplinary open access journal dedicated to the application of machine learning, artificial intelligence (AI) and data-driven computational methods across all areas of Earth, environmental and climate sciences including efforts to ensure a sustainable future. The journal publishes research reporting data-driven approaches that advance our knowledge of the Earth system, and of the interactions between biosphere, hydrosphere, cryosphere, atmosphere and geosphere. The journal also publishes research that presents methodological, theoretical, or conceptual advances in machine learning and AI with applications to Earth, environmental and climate science.

Machine Learning: Engineering is a multidisciplinary open access journal dedicated to the application of machine learning (ML), artificial intelligence (AI) and data-driven computational methods across all areas of engineering. The journal also publishes research that presents methodological, theoretical, or conceptual advances in machine learning and AI with applications to engineering.

Machine Learning: Health is a multidisciplinary open access journal dedicated to the application of machine learning, artificial intelligence (AI) and data-driven computational methods across healthcare and the medical, biological, clinical, and health sciences. The journal also publishes research that presents methodological, theoretical, or conceptual advances in machine learning and AI with applications to medicine and health sciences.

Machine Translation publishes original research papers on all aspects of MT, including (but not restricted to): - Statistical MT - Example-Based MT - Rule-Based MT - Hybrid MT - Spoken Language Translation - Discriminative MT - Evaluation in MT - MT Applications - Computer-Assisted Translation - Multilingual Corpus Resources - Tools for translators - The role of technology in translator training - MT and language teaching In addition, Machine Translation welcomes papers with a multilingual aspect from other areas of Computational Linguistics and Language Engineering, including: - text composition and generation - information retrieval - natural language interfaces - dialogue systems - message understanding systems - discourse phenomena - text mining - knowledge engineering - contrastive linguistics - morphology, syntax, semantics, pragmatics - computer-aided language instruction and learning - software localization and internationalization Machine Translation regularly focuses on issues of special interest, features a regular Book Review section, and welcomes other contributions of interest to the wide readership of the journal.

Sponsored by the International Association for Pattern Recognition, this journal publishes high-quality, technical contributions in machine vision research and development. Machine Vision and Applications features coverage of all applications and engineering aspects of image-related computing, including original contributions dealing with scientific, commercial, industrial, military, and biomedical applications of machine vision. The journal places particular emphasis on the engineering and technology aspects of image processing and computer vision. It includes coverage of the following aspects of machine vision applications: algorithms, architectures, VLSI implementations, AI techniques and expert systems for machine vision, front-END sensing, multidimensional and multisensor machine vision, real-time techniques, image databases, virtual reality and visualization.

Machines (ISSN 2075-1702) is an international, peer-reviewed journal on machinery and engineering. It publishes research articles, reviews, short communications and letters. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. Full experimental and/or methodical details must be provided. There are, in addition, unique features of this journal: manuscripts regarding research proposals and research ideas will be particularly welcomed electronic files or software regarding the full details of the calculation and experimental procedure - if unable to be published in a normal way - can be deposited as supplementary material

Machining Science and Technology publishes original scientific and technical papers and review articles on topics related to machining and traditional and nontraditional machining processes performed on all materials-metals, polymers, ceramics, and composites. In addition, this high-quality journal covers novel concepts for machining of advanced materials; measurement of surface quality and metrology including detection and characterization of machining damage; special cutting tools, coated inserts, new grinding wheels, special coolants, and cutting fluids; and design and implementation of in-process sensors for monitoring and control of surface quality and integrity. Publication office: Taylor & Francis, Inc., 325 Chestnut Street, Suite 800, Philadelphia, PA 19106.