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Knowledge-Based Systems is an international, interdisciplinary and applications-oriented journal. This journal focuses on systems that use knowledge-based (KB) techniques to support human decision-making, learning and action; emphases the practical significance of such KB-systems; its computer development and usage; covers the implementation of such KB-systems: design process, models and methods, software tools, decision-support mechanisms, user interactions, organizational issues, knowledge acquisition and representation, and system architectures.

This journal's current leading topics are but not limited to:

• Big data techniques and methodologies, data-driven information systems, and knowledge acquisition
• Cognitive interaction and intelligent human interfaces
• Recommender systems and E-service personalization
• Intelligent decision support systems, prediction systems and warning systems
• Computational and artificial intelligence based systems and uncertain information processes
• Swarm intelligence and evolutionary computing
• Knowledge engineering, machine learning-based systems and web semantics

The journal also welcomes papers describing novel applications of knowledge based systems in any human endeavor: ranging from financial technology to engineering to health science or any other domain impacted by Artificial Intelligence technologies and its associated techniques and systems.

To further the state of the art, Knowledge Based Systems now also reviews and publishes software and code through its new http://www.elsevier.com/about/content-innovation/original-software-publicationsOriginal Software Publication format to disseminate these important born digital knowledge elements.

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Stem cell research, cloning, GMOs ... How do regulations affect such emerging technologies? What impact do new technologies have on law? And can we rely on technology itself as a regulatory tool?

The meeting of law and technology is rapidly becoming an increasingly significant (and controversial) topic.  Law, Innovation and Technology is, however, the only journal to engage fully with it, setting an innovative and distinctive agenda for lawyers, ethicists and policy makers. Spanning ICTs, biotechnologies, nanotechnologies, neurotechnologies, robotics and AI, it offers a unique forum for the highest level of reflection on this essential area.

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: 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.

Mechatronics is the synergistic combination of precision mechanical engineering, electronic control and systems thinking in the design of products and manufacturing processes. It relates to the design of systems, devices and products aimed at achieving an optimal balance between basic mechanical structure and its overall control. The purpose of this journal is to provide rapid publication of topical papers featuring practical developments in mechatronics. It will cover a wide range of application areas including consumer product design, instrumentation, manufacturing methods, computer integration and process and device control, and will attract a readership from across the industrial and academic research spectrum. Particular importance will be attached to aspects of innovation in mechatronics design philosophy which illustrate the benefits obtainable by an a priori integration of functionality with embedded microprocessor control. A major item will be the design of machines, devices and systems possessing a degree of computer based intelligence. The journal seeks to publish research progress in this field with an emphasis on the applied rather than the theoretical. It will also serve the dual role of bringing greater recognition to this important area of engineering.Mechatronics publishes the following types of papers:• Regular Articles should describe original research of high quality in the field of Mechatronics.• Review Articles will generally be specially commissioned, however, suggestions for topics and authors are welcomed by the Editor-in-Chief.•Technical Notes provide rapid publication of important new contributions.Benefits to authorsWe also provide many author benefits, such as free PDFs, a liberal copyright policy, special discounts on Elsevier publications and much more. Please click here for more information on our author services.Please see our Guide for Authors for information on article submission. If you require any further information or help, please visit our support pages: http://support.elsevier.com

Medical Image Analysis provides a forum for the dissemination of new research results in the field of medical and biological image analysis, with special emphasis on efforts related to the applications of computer vision, virtual reality and robotics to biomedical imaging problems. The journal publishes the highest quality, original papers that contribute to the basic science of processing, analysing and utilizing medical and biological images for these purposes. The journal is interested in approaches that utilize biomedical image datasets at all spatial scales, ranging from molecular/cellular imaging to tissue/organ imaging. While not limited to these alone, the typical biomedical image datasets of interest include those acquired from:Magnetic resonanceUltrasoundComputed tomographyNuclear medicineX-rayOptical and Confocal MicroscopyVideo and range data imagesThe types of papers accepted include those that cover the development and implementation of algorithms and strategies based on the use of various models (geometrical, statistical, physical, functional, etc.) to solve the following types of problems, using biomedical image datasets: representation of pictorial data, visualization, feature extraction, segmentation, inter-study and inter-subject registration, longitudinal / temporal studies, image-guided surgery and intervention, texture, shape and motion measurements, spectral analysis, digital anatomical atlases, statistical shape analysis, computational anatomy (modelling normal anatomy and its variations), computational physiology (modelling organs and living systems for image analysis, simulation and training), virtual and augmented reality for therapy planning and guidance, telemedicine with medical images, telepresence in medicine, telesurgery and image-guided medical robots, etc.Benefits to authorsWe also provide many author benefits, such as free PDFs, a liberal copyright policy, special discounts on Elsevier publications and much more. Please click here for more information on our author services.Please see our Guide for Authors for information on article submission. If you require any further information or help, please visit our support pages: http://support.elsevier.com

Memetic Computing features articles on high quality research in hybrid metaheuristics (including evolutionary hybrids) for optimization, control and design in continuous and discrete optimization domains. It goes beyond current search methodologies towards innovative research on the emergence of cultural artifacts such as game, trade and negotiation strategies and, more generally, rules of behavior as they apply to, for example, robotic, multi-agent and artificial life systems. Memetic Computing is an avenue for the latest results in natural computation, artificial intelligence, machine learning, operational research and natural sciences, which are combined in novel ways so as to transcend the intrinsic limitations of a single discipline. Potential authors are invited to submit original research articles for publication consideration at any time. Reviews and short research communications are also welcomed. Further information on submission, format, lengths and style files is available through the journal website. All manuscripts should be submitted electronically using the Online Submission system. We aim to achieve a typical turnaround time of not more than 3 months for the review process. Some (but not all) of the topics covered by Memetic Computing are: Algorithmic Intelligence in Optimisation, Control and Design Hybrid (Parallel) Metaheuristics such as Tabu Search, Path relinking, Scatter Search, GRASP methods, Iterated Local Search, Simulated annealing, Variable Neighborhood Search, Evolutionary Algorithms, Learning Classifier Systems, Memetic Algorithms, Cultural Algorithms, etc. Approximate and exact algorithms for Combinatorial and Continuous Optimisation Integer and Linear Programming Ant Colony Computing Self-organisation, Self-Assembly, Self-Generation, Self-Healing of artificial systems Swarm Intelligence<, /LI> Neural networks Evolutionary Dynamics Memetic Theory Artificial Cultures in multi-agent systems, webbots and robots. Landscape Analysis Methodological aspects of experimental computing. Search based Software Engineering Genetic Programming Constraint Optimisation Representation and encoding studies Real-world applications Machine learning and Data Mining Multiobjective optimisation Artificial immune systems

Microprocessors and Microsystems: Embedded Hardware Design (MICPRO) is a journal covering all design and architectural aspects related to embedded systems hardware. It includes different embedded system hardware platforms ranging from custom hardware via reconfigurable systems and application specific processors to general purpose embedded processors. Special emphasis is put on novel complex embedded architectures, such as systems on chip (SoC), systems on a programmable/reconfigurable chip (SoPC) and multi-processors on a chip (MPoC) as well as their communication methods, such as network-on-chip (NoC).Design automation of such systems including methodologies, techniques and tools for their design as well as novel designs of hardware components fall within the scope of this journal. Novel applications that use embedded systems are also central in this journal. While software is not a part of this journal hardware/software co-design methods that consider interplay between software and hardware components with emphasis on hardware are also relevant here.Benefits to authorsWe also provide many author benefits, such as free PDFs, a liberal copyright policy, special discounts on Elsevier publications and much more. Please click here for more information on our author services.Please see our Guide for Authors for information on article submission. If you require any further information or help, please visit our support pages: http://support.elsevier.com

Affiliated with the Society for Machines and Mentality, the journal Minds and Machines fosters a tradition of criticism within the AI and philosophical communities on problems and issues of common concern. Its scope explicitly encompasses philosophical aspects of computer science. The journal affords an international forum for the discussion and debate of important and controversial issues concerning significant developments within its areas of editorial focus. It features special issues devoted to specific topics, critical responses to previously published pieces, and review essays discussing current problem situations.

Multidimensional Systems and Signal Processing publishes research and selective surveys papers ranging from the fundamentals to important new findings. The journal responds to and provides a solution to the widely scattered nature of publications in this area, offering unity of theme, reduced duplication of effort, and greatly enhanced communication among researchers and practitioners in the field.A partial list of topics addressed in the journal includes multidimensional control systems design and implementation;  multidimensional stability and realization theory; prediction and filtering of multidimensional processes; Spatial-temporal signal processing; multidimensional filters and filter-banks; array signal processing; and applications of multidimensional systems and signal processing to areas such as healthcare and 3-D imaging techniques.

Natural Computing refers to computational processes observed in nature, and human-designed computing inspired by nature. When complex natural phenomena are analyzed in terms of computational processes, our understanding of both nature and the essence of computation is enhanced. Characteristic for human-designed computing inspired by nature is the metaphorical use of concepts, principles and mechanisms underlying natural systems. Natural computing includes evolutionary algorithms, neural networks, molecular computing and quantum computing. The journal Natural Computing provides a forum for discovery in natural computing, offering links among researchers and insight into trends in an emerging specialty. The journal reports on theory, experiments, and applications, and covers natural computing from a very broad perspective, including use of algorithms to consider evolution as a computational process, and neural networks in light of computational trends in brain research. Now indexed in ISI.

The Journal provides a forum for integrating theoretical and experimental findings in computational neuroscience across relevant interdisciplinary boundaries. Rapidly accumulating empirical data in the neurobiological, psychological and cognitive domains provide important constraints for new theoretical models. The Journal aims to present such theoretical results, and make them accessible to neurobiologists, psychologists and cognitive scientists. Read More: http://informahealthcare.com/page/net/Description.