Journals in Machine learning

AI Open

ISSN: 2666-6510

AI Open is a freely accessible platform to share actionable knowledge and forward-thinking perspectives on the theory of artificial intelligence and its applications. The journal welcomes research articles, review papers, perspectives, short communications and technical notes on all aspects of artificial intelligence and its applications.Topics covered include, but are not limited to:Deep learning and representation learningGraph theory and graph miningConstraints, satisfiability, and searchKnowledge representation, reasoning, and logicMachine learning and data miningKnowledge graph and applicationsAgent-based and multi-agent systemsWeb and knowledge-based information systemsNatural language processingImage processing and analysisUncertaintyBrain-based LearningImplicit Cognition and LearningEditorial Board

AI Thermal Fluids

ISSN: 3050-5852

AI-Thermal/Fluids (AITF) is dedicated to exploratory research and advancements at the intersection of artificial intelligence (AI) and thermal-fluid science and engineering. The journal publishes full length Research Articles, Rapid Communications, Reviews, Letters to the Editor, and Vision Articles. It also publishes Original Software articles that describe and provide access to open-source software and Data Articles that describe and provide access to research data.AITF welcomes research that leverages AI techniques, such as machine learning, neural networks and data analytics, to promote fundamental understanding, improve prediction capabilities, solve problems and design or optimize thermal-fluid systems, with a view to stimulating innovation across a broad range of science and engineering applications. Contributions may include AI-driven modelling, simulation, data enhancement, AI-assisted experimentation, and control methodologies, among others.The list of focus areas includes (but is not limited to):Experimental Methodologies and Measurement Techniques: AITF showcases efforts to develop AI-based experimental methodologies, measurement techniques, and instrumentation for characterizing the thermal, mass transfer, reaction and/or fluid-flow dynamics aspects of a wide range of transport phenomena and processes. Modelling and Simulation: AITF publishes studies on the development of AI-based methodologies to design, model or predict material properties, transport phenomena and processes, enhance the capabilities of physics-based modelling and simulation tools, and analyse the data they generate, either in support of or independently from experimental measurements. Fluid Dynamics and Thermal Transport Phenomena: AITF seeks contributions aiming to elucidate transport phenomena. Examples are fluid flow behaviour, including turbulence, and transport processes within diverse flows, including single phase and multiphase flows, both isothermal and diabatic, as well as flows in the presence of phase change and reactive flows. AI-based approaches to process and analyse high throughput data, characterize flow dynamics, phase distribution, heat and mass transfer, reactions, and fluid-solid interactions are encouraged. Process Enhancement: AITF encourages the submission of articles focusing on innovative approaches to enhance heat and mass transfer or chemical processes in a wide range of devices, systems and applications. This encompasses design and operational optimization of heat exchangers, absorbers, separators, or reactors, the development of novel management approaches, surface modification techniques, and other passive and active enhancement strategies. Materials Properties: AITF welcomes research focused on the evaluation of thermophysical, thermochemical or thermodynamic materials properties relevant to the phenomena, problems, and applications of interest to the journal. This includes AI-based methods for predicting properties such as thermal conductivity, viscosity, density, phase behaviour, and other crucial attributes for accurate modelling, simulation, and optimization. Energy Exchange and Conversion: AITF features research addressing energy exchange and transformation mechanisms and their implications in engineering applications. This includes AI-driven optimization of energy harvesting techniques, energy conversion processes, energy storage, and sustainable energy technologies.Environmental and Sustainable Applications: AITF promotes efforts to address the economic and environmental implications of relevant processes, including renewable energy technologies, energy recover and management and energy efficiency in industry, buildings and the built environment, pollution mitigation, carbon capture and storage, and a wide range of other sustainable energy production, storage, or utilization technologies.

Information Fusion

ISSN: 1566-2535
5 Year impact factor: 16.1
Impact factor: 14.7

An International Journal on Multi-Sensor, Multi-Source Information FusionThe journal is intended to present within a single forum all of the developments in the field of multi-sensor, multi-source, multi-process information fusion and thereby promote the synergism among the many disciplines that are contributing to its growth. The journal is the premier vehicle for disseminating information on all aspects of research and development in the field of information fusion. Articles are expected to emphasize one or more of the three facets: architectures, algorithms, and applications. Papers dealing with fundamental theoretical analyses as well as those demonstrating their application to real-world problems will be welcome. The journal publishes original papers, letters to the Editors and from time to time invited review articles, in all areas related to the information fusion arena including, but not limited to, the following suggested topics:• Data/Image, Feature, Decision, and Multilevel Fusion • Multi-classifier/Decision Systems • Multi-Look Temporal Fusion • Multi-Sensor, Multi-Source Fusion System Architectures • Distributed and Wireless Sensor Networks • Higher Level Fusion Topics Including Situation Awareness And Management • Multi-Sensor Management and Real-Time Applications • Adaptive And Self-Improving Fusion System Architectures • Active, Passive, And Mixed Sensor Suites • Multi-Sensor And Distributed Sensor System Design • Fusion Learning In Imperfect, Imprecise And Incomplete Environments • Intelligent Techniques For Fusion Processing • Fusion System Design And Algorithmic Issues • Fusion System Computational Resources and Demands Optimization • Special Purpose Hardware Dedicated To Fusion Applications • Mining Remotely Sensed Multi-Spectral/Hyper-Spectral Image Data Bases • Information Fusion Applications in Intrusion Detection, Network Security, Information Security and Assurance arena • Applications such as Robotics, Space, Bio-medical, Transportation, Economics, and Financial Information Systems • Real-World Issues such as Computational Demands, Real-Time Constraints in the context of Fusion systems.Benefits to authors We 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 Center

International Journal of Cognitive Computing in Engineering

ISSN: 2666-3074

Cognitive computing is the creation of self-learning systems that use data mining, pattern recognition and natural language processing (NLP) to solve complicated problems without constant human oversight. It is a field that is highly transdisciplinary in nature, combining ideas, principles and methods of psychology, computer and internet technologies, linguistics, philosophy, neuroscience, etc.The goal of the International Journal of Cognitive Computing in Engineering is to explore how these data science technologies and new cognitive methods can be integrated to address real world engineering problems and challenges. For example, the journal welcomes submissions that look at the opportunities offered by combining existing data technologies with the knowledge of experts in the field and artificial intelligence. One of the benefits of cognitive computing is that it offers new analytics opportunities: the journal also welcomes designs for cognitive embedded data technologies that can process and analyse the large amount of data generated and aid decision-making.Submissions on a variety of cognitive computing-related topics will be considered.Editorial Board

International Journal of Intelligent Networks

ISSN: 2666-6030

International Journal of Intelligent Networks (IJIN) presents the most recent challenges and developments in computer networks with the objective of promoting awareness and best practices for the real-world problems. It aims to present new directions for further research and technology improvements in this important area.International Journal of Intelligent Networks (IJIN) presents research findings related to intelligent network of computing resources. It therefore invites original and timely research articles in the field of intelligent communication, technology, sustainability and social communication related research areas of current importance. IJIN will publish original papers, review papers, technical reports, case studies and book reviews.Topics of interest to the journal include, but are not limited to:Signal processing architectures, algorithms and applicationsImage computing and data science technologySmart sensor and agent networksGreen technologies in information, computing and communication systemsIntelligent computing for sustainable energyCommunication network architectures and securityBioinformatics and network computationWireless communication and network protocolsNetwork operation and managementIntelligent and information systemsNetwork coding theory and algorithmsSmart vehicular communication and technologyFuture AdvancementsEditorial Board

Journal of Automation and Intelligence

ISSN: 2949-8554

Artificial intelligence (AI) is almost everywhere due to the rapid development of modern technology and popularity of intelligent devices. While control theory and machine learning techniques as two enabling technologies have shown enormous power in their own right, a rapprochement of them is required to handle nonlinearity, uncertainty and scalability induced by high complexity of modern systems, huge quantity of real-time data, and large scale of agent networks.Journal of Automation and Intelligence (JAI) aims to provide a platform for researchers and practitioners from both academia and industry to exchange their ideas and present new developments across multiple disciplines relevant to automation and artificial intelligence with particular attention to machine learning.The JAI welcomes original high-quality contributions associated with theory, design, and applications of control, optimization and machine learning. Potential areas of interest include but are not limited to:1): control theory and related applications2): learning driven decision and optimization3): AI driven automation and autonomyEditorial Board

Machine Learning with Applications

ISSN: 2666-8270

Machine Learning with Applications (MLWA) is a peer reviewed, open access journal focused on research related to machine learning. The journal encompasses all aspects of research and development in ML, including but not limited to data mining, computer vision, natural language processing (NLP), intelligent systems, neural networks, AI-based software engineering, bioinformatics and their applications in the areas of engineering, medicine, biology, education, business and social sciences. It covers a broad spectrum of applications in the community, from industry, government, and academia.The journal publishes research results in addition to new approaches to ML, with a focus on value and effectiveness. Application papers should demonstrate how ML can be used to solve important practical problems. Research methodology papers should demonstrate an improvement to the way in which existing ML research is conducted.Submissions must be novel, technically sound, and clearly presented. MLWA accepts both regular papers and technical notes (technical notes are limited to a maximum of 10 pages). In addition, survey articles and discussion papers on ML are welcome.Submissions meeting journal criteria will undergo a single-blind review process, utilizing a minimum of two (2) external referees. Our dedicated editorial team, together with active researchers from all areas of ML, ensure that papers move through the evaluation and review as fast as possible without compromising on the quality of the process.The journal audience comprises academia, industry, and practitioners. Authors are strongly encouraged to make their datasets publicly accessible via a repository of their choosing. Please see our Guide for Authors for information on article submission. If you require any further information or help, please visit our Support Center.We invite you to convert your open source software into an additional journal publication in Software Impacts, a multi-disciplinary open access journal. Software Impacts provides a scholarly reference to software that has been used to address a research challenge. The journal disseminates impactful and re-usable scientific software through Original Software Publications which describe the application of the software to research and the published outputs.For more information contact us at: [email protected]

Robotics and Autonomous Systems

ISSN: 0921-8890
5 Year impact factor: 4.4
Impact factor: 4.3

Affiliated with the Intelligent Autonomous Systems (IAS) SocietyRobotics and Autonomous Systems will carry articles describing fundamental developments in the field of robotics, with special emphasis on autonomous systems. An important goal of this journal is to extend the state of the art in both symbolic and sensory based robot control and learning in the context of autonomous systems.Robotics and Autonomous Systems will carry articles on the theoretical, computational and experimental aspects of autonomous systems, or modules of such systems.Benefits to authors We 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 Center

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