期刊信息

Expert Systems with Applications (ESWA)

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影响因子:
7.5
出版商:
Elsevier
ISSN:
0957-4174
浏览:
57719
关注:
56

征稿

Expert Systems with Applications (ESWA) is an academic journal published by Elsevier. (ISSN 0957-4174, impact factor 7.5, CCF C).

Expert Systems With Applications is a refereed international journal whose focus is on exchanging information relating to expert and intelligent systems applied in industry, government, and universities worldwide. The thrust of the journal is to publish original papers dealing with the design, development, testing, implementation, and/or management of expert and intelligent systems, and also to provide practical guidelines in the development and management of these systems. The journal will publish papers in expert and intelligent systems technology and application in the areas of, but not limited to: finance, accounting, engineering, marketing, auditing, law, procurement and contracting, project management, risk assessment, information management, information retrieval, crisis management, stock trading, strategic management, network management, telecommunications, space education, intelligent front ends, intelligent database management systems, medicine, chemistry, human resources management, human capital, business, production management, archaeology, economics and energy. Papers in multi-agent systems, knowledge management, neural networks, knowledge discovery, data and text mining, multimedia mining, and genetic algorithms will also be published in the journal. The journal no longer considers papers that contain applications to military/defense systems. Papers detailing algorithms which repurpose existing concepts within the framework of metaphors inspired by various systems and processes, such as natural, technical, or social ones are discouraged as this can hinder effective algorithm comparisons and scientific advancement. Submissions from this field are required to convincingly demonstrate their contribution to the field in order to be considered for further evaluation. Authors are urged to present their methods with clarity, employing standard optimization terminology, and to provide compelling explanations for how their components are adapted to specific problem-solving contexts. The emphasis is on genuine innovation rather than the renaming of existing concepts. Natural metaphor articles - new nature-inspired proposals detailed in papers must provide formal, mathematically grounded explanations for how they differ from established methods and the use of metaphors as superficial embellishments is discouraged.
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