Intelligent Systems with Applications

Aims & Scope

Intelligent Systems with Applications (ISWA) is a peer reviewed open-access journal which focuses on the achievements of research and applications related to intelligent systems (IS).

IS can be applied to all aspects of human enterprize, such as business & finance, manufacturing & supply chains, agriculture, transportation, engineering, medicine and health, education, entertainment, culture, travel, media, the Internet, etc.

ISWA covers a broad spectrum of applications in the community, including industry, government, and academia.

The journal aims to publish papers dealing with, but not limited to, the following research fields: Knowledge Representation and Reasoning, Machine Learning (ML) and Neural Computing, Evolutionary Computation, Fuzzy Systems, Intelligent Information Processing, Intelligent Control and Robotics, Multi-agent Systems and Programming.

The journal welcomes submissions of IS-applications in various areas: Intelligent Cities, Industries, Consuming, Medical Treatment and Health, Agriculture, Business and Finance, Internet of Things (IoT).

Research addressing IS-applications in other fields is also encouraged.

Submissions must be novel, technically sound, and clearly presented.

ISWA accepts both regular papers and survey articles.

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 related fields, will ensure that papers move through the evaluation and review as rapidly as possible without compromising on the quality of the process.

Metrics & Ranking

Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar
• Directory of Open Access Journals (DOAJ)

Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Computer Science, designed to support cutting-edge academic discovery.

Licensing & Copyright

This journal operates under an Open Access model. Articles are freely accessible to the public immediately upon publication. The content is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), allowing users to share and adapt the work with proper attribution.

Copyright remains with the author(s), and no permission is required for non-commercial use, provided the original source is cited.

Policy Links

This section provides access to essential policy documents, guidelines, and resources related to the journal’s publication and submission processes.

• Aims scope
• Homepage
• Oa statement
• Author instructions
• License terms
• Review url
• Board url
• Copyright url
• Plagiarism url
• Preservation url
• Apc url
• License

Plagiarism Policy

This journal follows a plagiarism policy. All submitted manuscripts are screened using reliable plagiarism detection software to ensure originality and academic integrity. Authors are responsible for proper citation and acknowledgment of all sources, and any form of plagiarism, including self-plagiarism, will not be tolerated.

For more details, please refer to our official: Plagiarism Policy.

APC Details

The journal’s Article Processing Charge (APC) policies support open access publishing in Computer Science, ensuring accessibility and quality in research dissemination.

This journal requires an Article Processing Charge (APC) to support open access publishing, covering peer review, editing, and distribution. The current APC is 1,500.00 USD. Learn more.

Explore journals without APCs for alternative publishing options.

Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• Machine learning methods for sign language recognition: A critical review and analysis

  Citation: 129

  Authors: I.A., O.O., M.A.

• House price forecasting with neural networks

  Citation: 100

  Authors: Xiaojie, Yun

• A systematic survey on multimodal emotion recognition using learning algorithms

  Citation: 92

  Authors: Naveed, Zaher Al, Shini

• Automated machine learning: AI-driven decision making in business analytics

  Citation: 69

  Authors: Marc

• Deep learning for precision agriculture: A bibliometric analysis

  Citation: 64

  Authors: Solemane, Bernard, Dantouma, Daouda

• Speech emotion recognition using machine learning — A systematic review

  Citation: 58

  Authors: Samaneh, Talen, Olayinka, John Michael, Christian, Dave, Sandra L.

• A comprehensive review on multiple hybrid deep learning approaches for stock prediction

  Citation: 58

  Authors: Jaimin, Darsh, Manan

• AI-driven ensemble three machine learning to enhance digital marketing strategies in the food delivery business

  Citation: 53

  Authors: Chairote, Achmad Nizar

• Improved multi-classification of breast cancer histopathological images using handcrafted features and deep neural network (dense layer)

  Citation: 47

  Authors: Agaba, Mohammed, Sahalu Balarabe, Hayatu, Haruna

• Soybean and Soybean Oil Price Forecasting through the Nonlinear Autoregressive Neural Network (NARNN) and NARNN with Exogenous Inputs (NARNN–X)

  Citation: 47

  Authors: Xiaojie, Yun