About US

About Journal

Journal of Artificial Intelligence, Machine Learning and Data Science (JAIMLD - ISSN: 2583-9888) is an international, peer-reviewed, open access online journal on all aspects of artificial intelligence (AI), machine learning and data science published quarterly and started in 2023. As technology continues to rapidly evolve, our journal serves as a platform for researchers, academics, and industry professionals to explore and contribute to the latest developments in these exciting fields.


Journal Features

- Subject: Computer Science & Engineering; Science & Technology

- All papers are published in English and online

- Frequency of publication is quarterly

- ISSN: 2583-9888

- DOI: doi.org/10.51219/JAIMLD/....

- Double blind peer review process


Aim & Scope

Aim: This journal aims to provide scholarly input to the debate of the future impact of AI on society, as well as to host a forum in which research in AI focused on social good can be presented. JAIMLD is designed to meet the needs of a wide range of AI researchers, data scientists in academic and industrial research. With the rapid advancement of technology, AI, machine learning, and data science have become critical components in various industries, including healthcare, finance, transportation, and manufacturing.

 

The journal aims to publish research that is both theoretical and empirical, including papers that present novel algorithms, methodologies, models, and frameworks for solving challenging problems in AI, machine learning, and data science.

 

Scope: The scope of the journal extends to the application of intelligent systems in industry, medicine, and daily life. JAIMLD covering a wide range of issues from the tools and languages of artificial intelligence (AI) to philosophical implications. The journal provides a vigorous forum for the publication of both theoretical and experimental research, as well as surveys and impact studies. We prioritize original research that presents new methods, techniques, or applications in AI, machine learning, and data science, as well as innovative interdisciplinary research that combines these fields with other disciplines such as mathematics, electrical and electronics engineering, biomedical engineering, mechanical engineering, AI ethics and psychology.

 

At our journal, we cover a broad range of topics, including but not limited to:

Deep learning and neural networks, Natural language processing and speech recognition, Computer vision and image processing, Reinforcement learning and decision-making, Data mining and knowledge discovery, Big data analytics, Cloud computing, Internet of Things (IoT), Robotics and autonomous systems, Human-computer interaction, Machine learning algorithms and techniques, Explainable AI and interpretability, AI applications in healthcare, finance, marketing, and other fields, Ethical and social implications of AI and machine learning, Ethics and social implications of AI, Bayesian inference, Statistical modelling, Algorithm design and optimization, Predictive modelling, Cloud computing, Internet of Things (IoT), Predictive modelling, Statistical learning. Cognitive computing, Machine Learning Algorithms and Models, Cybersecurity and Privacy, Social Network Analysis, Sensor Networks, Bayesian networks and probabilistic reasoning, Ethics and social implications of AI, Explainable AI and interpretability, AI algorithms and architectures, Optimization and decision making, Bayesian networks and probabilistic graphical models, Ethical, legal, and social implications of AI.

 

JAIMLD also operates a double-blind peer-review process, which ensures the impartiality and objectivity of the review process. Categories of contributions accepted for the journal are research articles, reviews, debates, short communications, reviews of books, perspectives.

 

In order to reach the worldwide community of artificial intelligence (AI), the Journal of Artificial Intelligence, Machine Learning & Data Science (JAIMLD) is dedicated to rapid dissemination of important research results.


About special issues: Journal of Artificial Intelligence, Machine Learning and Data Science (JAIMLD) runs special issues to create collections of papers on specific topics. The aim is to build a community of authors and readers to discuss the latest research and develop new ideas and research directions. Special Issues are led by Guest Editors who are experts in the subject and oversee the editorial process for papers. Papers published in a Special Issue will be collected together on a dedicated page of the journal website. For any inquiries related to a Special Issue, please contact the Editorial Office at editorial.office@urfpublishers.com.


Thank you for visiting JAIMLD, and we hope that our journal will serve as a valuable resource for the academic community and industry professionals interested in the latest developments in AI, machine learning, and data science.

 


 

Topics

Artificial Intelligence & AI algorithms
Computer networks and communication
Natural language processing (NLP)
Explainable artificial intelligence (XAI)
Robotics and automation
Imbalanced learning
AI-driven automation and robotics
Predictive modeling and forecasting
Software engineering and development
Reinforcement learning for real life
Quantum machine learning
AI and ML tools and platforms
Deep learning theories and models
Data science & Big data analytics
Data preprocessing and cleaning
Data privacy and security
AI and ML in the internet of things (IoT)
Real-world AI and data science applications
Data warehousing and business intelligence
Computer architecture and hardware design
Machine learning foundations for data science
AI in speech recognition for healthcare records
Explainable AI in finance: Risk assessment models
Human emotion recognition with machine learning
Machine learning for cybersecurity threat detection
Emerging Impactful Machine Learning Applications
Artificial Intelligence (AI) Ethics and Social Implications
Quantum machine learning: Algorithms and applications
AI and ML in healthcare, finance, education, and industry
Deep Learning Applications in Natural Language Processing
Machine learning for healthcare, finance, and other domains
Deep learning for multimodal data fusion and analysis
Machine learning for predictive maintenance in industry 4.0
Transfer learning techniques for small and imbalanced datasets
Artificial Intelligence (AI) in Aerospace Science and Engineering
Applications of AI in the Aerospace and Defense Industry
Artificial Intelligence (AI) Developments for Healthcare Applications
Responsible AI and data science: Bias detection and mitigation
Data science techniques for anomaly detection in IoT networks
Machine learning for human-robot interaction and collaboration
Blockchain technology for secure and transparent data sharing
AI-powered mental health diagnostics: Ethical considerations
AI-enhanced cybersecurity strategies for critical infrastructure
Cognitive neuroscience meets AI: Insights into human intelligence
AI in criminal justice: Bias detection and fairness in sentencing
AI and ML ethics in autonomous weapons systems
AI in gaming: Player behavior prediction and content generation

Journal Features:

International peer review research journal that publishes articles on multidisciplinary fields.

Prompt acknowledgement after receiving the article.

Rigorous double blind peer review evaluation.

No charges for article submission.

Rapid publication after the acceptance of article through eminent reviewers and editor.

Publication of manuscripts with the fulfilment of excellence, novelty and originality.

Nominal publication charges.

Greater visibility of your valuable published work.

Best platform for sharing the knowledge globally.

Issue of Publication Certificate to author.

Issue of Editorial Board Member Certificate to EBM member.

Issue of reviewer Certificate to reviewer.

About UR Forum Publishers

UR Forum Publishers is a scientifically independent open access publisher whose mission is to publish new research by bridging the gap between research and practise, with the goal of resulting in societal benefits. Our peer-reviewed publications cover a wide range of medical research, healthcare, and other topics. We are dedicated to publishing high-quality research articles that have an impact and are long-lasting, resulting in a better future for future generations.

Researchers and academics can use UR Forum Publishers to disseminate scientific knowledge to a wider audience, gain media attention, and demonstrate professional progress through publication. The Open Access concept allows for unrestricted access to published content that is not time-limited, as well as the ability to reprint and distribute original content. We publish the work of eminent scientists, young researchers, and academic and industrial professionals. Because our journals adhere to the criteria and principles of the Directory of Open Access Journals, viewing the full text of articles does not require a subscription (DOAJ).

We are a committed group of educators and technocrats with extensive industry experience. Our torchbearers are eminent scientists, educators, industrialists, and officials from around the world who serve on the editorial board. Our team is working hard to connect available talent and resources in order to form partnerships that will promote research collaboration. Our common goal is to support research and discoveries that benefit the community and advance technology.

By embracing best practises and technology to deliver unique science books, journals, and series, UR Forum has remained at the forefront of academic publishing. Our entry into the exciting world of online publications and eBooks was a natural and seamless transition. This step is an extension of our vision to publish the best works by outstanding scientists from around the world and make them available to as many people as possible.

In almost every country, UR Forum has representatives, agents, and distributors. They provide review copies upon request, advertise academic journals in specialised catalogues, and exhibit at international trade shows such as the London, US, and international academic book events.