Introduction
The Journal of Machine Learning and Applications (JMLA) is a peer-reviewed and open-access journal publication committed to enlightening innovative research and development in machine learning and its applications. Given its focus on evolving topics, including multimodal AI, real-time machine learning, Agentic AI, advanced algorithms, and intelligent robotics, the journal inspires a conversation about both theory and practice in these fields. In both India and abroad, the journal welcomes submissions that connect and strengthen academia, industry, and practice in the conduct of new studies that define the future of artificial intelligence (AI).
Multimodal & Generative AI
Multimodal AI and generative AI are modifying intelligent systems by allowing machines to process and reason through different types of data (text, images, audio, and video). Topics of interest cover deep generative models, cross-modal fusion architectures, and safety mechanisms to maintain robustness. The applications of this research domain are multiple, rustic, and concessionary examples from health care in terms of imaging and clinical text, to retail, where vision is combined with language. The journal promotes submissions of reproducible research, sharing of datasets, and open benchmarks for the community. Researchers can accelerate the discovery of trustworthy, integrated intelligent systems that address meaningful issues across contexts by merging advances across multimodal AI and generative AI.
Real-Time Machine Learning & Edge AI
The concept of real-time machine learning is fundamental to any application that requires fast inference and adaptation, such as in fintech fraud detection, predictive maintenance and upkeep, and public safety monitoring. Edge AI can provide large-scale, low-latency decision making at the edge, where data can be originally generated, which lowers latency concerns. Topics also contain the identification of concept drift and continuous monitoring to ensure performance in space-time domains. In scope topics also include lightweight model performance, resource-effective planning and use, and MLOps best practices for the edge ecosystem. The journal seeks contributions that advance practices in real-time machine learning and edge AI to advance resilient and energy-efficient systems that can drive actionable intelligence in fast-paced settings formed by data-rich environments.
Agentic & Human-Centric AI
Agentic AI and human-centric AI are emerging areas of research that underscore the importance of interaction, collaboration, and effective decision-making between humans and intelligent systems. Research in these areas includes interactive AI, explainable AI, and responsibility-as-practice, creating a connection to the advancement of trust and transparency. Some core areas focus on tool-using agents, human-computer interaction frameworks, and evaluation processes that place human actions in the loop. We welcome submissions that focus on ethics in all aspects of decision-making, reporting, and ensuring transparency and accountability built into systems.
Machine Learning Algorithms & Theory in Practice
The journal welcomes papers that build upon machine learning algorithms in supervised learning, unsupervised learning, and reinforcement learning. Research interventions specific to the scope of the journal about machine learning algorithms should examine computational efficiency, resilience against adversarial conditions, and interpretability to engender trust in predictions. We encourage evaluations to be rigorous, including strong ablations, classification model baselines, and in-depth analyses of errors that explain system behaviours. Papers that connect theoretical contributions with system deployment will be especially welcomed. From scalable optimization algorithms to algorithmic models that are purposefully constrained by resources, we are concerned in approaches that advance theoretical limits while also enabling robust and deployable machine learning systems that can solve real-world high-impact problems.
Robotics, Autonomy & AI Applications
Intelligent robotics and autonomous robotics will be at the heart of transforming the way the world operates with AI-enabled technology. Intelligent robotics will integrate perception, planning, and control within a robotic system to permit complex, multi-step tasks. In-scope topics may include sim-to-real transfer and approaches for validating that the system models trained in simulation will effectively translate to real-world situations. Research into human-robot interaction, establishing safety protocols, and ensuring the reliability of operational systems is also encouraged and essential for practical adoption and scalability.
Article Types We Welcome
The Journal of Machine Learning and Applications (Reseapro Journal) accepts a diverse range of article types that support both foundational research and applied innovation. We welcome
- Research article
- Review
- Mini Review
- Commentary
- Application Papers
- Technical Notes
- Reproducibility Reports
- Surveys/Tutorials
- Dataset/Benchmark papers
- Short Communications
- Special Issues.
Submission & Peer Review
The Reseapro Journal | Journal of Machine Learning and Applications (JMLA) has an open and rigorous peer-review process. Authors are advised to read the Author Guidelines, Aims & Scope, and Editorial Board before submission. All manuscripts are subject to double-blind peer review. The journal is committed to fast publication and strict publication ethics, promoting original, transparent, and fair practices in publication.
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Who Can Submit?
The Journal of Machine Learning and Applications (Reseapro Journal) invites submissions from academics, researchers, engineers, data scientists, product teams, research labs, centres, start-ups, industry R&D units, and public sector organizations. The journal also has a strong interest from researchers in India and the wider APAC region, so an invitation is extended for contributors around the globe to submit emerging ideas in terms of theory and practice of AI whether academic or writing from industry. Accepted contributions can work with AI/machine learning applied in novel ways with high visibility for our global audience.
FAQs
Q: What topics are in scope?
A: Multimodal and generative AI, real-time machine learning and edge AI, agentic and human-centric AI, machine learning algorithms, and intelligent robotics with practical applications.
Q: Do you accept application-focused studies?
A: Yes. We welcome rigorous applications that demonstrate real-world impact, clear evaluation and reproducibility.
Q: Are datasets and benchmarks encouraged?
A: Yes. Dataset, benchmark and reproducibility reports are encouraged when they advance evaluation quality and community standards.
Q: How do I propose a Special Issue?
A: Email a short concept (theme, guest editors, timeline) to the editorial office. We prioritize timely, well-scoped topics aligned with the journal’s aims.
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