About the Journal

Aim :

Journal of Research in Artificial Intelligence for Systems and Applications (RAISA) aims to publish high-quality, peer-reviewed research that advances both the theoretical and practical aspects of AI across a wide range of fields. Its primary objectives include promoting AI research by publishing original studies on AI theories, algorithms, and methodologies, as well as highlighting innovative applications of AI in industries such as healthcare, agriculture, finance, software engineering, game, and robotics. The journal fosters interdisciplinary collaboration, encouraging research that integrates AI with areas like machine learning, data science, and ethics. It also focuses on reporting cutting-edge technological advancements, including breakthroughs in deep learning, neural networks, and autonomous systems, while providing essential insights into the foundational models and algorithms that underpin AI. Through these efforts, RAISA aims to support the responsible and impactful development of AI technologies.

Scope : 

The scope of the Journal of Research in Artificial Intelligence for Systems and Applications (RAISA) encompasses a wide range of topics within artificial intelligence, emphasizing both theoretical advancements and practical applications. The journal’s primary areas of focus include:

  1. AI Theories and Algorithms: Research on the foundational theories, algorithms, and mathematical models underpinning AI techniques, such as machine learning, neural networks, optimization methods, and probabilistic reasoning.
  2. AI Applications: Studies exploring the application of AI technologies across various industries, including healthcare, agriculture, finance, software engineering, game, and robotics. The journal often features case studies and real-world examples of AI in practice.
  3. Machine Learning and Deep Learning: Research on machine learning paradigms (supervised, unsupervised, and reinforcement learning) and advanced deep learning techniques, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative models.
  4. Data Science and Big Data: The use of AI to process, analyze, and extract insights from large-scale datasets, including data mining, predictive analytics, and AI-driven data visualization.
  5. Natural Language Processing (NLP): Research on AI techniques for understanding, generating, and interacting with human language, including machine translation, sentiment analysis, and chatbot development.
  6. Computer Vision: Studies on AI applications in image and video recognition, object detection, facial recognition, and visual perception systems.
  7. Systems and Applications in Data Signals: Research on AI techniques for signal processing, focusing on how AI can enhance the analysis, interpretation, and optimization of data signals across various industries and applications.
  8. Interdisciplinary AI Research: AI applications and theories intersecting with fields such as cognitive science, psychology, neuroscience, economics, education, manufacturing, and social sciences.

By addressing these areas, the journal provides a comprehensive platform for advancing AI research, promoting interdisciplinary collaboration, and encouraging the responsible and impactful application of AI technologies across diverse industries.