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Peer reviewed only Open Access

Journal of Artificial Intelligence and Machine Learning (JAIML)

Publisher : IAEME Publication Private Limited
Artificial Intelligence Machine Learning Adaptive autonomous robots
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e-ISSN 5979-A561
Issue Frequency Monthly
Impact Factor 22.45
Est. Year 2021
Mobile 9884798314
DOI YES
Language English
APC YES
Impact Factor Assignee Google Scholar
Email iaemedu@gmail.com

Journal Descriptions

The Journal of Artificial Intelligence and Machine Learning (JAIML) is an open-access, peer-reviewed international journal published by IAEME Publication. It serves as a platform for exploring and investigating various aspects of artificial intelligence and machine learning. This journal delves into the realms of living systems intelligence, human-level cognition in artificial systems, self-learning algorithms, and intelligent autonomous robots. It also provides a forum for researchers and practitioners to access and contribute to the latest advancements in machine learning methods, tools, and algorithms. JAIML promotes research in the emerging fields of artificial life and machine learning, seeking to enhance our understanding of real-world problems through bioinspired or human-like systems. The journal welcomes submissions of concepts, theories, systems, technologies, and procedures that demonstrate properties, phenomena, or capabilities akin to those found in living systems or humans. Aim and Scope: The primary aims and scope of the Journal of Artificial Intelligence and Machine Learning (JAIML) encompass a wide range of topics and areas, including but not limited to: Adaptive Autonomous Robots: Exploring the development and application of robots capable of adapting to changing environments. Genetic Algorithms: Investigating the use of genetic algorithms for optimization and problem-solving. Agent-Based Systems and Modeling: Studying systems and models based on autonomous agents and their interactions. Genetic Programming: Examining genetic programming techniques for evolving computer programs.

Journal of Artificial Intelligence and Machine Learning (JAIML) is :-

  • International, Peer-Reviewed, Open Access, Refereed, Artificial Intelligence, Machine Learning, Adaptive autonomous robots, Big data and data mining, Cognitive intelligence and modeling, Computer Vision, Human machine interaction, Swarm intelligence and swarming behaviors, Virtual Worlds, Neuro-computing , Online , Monthly Journal

  • UGC Approved, ISSN Approved: P-ISSN E-ISSN: 5979-A561, Established: 2021, Impact Factor: 22.45
  • Provides Crossref DOI

  • Not indexed in Scopus, WoS, DOAJ, PubMed, UGC CARE

Indexing

Publications of JAIML

MULTI-OBJECTIVE OPTIMIZATION USING EVOLUTIONARY AI FOR TEST TIME REDUCTION IN SEMICONDUCTOR PRODUCTION LINES
Thenmozhi Natarajan April, 2024
The semiconductor industry is under continuous pressure to optimize production efficiency while maintaining rigorous quality standards. One of the key challenges lies in reducing test time w...
PREDICTING OCEAN FREIGHT RATES USING MACHINE LEARNING METHODS
Prithviraj Lakkakula February, 2025
Ocean freight rates (hereafter referred to as ocean rates) have seen unprecedented growth (over 150-175% increase) and volatility in recent years due to many factors, including energy prices...
A CONTINUOUS INTEGRATION AND CONTINUOUS DEPLOYMENT FRAMEWORK FOR SAGEMAKER REAL-TIME INFERENCE
Gautham Ram October, 2024
Real-time inference in a modern machine learning workflow requires robust deployment and monitoring to ensure models are delivering accurate and timely predictions. This paper elaborates on ...
ROBUST IMAGE PROCESSING ALGORITHM TO COUNT SAREES/T-SHIRTS PLACED IN SHELVES OF A RETAIL SHOP
Lakshmi Namratha April, 2021
This study presents a robust image processing algorithm for accurately counting sarees/T-shirts placed on retail shop shelves. The proposed algorithm employs k-means clustering for color qua...
INVESTIGATING THE ROLE OF REINFORCEMENT LEARNING IN OPTIMIZING AUTOMATED TASK PIPELINES ACROSS HETEROGENEOUS SYSTEMS
mj iaeme November, 2021
Reinforcement Learning (RL) has emerged as a powerful tool for optimizing decision-making processes in complex environments. This paper explores its application to automate and optimize task...