About the Journal

Aim and Scope   [ISSN: 2583-5521 (Online)]

BOHR International Journal of Internet of things, Artificial Intelligence and Machine Learning (BIJIAM) is an open access peer-reviewed journal that publishes articles which contribute new results in all the areas of Internet of things, Artificial Intelligence and Machine Learning. Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in this area.

Journal Particulars

Frequency of Publication

: Annual (Jan-Dec)

Publication Format

: Online

Language

: English

Starting Year

: 2022

DOI

: 10.54646/bijiam

License

: CC BY 4.0

Copyright Statement

: Copyright © 2024; The Author(s).

The subject areas covered by the BIJIAM include (but are not limited to):

Internet of things
  • Artificial Intelligence-Based Security and Data Protection
  • Big Data and Information Integrity in IoT
  • Cloud Computing, Fog Computing, and Edge Computing
  • Cognitive and Reasoning about Things and Smart Objects
  • Cross-layer Attacks in IoT
  • Cryptography, Key Management, Authentication and Authorization for IoT
  • Cyber Security
  • Electronics for the Internet of Things
  • Green IoT: Sustainable Design and Technologies
  • Horizontal Application Development for IoT
  • Internet of Things Communication Systems and Network Infrastructures
  • IoT and Blockchain
  • IoT and Machine Learning
  • IoT Application Programming Interface (API)
  • IoT Deployment in Agriculture, Retail, Smart Cities, etc.
  • IoT Interconnections Among ISPs Analysis-QoS, Scalability, Performance, Interference
  • IoT Interoperability and Multi-Platform Integration
  • IoT Large-Scale Pilots and Portability
  • IoT Protocols and Standards (IPv6, 6LoWPAN, RPL, 6TiSCH, WoT, oneM2M, etc.)
  • IoT Security (Authentication, Access Control, Security Models) and Privacy Preservation
  • Medical and Healthcare Systems via the Internet
  • Mobile Cloud Computing (MCC) and IoT
  • Mobile Edge Computing
  • Multi-Objective IoT System Modeling and Analysis-Performance, Energy, Reliability, Robustness
  • Physical/MAC/Network Attacks in Internet of Things
  • Semantic Technologies, Collective Intelligence
  • Sensors Data Management, IoT Mining, and Analytics
  • Smart Home and IoT-based Building Automation
  • Wireless Sensor Network for IoT Security
Artificial Intelligence
  • Advanced Actuators and Sensors
  • Artificial Neural Network
  • Automated Reasoning and Logic Programming
  • CAD Design & Testing
  • Cognitive, Swarm, and Neuro Robotics
  • Deep Neural Networks
  • Force Sensors, Accelerometers, and Other Measuring Devices
  • Fuzzy Logic
  • Healthcare, Medical, and Assistive Robotics
  • Heuristic and AI Planning Strategies and Tools
  • Human-Robot Interaction and Social Robotics
  • Hybridisation of Intelligent Models/Algorithms
  • Information Retrieval
  • Intelligent Robotics, Mechatronics, and Biomimetics
  • IoT, Intelligent Cloud, and Fog Computing
  • Machine Learning, Big Data, and Analytics
  • Mathematical and Computational Methodologies in Robotics
  • Mobile Robots and Intelligent Autonomous Systems
  • Natural Language Processing
  • Parallel and Distributed Realisation of Intelligent Algorithms/Systems
  • Path/Trajectory Planning
  • Reinforcement Learning
  • Space and Underwater Robotics
  • Visual/linguistic Perception
  • Web Intelligence Applications & Search
Machine Learning
  • Adaptive Control, Robotics, and Control, Game Playing & Computer Vision
  • Automated Knowledge Acquisition
  • Bayesian and Other Probabilistic Methods
  • Biological Justification
  • Cheminformatics & Benchmark Studies
  • Codes and Datasets, Big Data, Data Mining & Web Mining
  • Cognitive Model & Cognitive Science
  • Combinatorial Optimization
  • Computational Anatomy, Novel Computing Architectures & Computational Neuroscience
  • Explainability, Causality, and Robustness
  • Information management and interdisciplinary areas
  • Information systems & Information science
  • Kernel Methods, Reinforcement Learning & Analogical Learning Methods
  • Learning in Integrated Architectures
  • Learning in Knowledge-Intensive Systems
  • Machine Teaching, Machine Perception, Brain-Machine Interfaces & Machine Learning Algorithms
  • Metascience
  • Multistrategy learning, Multi-agent learning & New (physics-inspired) learning algorithms
  • Natural Language Processing & Object Recognition
  • Neural Network Architectures & Bayesian Network
  • Problem Solving and Planning
  • Reasoning and Inference & Design and Diagnosis
  • Reinforcement Learning, Predictive Learning & Deep Learning
  • Science of Science Policy
  • Scientific Discovery & Information Retrieval
  • Search Engines, Internet Fraud Detection & Automatic Reasoning
  • Sociology of Science
  • Supervised Machine Learning & Unsupervised Machine Learning
  • Vision and Speech Perception
  • Visualization of Patterns in Data

Important Dates

Submission Deadline : May 21, 2024
Notification : June 21, 2024
Final Manuscript Due : June 28, 2024
Publication Date : Determined by the Editor-in-Chief