Aim

The primary aim of the International Journal of Future Technologies is to:

• Promote cutting-edge research on technologies that have the potential to influence future industries, societies, and scientific disciplines.

• Encourage interdisciplinary collaboration across fields such as engineering, computer science, artificial intelligence, robotics, data science, sustainable technologies, and human-technology interaction.

• Disseminate high-impact scholarly work that addresses current challenges and anticipates future technological trends.

• Support innovation and practical applications by sharing research that bridges fundamental theory and real

  Scope

  The journal welcomes high-quality contributions in, but not limited to, the following areas:

  • Generative Artificial Intelligence
    
    • Large Language Models (LLMs)
    • AI Content Generation
    • Generative Adversarial Networks (GANs)
    • AI-based Text, Image, Audio, and Video Generation
    • Prompt Engineering
    • AI Agents and Autonomous Systems
  • Machine Learning and Deep Learning
    
    • Supervised and Unsupervised Learning
    • Predictive Analytics
    • Deep Neural Networks
    • Transfer Learning
    • Ensemble Learning
    • Intelligent Recommendation Systems
  • Natural Language Processing (NLP)
    
    • Text Mining and Sentiment Analysis
    • Chatbots and Conversational AI
    • Language Translation Systems
    • Speech Recognition
    • Information Retrieval
    • Named Entity Recognition
  • Computer Vision and Image Processing
    
    • Object Detection and Recognition
    • Facial Recognition Systems
    • Medical Image Analysis
    • Pattern Recognition
    • Video Analytics
    • Augmented Reality and Visual Intelligence
  • Intelligent Computing Systems
    
    • Intelligent Decision Support Systems
    • Smart Computing Architectures
    • Adaptive Computing Models
    • Intelligent Automation
    • Context-Aware Systems
    • Smart Monitoring and Control Systems
  • Neural Networks and Cognitive Computing
    
    • Artificial Neural Networks (ANN)
    • Convolutional Neural Networks (CNN)
    • Recurrent Neural Networks (RNN)
    • Cognitive Learning Models
    • Brain-Inspired Computing
    • Self-Learning Intelligent Systems