• Editorial
  • I Artificial Intelligence and Machine Learning
    • 1 Data Driven learning
      • 1.1 Data
      • 1.2 5Vs of Big Data
      • 1.3 Significance of Data in Machine Learning
      • 1.4 Types of Data
      • 1.5 Generating data using GAN
      • 1.6 Data for ChatGPT
      • 1.7 Conclusion
    • 2 Machines that can understand Human Languages
      • 2.1 Lexical Analysis
      • 2.2 Syntactic analysis
      • 2.3 Semantic Analysis
      • 2.4 Disclosure Integration
      • 2.5 Pragmatic analysis
    • 3 AI over a Coffee
      • 3.1 The dumb and powerful computers of the past
      • 3.2 New-Age computers that can learn
      • 3.3 A.I. in science and engineering
      • 3.4 Primitive learning algorithms
      • 3.5 Artificial Neural Networks
  • II Astronomy and Astrophysics
    • 1 Applications of Satellite Imaging
      • 1.1 Use cases of Satellite imaging
      • 1.2 Challenges
    • 2 Eclipsing Binaries Part- 2
      • 2.1 Introduction
      • 2.2 Classification according to the physical characteristics of the components
      • 2.3 Classification based on the degree of filling of inner Roche lobes
      • 2.4 Some examples of Eclipsing binaries
  • III Biosciences
    • 1 An introduction to Molecular Biology
      • 1.1 Central Dogma
      • 1.2 How does the Gene expression connected to the Central Dogma of Molecular Biology?
  • IV Computer Programming
    • 1 Remote Sensing and Artificial Intelligence in Agriculture
  • V Fiction
    • 1 Curves and Curved Trajectories