Table of Contents
Preface
Acknowledgements
Abbreviations
Chapter 1. Introduction
What Is AI?
Brief History
Machine Learning
Deep Learning
Generative AI
Foundation Models
Time Series
Multivariant Time Series
Autocorrelation
Partial Autocorrelation Function (PACF)
Stationarity Check: Augmented Dickey Fuller (ADF) & Kwiatkowski-Phillips-Schmidt-Shin (KPSS) Tests
Forecasting Models
Sound Spectrum Engineering Principles and Algorithms
MFCC
CQT
Machine Learning: MFCC of Audio
Step 1: Load Audio File of General Conversation
Step 2: Load Audio File of General Conversation
Step 3: Plot the Audio and Visualize a Waveform in the Time Domain
Step 4: Display Spectrogram
Step 5: Mel-Frequency Cepstral Coefficients (MFCCs)
Step 6: Visualize MFCCs
Step 6: Extract Chroma Features
Step 7: Extract Power Spectrum (CQT)
Use Case: Golden Walls and Other Legends of Padmanabhaswamy Temple
Commerce and Wealth Connection!
To Open or Leave the Temple’s Vault Alone?
Machine Learning Model: Garuḍa Mantra Audio Waves & Vault B Connection
Step 1: Load Audio File of General Conversation
Step 2: Load Audio File of General Conversation
Step 3: Plot the Audio and Visualize a Waveform in the Time Domain
Step 4: Display Spectrogram
Step 5: Mel-Frequency Cepstral Coefficients (MFCCs)
Step 6: Visualize MFCCs
Step 7: Extract Chroma Features
Step 8: Extract Power Spectrum (CQT)
Supervised Learning
Regression Models
Metrics for Regression Models
Random Forest Trees
Gradient Boosting Regressor
Polynomial Regression
Chapter Machine Learning Experiments
ML Experimient. 1 Golconda Fort Sound Security Systems Signature
ML Experiment. 2 Palm Leaf Manuscripts and Language Understanding
References
Chapter 2. Jyotirlingas and Time
Hinduism: Karma Siddhanta
View Grided Data
Cosmic Rays
Incidents of Cosmic Rays & Electronic Bit Flipping
Cosmic Rays and Toyota Recall
Cosmic Rays & Statistics
Cosmic Rays and Early Life!
General Principles of Calendar
Time Reckoning
Time Frequency
Lunar Cycle
The Importance of the Prediction of Tidal Currents
Properties of Physics & Harmonic Model of Tides
Data Science Model: Understanding the Currents, Tides, and Harmonic Constituents of Tides
Step 1: Prepare Data Science & Python Libraries to Analyze Tides
Step 2: Load New York Tides Data
Step 3: Plot Statistical Distribution
Step 4: Tides Harmonic Analysis – Full Moon Phases
Step 5: Tides Harmonic Analysis – Dark Moon Phases
Data Science Linear Mathematical Model Approximation: Relative Tides Height & Somanath Jyotirlingam
Steps 1 & 2: Prepare Data Science & Python Libraries to Analyze Tides
Step 3: Plot the Tides Data Distribution
High Tide Height (Minima)
Low Tide Height (Maxima)
Historical Note
Natural Time-Series Analysis
Luni-Solar Calendar
Jyotirlinga
Material Science
Chapter Machine Learning Experiments
ML Experimient.1 Relative Sea Level Trend 600-041 Fort Phrachula Chomklao, Thailand
ML Experimient.2 Cooling of the Human Body
Experiment 3: Cosmic Rays
References
Chapter 3. Jyotirlinga and Locations
Locations
Longitude and Latitudes
Haversine Distances
Jyotirlingas and Locations
Meridian and Cluster Techniques
Data Science Model: Jyotirlinga Locations & Cluster Model
Types of Data in Cluster
Distance Calculations
Clustering Approaches
Hierarchical Clustering – Merging Clusters
Non-Hierarchical Clustering
Step 1: Prepare Data Science & Python Libraries to Analyze Tides
Step 2: Prepare Data Relating to Jyotirlinga Locations
Step 3: Empty Array to Store Distances
Step 4: Calculate Haversine
Step 5: Perform Square Distance and Single Linkage Cluster
Step 6: Perform Square Distance and Complete Linkage Cluster
Step 7: Perform Square Distance and Ward Linkage Cluster
Deccan Traps, Jyotirlingas and Shaktipeethas
Chapter Machine Learning Experiments
ML Experiment: Jyotirlinga Locations & K-Means Cluster
References
Chapter 4. Jyotirlingas and Time Series Patterns
Somanath
Time Series
Data Science Time Series Model: Estimate Height of Somanath Jyotirlingam
Steps 1 & 2: Prepare Data Science & Python Libraries to Analyze Tides
Step 3: Copy Tidal Data to Prepare Time Series Analysis
Step 4: Plot the Tidal Data
Step 5: Check Normality of the Data
Step 6: Perform Time Series Checks – Augmented_Dickey_Fuller and Shapiro-Wilk
Step 7: Perform Kurtosis and Skewness
Step 8: Create Time Series
Step 9: Perform Time Series Prediction
Historical Note
Mallikarjuna Jyotirlinga
Mahakaleshwar Jyotirlinga
Data Science Model: Global and Regional Vegetation Health (VH) of the Earth
Step 1: Prepare Data Science & Python Libraries to Analyze VH Index
Step 2: Load VH Data for MP, India 1982:2022
Step 3: Load VH Data for MP, India
Step 4: Analyze Distributions
White Noise: Stationary Time Series
Omkareshwar Jyotirlinga
White Noise
Data Science Model: Understand Sounds of the Universe
Step 1: Load Audio Signal Data – Kepler: Star KIC12268220C Light Curve Waves to Sound
Step 2: Plot the Audio Signal
Step 3: Draw the Spectrogram
Step 1: Load Audio Signal Data – Plasma Waves
Step 2: Plot the Audio Signal
Step 3: Compute Spectral Centroids
Step 1: Load Audio Signal Data OM Chanting
Step 2: Plot the Audio Signal
Step 3: Compute Spectral Centroids
Vaidyanath Jyotirlinga
Deccan Traps, Jyotirlingas and Shaktipeethas
Bhimashankar Jyotirlinga
Star Gazing & Celestial Objects
Chapter Machine Learning Experiments
ML Experiment. Solar System & Beyond: NASA Spooky Space ‘Sounds’ – Feature Engineering Unstructured Data (Audio & Wave Files)
References
Chapter 5. Conclusion
About Author
Index
Author’s ORCID iD
Chandrasekar Vuppalapati – 0000-0003-2261-759X
