*This book covers key areas of mathematics and computer science. The contributions by the authors include** algorithm of Collatz, variables, Emiliano theorem, Diophantic equations, integral representation, super-singular kernels, invers formula, three-dimensional integral equations, Dirichlet type boundary value problem, three-body problem, Liberation points, Linear stability, zero velocity curve, **handwriting recognition**,** database scheme**,** database interface, i**nternet router, self-similarity, Markovian arrival process, queueing theory, matrix analytic methods, partial buffer sharing, packet loss probability, function zeta, convergent series,** Abel**-**Ruffini Theorem, Enfer Diez equation, congruence method, polynomials, dimension reduction, multidimensional information visualization, Euclidean distance, embedding algorithms, pattern classification, infinite algorithms, hierarchical scale space, multiscale, feature extraction, segmentation, fuzzy inference system, landsat- 8, fuzzy knowledge rules, object oriented image analysis, google earth engine, machine learning, algorithms, supervised learning methods, predictive analytics, data analytics, climate change, support vector machine, linear discriminant analysis, feature extraction, feature selection, operational data center, MPLS computer networks, VDI, Model-view-controller, customized algorithm, spatio-temporal databases, Region of Interest, geospatial analysis, parallel programming, concurrency, autoregression, contaminated errors, distribution mixes. This book contains various materials suitable for students, researchers and academicians in the field of mathematics and computer science.*

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Mixtures of Distributions and Volatility: A Theoretical Explanation

Design of Spatio-temporal Data Archival for Big-data Satellite Imagery: Use Cases

Hierarchical Scale-space Representational Measure for Estimating Land Cover

An Application of the Algorithm (3a+1) in the Problem of Computing O\((2^n)\)

A Novel Approach to Visualization of High-Dimensional Data by Pairwise Fusion Matrices Using t-SNE

Polynomials of Degree Greater Than or Equal to FIVE are Solved by a Resolvable Finite Group

Mixtures of Distributions and Volatility: A Theoretical Explanation

Design of Spatio-temporal Data Archival for Big-data Satellite Imagery: Use Cases

Hierarchical Scale-space Representational Measure for Estimating Land Cover

An Application of the Algorithm (3a+1) in the Problem of Computing O\((2^n)\)

A Novel Approach to Visualization of High-Dimensional Data by Pairwise Fusion Matrices Using t-SNE

Polynomials of Degree Greater Than or Equal to FIVE are Solved by a Resolvable Finite Group

Mixtures of Distributions and Volatility: A Theoretical Explanation

Design of Spatio-temporal Data Archival for Big-data Satellite Imagery: Use Cases

Hierarchical Scale-space Representational Measure for Estimating Land Cover

An Application of the Algorithm (3a+1) in the Problem of Computing O\((2^n)\)

A Novel Approach to Visualization of High-Dimensional Data by Pairwise Fusion Matrices Using t-SNE

Polynomials of Degree Greater Than or Equal to FIVE are Solved by a Resolvable Finite Group

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