JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD

Course Objectives:

Provides an in-depth study of the rapidly changing and fascinating field of computer forensics. Combines both the technical expertise and the knowledge required to investigate, detect and prevent digital crimes. Knowledge on digital forensics legislations, digital crime, forensics processes and procedures, data acquisition and validation, e-discovery tools E-evidence collection and preservation, investigating operating systems and file systems, network forensics, art of steganography and mobile device forensics

Course Outcomes

Upon the Successful Completion of the Course, the Students would be able to:

Understand relevant legislation and codes of ethics. Computer forensics and digital detective and various processes, policies and procedures. E-discovery, guidelines and standards, E-evidence, tools and environment. Email and web forensics and network forensics.

UNIT - I

Single Variable Non-Linear Unconstrained Optimization: One dimensional Optimization methods, Uni-modal function, elimination methods, Fibonacci method, golden section method, interpolation methods – quadratic & cubic interpolation methods..

UNIT - II

Multi variable non-linear unconstrained optimization: Direct search method – Univariant method – pattern search methods – Powell’s- Hook -Jeeves, Rosenbrock search methods- gradient methods, gradient of function, steepest decent method, Fletcher Reeves method, variable metric method.

UNIT - III

Linear Programming: Formulation – Sensitivity analysis. Change in the constraints, cost coefficients, coefficients of the constraints, addition and deletion of variable, constraints. Simulation – Introduction – Types- steps – application – inventory – queuing – thermal system

UNIT - IV

Integer Programming: Introduction – formulation – Gomory cutting plane algorithm – Zero or one algorithm, branch and bound method Stochastic programming: Basic concepts of probability theory, random variables- distributions-mean, variance, correlation, co variance, joint probability distribution- stochastic linear, dynamic programming.

UNIT - V

Geometric Programming: Polynomials – arithmetic – geometric inequality – unconstrained G.P constrained G.P (<= TYPE ONLY) Non-traditional optimization Techniques: Genetic Algorithms-Steps- Solving simple problems Comparisons of similarities and dissimilarities between traditional and non- traditional techniques-Particle Swarm Optimization (PSO)- Steps(Just understanding)-Simulated Annealing-Steps-Simple problems.

Text Books:

Optimization theory & Applications, S.S. Rao, New Age International. Engineering Optimization-Kalyan Deb, PHI. Optimization Techniques theory and practice, M. C. Joshi, K. M. Moudgalya/ Narosa Publications.

M.Tech. (Data Science) II Year I Semester