MTECH PROJECTS

MTech CSE Projects is required for post graduate degree in computer science and engineering. We offer M.Tech cse projects for engineering students with efficient technologies, algorithm protocol and techniques. We establish M.Tech cse final year projects with advanced knowledge in various areas in computer science such as operating system configuration, problem solving, parallel programming language and distributed algorithm. We also provide M.Tech CSE thesis for research scholars. We ensure research guidance timely delivery services and brief explanation to M.Tech students.

We implement algorithm as step by step procedure to construct processing elements from Elsevier paper as:

- We perform serial algorithm based on computing assumptions and execute one instruction per second.
- We provide parallel algorithm opposite to serial algorithm which execute several instruction per second.

- We use this algorithm to construct shortest path among two adjacent vertices.
- Logical or procedural: to detect logical control we use this algorithm.

- We refer this algorithm as decrease and conquer algorithm to reduce small instance problem.

- We execute recursive algorithm until it specifies the given condition which is suitable for functional programming.

- Deterministic algorithms are used to solve predefined problem in computing application.
- Non deterministic algorithms perform in basic of guess and provide better solution for each step by heuristics.

We developed more than 90+ projects with various algorithms for MTech students. Some of the algorithms are given below:

**Ford Fulkerson algorithm:**

- We adopt ford Fulkerson algorithm to find maximal flow in network with integer edge capacities.

**Knuth morries Pratt algorithm:**

- We aid this algorithm to check whether a substring occurs in given text in linear time or not.

**Hill climbing:**

- By this algorithm we introduce optimization over non exhaustible domain which leads to whole array of method like simulated annealing, and gradient descent. Optimization permit decision making.

**Naive bayes:**

- In this algorithm we provide classification as first step to machine learning. Naïve bayes classifier combine trending concept of Bayesian inference it address key problems like spam classification.

**Kruskal algorithm for MSI:**

- We adopt this algorithm in data structure to find minimum spanning forest graph.

**Markov chain Monte Carlo:**

- We propose expected value computation and approximate integrate based on condition specification of probability distribution.

**Generic algorithm:**

- We use this algorithm to find near optimal solution to NP complete problem as traveling sale man problem.MTech CSE Projects