Anthony Manojh F. Profile

5.0

(17 reviews)

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$40 USD / hour

rostock,

germany

$40 USD / hour

It's currently 8:48 AM here

Joined February 25, 2016

1 Recommendation

Anthony Manojh F.

@ashyantony7

5.0

(17 reviews)

4.9

100%

$40 USD / hour

rostock,

germany

$40 USD / hour

90%

Jobs Completed

100%

On Budget

90%

On Time

24%

Repeat Hire Rate

Optimization

Programming Languages : Python, C++, JavaScript Programming for the last 15 years with a decade of professional experience.

See all Matlab and Mathematica Engineers in Germany

Contact Anthony Manojh F. about your job

Portfolio

The Project investigates the improvement in clustering accuracy of various datasets using general Fuzzy C-means Clustering and several variants of them

Fuzzy C-Means Clustering algorithms

Reviews

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Showing 1 - 5 out of 17 reviews

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1.0

$200.00 CAD

Project 17993600 has been deleted

Couldn't complete the project even after several deadline extensions

Closed User

@FinVictory

•

5 years ago

4.8

£150.00 GBP

Configurable Timeseries AR, plots and Tests

Anthony has good analytical mind and clearly enjoys what he does. He is very professional, mature and keen to deliver to satisfaction. I'd hire Anthony again for sure.

Python

Software Architecture

Statistics

Machine Learning (ML)

Kris G.

@Psimonix

•

6 years ago

5.0

$420.00 USD

Project 17801672 has been deleted

As the first project, it was an absolute pleasure to work with Anthony. Above expectations!

Jan G.

@jangimph

•

6 years ago

5.0

$166.00 USD

Project 17742307 has been deleted

It was a pleasure to work with him. Anthony is not only very smart and had good own ideas, but he worked so structured and very fast. I can definitely recommend him!

Jan G.

@jangimph

•

6 years ago

5.0

$35.00 USD

optimize my python code with multithreading or GPU

very good communication!

Python

S Z.

@salizym

•

6 years ago

Publications

Deterministic oscillatory search: a new meta-heuristic optimization algorithm

Springer

The algorithm combines the deterministic nature of classical methods of optimization and global converging characteristics of meta-heuristic algorithms. Common traits of nature-inspired algorithms like randomness and tuning parameters (other than population size) are eliminated. The proposed algorithm is tested with mathematical benchmark functions and compared to other popular optimization algorithms.