Dr. Samad Dadvandipour

Research Activities

- Machine Learning;

- Artificial Neural Networks in different fields;

- Image Processing System (IPS);

- Image Processing System and Neural Networks;

- Electro-Discharge Machining Processing (EDM);

- Integration of CAPP and CAPC in Discrete Manufacturing Systems;

- Optimization of Total Cost of Turning Processes using Design and Mathematical Analysis;

- Design and Manufacturing of TI-135 Type Truck Exhaust and Intake Pipes Using CAD/CAM Systems;

- Simulation and Optimization of Non-Linear Motion of Four-Axis Scara Robot;

- Experimental Process of EDM (Electro-Discharge Machining) with different kinds of electrodes.

Projects Activities

- Solving Some Optimization Problems of CAPP in the CIM Environment (a Part of PhD-thesis);

- Notch Effect on The Reliability of Quasi-Static Loaded Structures (a Part of PhD-thesis);

- Analysing and Documenting Simple and Complex Industrial Components Using the Finite Element Method (Bay Zoltan Interior Project);

- Hungary-Turkey R&D Inter-governmental Project: Developing of CAQC Software for Elimination Turning Process Error;

- Hungary-Greece R&D Inter-governmental Project: Notch Effect in Engineering Structure;

- EU Inco-Copernicus Project: Hungary, Germany, Slovenia, and Belgium: Rapid Sheet Metal Product Development Chain by Laser Sintered Prototype Tool;

- Hungary-Germany R&D Inter-governmental Project: Abrasive Water Jet Cutting Systems in CAD/CAM Environment;

- TAMOP–4.2.1.B-10/2/KONV-2010-0001;

- MeMOOC project (TÁMOP-4.1.2. F-15/1-2015-0001);

- EFOP-3.6.1-16-2016-00011

Publications (Recent)

1. Samad Dadvandipour, Yahya Layth Khaleel: “Application of deep learning algorithms detecting fake and correct textual or verbal news,” DOI: https://doi.org/10.32968/psaie.2022.2.4.

2. Samad Dadvandipour, Aadil Gani Ganie: “An Approach to Implementation of Autoencoders in Intelligent Vehicles,” VAE 2022: Vehicle and Automotive Engineering pp 3–10, Springer https://doi.org/10.1007/978-3-031-15211-5_1

3. Aadil Gani Ganie, Samad Dadvandipour: “Identification of online harassment using ensemble fine-tuned pre-trained Bert”, Pollack Periodica, 2022, 17 (2022) 3, 13–18, DOI:10.1556/606.2022.00608.

4. Owais Mujtaba Khanday, Samad Dadvandipour, Mohd Aaqib Lone: “Effect of filter sizes on image classification in CNN: a case study on CFIR10 and Fashion-MNIST datasets”, Vol. 10, No. 4, December 2021, pp. 872-878 ISSN: 2252-8938, IAES International Journal of Artificial Intelligence, DOI: 10.11591/ijai.v10.i4.

5. Owais Mujtaba Khanday, Samad Dadvandipour: “Analysis of machine learning algorithms for character recognition,” A Case Study on Handwritten Digit Recognition, Indonesian, Journal of Electrical Engineering and Computer Science Vol. 21, No. 1, January 2021, pp. 574-581 ISSN: 2502-4752, DOI: 10.11591/ijeecs.v21.i1. Pp 574-581.

Previously performed research work: 2021 Sept-2022 April

Autoencoder neural networks are unsupervised machine learning algorithms. They apply backpropagation, setting the target values equal to the inputs. They are algorithms similar to PCA but minimize the same objective function. An autoencoder is a neural network whose target output is its input. Autoencoders are pretty identical to PCA, but they are more flexible when compared to the others. For example, autoencoders can represent linear and non-linear transformations in encoding, but PCA can perform linear transformations.

Research Results

1.     How do the autoencoders work- Discussing theoretical and practical implementation views.

2.  The autoencoders application for training the networks, ignoring signal noise, and learning a data set commonly for dimensionality reduction purposes.

3.     Implementation of Autoencoders in Image Recognition

4.     Medical Application of  Autoencoder.

Further recommendation

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Application of Robotic Processing Automation Using Different Machine Learning Methods In Industrial Administration Affairs.  

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