Santosh Kumar Nanda

Work place: Computer Science and Engineering Department, Eastern Academy of Science & Technology, Bhubaneswar, Odisha– 754001, India



Research Interests: Swarm Intelligence, Artificial Intelligence, Software Development Process, Computer Science & Information Technology, Information Technology Management, Image Processing


Dr. Santosh Kumar Nanda is working as Professor in Eastern Academy of Science and Technology, Bhubaneswar, Orissa, India. He completed his PhD from National Institute of Technology, Rourkela. His research interests are Soft Computing, Artificial Intelligence, Image Processing, Prediction of machinery noise and vibration, Noise and vibration control, Mathematical modelling, Pattern Recognition. He has more than 60 research articles in reputed International Journals and International conferences etc. He is also serving more than 15 journals as Editor/Reviewer. So far, Dr. Nanda has produced 1 PhD and more than 15 M.Tech Scholars. Now-a-days he is supervising another 1 PhD and 6 M.Tech students, respectively. He is also International Program Committee Member of 18th Online World Conference on Soft Computing in Industrial Applications (WSC18) 2014, Committee Member of 17th Online World Conference on Soft Computing in Industrial Applications (WSC17) 2012,16th Online World Conference on Soft Computing in Industrial Applications (WSC16) 2011 and 15th Online World Conference on Soft Computing in Industrial Applications (WSC15) 2010. He is now selected as International Program Committee of 2014 and 2013 World Conference on Information Systems and Technologies (WorldCIST, France). Currently he is an Individual Member in International Rough Set Society and Member of International Association of Engineers (IAENG). His name was selected for Marquis Who’s and Who 2011, 2012 and 2013. In 2014, he was selected as a Young Researcher Member in World federation of Soft Computing, USA.

Author Articles
Model Driven Test Case Optimization of UML Combinational Diagrams Using Hybrid Bee Colony Algorithm

By Rajesh Ku. Sahoo Santosh Kumar Nanda Durga Prasad Mohapatra Manas Ranjan Patra

DOI:, Pub. Date: 8 Jun. 2017

To detect faults or errors for designing the quality software, software testing tool is used. Testing manually is an expensive and time taking process. To overcome this problem automated testing is used. Test case generation is a vital concept used in software testing which can be derived from requirements specification. Automation of test cases is a method where it can generate the test cases and test data automatically by using search based optimization technique. Model-driven testing is an approach that represents the behavioral model and also encodes the system behavior with certain conditions. Generally, the model consists of a set of objects that defined through variables and object relationships. This piece of work is used to generate the automated optimized test cases or test data with the possible test paths from combinational system graph. A hybrid bee colony algorithm is proposed in this paper for generating and optimizing the test cases from combinational UML diagrams.

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Prediction of Rainfall in India using Artificial Neural Network (ANN) Models

By Santosh Kumar Nanda Debi Prasad Tripathy Simanta Kumar Nayak Subhasis Mohapatra

DOI:, Pub. Date: 8 Nov. 2013

In this paper, ARIMA(1,1,1) model and Artificial Neural Network (ANN) models like Multi Layer Perceptron (MLP), Functional-link Artificial Neural Network (FLANN) and Legendre Polynomial Equation ( LPE) were used to predict the time series data. MLP, FLANN and LPE gave very accurate results for complex time series model. All the Artificial Neural Network model results matched closely with the ARIMA(1,1,1) model with minimum Absolute Average Percentage Error(AAPE). Comparing the different ANN models for time series analysis, it was found that FLANN gives better prediction results as compared to ARIMA model with less Absolute Average Percentage Error (AAPE) for the measured rainfall data.

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Digital Image Texture Classification and Detection Using Radon Transform

By Satyabrata Sahu Santosh Kumar Nanda Tanushree Mohapatra

DOI:, Pub. Date: 8 Oct. 2013

A novel and different approach for detecting texture orientation by computer was presented in this research work. Many complex real time problem example detection of size and shape of cancer cell, classification of brain image signal, classification of broken bone structure, detection and classification of remote sensing images, identification of foreign particle in universe, detection of material failure in construction design, detection and classification of textures in particularly fabrications etc where edge detection and both vertical and horizontal line detection are essential. Thus researches need to develop different algorithm for this above complex problem. It is seen from literature that conventional algorithm DCT, FFT are all highly computational load and hence impossible task to implemented in hardware. These difficulties were solved in this particular research work by applying DWT and radon transform. It was seen from the simulation result that with very high computational load the entire algorithm takes very less CPU time and proved its robustness.

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Location Prediction of Mobility Management Using Soft Computing Techniques in Cellular Network

By Smita Parija Santosh Kumar Nanda Prasanna Kumar Sahu Sudhansu Sekhar Singh

DOI:, Pub. Date: 8 May 2013

This work describes the neural network technique to solve location management problem. A multilayer neural model is designed to predict the future prediction of the subscriber based on the past predicted information of the subscriber. In this research work, a prediction based location management scheme is proposed for locating a mobile terminal in a communication without losing quality maintains a good response. There are various methods of location management schemes for prediction of the mobile user. Based on individual characteristic of the user, prediction based location management can be implemented. This work is purely analytical which need the past movement of the subscriber and compared with the simulated one. The movement of the mobile target is considered as regular and uniform. An artificial neural network model is used for mobility management to reduce the total cost. Single or multiple mobile targets can be predicted. Among all the neural techniques multilayer perceptron is used for this work. The records are collected from the past movement and are used to train the network for the future prediction. The analytical result of the prediction method is found to be satisfactory.

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Development of Regression Models for Assessing Fire Risk of Some Indian Coals

By Devidas S. Nimaje D.P. Tripathy Santosh Kumar Nanda

DOI:, Pub. Date: 8 Jan. 2013

Spontaneous combustion of coals leading to mine fires is a major problem in Indian coal mines that creates serious safety and mining risk. A number of experimental techniques based on petrological, thermal and oxygen avidity studies have been used for assessing the spontaneous heating liability of coals all over the world. Crossing point temperature (CPT) is one of the most common methods in India to assess the fire risk of coal so that appropriate strategies and effective action plans could be made in advance to prevent occurrence and spread of fire and hence minimize coal loss. In this paper, the spontaneous heating risks of some of the Indian coals covering few major coalfields were assessed using CPT apparatus. Statistical analysis was carried out between CPT and the proximate analysis parameters and it was found that the Mixture Surface Regression (MSR) model was more effective and gave very good residual values as compared to the polynomial and simple multiple regression models. The performance of Anderson-Darling testing was done between the prediction results of MSR model and measured value of CPT showed that the residual follows normal distribution hence justifies the suitability of model for the prediction of spontaneous heating liability of coal.

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A Novel Application of Artificial Neural Network for the Solution of Inverse Kinematics Controls of Robotic Manipulators

By Santosh Kumar Nanda Swetalina Panda P Raj Sekhar Subudhi Ranjan Kumar Das

DOI:, Pub. Date: 8 Aug. 2012

In robotic applications and research, inverse kinematics is one of the most important problems in terms of robot kinematics and control. Consequently, finding the solution of Inverse Kinematics in now days is considered as one of the most important problems in robot kinematics and control. As the intricacy of robot manipulator increases, obtaining the mathematical, statistical solutions of inverse kinematics are difficult and computationally expensive. For that reason, now soft-computing based highly intelligent based model applications should be adopted to getting appropriate solution for inverse kinematics. In this paper, a novel application of artificial neural network is used for controlling a robotic manipulator. The proposed methods are based on the establishments of the non-linear mapping between Cartesian and joint coordinates using multi layer perceptron and functional link artificial neural network.

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