Mahendra Kumar Gourisaria

Work place: School of Computer Engineering, KIIT University, Bhubaneswar, Odisha

E-mail: mkgourisaria2010@gmail.com

Website: https://orcid.org/0000-0002-1785-8586

Research Interests: Data Structures and Algorithms, Data Mining, Autonomic Computing, Computer systems and computational processes

Biography

Mahendra Kumar Gourisaria is presently working as an Assistant Professor in the School of Computer Engineering at KIIT University, Bhubaneswar, Odisha. He has received his Master degree in Computer Application from Indira Gandhi National Open University, New Delhi and M.Tech in Computer Science and Engineering from Biju Patnaik University of Technology, Rourkela. He is pursuing his Ph.D. from KIIT University. He has published number of research papers in different international journals and conferences of repute. His area of research includes Cloud Computing, Data Mining, Soft Computing and Internet and Web Technology. He is a member of IAENG, UACEE and life member of ISTE, CSI and ISCA.

Author Articles
Climate Literacy: Creating a Multilevel Interactive Platform for Climate Education

By Ruth George Phiri Lameck Nsama Ngula Walubita Swati Samantaray Sudhansu Shekhar Patra Manoj Ranjan Mishra Mahendra Kumar Gourisaria

DOI: https://doi.org/10.5815/ijmecs.2025.05.03, Pub. Date: 8 Oct. 2025

Climate literacy is crucial to increasing public understanding and engagement with the global climate catastrophe. However, current climate education approaches often fail to effectively raise concern and action, particularly across diverse age groups. This study makes a modest attempt to detail the design and development of a novel multilevel interactive digital climate education platform for early learners, adolescents, and adults using adaptive learning pathways, personalized content delivery, multimedia interactivity, and gamification features to promote learner engagement as well as learning outcomes across different age levels. A mixed-methods research design was used involving pre and post-survey quantitative measures as well as qualitative user experience testing. Post-survey results indicated significant improvement in climate literacy knowledge, attitudes towards the environment, and self-efficacy beliefs regarding individual efforts to mitigate future climate impacts (response efficacy), regardless of learner age group. The comparative analysis thus revealed certain content preferences by age as well as interaction patterns among functionalities and learning gains between groups based on user perspectives that point towards differentiated preference areas linked with diverse ages. The resulting platform exemplifies interactive digital technologies’ potential for achieving sustainable behavior change through optimised synergies with large-scale educational interventions for inducing positive spillover effects in terms of broader widespread climate change engagement impact over generational transition pragma.

[...] Read more.
Heart Disease Prediction Using Frequent Item Set Mining and Classification Technique

By Sinkon Nayak Mahendra Kumar Gourisaria Manjusha Pandey Siddharth Swarup Rautaray

DOI: https://doi.org/10.5815/ijieeb.2019.06.02, Pub. Date: 8 Nov. 2019

The heart is the most important part of the human body. Any abnormality in heart results heart related illness in which it obstructs blood vessels which causes heart attack, chest pain or stroke. Care and improvement of the health by the help of identification, prevention, and care of any kind of diseases is the main goal. So for this various prediction analysis methods are used which job is to identify the illness at prelim phase so that prevention and care of heart disease is done. This paper emphasizes on the care of heart diseases at a primitive phase so that it will lead to a successful cure. In this paper, diverse data mining classification method like Decision tree classification, Naive Bayes classification, Support Vector Machine classification, and k-NN classification are used for determination and safeguard of the diseases.

[...] Read more.
A Parallel Soft Computing Model for Identifying Lost Student in an Incomplete and Imprecise Environment

By Mahendra Kumar Gourisaria Susil Rayaguru Satya Ranjan Dash Sudhansu Shekhar Patra

DOI: https://doi.org/10.5815/ijisa.2018.04.07, Pub. Date: 8 Apr. 2018

The numbers of educational institutions are growing at par with the lost student rate in a country like India. When a missing student is found we need to identify the student on the strength of some common parameter like student name, his/her institution name, branch or class etc. But we never get accurate and complete information in most of the cases to identify or recognize a lost student. In such a situation, a soft computing model can be a striking choice to track a lost student on the basis of partial information. In the past we propose soft computing model for the same. This paper proposes a more optimized parallel soft computing model which takes half of the time taken by the earlier single thread model for identifying a lost student on the basis of imprecise and partial information. The system is tested meticulously on a database of 50000 records and an efficiency of 94% is obtained.

[...] Read more.
A Survey Work on Optimization Techniques Utilizing Map Reduce Framework in Hadoop Cluster

By Bibhudutta Jena Mahendra Kumar Gourisaria Siddharth Swarup Rautaray Manjusha Pandey

DOI: https://doi.org/10.5815/ijisa.2017.04.07, Pub. Date: 8 Apr. 2017

Data is one of the most important and vital aspect of different activities in today's world. Therefore vast amount of data is generated in each and every second. A rapid growth of data in recent time in different domains required an intelligent data analysis tool that would be helpful to satisfy the need to analysis a huge amount of data. Map Reduce framework is basically designed to process large amount of data and to support effective decision making. It consists of two important tasks named as map and reduce. Optimization is the act of achieving the best possible result under given circumstances. The goal of the map reduce optimization is to minimize the execution time and to maximize the performance of the system. This survey paper discusses a comparison between different optimization techniques used in Map Reduce framework and in big data analytics. Various sources of big data generation have been summarized based on various applications of big data.The wide range of application domains for big data analytics is because of its adaptable characteristics like volume, velocity, variety, veracity and value .The mentioned characteristics of big data are because of inclusion of structured, semi structured, unstructured data for which new set of tools like NOSQL, MAPREDUCE, HADOOP etc are required. The presented survey though provides an insight towards the fundamentals of big data analytics but aims towards an analysis of various optimization techniques used in map reduce framework and big data analytics.

[...] Read more.
Other Articles