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Dr. Hrudaya Kumar Tripathy presently working as Associate Professor at School of Computer Engineering, KIIT (Deemed to be University), Bhubaneswar, India. He had been a visiting faculty at Asia Pacific University, Kuala Lumpur, Malaysia, and University Utara Malaysia, Sintok, Malaysia. He is having 20 years of teaching experience with post-doctorate research experience in the field of Soft Computing, Machine Learning, Speech Processing, AI, Mobile Robotics, and Big Data Analysis. Received many certificates of merits and highly applauded in a presentation of research papers at International conferences. He has published a number of research papers in reputed international & national refereed journals & conferences. He is a senior member of IEEE, a life member of CSI & having membership in other different professional bodies such as IET, IACSIT, IAENG.
In recent years, there is an increasing interest in designing autonomous vehicles such as Mobile Robots. However, one of the fundamental needs of Mobile Robots is a collision-free navigation with an optimal path from the source to the destination. In this paper, a collision-free low-complexity Mobile Robot navigation scheme called Collision Aware Mobile Robot navigation in Grid-Environment is designed. The proposed scheme uses the Radio Frequency based Identification method for Mobile Robot localization, the hybrid approach for the path planning, and a predefined decision table for the navigation. The algorithms are implemented in two stages, construction of virtual world and generation of optimal shortest path. The algorithms’ performance is analyzed in grid-based environment of size 5×5,20×20,35×35, and 50 × 50 with four different cases. It is observed that for the environment with no obstacles, the robot explores fewer cells in finding the shortest path. However, the number of turning in the shortest path is always less in the environment with some obstacles than in the case of whole virtual world exploration. The performance results show the effectiveness of the proposed scheme for Mobile Robot navigation in the grid environment.
Hrudaya Kumar Tripathy; Sushruta Mishra; Hiren Kumar Thakkar; Deepak Rai. CARE: A Collision-Aware Mobile Robot Navigation in Grid Environment using Improved Breadth First Search. Computers & Electrical Engineering 2021, 94, 107327 .
AMA StyleHrudaya Kumar Tripathy, Sushruta Mishra, Hiren Kumar Thakkar, Deepak Rai. CARE: A Collision-Aware Mobile Robot Navigation in Grid Environment using Improved Breadth First Search. Computers & Electrical Engineering. 2021; 94 ():107327.
Chicago/Turabian StyleHrudaya Kumar Tripathy; Sushruta Mishra; Hiren Kumar Thakkar; Deepak Rai. 2021. "CARE: A Collision-Aware Mobile Robot Navigation in Grid Environment using Improved Breadth First Search." Computers & Electrical Engineering 94, no. : 107327.
Energy consumption is a crucial domain in energy system management. Recently, it was observed that there has been a rapid rise in the consumption of energy throughout the world. Thus, almost every nation devises its strategies and models to limit energy usage in various areas, ranging from large buildings to industrial firms and vehicles. With technological advancements, computational intelligence models have been successfully contributing to the prediction of the consumption of energy. Machine learning and deep learning-based models enhance the precision and robustness compared to traditional approaches, making it more reliable. This article performs a review analysis of the various computational intelligence approaches currently being utilized to predict energy consumption. An extensive survey procedure is conducted and presented in this study, and relevant works are discussed. Different criteria are considered during the aggregation of the relevant studies relating to the work. The author’s perspective, future trends and various novel approaches are also presented as a part of the discussion. This article thereby lays a foundation stone for further research works to be undertaken for energy prediction.
Sunil Mohapatra; Sushruta Mishra; Hrudaya Tripathy; Akash Bhoi; Paolo Barsocchi. A Pragmatic Investigation of Energy Consumption and Utilization Models in the Urban Sector Using Predictive Intelligence Approaches. Energies 2021, 14, 3900 .
AMA StyleSunil Mohapatra, Sushruta Mishra, Hrudaya Tripathy, Akash Bhoi, Paolo Barsocchi. A Pragmatic Investigation of Energy Consumption and Utilization Models in the Urban Sector Using Predictive Intelligence Approaches. Energies. 2021; 14 (13):3900.
Chicago/Turabian StyleSunil Mohapatra; Sushruta Mishra; Hrudaya Tripathy; Akash Bhoi; Paolo Barsocchi. 2021. "A Pragmatic Investigation of Energy Consumption and Utilization Models in the Urban Sector Using Predictive Intelligence Approaches." Energies 14, no. 13: 3900.
Customization of products or services is a strategy that the business sector has embraced to build a better relationship with the customers to cater to their individual needs and thus providing them a fulfilling experience. This whole process is known as customer relationship management (CRM). In this context, we extensively surveyed 138 papers published between 1996 and 2021 in the area of analytical CRM. Although this study consisted of papers from different business sectors, a fair share of focus was directed to the telecommunication industry and generalized CRM techniques usages. Different science and engineering-based data repositories were studied to ascertain significant studies published in scientific journals, conferences, and articles. The research works on CRM were considered and separated into IT and non-IT-based techniques to study the methods used in different business sectors. The main target behind implementing CRM is for the better revenue growth of the company. Different IT and non-IT-based techniques are used in the analytical CRM area to achieve this target, and researchers have been actively involved in this domain. The purpose of the research was to show the impact of IT-based techniques in the business world. A detailed future course of research in this area was discussed.
Lewlisa Saha; Hrudaya Tripathy; Soumya Nayak; Akash Bhoi; Paolo Barsocchi. Amalgamation of Customer Relationship Management and Data Analytics in Different Business Sectors—A Systematic Literature Review. Sustainability 2021, 13, 5279 .
AMA StyleLewlisa Saha, Hrudaya Tripathy, Soumya Nayak, Akash Bhoi, Paolo Barsocchi. Amalgamation of Customer Relationship Management and Data Analytics in Different Business Sectors—A Systematic Literature Review. Sustainability. 2021; 13 (9):5279.
Chicago/Turabian StyleLewlisa Saha; Hrudaya Tripathy; Soumya Nayak; Akash Bhoi; Paolo Barsocchi. 2021. "Amalgamation of Customer Relationship Management and Data Analytics in Different Business Sectors—A Systematic Literature Review." Sustainability 13, no. 9: 5279.
There is a consistent rise in chronic diseases worldwide. These diseases decrease immunity and the quality of daily life. The treatment of these disorders is a challenging task for medical professionals. Dimensionality reduction techniques make it possible to handle big data samples, providing decision support in relation to chronic diseases. These datasets contain a series of symptoms that are used in disease prediction. The presence of redundant and irrelevant symptoms in the datasets should be identified and removed using feature selection techniques to improve classification accuracy. Therefore, the main contribution of this paper is a comparative analysis of the impact of wrapper and filter selection methods on classification performance. The filter methods that have been considered include the Correlation Feature Selection (CFS) method, the Information Gain (IG) method and the Chi-Square (CS) method. The wrapper methods that have been considered include the Best First Search (BFS) method, the Linear Forward Selection (LFS) method and the Greedy Step Wise Search (GSS) method. A Decision Tree algorithm has been used as a classifier for this analysis and is implemented through the WEKA tool. An attribute significance analysis has been performed on the diabetes, breast cancer and heart disease datasets used in the study. It was observed that the CFS method outperformed other filter methods concerning the accuracy rate and execution time. The accuracy rate using the CFS method on the datasets for heart disease, diabetes, breast cancer was 93.8%, 89.5% and 96.8% respectively. Moreover, latency delays of 1.08 s, 1.02 s and 1.01 s were noted using the same method for the respective datasets. Among wrapper methods, BFS’ performance was impressive in comparison to other methods. Maximum accuracy of 94.7%, 95.8% and 96.8% were achieved on the datasets for heart disease, diabetes and breast cancer respectively. Latency delays of 1.42 s, 1.44 s and 132 s were recorded using the same method for the respective datasets. On the basis of the obtained result, a new hybrid Attribute Evaluator method has been proposed which effectively integrates enhanced K-Means clustering with the CFS filter method and the BFS wrapper method. Furthermore, the hybrid method was evaluated with an improved decision tree classifier. The improved decision tree classifier combined clustering with classification. It was validated on 14 different chronic disease datasets and its performance was recorded. A very optimal and consistent classification performance was observed. The mean values for accuracy, specificity, sensitivity and f-score metrics were 96.7%, 96.5%, 95.6% and 96.2% respectively.
Sushruta Mishra; Pradeep Mallick; Hrudaya Tripathy; Akash Bhoi; Alfonso González-Briones. Performance Evaluation of a Proposed Machine Learning Model for Chronic Disease Datasets Using an Integrated Attribute Evaluator and an Improved Decision Tree Classifier. Applied Sciences 2020, 10, 8137 .
AMA StyleSushruta Mishra, Pradeep Mallick, Hrudaya Tripathy, Akash Bhoi, Alfonso González-Briones. Performance Evaluation of a Proposed Machine Learning Model for Chronic Disease Datasets Using an Integrated Attribute Evaluator and an Improved Decision Tree Classifier. Applied Sciences. 2020; 10 (22):8137.
Chicago/Turabian StyleSushruta Mishra; Pradeep Mallick; Hrudaya Tripathy; Akash Bhoi; Alfonso González-Briones. 2020. "Performance Evaluation of a Proposed Machine Learning Model for Chronic Disease Datasets Using an Integrated Attribute Evaluator and an Improved Decision Tree Classifier." Applied Sciences 10, no. 22: 8137.
Disease diagnosis is a critical task which needs to be done with extreme precision. In recent times, medical data mining is gaining popularity in complex healthcare problems based disease datasets. Unstructured healthcare data constitutes irrelevant information which can affect the prediction ability of classifiers. Therefore, an effective attribute optimization technique must be used to eliminate the less relevant data and optimize the dataset for enhanced accuracy. Type 2 Diabetes, also called Pima Indian Diabetes, affects millions of people around the world. Optimization techniques can be applied to generate a reliable dataset constituting of symptoms that can be useful for more accurate diagnosis of diabetes. This study presents the implementation of a new hybrid attribute optimization algorithm called Enhanced and Adaptive Genetic Algorithm (EAGA) to get an optimized symptoms dataset. Based on readings of symptoms in the optimized dataset obtained, a possible occurrence of diabetes is forecasted. EAGA model is further used with Multilayer Perceptron (MLP) to determine the presence or absence of type 2 diabetes in patients based on the symptoms detected. The proposed classification approach was named as Enhanced and Adaptive-Genetic Algorithm-Multilayer Perceptron (EAGA-MLP). It is also implemented on seven different disease datasets to assess its impact and effectiveness. Performance of the proposed model was validated against some vital performance metrics. The results show a maximum accuracy rate of 97.76% and 1.12 s of execution time. Furthermore, the proposed model presents an F-Score value of 86.8% and a precision of 80.2%. The method is compared with many existing studies and it was observed that the classification accuracy of the proposed Enhanced and Adaptive-Genetic Algorithm-Multilayer Perceptron (EAGA-MLP) model clearly outperformed all other previous classification models. Its performance was also tested with seven other disease datasets. The mean accuracy, precision, recall and f-score obtained was 94.7%, 91%, 89.8% and 90.4%, respectively. Thus, the proposed model can assist medical experts in accurately determining risk factors of type 2 diabetes and thereby help in accurately classifying the presence of type 2 diabetes in patients. Consequently, it can be used to support healthcare experts in the diagnosis of patients affected by diabetes.
Sushruta Mishra; Hrudaya Kumar Tripathy; Pradeep Kumar Mallick; Akash Kumar Bhoi; Paolo Barsocchi. EAGA-MLP—An Enhanced and Adaptive Hybrid Classification Model for Diabetes Diagnosis. Sensors 2020, 20, 4036 .
AMA StyleSushruta Mishra, Hrudaya Kumar Tripathy, Pradeep Kumar Mallick, Akash Kumar Bhoi, Paolo Barsocchi. EAGA-MLP—An Enhanced and Adaptive Hybrid Classification Model for Diabetes Diagnosis. Sensors. 2020; 20 (14):4036.
Chicago/Turabian StyleSushruta Mishra; Hrudaya Kumar Tripathy; Pradeep Kumar Mallick; Akash Kumar Bhoi; Paolo Barsocchi. 2020. "EAGA-MLP—An Enhanced and Adaptive Hybrid Classification Model for Diabetes Diagnosis." Sensors 20, no. 14: 4036.
Prabin Kumar Panigrahi; Hrudya Kumar Tripathy. Analysis on Intelligent Based Navigation and Path Finding of Autonomous Mobile Robot. Advances in Intelligent Systems and Computing 2015, 339, 219 -232.
AMA StylePrabin Kumar Panigrahi, Hrudya Kumar Tripathy. Analysis on Intelligent Based Navigation and Path Finding of Autonomous Mobile Robot. Advances in Intelligent Systems and Computing. 2015; 339 ():219-232.
Chicago/Turabian StylePrabin Kumar Panigrahi; Hrudya Kumar Tripathy. 2015. "Analysis on Intelligent Based Navigation and Path Finding of Autonomous Mobile Robot." Advances in Intelligent Systems and Computing 339, no. : 219-232.
Prabin Kumar Panigrahi; Hrudaya Kumar Tripathy. Low Complexicity Graph Based Navigation and Path Finding of Mobile Robot Using BFS. Proceedings of the 2nd International Conference on Cryptography, Security and Privacy 2015, 189 -195.
AMA StylePrabin Kumar Panigrahi, Hrudaya Kumar Tripathy. Low Complexicity Graph Based Navigation and Path Finding of Mobile Robot Using BFS. Proceedings of the 2nd International Conference on Cryptography, Security and Privacy. 2015; ():189-195.
Chicago/Turabian StylePrabin Kumar Panigrahi; Hrudaya Kumar Tripathy. 2015. "Low Complexicity Graph Based Navigation and Path Finding of Mobile Robot Using BFS." Proceedings of the 2nd International Conference on Cryptography, Security and Privacy , no. : 189-195.
This paper proposed an online path planning of mobile robot in a grid-map environment using modified real time A* algorithm. This algorithm has implemented in simulated and Khepera-II environment and find the optimized path from an initial predefine position to a predefine target position by avoiding the obstacles in its trajectory of path. The path finding strategy is designed in a grid-map and cluttered environment with static and dynamic obstacles with quadrant concept. The optimization the path is found using this algorithm as the goal is present in any of the four quadrant and restricted the movement of the robot to only one quadrant. Robot will plan an optimal path by avoiding obstructions in its way and minimizing time, energy, and distance as the cost, but the original A* algorithm find the shortest path not optimized. Finally, it is compared with other heuristic algorithms.
Prasanta Kumar Das; H. S. Behera; S. K. Pradhan; Hrudaya Kumar Tripathy; Puspanjali Jena. A Modified Real Time A* Algorithm and Its Performance Analysis for Improved Path Planning of Mobile Robot. Blockchain Technology and Innovations in Business Processes 2014, 221 -234.
AMA StylePrasanta Kumar Das, H. S. Behera, S. K. Pradhan, Hrudaya Kumar Tripathy, Puspanjali Jena. A Modified Real Time A* Algorithm and Its Performance Analysis for Improved Path Planning of Mobile Robot. Blockchain Technology and Innovations in Business Processes. 2014; ():221-234.
Chicago/Turabian StylePrasanta Kumar Das; H. S. Behera; S. K. Pradhan; Hrudaya Kumar Tripathy; Puspanjali Jena. 2014. "A Modified Real Time A* Algorithm and Its Performance Analysis for Improved Path Planning of Mobile Robot." Blockchain Technology and Innovations in Business Processes , no. : 221-234.
In this present scenario, the application of speech science has a vital role to produce the biometric applications. After so many research and improvement of Automatic Speech Recognition, accuracy of speech recognition is one of the challenging task. Various feature extraction is one of the Linear Predictive Coding, cepstral analysis, Local Discriminant Base, Restricted Boltzmann Machines have been discussed since past days. Similarly, a lot of debates have been arranged among the researchers for feature matching. Some of them are Hidden Markov Model (HMM), Dynamic time warping (DTW), Deep Belief Network.This paper is a clear reflection of automatic speech recognition. It describes various feature extraction and matching and focuses on analytical study based on performance metrics like Word Error Rate (WER) and accuracy of these techniques.
Ruchismita Tripathy; Hrudaya Kumar Tripathy. Unalike methodologies of feature extraction & feature matching in Speech Recognition. 2014 International Conference on High Performance Computing and Applications (ICHPCA) 2014, 1 -6.
AMA StyleRuchismita Tripathy, Hrudaya Kumar Tripathy. Unalike methodologies of feature extraction & feature matching in Speech Recognition. 2014 International Conference on High Performance Computing and Applications (ICHPCA). 2014; ():1-6.
Chicago/Turabian StyleRuchismita Tripathy; Hrudaya Kumar Tripathy. 2014. "Unalike methodologies of feature extraction & feature matching in Speech Recognition." 2014 International Conference on High Performance Computing and Applications (ICHPCA) , no. : 1-6.
Covering based rough sets have been introduced as an extension of the basic rough sets introduced by Pawlak. There is only one way to define the lower approximation of a set using covering. However, as many as four definitions have been proposed for covering based upper approximation. Accordingly, we find four types of covering based rough sets in the literature. In earlier work the concept of rough equivalences of sets, which extended the corresponding notions of approximate equalities of Novotny and Pawlak were introduced and their properties were studied. In this article we further generalize these concepts to the setting of covering based rough sets and establish some of their properties.
B. K. Tripathy; Hrudaya Ku. Tripathy. Covering Based Rough Equivalence of Sets and Comparison of Knowledge. 2009 International Association of Computer Science and Information Technology - Spring Conference 2009, 303 -307.
AMA StyleB. K. Tripathy, Hrudaya Ku. Tripathy. Covering Based Rough Equivalence of Sets and Comparison of Knowledge. 2009 International Association of Computer Science and Information Technology - Spring Conference. 2009; ():303-307.
Chicago/Turabian StyleB. K. Tripathy; Hrudaya Ku. Tripathy. 2009. "Covering Based Rough Equivalence of Sets and Comparison of Knowledge." 2009 International Association of Computer Science and Information Technology - Spring Conference , no. : 303-307.
Recently, new applications have emerged that require database management systems with uncertainty capabilities. Many of the existing approaches to modeling uncertainty in database management systems are based on the theory of fuzzy sets. This paper introduces basic parallelism in fuzzy relational databases and its queries. Article describes basic characteristics of fuzzy relational databases and their properties, compares fuzzy logic with Boolean one in connection with fuzzy relational databases. Deal with data classification and form of storage fuzzy data in database systems. Paper describes fuzzy queries and parallelism in queries from view of today's systems and their connection to SQL language and its extension fuzzy SQL. Discuss about pipeline parallelism of query operators and its benefits.
Hrudaya Ku Tripathy; B. K. Tripathy; Pradip K. Das; Saraju Pr. Khadanga. Application of Parallelism SQL in Fuzzy Relational Databases. 2008 International Conference on Computer Science and Information Technology 2008, 553 -557.
AMA StyleHrudaya Ku Tripathy, B. K. Tripathy, Pradip K. Das, Saraju Pr. Khadanga. Application of Parallelism SQL in Fuzzy Relational Databases. 2008 International Conference on Computer Science and Information Technology. 2008; ():553-557.
Chicago/Turabian StyleHrudaya Ku Tripathy; B. K. Tripathy; Pradip K. Das; Saraju Pr. Khadanga. 2008. "Application of Parallelism SQL in Fuzzy Relational Databases." 2008 International Conference on Computer Science and Information Technology , no. : 553-557.
Data mining and/or knowledge discovery is a very important part of today's e-business. The World Wide Web has become in reality, the largest online information available practically to anyone with access to Internet. An e-business framework is proposed in the paper, as well as the knowledge discovery technique to personalize e-business, increase cross selling, and improve the customer relationship management. Due to the enormous size of the Web and low precision of user queries, results returned from present Web search engines can reach hundreds or even thousands data. Therefore, finding the right information can be difficult if not impossible. One approach that tries to solve this problem is by using clustering techniques for grouping similar data together in order to facilitate presentation of results in more compact form and enable browsing of the results set. In this paper, a data clustering techniques is presented with emphasis on application to Web search results. An algorithm for clustering Web data based on Rough Set is presented and its practical implementation is discussed.
Hrudaya Ku. Tripathy; B. K. Tripathy. A Rough Set Approach for Clustering the Data Using Knowledge Discovery in World Wide Web for E-Business. IEEE International Conference on e-Business Engineering (ICEBE'07) 2007, 717 -722.
AMA StyleHrudaya Ku. Tripathy, B. K. Tripathy. A Rough Set Approach for Clustering the Data Using Knowledge Discovery in World Wide Web for E-Business. IEEE International Conference on e-Business Engineering (ICEBE'07). 2007; ():717-722.
Chicago/Turabian StyleHrudaya Ku. Tripathy; B. K. Tripathy. 2007. "A Rough Set Approach for Clustering the Data Using Knowledge Discovery in World Wide Web for E-Business." IEEE International Conference on e-Business Engineering (ICEBE'07) , no. : 717-722.