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1,176 results on '"Mishra, Om"'

1. Boundedness for Unions of Conjunctive Regular Path Queries over Simple Regular Expressions

Author

Figueira, Diego, Krishna, S., Mishra, Om Swostik, and Padmanabha, Anantha

Subjects
Computer Science - Databases
Abstract

The problem of checking whether a recursive query can be rewritten as query without recursion is a fundamental reasoning task, known as the boundedness problem. Here we study the boundedness problem for Unions of Conjunctive Regular Path Queries (UCRPQs), a navigational query language extensively used in ontology and graph database querying. The boundedness problem for UCRPQs is ExpSpace-complete. Here we focus our analysis on UCRPQs using simple regular expressions, which are of high practical relevance and enjoy a lower reasoning complexity. We show that the complexity for the boundedness problem for this UCRPQs fragment is $\Pi^P_2$-complete, and that an equivalent bounded query can be produced in polynomial time whenever possible. When the query turns out to be unbounded, we also study the task of finding an equivalent maximally bounded query, which we show to be feasible in $\Pi^P_2$. As a side result of independent interest stemming from our developments, we study a notion of succinct finite automata and prove that its membership problem is in NP.

Published
2024

3. An efficient quantifier elimination procedure for Presburger arithmetic

Author

Haase, Christoph, Krishna, Shankara Narayanan, Madnani, Khushraj, Mishra, Om Swostik, and Zetzsche, Georg

Subjects
Computer Science - Logic in Computer Science, Computer Science - Formal Languages and Automata Theory, Mathematics - Logic
Abstract

All known quantifier elimination procedures for Presburger arithmetic require doubly exponential time for eliminating a single block of existentially quantified variables. It has even been claimed in the literature that this upper bound is tight. We observe that this claim is incorrect and develop, as the main result of this paper, a quantifier elimination procedure eliminating a block of existentially quantified variables in singly exponential time. As corollaries, we can establish the precise complexity of numerous problems. Examples include deciding (i) monadic decomposability for existential formulas, (ii) whether an existential formula defines a well-quasi ordering or, more generally, (iii) certain formulas of Presburger arithmetic with Ramsey quantifiers. Moreover, despite the exponential blowup, our procedure shows that under mild assumptions, even NP upper bounds for decision problems about quantifier-free formulas can be transferred to existential formulas. The technical basis of our results is a kind of small model property for parametric integer programming that generalizes the seminal results by von zur Gathen and Sieveking on small integer points in convex polytopes., Comment: Accepted for publication at ICALP 2024

Published
2024

7. Analyzing Twitter Data Using Apache Hive—A Big Data Technology Exploration

Author

Sharma, Kanhaiya, Kapshe, Mansi, Bhargava, Parth, Trivedi, Prakhar, Changde, Sanika, Mishra, Om, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Chillarige, Raghavendra Rao, editor, Distefano, Salvatore, editor, and Rawat, Sandeep Singh, editor

Published
2024
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8. Sustainable Growth of Automobile Industry in India

Author

Singh, Surender, Kumar, Krishan, Mishra, Om Prakash, Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Ivanov, Vitalii, Series Editor, Haddar, Mohamed, Series Editor, Cavas-Martínez, Francisco, Editorial Board Member, di Mare, Francesca, Editorial Board Member, Kwon, Young W., Editorial Board Member, Trojanowska, Justyna, Editorial Board Member, Xu, Jinyang, Editorial Board Member, Bhardwaj, Arvind, editor, Pandey, Pulak Mohan, editor, and Misra, Aviral, editor

Published
2024
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20. Convolutional Neural Network-Based Quality of Fruit Detection System Using Texture and Shape Analysis

Author

Mishra, Om, Parashar, Deepak, Harikrishanan, Gaikwad, Abhinav, Gagare, Anubhav, Darade, Sneha, Bhalla, Siddhant, Pandey, Gaurav, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Mishra, Anurag, editor, Gupta, Deepak, editor, and Chetty, Girija, editor

Published
2023
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21. Payroll Management Using Blockchain

Author

Giri, Govinda, Chakate, Kunal, Gonge, Sudhanshu, Joshi, Rahul, Kotecha, Ketan, Mishra, Om, Parashar, Deepak, Mulay, Preeti, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Hassanien, Aboul Ella, editor, Castillo, Oscar, editor, Anand, Sameer, editor, and Jaiswal, Ajay, editor

Published
2023
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33. Human Action Recognition Using a New Hybrid Descriptor

Author

Mishra, Om, Kavimandan, Pranoti S., Tripathi, M. M., Kapoor, Rajiv, Yadav, Kalpana, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Harvey, David, editor, Kar, Haranath, editor, Verma, Shekhar, editor, and Bhadauria, Vijaya, editor

Published
2021
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34. Osteopetrosis and renal tubular acidosis: Answers

Author

Singh, Ankur, Rajawat, Jaideep, Singh, Akanksha, Abhinay, Abhishek, Prasad, Rajniti, and Mishra, Om Prakash

Subjects
Osteopetrosis -- Diagnosis -- Patient outcomes -- Tests, problems and exercises, Acidosis -- Diagnosis -- Patient outcomes -- Tests, problems and exercises, Health
Abstract

Author(s): Ankur Singh [sup.1] , Jaideep Rajawat [sup.2] , Akanksha Singh [sup.3] , Abhishek Abhinay [sup.3] , Rajniti Prasad [sup.2] , Om Prakash Mishra [sup.3] Author Affiliations: (1) grid.411507.6, 0000 [...]

Published
2021
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41. Epileptic seizure classification using fuzzy lattices and Neural Reinforcement Learning.

Author

Kukker, Amit, Sharma, Rajneesh, Mishra, Om, and Parashar, Deepak

Subjects
EPILEPSY, REINFORCEMENT learning, SCHRODINGER equation, DATABASES, KINETIC energy, CLASSIFICATION, THETA rhythm
Abstract

This work uses Fuzzy Lattices and Neural Reinforcement Learning techniques for seizure classification. EEG database of Bonn University and CHB-MIT has been used for the evaluation of the proposed method. Here, features are represented in the form of fuzzy lattices, and feature vectors are created in the form of Kinetic Energy (K.E.) using Schrödinger equation. Then, the highest K.E.-based seven fuzzy lattices have been used for classification using Neural Reinforcement Learning classifier. Neural Reinforcement Learning classifier is a self-learner method that classifies different seizure sub-classes (healthy eyes open, healthy eye closed, Epileptogenic Zone, hippocampal formation of opposite hemisphere, epileptic seizure). The effectiveness of the proposed method has been tested on two different public datasets. The average classification accuracy achieved is 97.6% and 97.5% for Bonn and CHB-MIT datasets, respectively. Results are compared with existing techniques to show the precedence of proposed approach. Also, computation speed of proposed classifier is more than 1.5 times compared to Fuzzy Q Learning classifier. The objective is to develop a hybrid model integrating fuzzy lattices and neural reinforcement learning (adaptive methods) for accurate classification, aiming to enhance and speeding up seizure detection and diagnosis to improve patient care through advanced computational techniques. [ABSTRACT FROM AUTHOR]

Published
2024
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