Hanumat Sastry

University of petroleum & energy studies, Information Technology, Faculty Member

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Beat Signer

Vrije Universiteit Brussel

B. Harun Küçük

University of Pennsylvania

Adrian Cussins

Universidad Nacional de Colombia (National University of Colombia)

Stefano Maffei

Politecnico di Milano

David Seamon

Kansas State University

Armando Marques-Guedes

UNL - New University of Lisbon

Lev Manovich

Graduate Center of the City University of New York

Hamza Aldabbas

Al-Balqa Applied University

Marica Amadeo

Mediterranean University of Reggio Calabria

Rene Mulder

University Medical Center Groningen

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Papers by Hanumat Sastry

Stock indices price prediction in real time data stream using deep learning with extra-tree ensemble optimisation

by Hanumat Sastry and monika Arya

International Journal of Computational Science and Engineering

Optimal solution approach on large scale data to avoid deadlocks in resource allocations

Materials Today: Proceedings

Abstract Resources and Processes are two elements of the computational approach that make large-s... more Abstract Resources and Processes are two elements of the computational approach that make large-scale data analytics applications for databases and database management systems possible. Processes are the actions that various components take in order to conduct analysis and deal with massive databases. Resources are necessary for process management because they enable computational methodologies and functions and offer crucial hard and soft frameworks that aid in improved process management. The purpose of this research is to provide a graph theoretic technique for allocating resources to processes in order to avoid deadlocks. The set of resources and processes are combined in this work to provide the set of vertices for a directed graph. The graph's edges have been collected as ordered pairs, with each ordered pair denoting a process’s request either for any resource or for a process's allocation of resources. Furthermore, centrality metrics have been utilized to find cycles in the graph and after the cycles have been acquired; those cycles with potential deadlocks have been found. The ranking of vertices based on centralities is used to identify cycles with the potential to generate deadlocks. After getting the deadlock cycles, those edges are identified using a matrix operation, which may be deleted or postponed for a fixed period in order to prevent deadlock or locate other allocations. The essential edges, or resource demands, necessary to ensure a smooth, deadlock-free processing are identified. The findings of this study are essential for resource managers to comprehend the meaning and use of reserved resources that can be utilized in small quantities to avoid resource allocation deadlocks.

Semantic Information Extraction from Multi-Corpora Using Deep Learning

Computers, Materials & Continua

Information extraction plays a vital role in natural language processing, to extract named entiti... more Information extraction plays a vital role in natural language processing, to extract named entities and events from unstructured data. Due to the exponential data growth in the agricultural sector, extracting significant information has become a challenging task. Though existing deep learningbased techniques have been applied in smart agriculture for crop cultivation, crop disease detection, weed removal, and yield production, still it is difficult to find the semantics between extracted information due to unswerving effects of weather, soil, pest, and fertilizer data. This paper consists of two parts. An initial phase, which proposes a data preprocessing technique for removal of ambiguity in input corpora, and the second phase proposes a novel deep learning-based long short-term memory with rectification in Adam optimizer and multilayer perceptron to find agricultural-based named entity recognition, events, and relations between them. The proposed algorithm has been trained and tested on four input corpora i.e., agriculture, weather, soil, and pest & fertilizers. The experimental results have been compared with existing techniques and it was observed that the proposed algorithm outperforms Weighted-SOM, LSTM+RAO, PLR-DBN, KNN, and Naïve Bayes on standard parameters like accuracy, sensitivity, and specificity.

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