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G. N. Vivekananda

Bio: G. N. Vivekananda is an academic researcher from Madanapalle Institute of Technology and Science. The author has contributed to research in topics: Computer science & Network congestion. The author has an hindex of 3, co-authored 9 publications receiving 27 citations. Previous affiliations of G. N. Vivekananda include Jawaharlal Nehru Technological University, Anantapur.

Papers
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Journal ArticleDOI
TL;DR: In this article, the authors proposed that land use/Land cover plays a vital role in planning and supervising the utilization of the natural resources based on the gradual increase in the human demands in the current ecosystem.
Abstract: Land use/Land cover (LULC) plays a vital role in planning and supervising the utilization of the natural resources based on the gradual increase in the human demands in the current ecosystem. This ...

66 citations

Journal ArticleDOI
28 Jul 2020
TL;DR: This work classifies offline three‐organized CBM with floats of ideas and awkwardness data, using an improved Dynamic AdaBoost for preparing a group classifier and an enhanced linear four rates (LFR) methodology is used by the classifier of nominal and continuous with synthetic minority oversampling technique (SMOTE) method.
Abstract: Nowadays, countless industrial IIoT contraptions and sensors are conveyed a sharp plant to gather tremendous information regarding system conditions and a computerized bodily framework for handling industrial plant's mist point of convergence by using keen assembling projects. By then, the system utilizes an array of condition‐based support model (CBM) procedures to predict when devices begin to unusually work and to keep them up or supplant their fragments ahead of time to avoid assembling colossal investigator items in smart manufacturing industries. CBM experiences problems of floating ideas (ie, conveying examples of deficiencies can change extra time) and information of lop‐sidedness (ie, information with issues represents a minority of all things considered). The condition‐based support assisted learning technique by the group that coordinates the assorted variety of numerous classifiers provides an elite response to address these issues. Therefore, in this work the proposed work classifies offline three‐organized CBM with floats of ideas and awkwardness data, using an improved Dynamic AdaBoost for preparing a group classifier and an enhanced linear four rates (LFR) methodology is used by the classifier of nominal and continuous (NC) with synthetic minority oversampling technique (SMOTE) method to tackle inconsistent information in recognizing concept floats in lop‐sidedness information. The investigational results scheduled datasets by varying notches anomaly demonstration that the future strategy has a high degree of accuracy in the identifiable evidence of minority knowledge, which is over 96%.

16 citations

Journal ArticleDOI
TL;DR: In this paper , an integrated remote supervision with machine learning algorithms (IRS-MLA) is proposed for the online English teaching audit process, which simulates the implementation of supervision methodologies in the teaching process according to English online teaching's real needs.
Abstract: The automated supervision system for online teaching is volatile in current teaching observation. Hence, it requires additional comprehensive, analytical, and realistic discussion on how the automatic supervision method can be applied to high school teaching. This paper integrated remote supervision with machine learning algorithms (IRS-MLA) proposed for the online English teaching audit process. Here, IRS-MLA simulates the implementation of supervision methodologies in the teaching process according to English online teaching’s real needs. Furthermore, searching the performance and stating the learning process for students from the teachers’ perspectives and their students measures the teacher’s teaching process. This paper presents the studies for evaluating the classic English language online supervision and explores this method’s functional impact. This analysis’s findings show that the model developed in this paper worked well and validated based on the case study report. This study validates the proposed IRS-MLA with the highest performance ratio of 97.8%, the accuracy of 96%, the efficiency of 99.3%, and a success ratio of 98%, compared to existing models.

5 citations

Journal ArticleDOI
TL;DR: A Packet Loss Minimising Approach (PLMA-SCTP) based on traffic prediction for multi-streaming application is proposed for the mobile ad hoc networks (MANETs) environment to reduce data loss and delay in multi- streaming communication.
Abstract: Wireless networks are extensively used for communication. Most of the devices that we use for communication are equipped with the wireless network interface and are capable of streaming data efficiently to the device within the communication range. Stream Control Transmission Protocol (SCTP) is designed for multi-streaming service. In this paper, a Packet Loss Minimising Approach (PLMA-SCTP) based on traffic prediction for multi-streaming application is proposed for the mobile ad hoc networks (MANETs) environment. The objective is to reduce data loss and delay in multi-streaming communication. The PLMA-SCTP method aims to provide an efficient route with optimal overhead and delay. Our simulation studies prove that PLMA-SCTP improves the various performance metrics of multi-streaming applications.

5 citations

Proceedings ArticleDOI
08 Oct 2015
TL;DR: In this paper justification the role of Cross-layer approach is presented, and critical analysis of it is done to identify its limitations.
Abstract: Internet has become everyday utility. Internet is ubiquitous. All the devices including household devices are connected to the Internet. Layer approach is used for the design of Internet. Layered approach is robust but is designed for wired networks. Cross-layer approach is examined extensively in mobile networks because of the layered approach inability to provide the required performance. There are many tradeoffs associated with the use of Cross-layer approach. It breaches the basic principles of layered approach and may compromise the robust nature of the Internet. But with wide use of multimedia applications and mobile devices, we cannot continue to use layered approach as it is. In this paper justification the role of Cross-layer approach is presented, and critical analysis of it is done to identify its limitations.

4 citations


Cited by
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01 Jan 2016
TL;DR: The remote sensing and image interpretation is universally compatible with any devices to read and is available in the digital library an online access to it is set as public so you can get it instantly.
Abstract: Thank you very much for downloading remote sensing and image interpretation. As you may know, people have look hundreds times for their favorite novels like this remote sensing and image interpretation, but end up in malicious downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they are facing with some malicious virus inside their computer. remote sensing and image interpretation is available in our digital library an online access to it is set as public so you can get it instantly. Our book servers spans in multiple countries, allowing you to get the most less latency time to download any of our books like this one. Merely said, the remote sensing and image interpretation is universally compatible with any devices to read.

1,802 citations

Journal ArticleDOI
20 Feb 2021-Sensors
TL;DR: In this paper, the authors present a review of the current literature concerning predictive maintenance and intelligent sensors in smart factories, focusing on contemporary trends to provide an overview of future research challenges and classification, using burst analysis, systematic review methodology, co-occurrence analysis of keywords, and cluster analysis.
Abstract: With the arrival of new technologies in modern smart factories, automated predictive maintenance is also related to production robotisation. Intelligent sensors make it possible to obtain an ever-increasing amount of data, which must be analysed efficiently and effectively to support increasingly complex systems' decision-making and management. The paper aims to review the current literature concerning predictive maintenance and intelligent sensors in smart factories. We focused on contemporary trends to provide an overview of future research challenges and classification. The paper used burst analysis, systematic review methodology, co-occurrence analysis of keywords, and cluster analysis. The results show the increasing number of papers related to key researched concepts. The importance of predictive maintenance is growing over time in relation to Industry 4.0 technologies. We proposed Smart and Intelligent Predictive Maintenance (SIPM) based on the full-text analysis of relevant papers. The paper's main contribution is the summary and overview of current trends in intelligent sensors used for predictive maintenance in smart factories.

92 citations

Journal ArticleDOI
TL;DR: In this article, the Google Earth engine (GEE) has been proposed, a free cloud-based computational platform that allows users to access and process remotely sensed data at petabyte scales.
Abstract: The sustainable management of natural heritage is presently considered a global strategic issue. Owing to the ever-growing availability of free data and software, remote sensing (RS) techniques have been primarily used to map, analyse, and monitor natural resources for conservation purposes. The need to adopt multi-scale and multi-temporal approaches to detect different phenological aspects of different vegetation types and species has also emerged. The time-series composite image approach allows for capturing much of the spectral variability, but presents some criticalities (e.g., time-consuming research, downloading data, and the required storage space). To overcome these issues, the Google Earth engine (GEE) has been proposed, a free cloud-based computational platform that allows users to access and process remotely sensed data at petabyte scales. The application was tested in a natural protected area in Calabria (South Italy), which is particularly representative of the Mediterranean mountain forest environment. In the research, random forest (RF), support vector machine (SVM), and classification and regression tree (CART) algorithms were used to perform supervised pixel-based classification based on the use of Sentinel-2 images. A process to select the best input image (seasonal composition strategies, statistical operators, band composition, and derived vegetation indices (VIs) information) for classification was implemented. A set of accuracy indicators, including overall accuracy (OA) and multi-class F-score (Fm), were computed to assess the results of the different classifications. GEE proved to be a reliable and powerful tool for the classification process. The best results (OA = 0.88 and Fm = 0.88) were achieved using RF with the summer image composite, adding three VIs (NDVI, EVI, and NBR) to the Sentinel-2 bands. SVM and RF produced OAs of 0.83 and 0.80, respectively.

79 citations

Journal ArticleDOI
TL;DR: In this article, the authors proposed that land use/Land cover plays a vital role in planning and supervising the utilization of the natural resources based on the gradual increase in the human demands in the current ecosystem.
Abstract: Land use/Land cover (LULC) plays a vital role in planning and supervising the utilization of the natural resources based on the gradual increase in the human demands in the current ecosystem. This ...

66 citations

Proceedings Article
19 Jun 2014
TL;DR: Evaluations with micro-benchmarks showed that the OS I/O path optimizations aimed to minimize scheduling delays caused by additional contexts such as interrupt bottom halves and background queue runs were capable of accommodating up to five, AHCI controller attached, SATA 3.0 SSD devices at 671k IOPS.
Abstract: In this paper, we present OS I/O path optimizations for NAND flash solid-state drives, aimed to minimize scheduling delays caused by additional contexts such as interrupt bottom halves and background queue runs. With our optimizations, these contexts are eliminated and merged into hardware interrupts or I/O participating threads without introducing side effects. This was achieved by pipelining fine grained host controller operations with the cooperation of I/O participating threads. To safely expose fine grained host controller operations to upper layers, we present a low level hardware abstraction layer interface. Evaluations with micro-benchmarks showed that our optimizations were capable of accommodating up to five, AHCI controller attached, SATA 3.0 SSD devices at 671k IOPS, while current Linux SCSI based I/O path was limited at 354k IOPS failing to accommodate more than three devices. Evaluation on an SSD backed key value system also showed IOPS improvement using our I/O optimizations.

32 citations