Performance Evaluation of an Independent Time Optimized Infrastructure for Big Data Analytics that Maintains Symmetry

Vats, Satvik; Sagar, Bharat Bhushan; Singh, Kritika; Ahmadian, Ali; Pansera, Bruno Antonio

doi:10.3390/sym12081274

Cited by 38 publications

(5 citation statements)

References 36 publications

(47 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The enhanced algorithm suggested in this study significantly reduces the uncertainty of data fusion when compared to the D-S evidence theory, Kmeans, and Bayesian algorithm. [7] S. Vats, et al (2020) reviewed the evaluation of the efficiency of a time-optimized, symmetrical big data analytics framework. In the time-optimization paradigm presented in this study, three Namenodes (Master Nodes), three Data-nodes, & a single Client-node all shared HDFS.…”

Section: Figure 2 Deep Learning Techniques 14 Optimizationmentioning

confidence: 99%

“…The proposed model is also capable of running any kind of algorithm on various data sets that are available in their native forms. [8] S. Riaz, et al (2020) focused on privacy and security concerns around big data in the cloud at the current time and where the field is headed in terms of research. In this paper, they analyse and critique existing frameworks and architectures for data security that is continually established against threats to improve how to keep and store data in the cloud setting, and we provide an overview of the characteristics and current state of big data and data security and privacy top threats, open issues, current challenges, and their impact on business for future research perspective.…”

Section: Figure 2 Deep Learning Techniques 14 Optimizationmentioning

confidence: 99%

See 1 more Smart Citation

Security of Big Data over IoT Environment by Integration of Deep Learning and Optimization

Alleema

Raman

Castro-Cayllahua

et al. 2022

Int. j. commun. netw. inf. secur.

View full text Add to dashboard Cite

This is especially true given the spread of IoT, which makes it possible for two-way communication between various electronic devices and is therefore essential to contemporary living. However, it has been shown that IoT may be readily exploited. There is a need to develop new technology or combine existing ones to address these security issues. DL, a kind of ML, has been used in earlier studies to discover security breaches with good results. IoT device data is abundant, diverse, and trustworthy. Thus, improved performance and data management are attainable with help of big data technology. The current state of IoT security, big data, and deep learning led to an all-encompassing study of the topic. This study examines the interrelationships of big data, IoT security, and DL technologies, and draws parallels between these three areas. Technical works in all three fields have been compared, allowing for the development of a thematic taxonomy. Finally, we have laid the groundwork for further investigation into IoT security concerns by identifying and assessing the obstacles inherent in using DL for security utilizing big data. The security of large data has been taken into consideration in this article by categorizing various dangers using a deep learning method. The purpose of optimization is to raise both accuracy and performance.

show abstract

Section: Figure 2 Deep Learning Techniques 14 Optimizationmentioning

confidence: 99%

Section: Figure 2 Deep Learning Techniques 14 Optimizationmentioning

confidence: 99%

Security of Big Data over IoT Environment by Integration of Deep Learning and Optimization

Alleema

Raman

Castro-Cayllahua

et al. 2022

Int. j. commun. netw. inf. secur.

View full text Add to dashboard Cite

show abstract

“…In the realm of data science, the performance efficiency of algorithms plays a critical role in the success of various applications [16,17]. As datasets continue to grow in size and complexity, data scientists are constantly seeking algorithms that can analyze and process data quickly and accurately [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Analyzing the Opportunities and Challenges Associated With Using Mathematical and Statistical Methods in Data Science

Kuppuswamy,

Al-Maliki,

Abdul Azees

et al. 2023

INDJCSE

View full text Add to dashboard Cite

Data science is an interdisciplinary field that applies scientific methods, processes, algorithms, and systems to extract meaningful insights and knowledge from structured and unstructured data. At the core of this field lies the utilization of mathematical algorithms to analyze vast datasets and make informed decisions. These algorithms, infused with statistical techniques, computational models, and predictive analytics, play a crucial role in understanding patterns, predicting trends, and making data-driven decisions. Data science utilizes mathematical tools, algorithms, and techniques to extract meaningful insights from data, make predictions, and drive decision-making processes. This article explores the strong relationship between data science and mathematics, delving into various mathematical concepts and methodologies employed in data science, and highlighting their applications and contributions to the field. Basically, there is a lot of confusion about using and implementing perfect modeling algorithms in data science. In this article, the utilization of different machine learning and Artificial Intelligence algorithms applied to data science are described. Moreover, in this article we try to explore the concept of performance efficiency in data science algorithms, highlighting the factors that influence efficiency, discussing common metrics for evaluation, and presenting optimization techniques to enhance algorithm performance.

show abstract

“…The most readily used modeling methods include numerical, statistical, deterministic, and stochastic models. However, these models have some limitations, such as inadequate competencies and complex structures, and require exhaustive details about the model development [9][10][11][12]. Moreover, these traditional models showed relatively low prediction accuracies and unbalanced forecasts for various levels of water quality.…”

Section: Introductionmentioning

confidence: 99%

Comparative Assessment of Individual and Ensemble Machine Learning Models for Efficient Analysis of River Water Quality

et al. 2022

View full text Add to dashboard Cite

The prediction accuracies of machine learning (ML) models may not only be dependent on the input parameters and training dataset, but also on whether an ensemble or individual learning model is selected. The present study is based on the comparison of individual supervised ML models, such as gene expression programming (GEP) and artificial neural network (ANN), with that of an ensemble learning model, i.e., random forest (RF), for predicting river water salinity in terms of electrical conductivity (EC) and dissolved solids (TDS) in the Upper Indus River basin, Pakistan. The projected models were trained and tested by using a dataset of seven input parameters chosen on the basis of significant correlation. Optimization of the ensemble RF model was achieved by producing 20 sub-models in order to choose the accurate one. The goodness-of-fit of the models was assessed through well-known statistical indicators, such as the coefficient of determination (R2), mean absolute error (MAE), root mean squared error (RMSE), and Nash–Sutcliffe efficiency (NSE). The results demonstrated a strong association between inputs and modeling outputs, where R2 value was found to be 0.96, 0.98, and 0.92 for the GEP, RF, and ANN models, respectively. The comparative performance of the proposed methods showed the relative superiority of the RF compared to GEP and ANN. Among the 20 RF sub-models, the most accurate model yielded the R2 equal to 0.941 and 0.938, with 70 and 160 numbers of corresponding estimators. The lowest RMSE values of 1.37 and 3.1 were yielded by the ensemble RF model on training and testing data, respectively. The results of the sensitivity analysis demonstrated that HCO3− is the most effective variable followed by Cl− and SO42− for both the EC and TDS. The assessment of the models on external criteria ensured the generalized results of all the aforementioned techniques. Conclusively, the outcome of the present research indicated that the RF model with selected key parameters could be prioritized for water quality assessment and management.

show abstract

Performance Evaluation of an Independent Time Optimized Infrastructure for Big Data Analytics that Maintains Symmetry

Cited by 38 publications

References 36 publications

Security of Big Data over IoT Environment by Integration of Deep Learning and Optimization

Security of Big Data over IoT Environment by Integration of Deep Learning and Optimization

Analyzing the Opportunities and Challenges Associated With Using Mathematical and Statistical Methods in Data Science

Comparative Assessment of Individual and Ensemble Machine Learning Models for Efficient Analysis of River Water Quality

Contact Info

Product

Resources

About