Impact of Big Data Analysis on Nanosensors for Applied Sciences Using Neural Networks

Abstract

In the current-generation wireless systems, there is a huge requirement on integrating big data which can able to predict the market trends of all application systems. Therefore, the proposed method emphasizes on the integration of nanosensors with big data analysis which will be used in healthcare applications. Also, safety precautions are considered when this nanosensor is integrated where depth and reflection of signals are also observed using different time samples. In addition, to analyze the effect of nanosensors, six fundamental scenarios that provide good impact on real-time applications are also deliberated. Moreover, for proving the adeptness of the proposed method, the results are equipped in both online and offline analyses for investigating error measurement, sensitivity, and permeability parameters. Since nanosensors are introduced, the efficiency of the projected technique is increased by implementing media access control (MAC) protocol with recurrent neural network (RNN). Further, after observing the simulation results, it is proved that the proposed method is more effective for an average percentile of 67% when compared to the existing methods.

A Radical Safety Measure for Identifying Environmental Changes Using Machine Learning Algorithms

“…In [11] a technique for analyzing water quality using algorithms, such as Random Forest as well as multi-label classifiers has been developed. Multiclass classifiers were also shown to be greater than the corresponding forests by the researchers and in [12] a carbon emissions assessment approach for Bengaluru has been suggested. For the examination of air contaminants, the author used the ZeroR method.…”

“…In [4], improved identification and model accuracy were achieved by combining hybrid machine learning with Pareto-optimal solutions for a wide variety of information, such as standard performance and feature sets from a variety of growing domains [9][10][11][12][13]. The methodologies employed in numerous research projects were beneficial among the diverse assessment criteria in information technology, computational science, and cloud-based services.…”

“…In [18] Vehicle emissions forecasts were made in Spain using an SVM-based logistic regression that included the most important inductive reasoning in order to provide a good prediction of the main pollutants. The major findings of the existing methods [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18] are that many different forms of pollution that are caused due to human activities are continuously monitored using several techniques, but as per the technological aspect, it is not possible to stop the spread of polluting contents. However, the presence of several chemicals in the atmosphere can be reduced by preventing the burning ratio of fossil fuels and other residues that introduces pollution to the surroundings.…”

Deep Learning Approaches for Prognosis of Automated Skin Disease

“…The matching algorithm unit tests the characteristics needed to categorize skin disorders. The process of extracting features from testing pictures is linked with extracting features from previously learned images [ 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 ]. The matching algorithm is designed so that if illnesses are identified, they are categorized, and, ultimately, patients receive an e-prescription; if no diseases are found, the system classifies the skin as healthy.…”

Perception Exploration on Robustness Syndromes With Pre-processing Entities Using Machine Learning Algorithm

“…While conducting a depression analysis, several fields of doctors' opinions on depression are examined. Types and causes of depression are investigated to develop a solution for prediction ( 17 , 23 – 27 ).…”