Impact of Big Data Analysis on Nanosensors for Applied Sciences Using Neural Networks
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...
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| Veröffentlicht in: | Journal of nanomaterials 2021, Vol.2021, p.1-9 |
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| container_title | Journal of nanomaterials |
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| creator | Shitharth, S. Meshram, Pratiksha Kshirsagar, Pravin R. Manoharan, Hariprasath Tirth, Vineet Sundramurthy, Venkatesa Prabhu |
| description | 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. |
| doi_str_mv | 10.1155/2021/4927607 |
| format | Article |
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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.</description><identifier>ISSN: 1687-4110</identifier><identifier>EISSN: 1687-4129</identifier><identifier>DOI: 10.1155/2021/4927607</identifier><language>eng</language><publisher>New York: Hindawi</publisher><subject>Access control ; Algorithms ; Artificial intelligence ; Big Data ; Cognition & reasoning ; Coronaviruses ; Data analysis ; Design ; Error analysis ; Impact analysis ; Methods ; Nanomaterials ; Nanoparticles ; Nanosensors ; Nanotechnology ; Parameter sensitivity ; Recurrent neural networks ; Sensors ; Signal reflection</subject><ispartof>Journal of nanomaterials, 2021, Vol.2021, p.1-9</ispartof><rights>Copyright © 2021 S. Shitharth et al.</rights><rights>Copyright © 2021 S. Shitharth et al. 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| subjects | Access control Algorithms Artificial intelligence Big Data Cognition & reasoning Coronaviruses Data analysis Design Error analysis Impact analysis Methods Nanomaterials Nanoparticles Nanosensors Nanotechnology Parameter sensitivity Recurrent neural networks Sensors Signal reflection |
| title | Impact of Big Data Analysis on Nanosensors for Applied Sciences Using Neural Networks |
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