Ambient air quality parameters were correlated with the biochemical characteristics of plant leaves and significant changes were observed in the plants biochemical characteristics due to the air pollution stress.
Rapid improvements in ultrasound imaging technology have made it much more useful for screening and diagnosing breast problems. Local-speckle-noise destruction in ultrasound breast images may impair image quality and impact observation and diagnosis. It is crucial to remove localized noise from images. In the article, we have used the hybrid deep learning technique to remove local speckle noise from breast ultrasound images. The contrast of ultrasound breast images was first improved using logarithmic and exponential transforms, and then guided filter algorithms were used to enhance the details of the glandular ultrasound breast images. In order to finish the pre-processing of ultrasound breast images and enhance image clarity, spatial high-pass filtering algorithms were used to remove the extreme sharpening. In order to remove local speckle noise without sacrificing the image edges, edge-sensitive terms were eventually added to the Logical-Pool Recurrent Neural Network (LPRNN). The mean square error and false recognition rate both fell below 1.1% at the hundredth training iteration, showing that the LPRNN had been properly trained. Ultrasound images that have had local speckle noise destroyed had signal-to-noise ratios (SNRs) greater than 65 dB, peak SNR ratios larger than 70 dB, edge preservation index values greater than the experimental threshold of 0.48, and quick destruction times. The time required to destroy local speckle noise is low, edge information is preserved, and image features are brought into sharp focus.
Present day world have evolved from traditional environment to smart industries using IoT scheme which in turn forms Industrial Internet of Things (IIoT), which significantly elaborated by providing enhance integration using smart communication through IoT based sensors. IIoT has been providing cost reduction and enhancement in technology by bringing availability, flexibility and data sharing through real time scenario. Despite being unsecure environment of cloud, the privacy of data transfer and information confidentiality is guaranteed. In this context, this work presents a Public Key Encryption with Equality Test based on DLP with double decomposition problems over near-ring. Computation Diffie-Hellman is utilized in algebraic structure which involves DLP with Double Decomposition problem for proposing a Public Key Encryption withEquality Test which provides more security to the scheme. The proposed method is highly secure and it solves the problem of quantum algorithm attacks in IIoT systems. Further, the suggested system is significantly secure and it prevents the chosen-ciphertext attack in type-I rival and it is indistinguishable against the random oracle model for the type-II rival. The recommended scheme is highly secure and the security analysis measures are comparatively stronger than existing techniques. Search time of the proposed scheme is 150 milliseconds for which the number of attributes is 50 and when comparing to the decryption time of the proposed model which is lower when compared to other existing scheme for 50 attributes.
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