The present work deals with the ultrasound assisted green synthesis of iron oxide nano particle using Coriandrum sativum leaf extract as a reducing agent. The synthesized iron oxide nanoparticle was confirmed by UV spectra. The characterization was done to know more about morphology and size of the particle by SEM analysis which shows spherical particles with size ranging from 20 to 90nm. The antimicrobial activity of the leaf extract and the synthesized nanoparticles was studied against the pathogens Micrococcus luteus, Staphylococcus aureus and Aspergillus niger. The ultrasound assisted iron oxide nanoparticle shows higher scavenging activity and antimicrobial activity compared with iron oxide nanoparticle synthesized by magnetic stirrer and Coriandrum sativum leaf extract.
Biometrics combined with cryptography can be employed to solve the conceptual and factual identity frauds in digital authentication. Biometric traits are proven to provide enhanced security for detecting crimes because of its interesting features such as accuracy, stability, and uniqueness. Although diverse techniques have been raised to address this objective, limitations such as higher computational time, minimal accuracy, and maximum recognition time remain. To overcome these challenges, an enhanced iris recognition approach has been proposed based on hyperelliptic curve cryptography (HECC). The proposed study uses the 2D Gabor filter approach for perfect feature extraction in iris preprocessing. A lightweight cryptographic scheme called HECC was employed to encrypt the iris template to avoid intentional attack by the intruders. The benchmark CASIA Iris V-4 and IITD iris datasets were used in the proposed approach for experimental analysis. The result analysis witnessed that the prime objective of the research such as lesser false acceptance rate, lesser false rejection rate, maximum accuracy of 99.74%, maximum true acceptance rate of 100%, and minimal recognition time of 3 seconds has been achieved. Also, it has been identified that the proposed study outperforms other existing well-known techniques.
In the field of agriculture, the development of an early warning diagnostic system is essential for timely detection and accurate diagnosis of diseases in rice plants. This research focuses on identifying the plant diseases and detecting them promptly through the advancements in the field of computer vision. The images obtained from in-field farms are typically with less visual information. However, there is a significant impact on the classification accuracy in the disease diagnosis due to the lack of high-resolution crop images. We propose a novel Reconstructed Disease Aware-Convolutional Neural Network (RDA-CNN), inspired by recent CNN architectures, that integrates image super resolution and classification into a single model for rice plant disease classification. This network takes low-resolution images of rice crops as input and employs the super resolution layers to transform low-resolution images to super-resolution images to recover appearance such as spots, rot, and lesion on different parts of the rice plants. Extensive experimental results indicated that the proposed RDA-CNN method performs well under diverse aspects generating visually pleasing images and outperforms better than other conventional Super Resolution (SR) methods. Furthermore, these super-resolution images are subsequently passed through deep classification layers for disease classification. The results demonstrate that the RDA-CNN significantly boosts the classification performance by nearly 4-6% compared with the baseline architectures.
Security is a key factor in today’s fast communicating world. Many cryptographic algorithms are tested and put into use efficiently. Random numbers are used in diverse forms like nonces, secret key, initialization vector, etc. They find place in encryption, digital signature, hashing algorithms. A deterministic algorithms takes an intial seed value as input and produces pseudo random numbers with falsely induced randomness. This research work extensively surveys large set of state-of-the-art PRNGs and categorizes them based on methodology used to produce them. We compared their statistical results obtained from various statistical test tools like NIST SP 800-22, TestU01. Finally, security analyses of various PRNGs were done quantitatively for their key space, key sensitivity, entropy, speed of bit generation, linear complexity. At last, we concluded the results with some future directions for researchers to carry out their research in improving the PRNGs.
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