Agriculture exhibitions an important role in the progression and enlargement of the economy of any country. Prediction of crop yield will be useful for farmers, but it is difficult to predict crop yield because of the climatic factors such as rainfall, soil factors and so on. To tackle these issues, we are implementing a novel algorithm called Lemuria by applying data mining in agriculture especially for crop yield analysis and prediction. This novel algorithm is the hybridization of classifiers for pre-training, training and testing: deep belief network for feature learning, k-means clustering together with particle swarm optimization (PSO) to get the global solution as well as naïve Bayes clustering with PSO for testing. The performance of the Lemuria algorithm is evaluated in Python, which provides an accuracy of 97.74% for crop prediction by considering the rainfall dataset and also stated that this gives the optimum results in comparison with the existing methodologies.
The transmission of significant masses of sensitive and secret images over a public network is inevitable, and demands effective tools and technology to safeguard and conceal the data. In this paper, a symmetric multiple color image encryption technique is proposed by adopting a dual permutation and dual substitution framework. Firstly, the input images are combined into a large image and then segmented into many small and equal-sized pure-image elements. Secondly, using the elementary cellular automata Rule-30, these pure-image elements are permuted to obtain mixed-image elements. Thirdly, second-level permutation is undertaken on the mixed-image elements by applying zigzag pattern scanning. Fourthly, pixel values are substituted by employing the circular shift method; subsequently, second-level pixel substitution is realized through using chaotic random sequences from a 2D logistic map. Finally, the big encrypted image is segmented into smaller encrypted images. Additionally, the keys are calculated from the input images to attain input sensitivity. The efficiency of this method is quantified, based on the unified average changing intensity (UACI), information entropy, number of pixels change rate (NPCR), key sensitivity, key space, histogram, peak signal-to-noise ratio (PSNR) and correlation coefficient (CC) performance metrics. The outcome of the experiments and a comparative analysis with two similar methods indicate that the proposed method produced high security results.
Digital raw images obtained from the data set of various organizations require authentication, copyright protection, and security with simple processing. New Euclidean space point’s algorithm is proposed to authenticate the images by embedding binary logos in the digital images in the spatial domain. Diffie–Hellman key exchange protocol is implemented along with the Euclidean space axioms to maintain security for the proposed work. The proposed watermarking methodology is tested on the standard set of raw grayscale and RGB color images. The watermarked images are sent in the email, WhatsApp, and Facebook and analyzed. Standard watermarking attacks are also applied to the watermarked images and analyzed. The finding shows that there are no image distortions in the communication medium of email and WhatsApp. But in the Facebook platform, raw images experience compression and observed exponential noise on the digital images. The authentication and copyright protection are tested from the processed Facebook images. It is found that the embedded logo could be recovered and seen with added noise distortions. So the proposed method offers authentication and security with compression attacks. Similarly, it is found that the proposed methodology is robust to JPEG compression, image tampering attacks like collage attack, image cropping, rotation, salt-and-pepper noise, sharpening filter, semi-robust to Gaussian filtering, and image resizing, and fragile to other geometrical attacks. The receiver operating characteristics (ROC) curve is drawn and found that the area under the curve is approximately equal to unity and restoration accuracy of [67 to 100]% for various attacks.
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