This paper deals with the novel approach to generate cubical key that symbolises the message and key in six-face cubical structure. The cubical message is hybridised to generate the cipher in the encryption. Hybridisation of cubes is performed by using the XOR operation to the six-face cubical original message and six-face random sequence which is a part of Key-Cube. Key-Cube consists of Rotation Type (RT-Cube), Rotation Angle (RA-Cube) and Random Sequence (R-Cube). As a product cipher technique we generate the S-Cube and P-Cube in each of the iterations and it symbolises the substitution and permutation, respectively. Here, total 36 round phase is carried out to generate the final cipher. To guarantee the randomness in each phase of hybridization, cubical message and cubical form of random sequence are shuffled in each of the iterations based on RA-Cube and RT-Cube. Cubical message representation, cube hybridisation and shuffling the message using Rubik Cube technique ensure the security hardness in the final cipher. We analysed the strength of our proposed approach by compared the cipher variance with the original message and analysed our encryption technique with well-known block cipher attaches. The results and analysis show betterment in our proposed approach. 8 IET Netw.
Background: Agriculture is one of the most essential industry that fullfills people’s need and also plays an important role in economic evolution of the nation. However, there is a gap between the agriculture sector and the technological industry and the agriculture plants are mostly affected by diseases, such as the bacterial, fungus and viral diseases that lead to loss in crop yield. The affected parts of the plants need to be identified at the beginning stage to eliminate the huge loss in productivity. Methods: In the present scenario, crop cultivation system depend on the farmers experience and the man power, but it consumes more time and increases error rate. To overcome this issue, the proposed system introduces the Double Line Clustering technique based disease identification system using the image processing and data mining methods. The introduced method analyze the Anthracnose, blight disease in grapes, tomato and cucumber. The leaf images are captured and the noise has been removed by non-local median filter and the segmentation is done by double line clustering method. The segmented part compared with diseased leaf using pattern matching algorithm. Methods: In the present scenario, crop cultivation system depend on the farmers experience and the man power, but it consumes more time and increases error rate. To overcome this issue, the proposed system introduces the Double Line Clustering technique based disease identification system using the image processing and data mining methods. The introduced method analyze the Anthracnose, blight disease in grapes, tomato and cucumber. The leaf images are captured and the noise has been removed by non-local median filter and the segmentation is done by double line clustering method. The segmented part compared with diseased leaf using pattern matching algorithm. Conclusion: The result of the clustering algorithm achieved high accuracy, sensitivity, and specificity. The feature extraction is applied after the clustering process which produces minimum error rate.
In the current scenario, handheld devices play a major role in the human life. Handheld devices become an essential kit, not only acting as a conduit for social media, but also in medicine. Several new opportunities for the different applications of mobile image processing exist, such as to improve the visual quality, and image recognition. Captured images do not provide an effective visualization due to the poor specifications of the device camera, low light, poor sensing features, etc. In this article, an adaptive histogram equalization for contrast enhancement using a linear mapping function scheme is proposed to improve the images. The image from the mobile device is fed into a contrast improvement phase. The intensity value of each pixel is processed to improve the image visuals. The pixel density value is measured and according to it, the low-density value is changed. Hence, the image is tuned finely to yield better results.
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