Pattern recognition is attracting the interest of researchers in the recently few years as a machine learning approaches due to its vast extending application areas. he application area includes communications, medicine, automations, data mining, military intelligence, document classification, bioinformatics, speech recognition and business. In this research convolutional neural networks (CNN) using for building system to recognize diseases that are happened in citrus. In this study presented dataset for seven classes of citrus diseases which contains 2450 images such as anthracnose, brown rot, citrus black spot, citrus canker, citrus scob, melanose and sooty mold citrus. The proposed system recognizes learned via CNN. The experimental result shows our model has ability to recognize citrus diseases with high and robustness accuracy. The study presented here gives 88% recognition of citrus diseases for the entire database.Povzetek: Konvolucijske nevronske mreže so uporabljene za detekcijo oz. klasifikacijo bolezni citrusov.

Currently, information hiding has become an effective technique and has increased interest due to the rapid growth of Internet use. Many techniques can hide and transmit private information. The science of hiding information within the carrier's body ensures secure communication over the Internet so that it can only be recognized and detected by the sender and recipient. Thus, we can use many forms of a carrier, such as images, video, protocol, and audio. However, digital images are most commonly used because of their frequency on the Internet. There are many techniques for hiding information, each with advantages and disadvantages. In this study, we reviewed the techniques used to investigate the term "hiding" by reviewing and collecting various studies related to this field published between 2015 and 2021. This study assessed several ways of addressing this problem. Four measures -image quality, message capacity, and securitywere used to assess the additional computational complexity of each method. Finally, the results presented and summarized as performance, security, and hidden image quality are essential for evaluating the approach.

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The use of medical imaging for the purposes of medical monitoring and diagnosis has seen tremendous growth in recent years, which has resulted in a rising need for suitable data storage and accessibility. The primary goal of image enhancement is to generate a processed image that is suitable for a specific application. This can be accomplished by acquiring the image in the highest resolution possible or by using a variety of techniques to extract the important information while ignoring the irrelevant information. Image processing can then be performed on the processed image. Image enhancement techniques are used in medical imaging to increase the quality of the visual representation of an image by reducing the amount of noise present and sharpening the features. This study examines the Curvelet transform-based approach for increasing medical image quality. The contribution of the proposed review two steps are advised for optimizing the images. The first step is the optimization step utilizing the Curvelet transform, which featured a series of image-preparation processors. Curvelet's ability to extract complete spectrum information from an image with varying edge orientations makes it valuable for assessing data about wave propagation. In light of this, the curvelet transform is considered an excellent filter for improving and denoising images, and it is intended to deal with components in several orientations and scales. The primary objective of this treatment is to boost visibility and eliminate noise, which is superior to conventional techniques of enhancement in which the enhancement step is performed by discrete curvelet transform through wrapping. The contribution of the proposed review the usual vector is the second step of this review approach. The vector image is distributed randomly to each pixel in images that are unclear and noisy. This procedure eliminates the unpredictability in the previously computed directions and makes them regular, producing more precise results and enhancing image clarity, particularly at the image's edges. By comparing the results of the proposed technique of improvement to the results of existing improvement methods, it was determined that the proposed approach yields superior performance and is more efficient. The efficiency and efficacy of the suggested technique may be determined based on the results of the trials, which demonstrated that the method performs very well and yields accurate and precise results.