فرح عباس عبيد ساري

The use of digital technologies in agriculture has become very important to ensure the protection of trees from disease and limit their development, which leads to increased production, so the paper proposes a modified analytical model to analyze the data and graphical parts of the leaves of fruit trees using priority fuzzy C-means (PFCM). Based on the proposed distance scale to obtain a clustering with a less error rate and fairly close to accuracy for the purpose of monitoring the development of diseases of fruit trees, by classifying the diseases and medications needed for each disease, a database was created containing large samples of data and images, where the results of Analysis of previous studies that analyzes of large amounts of data give accurate results. The proposed method was used in smart gardens with large areas and we got the desired results.

The development of a mathematical model of the risks of cardiovascular events in humans based on risk factors for cardiovascular diseases is an unresolved problem of modern knowledge. We have identified and grouped by type the following risk factors for cardiovascular diseases: biological indicators (anthropometric, biochemical, morphological, physiological), indicators of diseases and social indicators. These risk factors are designated as corresponding linguistic variables. A logical-linguistic model has been built. Further, the risk degree of each of the indicators is assessed, where the risk degree is first calculated using the membership formula, then using a logical-linguistic model, the hazard class is assessed according to the degree of risk.

A patient who has fully recovered from COVID-19 can help patients currently fighting infection by donating plasma. Because it is an infection killer, the plasma now contains antibodies against COVID-19. These antibodies provided the immune system with one way to fight the virus when it was sick, so plasma can be used to fight diseases. Therefore, this paper monitoring recovering patients based on the clustering of data and classifying them using fuzzy hierarchical clustering to reach the plasma as soon as possible.

The paper presents the formulation and solution of the fuzzy problem of the distribution of nodes in the structure of an information system with a star-tree structure under uncertainty. Fuzzy parameters are specified as fuzzy numbers of the L-R type.

The Encryption important Images like documents and archaeologist images area became extremely important now days , so that is why proposed new technique called H.A.F(Hind ,Ali and Farah) to encrypt image with complex steps to make it hard on hackers to hacked it , which need to reverse the block exchange then should be try to decrypt image by inverse math module use specific keys , these keys created using many probability with specific criteria . in this paper have twice keys with shifting blocks of images , in additional encryption method will apply on parts not the entire image . Experimental result and security analysis indicates the robustness and advantages of the new proposed algorithm. Technique is possible to reproduce the original image with no loss of information for the encryption and decryption process. It is fast and simple enough to be comparable to other recent methods, and it has passed all the security requirements and it is fast and secure to be used in very broad range of documents and archaeologist images encryption applications.

This paper presents Evaluation K-mean and Fuzzy c-mean image segmentation based Clustering classifier. It was followed by thresholding and level set segmentation stages to provide accurate region segment. The proposed stay can get the benefits of the K-means clustering. The performance and evaluation of the given image segmentation approach were evaluated by comparing K-mean and Fuzzy c-mean algorithms in case of accuracy, processing time, Clustering classifier, and Features and accurate performance results. The database consists of 40 images executed by K-mean and Fuzzy c-mean image segmentation based Clustering classifier. The experimental results confirm the effectiveness of the proposed Fuzzy c-mean image segmentation based Clustering classifier. The statistical significance Measures of mean values of Peak signal-to-noise ratio (PSNR) and Mean Square Error (MSE) and discrepancy are used for Performance Evaluation of K-mean and Fuzzy c-mean image segmentation. The algorithm’s higher accuracy can be found by the increasing number of classified clusters and with Fuzzy c-mean image segmentation.