Tomato is one of the most essential and consumable crops in the world. Tomatoes differ in quantity depending on how they are fertilized. Leaf disease is the primary factor impacting the amount and quality of crop yield. As a result, it is critical to diagnose and classify these disorders appropriately. Different kinds of diseases influence the production of tomatoes. Earlier identification of these diseases would reduce the disease’s effect on tomato plants and enhance good crop yield. Different innovative ways of identifying and classifying certain diseases have been used extensively. The motive of work is to support farmers in identifying early-stage diseases accurately and informing them about these diseases. The Convolutional Neural Network (CNN) is used to effectively define and classify tomato diseases. Google Colab is used to conduct the complete experiment with a dataset containing 3000 images of tomato leaves affected by nine different diseases and a healthy leaf. The complete process is described: Firstly, the input images are preprocessed, and the targeted area of images are segmented from the original images. Secondly, the images are further processed with varying hyper-parameters of the CNN model. Finally, CNN extracts other characteristics from pictures like colors, texture, and edges, etc. The findings demonstrate that the proposed model predictions are 98.49% accurate.

Recognition-based graphical password have been proposed as an alternative to overcome the drawbacks of the alphanumeric password in user authentication. A web-based study was performed to determine the cultural impact on the usability of recognition-based graphical password. A number of participants (Saudi and British) selected their graphical passwords from a set of pictures representing different cultures. After three months, they were asked to login using their graphical passwords.For the registration stage, our results show that users were highly affected by their culture when they chose pictures for their graphical password. However, Saudis were significantly more affected by their culture than British. Also, there was no evidence that gender can affect the participant's choice when it comes to select graphical password from pictures belonging to their culture.For the authentication stage, the main results show that the memorability rate for graphical passwords consisting of only pictures belonging to participants' culture was higher than the memorability rate for graphical passwords consisted of pictures which did not belong to participants' culture. Also, there was no evidence that the participants, who had graphical passwords based on their cultures, will have less time to login than other participants, who had non-cultural based graphical passwords.

The COVID-19 pandemic has profoundly affected almost all facets of peoples’ lives, various economic areas and regions of the world. In such a situation implementation of a vaccination can be viewed as essential but its success will be dependent on availability and transparency in the distribution process that will be shared among the stakeholders. Various distributed ledgers (DLTs) such as blockchain provide an open, public, immutable system that has numerous applications due the mentioned abilities. In this paper the authors have proposed a solution based on blockchain to increase the security and transparency in the tracing of COVID-19 vaccination vials. Smart contracts have been developed to monitor the supply, distribution of vaccination vials. The proposed solution will help to generate a tamper-proof and secure environment for the distribution of COVID-19 vaccination vials. Proof of delivery is used as a consensus mechanism for the proposed solution. A feedback feature is also implemented in order to track the vials lot in case of any side effect cause to the patient. The authors have implemented and tested the proposed solution using Ethereum test network, RinkeyBy, MetaMask, one clicks DApp. The proposed solution shows promising results in terms of throughput and scalability.

Radial Based Function Neural Network models (RBFNN) are currently used deep-rooted methods for assessing the stages of diagnosis of chronic diseases. The goals of this research are to suggest a model for the diagnosis of breast cancer, and to be able to estimate the stages of development of premalignant breast tumors. The significance of the study is to develop an integrated RBF neural network with ensemble features using the boosting method. The importance of the ensemble boosting method is to generate a sequence of models to achieve more precise predictions. One of the ensemble boosting advantages is that it can take longer to build and to score than a RBF NN standard model. By using ensemble boosting, the accuracy of breast tumor diagnosis increased and thus it became easier to know the stage of the tumor, and whether it was malignant or benign. This will help doctors to select appropriate treatment for each tumor stage, consequently leading to the salvage of cancer patients with this type of tumor. The suggested RBFNN method was examined on the different type of UCI breast cancer datasets. The general diagnosis accuracy based on 10-fold cross validation using RBFNN method obtained 97.4%, 98.4%, 97.7% and 97.0% for the WBC, BCD, BCP, and WBCD UCI datasets respectively. The effectiveness of the proposed method was confirmed by comparing accuracy improvement both before and after using ensemble boosting, and it was found to be more accurate compared with other breast cancer diagnosis methods such as Logistic Regression (91.5%), KNN (96%), SVM (89%), Decision tree (95.13), CNN (97.66%), and Naive Bayes (91%).INDEX TERMS Cancer disease, ensemble boosting, prediction, radial based function, neural network.

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