This paper addresses the issue of assessing the quality of the clusters found by fuzzy and hard clustering algorithms. In particular, it seeks an answer to the question on how well cluster validity indexes can automatically determine the appropriate number of clusters that represent the data. The paper surveys several key existing solutions for cluster validity in the domain of image segmentation. In addition, it suggests two new indexes. The first one is based on Akaike's information criterion (AIC). While AIC was devoted to other domains such as statistical estimation of model fitting, it is implemented here for the first time as a validation index. The second index is developed from the well-established idea of cross-validation. The existing and new indexes are evaluated and compared on several synthetic images corrupted with noise of varying levels and volumetric MR data.

Egypt’s geographic location and natural resources make it an ideal candidate for Renewable Energy (RE) projects. The government has implemented initiatives and policies to promote RE development, resulting in notable projects such as the Benban Solar Park and wind farms in the Gulf of Suez. These endeavors aim to combat climate change, reduce greenhouse gas emissions, and foster sustainable development. Multicriteria Decision Making (MCDM) techniques are increasingly favored due to their ability to accommodate diverse criteria for selecting RE sources. This paper evaluates numerous RE options in Egypt, including solar, wind, hydropower, and biomass energy, and proposes a hybrid strategy for classifying RE sources for power generation based on criteria such as finance, environment, technology, and community. The study utilizes four MCDM methods: the Analytical Hierarchy Process (AHP), the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), Fuzzy TOPSIS (FTOPSIS), and Fuzzy AHP (FAHP). The key findings indicate that solar and wind energy are the most viable options for Egypt’s renewable energy strategy, with the FTOPSIS method proving to be the most effective for ranking these sources by providing a comprehensive evaluation framework that aligns well with related studies. These findings have significant implications for policy and investment, as they provide a robust decision-making framework that ensures balanced consideration of financial, environmental, technological, and social factors. The study underscores the value of using advanced MCDM techniques to guide RE development in Egypt, contributing to the global effort to combat climate change.

Coffee, the world’s most traded tropical crop, is vital to the economies of many producing countries. However, coffee leaf diseases pose a serious threat to coffee quality and sustainable production. Deep learning has shown strong performance in plant disease identification through automatic image classification. Nevertheless, reliance on a single convolutional neural networks (CNNs) architecture restricts feature variability and real-world generalization. Moreover, limited work has systematically combined feature selection/reduction with CNNs, which constrains the advancement of hybrid models capable of capturing complementary features while ensuring computational efficiency without accuracy loss. This article presents an enhanced deep learning-based framework for coffee disease classification incorporating a hybrid strategy that integrates CNNs and advanced feature selection algorithms. GoogLeNet and ResNet18 are paired for complementary feature extraction, Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) are employed for dimensionality reduction, and ANOVA and Chi-square are applied to select the most informative features. An Adam optimizer (learning rate = 0.001, batch size = 20, epochs = 50) with early stopping is used for training. Experiments on the BRACOL dataset achieved 99.78% accuracy, with precision, recall, and F1-score all exceeding 99% across classes. To the best of our knowledge, this study systematically integrates GoogLeNet and ResNet18 with PCA/SVD dimensionality reduction and analysis of variance (ANOVA)/Chi-square feature selection, for coffee disease classification, thereby addressing a key gap in prior research.

In this paper, we present a new encryption method based on discrete wavelet transform (DWT). This method provides a number of advantages as a pseudo randomness and sensitivity due to the variation of the initial values. We start by decomposing the image with spatial reconstruction by DWT, followed by preformation by fractional chaotic cryptovirology and Henon map keys for space encryption. Bearing in mind the rapid pace of change in the fields of fractional chaotic cryptovirology, our findings suggest that the proposed method is too powerful relative to the key sensitivity, histogram, entropy and correlation coefficient.