Nanomaterials, including nanoparticles such as iron oxide nanoparticles, have received great attention from researchers due to their unique properties and applications. There are several diverse methods, including chemical, physical, and green biological methods, to prepare iron oxide nanoparticles. The green method was chosen because it is safer, purer, and less toxic compared to other methods. Therefore, the green method is a promising and environmentally friendly method in the near future. The aqueous extract of Iraqi orange leaves was used to prepare nano iron oxide, it was examined structurally and spectrally by several techniques (X-ray diffraction- XRD, Fourier transform infrared - FT-IR, field emission scanning electron micr

In this study, a novel application of lab-scale dual chambered air-cathode microbial fuel cell (MFC) has been developed for simultaneous bio-treatment of real pharmaceutical wastewater and renewable electricity generation. The microbial fuel cell (MFC) was provided with zeolite-packed anodic compartment and a cation exchange membrane (CEM) to separate the anode and cathode. The performance of the proposed MFC was evaluated in terms of COD removal and power generation based on the activity of the bacterial consortium in the biofilm mobilized on zeolite bearer. The MFC was fueled with real pharmaceutical wastewater having an initial COD concentration equal to 800 mg/L and inoculated with anaerobic aged sludge. Results demo

      Data mining has the most important role in healthcare for discovering hidden relationships in big datasets, especially in breast cancer diagnostics, which is the most popular cause of death in the world. In this paper two algorithms are applied that are decision tree and K-Nearest Neighbour for diagnosing Breast Cancer Grad in order to reduce its risk on patients. In decision tree with feature selection, the Gini index gives an accuracy of %87.83, while with entropy, the feature selection gives an accuracy of %86.77. In both cases, Age appeared as the  most effective parameter, particularly when Age<49.5. Whereas  Ki67  appeared as a second effective parameter. Furthermore, K- Nearest Neighbor is based on the minimu

In digital images, protecting sensitive visual information against unauthorized access is considered a critical issue; robust encryption methods are the best solution to preserve such information. This paper introduces a model designed to enhance the performance of the Tiny Encryption Algorithm (TEA) in encrypting images. Two approaches have been suggested for the image cipher process as a preprocessing step before applying the Tiny Encryption Algorithm (TEA). The step mentioned earlier aims to de-correlate and weaken adjacent pixel values as a preparation process before the encryption process. The first approach suggests an Affine transformation for image encryption at two layers, utilizing two different key sets for each layer. Th

Text based-image clustering (TBIC) is an insufficient approach for clustering related web images. It is a challenging task to abstract the visual features of images with the support of textual information in a database. In content-based image clustering (CBIC), image data are clustered on the foundation of specific features like texture, colors, boundaries, shapes. In this paper, an effective CBIC) technique is presented, which uses texture and statistical features of the images. The statistical features or moments of colors (mean, skewness, standard deviation, kurtosis, and variance) are extracted from the images. These features are collected in a one dimension array, and then genetic algorithm (GA) is applied for image clustering.

With the escalation of cybercriminal activities, the demand for forensic investigations into these crimeshas grown significantly. However, the concept of systematic pre-preparation for potential forensicexaminations during the software design phase, known as forensic readiness, has only recently gainedattention. Against the backdrop of surging urban crime rates, this study aims to conduct a rigorous andprecise analysis and forecast of crime rates in Los Angeles, employing advanced Artificial Intelligence(AI) technologies. This research amalgamates diverse datasets encompassing crime history, varioussocio-economic indicators, and geographical locations to attain a comprehensive understanding of howcrimes manifest within the city. Lev