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

This work presents an investigation on the fabrication and characterization of Fe doped zeolitic imidazolate framework (ZIF-8) of 1:1 M ratio of Zn:Fe (Fe/Zn-ZIF-8) and adsorption performances of acquired materials. The synthesized Zn-ZIF-8, Fe-ZIF-8, and Fe/Zn-ZIF-8 materials were characterized for the phase structure, morphology, elemental analysis and surface area by using X-ray diffraction (XRD), Field emission scanning electron microscope (FESEM), Energy Dispersive X-Ray (EDX), and BET surface area, respectively. The results revealed the adsorption capacity was enhanced by incorporation of Fe into ZIF-8 structure. The CR dye adsorption capacities were 287, 219, and 412 mg/g for Zn-ZIF-8, Fe-ZIF-8, and Fe/Zn-ZIF-8 adsorbers, respectivel

In this research, Argon gas was used to generate atmospheric plasma in the manufacture of platinum nanomaterials, to study the resultant plasma spectrum and to calculate the cellular toxicity of those manufactured nanomaterials. This research is keen on the generation of nonthermal atmospheric pressure plasma using aqueous platinum salts (H₂PtCl₆ 6H₂O) with different concentrations and exposure of cold plasma with a different time period used to produce platinum nanoparticles, to ensure typical preparation of nanoparticles. Visible UV and X-rays were performed for this purpose, and the diameter of the system probe was (1[Formula: see text]mm) with the Argon gas flow of

One of the most difficult issues in the history of communication technology is the transmission of secure images. On the internet, photos are used and shared by millions of individuals for both private and business reasons. Utilizing encryption methods to change the original image into an unintelligible or scrambled version is one way to achieve safe image transfer over the network. Cryptographic approaches based on chaotic logistic theory provide several new and promising options for developing secure Image encryption methods. The main aim of this paper is to build a secure system for encrypting gray and color images. The proposed system consists of two stages, the first stage is the encryption process, in which the keys are genera

Numeral recognition is considered an essential preliminary step for optical character recognition, document understanding, and others. Although several handwritten numeral recognition algorithms have been proposed so far, achieving adequate recognition accuracy and execution time remain challenging to date. In particular, recognition accuracy depends on the features extraction mechanism. As such, a fast and robust numeral recognition method is essential, which meets the desired accuracy by extracting the features efficiently while maintaining fast implementation time. Furthermore, to date most of the existing studies are focused on evaluating their methods based on clean environments, thus limiting understanding of their potential a

A content-based image retrieval (CBIR) is a technique used to retrieve images from an image database. However, the CBIR process suffers from less accuracy to retrieve images from an extensive image database and ensure the privacy of images. This paper aims to address the issues of accuracy utilizing deep learning techniques as the CNN method. Also, it provides the necessary privacy for images using fully homomorphic encryption methods by Cheon, Kim, Kim, and Song (CKKS). To achieve these aims, a system has been proposed, namely RCNN_CKKS, that includes two parts. The first part (offline processing) extracts automated high-level features based on a flatting layer in a convolutional neural network (CNN) and then stores these features in a