In Hybrid Cloud Security, we combine symmetric encryption with key management to ensure anonymity. Our method provides a flexible and scalable deployment solution for cloud-based data security. To store, analyse, and deal with data, cloud computing relies on a core concept: sharing resources. Since cloud services are utilized by many individuals and are dispersed across the Internet, they provide a number of security vulnerabilities. Due to their worldwide accessibility, hybrid clouds open up a wide range of opportunities. The security solution works with a wide variety of PaaS, SaaS, and even IaaS cloud services (Infrastructure as a Service). It also works with most Cloud services out there. Hybrid public key cryptosystems can improve cloud security. Businesses are wary about cloud computing due to security concerns. The primary goal of this research is to strengthen the security of cloud computing by fusing the cryptographic models of Rabin and Rivest-Shamir-Adleman (RSA). Evaluating the efficacy of a hybrid approach to producing secret keys for data encryption and decryption. The purely RSA-based system has higher latency and is less computationally intensive than the hybrid system.

This paper aims at investigating the feasibility of adopting a secure e-voting based on the authenticity of biometrics in conducting the election of Jordan parliamentarians consol. It firstly investigates the Jordanian citizens’ acceptance level of an e-voting system based on biometric credentials as a supportive solution to their attitude, intention, and trust in actual participation. The successor phases are directed to validate the feasibility of adopting a biometric-based e-voting system by involving the Delphi method of three rounds for collecting and analysing data. The initial use case diagram, interview questions, and resultant queries are all mapped to construct the proposed conceptual framework. The results of multi phases methodology allow for development and recommend a conceptual framework for implementing an e-voting system with all acting schemas that represent the different stages of the election process. Moreover, the proposed conceptual framework was developed with thematic regulations that align with the experts' consensuses and the current Jordan parliament election law.

The death rate has increased in recent years due to the rising prevalence of encephaloma tumors across all age brackets. Because of their complex structure and background noise, tumors are difficult to detect in medical imaging and require a great deal of time and effort on the part of professionals. This is crucial since locating the tumor early on is key to successful treatment. Scans can detect and even forecast the presence of cancer at a variety of stages. A combination of these scans with segmentation and relegation techniques can aid in a rapid diagnosis, saving valuable time for the treating physician. Due to the complex nature of tumors and the gradual evolution of noise in MR imaging data, physical tumor identification has become a complicated and time-consuming process for medical professionals. Hence, early detection and localization of the tumor site is essential. Using segmentation and relegation techniques, medical imaging can pinpoint cancer tumors at multiple stages for a precise diagnosis. This study presents a machine learning-based method for automatically segmenting and labelling MRI scans of the brain to help in the detection of malignant growths. In addition, this framework employs a number of machine learning algorithms for tasks including image pre-processing, segmentation, feature extraction, and classification, including Nave Bayes, Nearest Neighbours, and Decision Table.