Artificial intelligence (AI) is simulating human intelligence processes by machines and software simulators to help humans in making accurate, informed, and fast decisions based on data analysis. The medical field can make use of such AI simulators because medical data records are enormous with many overlapping parameters. Using in-depth classification techniques and data analysis can be the first step in identifying and reducing the risk factors. In this research, we are evaluating a dataset of cardiovascular abnormalities affecting a group of potential patients. We aim to employ the help of AI simulators such as Weka to understand the effect of each parameter on the risk of suffering from cardiovascular disease (CVD). We are utilizing seven classes, such as baseline accuracy, naïve Bayes, k-nearest neighbor, decision tree, support vector machine, linear regression, and artificial neural network multilayer perceptron. The classifiers are assisted by a correlation-based filter to select the most influential attributes that may have an impact on obtaining a higher classification accuracy. Analysis of the results based on sensitivity, specificity, accuracy, and precision results from Weka and Statistical Package for Social Sciences (SPSS) is illustrated. A decision tree method (J48) demonstrated its ability to classify CVD cases with high accuracy 95.76%.

Blockchain, a breakthrough technology, offers solutions to several healthcare challenges, includes Managing and sharing data securely, Making different systems work together seamlessly, Increasing transparency in the supply chain, Giving patients more control over their health information, and Adhering to regulations and guidelines. This paper offers an in-depth study of current literature on blockchain in healthcare. It covers areas that have been investigated, studied and implemented by previous research, provides valuable insights, and introduces practical suggestions. Research findings indicate that blockchain has the capacity to improve data security, privacy, and immutability. It can also facilitate effortless data sharing among different parties involved, enhance transparency in pharmaceutical supply chains, empower patients in managing their health information, and address regulatory concerns. The results of this study recommends Healthcare organizations to consider implementing secure transaction protocols to ensure the confidentiality and integrity of their data. Also to explore the use of Hyperledger Fabric architecture, a blockchain framework specifically designed for enterprise applications, Implementation of Secure Transaction Protocols, Utilization of Blockchain for Data Sharing, Adoption of Lightweight Consensus Protocols. By leveraging blockchain technology, healthcare businesses may securely share data with other trusted parties, improving interoperability and data exchange. Additionally, adopting lightweight consensus methods can help streamline decision-making processes within the organization. Future research shall involve the integration of blockchain technology with artificial intelligence and data analytics, the establishment of decentralized parallel healthcare organizations, the integration of blockchain with cloud computing, and the resolution of regulatory and standards difficulties

Driver drowsiness is a severe problem that usually causes traffic accidents, classified as more dangerous. The record of the National Safety Council reported that drowsy driving is caused by 9.5 of all crashes (100,000 cases). Therefore, preventing and minimizing driver fatigue is a significant research area. This study aims to design a nonintrusive real-time drowsiness system based on image processing and fuzzy logic techniques. It is an enhanced approach for ViolaJones to examine different visual signs to detect the driver's drowsiness level. It extracted eye blink duration and mouth features to detect driver drowsiness based on the desired facial feature image in a specific driver video frame. The size and orientation of the captured features were tracked and handled for determining image features such as brightness, shadows, and clearness. Lastly, the fuzzy control system provides different alert sounds based on the tracked information from the face, eyes, and mouth in separate cases, such as race, wearing glasses or not, gender, and various illumination backgrounds. The experiments results show that the proposed approach achieved high accuracy of 94.5 in detecting driver status compared with other studies. Also, the fuzzy logic controller efficiently issued the required alert signal of the drowsy driver status that helps to save the driver's life.

The Holy Quran is the central religious verbal text of Islam. Muslim people are expected to read, understand, and apply teachings of the Holy Quran. Holy Quran was translated to Braille code as a normal Arabic text without included its reciting rules. This, obviously, mean that the users of this translation is not able to recite the Quran the right way. Through this work, Quran Braille Translator (QBT) is introduced to be as specific translator to translate Quran verses text into Braille code. QBT is using Extended Finite State Machine (EFSM) technique for detecting Quran Reciting Rules (QRR) from the Quran text and Markov Algorithm (MA) for translating the detected QRR and Quran text into the matched Braille code. The data entries for QBT are Arabic letters and diacritics. Detecting and translating measurement processes were done for translated text. The Holy Quran is the central religious verbal text of Islam. Muslim people are expected to read, understand, and apply teachings of the Holy Quran. Holy Quran was translated to Braille code as a normal Arabic text without included its reciting rules. This, obviously, mean that the users of this translation is not able to recite the Quran the right way. Through this work, Quran Braille Framework is introduced to be as specific Framework to translate Quran verses text and its reciting rules into Braille code. User Acceptance Technology had done for QBT and the results were positive.

This article concerned with reorganize new Braille symbols to represent the vibrations in the Arabic Quran, the aim of this study is building a system that translate the Quran verses to Braille symbols including new vibrations. This study limited for the (Noun + Scoon) vibrations, (Meem + Scoon) vibrations and (Lam + Scoon) vibrations. It builds on an existing translation system that combines a finite state machine with left and right context matching and a set of translation rules. This allows to translate the Arabic language from text to Braille symbols after detect the vibration for the Quran verses.

With the current continuous increase in the usage of communication technologies and data transmission amount, the available web services and mobile applications are used to access and complete several online activities such as online education, e-commerce, and online financial transactions. The needs for a strong and secure access control authentication solution of users’ data and the available online resources are crucial. Authentication can play a major rule in protection by implementing a solution, which allows users to prove their identities and provide access permission to all legitimate users only. This paper proposes two-factor authentication scheme based on the usage of a modified copy of the Schnorr Signcryption algorithm. The proposed scheme achieves the target goal of this paper, which provides the users with an efficient solution that can be implemented in any authentication solution as the second factor, with no special requirement and can be used to authenticate the users and control recourses access permission. Moreover, Java-based application is developed in order to examine the proposed scheme in a client/server architecture where it is found that the solution can successfully authenticate authorised users and reject any unauthorised access request with an adequate security protection level and acceptable performance.