Although the blockchain technology was first introduced through Bitcoin, extending its usage to nonfinancial applications, such as managing electronic medical records, is an attractive mission for recent research to balance the needs for increasing data privacy and the regular interaction among patients and health providers. Various systems that adopts the blockchain in managing medical records have been proposed. However, there is a need for more work to better characterize, understand and evaluate the employment of blockchain technology in the healthcare industry. In this paper, a design of blockchain based system, namely MedChain, for managing medical records is proposed. MedChain is designed to improve the current systems as it provides interoperable, secure, and effective access for medical records by patients, health care providers, and other third parties, while keeping the patients’ privacy. MedChain employs timed-based smart contracts for governing transactions and controlling accesses to electronic medical records. It adopts advanced encryption techniques for providing further security. This work proposes a new incentive mechanism that leverages the degree of health providers regarding their efforts on maintaining medical records and creating new blocks. Extensive experiments are conducted to evaluate the MedChain performance, and results indicate the efficiency of our proposal in handling a large dataset at low latency.

Although blockchain technology was first introduced through Bitcoin, extending its usage to non-financial applications, such as managing academic records, is a new mission for recent research to balance the needs for increasing data privacy and the regular interaction among students and universities. In this paper, a design for a blockchain-based system, namely UniChain, for managing Electronic Academic Records (EARs) is proposed. UniChain is designed to improve the current management systems as it provides interoperable, secure, and effective access to EARs by students, universities, and other third parties, while keeping the students’ privacy. UniChain employs timed-based smart contracts for governing transactions and controlling access to EARs. It adopts advanced encryption techniques for providing further security. This work proposes a new incentive mechanism that leverages the degree of universities regarding their efforts on maintaining academic records and creating new blocks. Extensive experiments were conducted to evaluate the UniChain performance, and the results indicate the efficiency of the proposal in handling a large dataset at low latency.

Several research on cyberbullying detection have employed different deep learning and machine learning methodologies to achieve promising outcomes. Nevertheless, most of them have primarily concentrated on using English data for both purposes: training and testing, with only a limited number considering native languages such as Arabic. Thus, there is a critical need to address cyberbullying in its native linguistic context. The dataset utilized in this research has been compiled and sourced from various Kaggle and Github repositories. Six collected benchmark datasets from Facebook, Twitter and Instagram in addition to a developed Arabic cyberbullying lexicon were utilized to evaluate the efficiency of the proposed hybrid model. Prior to classification, data cleaning was carried out to preprocess the text. Moreover, word embedding as a natural language processing method is utilized. Numerous machine learning and deep learning algorithms were assessed, encompassing naïve bayes, support vector machines, k-nearest neighbors, decision trees, random forest, multi-layer perceptron neural networks, convolutional ceural cetworks, recurrent neural networks, bidirectional long short-term memory, long short-term memory, and gated recurrent units, with a meticulous comparative analysis conducted. Given their demonstrated potential, hybrid techniques have emerged as promising model for effectively detecting instances of cyberbullying. Thus, the best performing algorithms is utilized to construct the hybrid model. This research introduces a hybrid deep learning model with stacked word embedding. This model consistently outperforms single models in terms of cyberbullying detection. We extensively investigated the performance of the proposed hybrid model across diverse data contexts. Through thorough study and validation, the proposed hybrid model demonstrates enhanced capabilities in feature extraction and accurate text classification.

This paper proposes a three-computing-layer architecture consisting of Edge, Fog, and Cloud for remote health vital signs monitoring. The novelty of this architecture is in using the Narrow-Band IoT (NB-IoT) for communicating with a large number of devices and covering large areas with minimum power consumption. Additionally, the architecture reduces the communication delay as the edge layer serves the health terminal devices with initial decisions and prioritizes data transmission for minimizing congestion on base stations. The paper also investigates different authentication protocols for improving security while maintaining low computation and transmission time. For data analysis, different machine learning algorithms, such as decision tree, support vector machines, and logistic regression, are used on the three layers. The proposed architecture is evaluated using CloudSim, iFogSim, and ns3-NB-IoT on real data consisting of medical vital signs. The results show that the proposed architecture reduces the NB-IoT delay by 59.9%, the execution time by an average of 38.5%, and authentication time by 35.1% for a large number of devices. This paper concludes that the NB-IoT combined with edge, fog, and cloud computing can support efficient remote health monitoring for large devices and large areas.

The agriculture sector stands as one of the most significant sectors sustaining 70 percent of the world’s population. In this sector, the supply consists of a series of interconnected stages, spanning from farming through production to the final delivery of goods to the end customer. A lack of transparency within the supply chain presents the largest gap between suppliers and retailers, such as ensuring the true value of products or services. This research introduces AgroChain, a Blockchain-based system that is designed to support the Agricultural Supply Chain (ASC) process. For scalability purposes, the proposed AgroChain solution comply with a process model that separates the registry of agricultural records from the record itself. The development of the AgroChain prototype along with its smart contracts that establish a transparent, yet secure, environment within the ASC under Quorum which is Ethereum oriented network is illustrated in this research. This research demonstrates that Blockchain networks offer a solution to manage the ASC ensuring traceability, privacy and integrity. Moreover, the research indicates that there is a pressing need to promote the standardization of ASC smart contracts, incorporating secure and straightforward process. Smart contracts have to ideally be implemented in consortium environments, enabling reliable validation of transactions by independent third parties without necessitating access to their content. Moreover, investigating the accountability for illegal activities become challenging.

Elections and voting play a crucial role in the development of a democratic society, enabling the public to express their views and participate in the decision-making process. Voting methods have evolved from paper ballot systems to e-voting systems to preserve the integrity of votes, ensuring a secure, transparent, and verifiable process. Continuous efforts have been made to develop a secure e-voting system that eliminates fraud attempts and provides accurate voting results. In this paper, we propose the architecture of a blockchain-based e-voting system called VoteChain. Developed to support the existing voting system in the state of Palestine, VoteChain aims to provide secure e-voting with features such as auditability, verifiability, accuracy, privacy, flexibility, transparency, mobility, availability, convenience, data integrity, and distribution of authority. The work introduces a smart contract designed to meet the demands of e-voting, governing transactions, monitoring computations, enforcing acceptable usage policies, and managing data usage after transmission. The proposed system also adopts advanced cryptographic techniques to enhance security. VoteChain features a web-based interface to facilitate user interaction, providing protection against multiple or double voting to ensure the integrity of the election. Furthermore, VoteChain is designed with a user-friendly and easily accessible administrator interface for managing voters, constituencies, and candidates. It ensures equal participation rights for all voters, fostering fair and healthy competition among candidates while preserving voter anonymity. A comparative analysis demonstrates VoteChain’s advancements in privacy, security, and scalability over both traditional and blockchain-based e-voting systems.