Amani Saad

Blockchainsand smart contracts are pivotal in transforming interactions between systems and individuals, offering secure, immutable, and transparent trust-building mechanisms without central oversight. However, Smart Contracts face limitations due to their reliance on blockchain-contained data, a gap addressed by ’Oracles’. These bridges to external data sources introduce the ’Oracle problem’, where maintaining blockchain-like security and transparency becomes vital to prevent data integrity issues. This paper presents Veritas, a novel decentralized oracle system leveraging a layer-2 scaling solution, enhancing smart contracts’ efficiency and security on Ethereum blockchains. The proposed architecture, explored through simulation and experimental analyses, significantly reduces operational costs while maintaining robust security protocols. An innovative node selection process is also introduced to minimize the risk of malicious data entry, thereby reinforcing network security. Veritas offers a solution to the Oracle problem by aligning with blockchain principles of security and transparency, and demonstrates advancements in reducing operational costs and bolstering network integrity. While the study provides a promising direction, it also highlights potential areas for further exploration in blockchain technology and oracle system optimization.

Heart Rate Variability (HRV) is a measure of variation in the time interval between consecutive heart beats.HRV analysis is highly sensitive for risks linked with cardiovascular diseases which are main causes of death in Egypt and all over the Middle East. Early detection of cardiac arrhythmia diseases achieves effective treatment by making it easy to choose appropriate anti-arrhythmic drugs, also very important for improving arrhythmia therapy and preventing number of death in individuals. In this paper, an efficient cardiac arrhythmia detection algorithm is introduced. Different classifiers are deployed and examined on ECG signals. Various oversampling techniques are investigated to handle imbalanced dataset. The ensemble classifier; support vector machine and Random forest with random sampling show accuracy of 98.18 % in 0.145 sec which is the best accuracy among all other classifiers. In addition, this paper also proposes a mobile based system architecture integrated with the algorithm for diagnosis and classification of cardiac arrhythmia diseases. The proposed system can be easily used by patients to check their heart health remotely and easily.