Abstract Quantum software systems represent a new realm in software engineering, utilizing quantum bits (Qubits) and quantum gates (Qgates) to solve the complex problems more efficiently than classical counterparts. Agile software development approaches are considered to address many inherent challenges in quantum software development, but their effective integration remains unexplored. This study investigates key causes of challenges that could hinders the adoption of traditional agile approaches in quantum software projects and develop an Agile-Quantum Software Project Success Prediction Model (AQSSPM). Firstly, we identified 19 causes of challenging factors discussed in our previous study, which are potentially impacting agile-quantum project success. Secondly, a survey was conducted to collect expert opinions on these causes and applied Genetic Algorithm (GA) with Naive Bayes Classifier (NBC) and Logistic Regression (LR) to develop the AQSSPM. Utilizing GA with NBC, project success probability improved from 53.17 to 99.68%, with cost reductions from 0.463 to 0.403%. Similarly, GA with LR increased success rates from 55.52 to 98.99%, and costs decreased from 0.496 to 0.409% after 100 iterations. Both methods result showed a strong positive correlation (rs = 0.955) in causes ranking, with no significant difference between them ( t = 1.195, p = 0.240 > 0.05). The AQSSPM highlights critical focus areas for efficiently and successfully implementing agile-quantum projects considering the cost factor of a particular project.

This paper introduces a novel approach for securing mobile crowd sensing (MCS) systems, with a focus on improving the safety and efficiency of crowd management during the Hajj pilgrimage through the integration of deep learning techniques within an IOTA-based framework. The proposed method employs a logit-boosted convolutional neural network (Logit-CNN) model to address significant security threats, such as jamming, spoofing, and faked sensing attacks, which are prevalent in large-scale, dynamic, and heterogeneous networks. Through comprehensive performance evaluations, the Logit-CNN model demonstrated superior accuracy and reliability, achieving a 99.5% accuracy, 99% precision, and 98% recall, outperforming traditional security methods by significant margins. These results highlight the model's ability to provide real-time anomaly detection, ensuring enhanced security and resource allocation. Furthermore, the study underscores the practical implications of deploying deep learning models in MCS systems, offering valuable insights into the challenges of real-world implementation and suggesting pathways for future research to further refine these security measures. The integration of deep learning with MCS systems not only elevates the overall security and management of large-scale events like the Hajj but also paves the way for its application in other similar environments.

This paper presents a hybrid blockchain architecture for Internet of Medical Things (IoMT) systems, aiming to enhance their security and performance. The proposed approach combines artificial intelligence (AI) models with blockchain technology to create a safe and efficient healthcare system. The study focuses on addressing the challenges related to data storage, data management, real-time medical applications, and system precision in IoMT. Through experimental evaluations, the effectiveness of the proposed techniques in terms of communication overhead, transaction performance, and privacy preservation is demonstrated. The results highlight the potential of leveraging AI and blockchain to improve the overall functionality of IoMT systems.