An AHP-Based Framework for Effective Requirement Management in Agile Software Development (ASD)

In Agile changes may be made at any time throughout the project lifetime in agile projects. These changes, however, often lead to longer turnaround times and higher costs while having a substantial influence on the activities and quality of project management. The suggested framework's main goal is to increase the effectiveness and flexibility of the requirement engineering process by successfully managing requirements changes, particularly in contexts where Agile Software Development (ASD) is practiced. The Agile methodology has gained popularity as a strategy for developing software because it can adjust to changing requirements and deliver software gradually. To make sure that the software being produced satisfies stakeholder expectations and adds value to the firm, good requirement management is essential. Using the Analytic Hierarchy Process (AHP) to prioritize requirements based on their relative relevance and urgency, this article introduces a framework for requirement management in Agile Projects. The most important requirements are taken care of first thanks to this strategy, which enables a more organized and informed decision-making process. We demonstrate the actual use of our framework in real-world contexts and highlight its efficacy in solving the issues faced by Agile projects by including a case study and an accompanying table. The suggested framework also supports the three core tenets of the Agile approach—transparency, cooperation, and continuous improvement—to foster an environment of excellence and ongoing learning within the Agile team. By developing these fundamental ideas, our framework not only supports Agile teams' continual growth and development but also helps them manage and prioritize requirements more efficiently, which ultimately improves project results and increases organizational value

Collaborative Federated Learning For Healthcare: Multi-Modal COVID-19 Diagnosis at the Edge

Despite significant improvements over the last few years, cloud-based healthcare applications continue to suffer from poor adoption due to their limitations in meeting stringent security, privacy, and quality of service requirements (such as low latency). The edge computing trend, along with techniques for distributed machine learning such as federated learning, have gained popularity as a viable solution in such settings. In this paper, we leverage the capabilities of edge computing in medicine by analyzing and evaluating the potential of intelligent processing of clinical visual data at the edge allowing the remote healthcare centers, lacking advanced diagnostic facilities, to benefit from the multi-modal data securely. To this aim, we utilize the emerging concept of clustered federated learning (CFL) for an automatic diagnosis of COVID-19. Such an automated system can help reduce the burden on healthcare systems across the world that has been under a lot of stress since the COVID-19 pandemic emerged in late 2019. We evaluate the performance of the proposed framework under different experimental setups on two benchmark datasets. Promising results are obtained on both datasets resulting in comparable results against the central baseline where the specialized models (i.e., each on a specific type of COVID-19 imagery) are trained with central data, and improvements of 16\% and 11\% in overall F1-Scores have been achieved over the multi-modal model trained in the conventional Federated Learning setup on X-ray and Ultrasound datasets, respectively. We also discuss in detail the associated challenges, technologies, tools, and techniques available for deploying ML at the edge in such privacy and delay-sensitive applications.Comment: preprint versio

Capability Indices for Non-Normal Distribution using Gini’s Mean Difference as Measure of Variability

This paper investigates the efficiency of Gini's mean difference (GMD) as a measure of variability in two commonly used process capability indices (PCIs), i.e., Cp and Cpk. A comparison has been carried out to evaluate the performance of GMD-based PCIs and Pearn and Chen quantile-based PCIs under low, moderate, and high asymmetry using Weibull distribution. The simulation results, under low and moderate asymmetric condition, indicate that GMD-based PCIs are more close to target values than quantile approach. Beside point estimation, nonparametric bootstrap confidence intervals, such as standard, percentile, and bias corrected percentile with their coverage probabilities also have been calculated. Using quantile approach, bias corrected percentile (BCPB) method is more effective for both Cp and Cpk, where as in case of GMD, both BCPB and percentile bootstrap method can be used to estimate the confidence interval of Cp and Cpk, respectively.1133Ysciescopu