Software requirement engineering uses different elicitation approaches from clients and other relevant stockholders communication. The elicitation techniques are used to elicit and analyse the requirement that is based on the presence of stakeholder availability. So, software products often fail to handle the demand of the stakeholder and users of the software. The requirement elicitation process gets complicated and labour-intensive when the traditional techniques are used with a larger population. This gives us the motivation to use a data-centred approach of Re engineering (RE) where requirements are elicited and collected on a larger scale. This study uses crowdsourcing integrated with the SCRUM approach of agile software development which is now popular in software development industries due to reducing cost and efficiency. Although it is important to identify the issues while integrating crowd source software development (CSSD) with SCRUM and resolve those issues. This study proposed a framework based on the integration of SCRUM with crowdsourcing development. The framework is designed in four major layers, the first layer is the vision and goal in which the raw document has been prepared, and the second layer is the prioritisation of task in which the product owner prioritised the task which should be developed first and which should develop later, the third layer is sprint planning and designing in which core document has been prepared and deliver it to the final project development, and the fourth layer is retrospective meeting in which all the development team and product owner and Scrum master conduct a meeting on a weekly basis to discuss the issues and changes that are made in the development process. This study suggested a conceptual framework that can combine CSSD and SCRUM to benefit from both software requirement development approaches and to address integration issues. The challenge for the researcher is to combine CSSD and SCRUM so that both software development methodologies can benefit from each other. Finally, issues are identified, and possible research directions are discussed for the future. K E Y W O R D Srequirements elicitation, requirements engineering, SCRUM, software engineering | INTRODUCTIONRequirement engineering (RE) is a difficult process concerned with communication and identifying the purpose of software development. For the software developer, this is critical to building the best software application with misinterpreted requirements to handle the user's demands. The best software system will be developed and fulfil the actual needs of the stakeholder when better requirements are being gathered from the stakeholder. RE is the process of identifying, defining, and maintaining a software application to understand the user's needs and find out the possible solution, specify the solution This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly...

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The attacks of cyber are rapidly increasing due to advanced techniques applied by hackers. Furthermore, cyber security is demanding day by day, as cybercriminals are performing cyberattacks in this digital world. So, designing privacy and security measurements for IoT-based systems is necessary for secure network. Although various techniques of machine learning are applied to achieve the goal of cyber security, but still a lot of work is needed against intrusion detection. Recently, the concept of hybrid learning gives more attention to information security specialists for further improvement against cyber threats. In the proposed framework, a hybrid method of swarm intelligence and evolutionary for feature selection, namely, PSO-GA (PSO-based GA) is applied on dataset named CICIDS-2017 before training the model. The model is evaluated using ELM-BA based on bootstrap resampling to increase the reliability of ELM. This work achieved highest accuracy of 100% on PortScan, Sql injection, and brute force attack, which shows that the proposed model can be employed effectively in cybersecurity applications.

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The cutting-edge studies on Automatic Speech Recognition approach have reported exceptional accuracy rates that are even comparable to human transcribersposing a question if machine has reached human performance. Automatic Speech Recognition can be used as a biometric authentication technique, which is essential in ciphering many applications used. In light of the Arabic language, only few studies have proposed to assess the effectiveness of using Automatic Speech Recognition in Arabic language; therefore, this study aims to implement Arabic speaker recognition using three different algorithms, including (i) Dynamic Time Warping (DTW), (ii) Gaussian mixture model (GMM), and (iii) Support Vector Machine (SVM). To measure the effectiveness of these algorithm in recognizing the Arabic speech, two datasets are used to train and test them, which are: (i) speech agent archive, and (ii) Arabic speech corpus. The results reveled that the DTW outperforms the GMM and SVM in terms of accuracy, precision, recall and fmeasure, as it achieves 95.7%, 96%, and 95%, and 96%, respectively.

Because of the existence of Covid-19 and its variants, health monitoring systems have become mandatory, particularly for critical patients such as neonates. However, the massive volume of real-time data generated by monitoring devices necessitates the use of efficient methods and approaches to respond promptly. A fog-based architecture for IoT healthcare systems tends to provide better services, but it also produces some issues that must be addressed. We present a bidirectional approach to improving real-time data transmission for health monitors by minimizing network latency and usage in this paper. To that end, a simplified approach for large-scale IoT health monitoring systems is devised, which provides a solution for IoT device selection of optimal fog nodes to reduce both communication and processing delays. Additionally, an improved dynamic approach for load balancing and task assignment is also suggested. Embedding the best practices from the IoT, Fog, and Cloud planes, our aim in this work is to offer software architecture for IoT-based healthcare systems to fulfill non-functional needs. 4 + 1 views are used to illustrate the proposed architecture.

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.