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Distributed Scrum of Scrums Agile Software Development (DSsASD) plays a crucial role in modern software development, enabling collaboration across vast distances in software development. However, existing tools and frameworks have struggled to effectively address communication, teamwork, and collaboration challenges within DSsASD teams. These challenges include transparency, trust, traceability, auditability, and security concerns, these issues are related to project delays, client dissatisfaction, contract cancellations, miscommunication collaboration, and payment disputes between stakeholders. To tackle these persistent concerns and issues, this article introduces ChainAgilePlus, a novel framework that integrates blockchain technology in the distributed scrum of scrum agile software development. ChainAgilePlus implements smart contracts by utilizing a private Ethereum blockchain to govern acceptance acknowledgment, deployment testing, secure payments, verification of developer payments, and automated payment distribution to team wallets. Smart contracts also enforce penalties for delayed payments and overdue tasks, promoting accountability and adherence to deadlines. Additionally, ChainAgilePlus mitigates blockchain scalability challenges by integrating the Interplanetary File System (IPFS) for off-chain storage. Empirical results from experimental processes demonstrate the effectiveness of ChainAgilePlus in enhancing communication, transparency, coordination, traceability, auditability, security, and trust among clients and developers in DSsASD projects.

Abstract Autism spectrum disorder (ASD) presents a neurological and developmental disorder that has an impact on the social and cognitive skills of children causing repetitive behaviours, restricted interests, communication problems and difficulty in social interaction. Early diagnosis of ASD can prevent from its severity and prolonged effects. Federated learning (FL) is one of the most recent techniques that can be applied for accurate ASD diagnoses in early stages or prevention of its long-term effects. In this article, FL technique has been uniquely applied for autism detection by training two different ML classifiers including logistic regression and support vector machine locally for classification of ASD factors and detection of ASD in children and adults. Due to FL, results obtained from these classifiers have been transmitted to central server where meta classifier is trained to determine which approach is most accurate in the detection of ASD in children and adults. Four different ASD patient datasets, each containing more than 600 records of effected children and adults have been obtained from different repository for features extraction. The proposed model predicted ASD with 98% accuracy (in children) and 81% accuracy (in adults).

Metaverse is a virtual world platform that enables users to create and explore 3D environments. Recently, this technology has gained traction due to its potential to revolutionize the way people interact and share information. This paper provides an overview of the current state of Metaverse technologies. It begins by introducing their core components and surveys recent research and development activities in the area. Next, the paper examines the potential applications of Metaverse technology in different domains, such as gaming, social networking, training, education, healthcare, and marketing. It then outlines the challenges and opportunities associated with the development and deployment of Metaverse technologies, including scalability, privacy, and security issues. Finally, the paper concludes with a discussion of the implications of Metaverse technologies for the future of digital communication and information sharing.

Behavior Driven Development (BDD) is a widely adopted agile methodology for software development that emphasizes the behavior of an application as a series of test cases, using the keywords, which include “Given,” “When,” and “Then.” It involves writing requirements in a structured and testable format that can be evaluated to ensure compliance with the expected behavior. Although a significant amount of research has been conducted to examine the impact of using BDD on software development process yet rare work is observed to synthesize these studies and identify areas for future exploration. This study presents a review of the state-of-the-art BDD by synthesizing the recent advancements in its uses and applications. It aims to systematically investigate the impact of BDD on software development process as well as on product quality by aiding to bridge the communication gap between the stakeholders. The results reveal that BDD is an effective technique to clarify requirements during the software development process as it helps minimizing the intrinsic ambiguities. This work proposes a taxonomy based on the role and applications of BDD in various contexts. It suggests a framework for applying BDD in software development and defines a workflow for its application in software development. Finally, this work highlights some pertinent future directions for the use of BDD in software development.

Women’s safety has been highlighted as one of the major concerns of any society where several women are dealing with various safety issues like harassment, rape, molestation, and domestic violence due to different social or cultural reasons. Internet of Things (IoT) is becoming a promising technology to support day-to-day concerns and provide support in handling various affairs. Many IoT-based devices have been introduced by the community to help women deal with their potential safety threats. This study presents a systematic literature review of research studies exhibiting the IoT devices for women’s safety, the main features these devices offer as well as the wearable, sensors used, and the machine learning algorithms used. The review is carried out by carefully examining and synthesizing the research articles published between 2016 to 2022 in well-reputed research venues. The results revealed that different types of sensors are used to capture the state of women undergoing safety issues where the pulse-rate, and pressure sensors are most commonly used sensors in these devices. In addition, the devices used different technology to transmit the alerts including global positing system (GPS), global system for mobile communication (GSM), and Raspberry pi. Furthermore, several machine learning algorithms such as logistic regression, hidden Markov, and decision trees are used to identify the potential under threat women and help prevent the undesirable situation for women beforehand. It was identified that despite producing notable research in the underlying domain the systems emphasizing auto-activation of alert generation with lesser human interaction and improved accuracies are required to be developed for effectively addressing the concern. In addition to reviewing the literature, this study suggests a taxonomy posing different techniques, features, wearables, and sensors used in IoT-based women safety devices. Furthermore, the gaps and challenges pertaining to the IoT devices and their usability for women’s safety have also been highlighted. In addition, this work proposes an architectural model that presents prominent components necessary to develop IoT-based women’s safety devices. Lastly, this study emphasizes the use of combinations of sensors to get multiple types of input data that could lead to determining the possibility of threat with better accuracies and precisions.

Floods are one of the most common natural disasters that occur frequently causing massive damage to property, agriculture, economy and life. Flood prediction offers a huge challenge for researchers struggling to predict floods since long time. In this article, flood forecasting model using federated learning technique has been proposed. Federated Learning is the most advanced technique of machine learning (ML) that guarantees data privacy, ensures data availability, promises data security, and handles network latency trials inherent in prediction of floods by prohibiting data to be transferred over the network for model training. Federated Learning technique urges for onsite training of local data models, and focuses on transmission of these local models on the network instead of sending huge data set towards central server for local model aggregation and training of global data model at the central server. In this article, the proposed model integrates locally trained models of eighteen clients, investigates at which station flooding is about to happen and generates flood alert towards a specific client with five days lead time. A local feed forward neural network (FFNN) model is trained at the client station where the flood has been expected. Flood forecasting module of local FFNN model predicts the expected water level by taking multiple regional parameters as input. The dataset of five different rivers and barrages has been collected from 2015 to 2021 considering four aspects including snow melting, rainfall-runoff, flow routing and hydrodynamics. The proposed flood forecasting model has successfully predicted previous floods happened in the selected zone during 2010 to 2015 with 84 % accuracy.

In recent years, the Internet of Things (IoT) has become one of the most familiar names creating a benchmark and scaling new heights. IoT an indeed future of the communication that has transformed the objects (things) of the real world into smarter devices. With the advent of IoT technology, this decade is witnessing a transformation from traditional agriculture approaches to the most advanced ones. In perspective to the current standing of IoT in agriculture, identification of the most prominent application of IoT-based smart farming i.e. greenhouse has been highlighted and presented a systematic analysis and investigated the high quality research work for the implementation of greenhouse farming. The primary objective of this study is to propose an IoT-based network framework for a sustainable greenhouse environment and implement control strategies for efficient resources management. A rigorous discussion on IoT-based greenhouse applications, sensors/devices, and communication protocols have been presented. Furthermore, this research also presents an inclusive review of IoT-based greenhouse sensors/devices and communication protocols. Moreover, we have also presented a rigorous discussion on smart greenhouse farming challenges and security issues as well as identified future research directions to overcome these challenges. This research has explained many aspects of the technologies involved in IoT-based greenhouse and proposed network architecture, topology, and platforms. In the end, research results have been summarized by developing an IoT-based greenhouse farm management taxonomy and attacks taxonomy.

Neurology is considered as one of the most challenging fields of medical science due to the complexity of the nervous system in human body. The treatment processes of the patients suffering from neurological disorders are greatly influenced by the advancements in technology. In this regard, the infusion of augmented reality (AR) has immensely improved patients’ and healthcare workers’ experiences in the field of neurology. Several efforts are being made by the community to introduce various applications of AR for patients as well as for healthcare workers in neurology. This study systematically examines the applications of AR in neurology and synthesizes their uses and impacts in the treatment and assistance of the patients suffering from neurological disorders as well as in the assistance of healthcare workers. The results reveal that most of the AR applications are used for motor disorders while lesser systems are designed for patients with degenerative diseases, most of them being task assistance systems. Moreover, the use of AR applications leaves both long-term and short-term impacts in improving the quality of patient’s life. Furthermore, there is a need of introducing more interactive AR systems for patients with degenerative diseases as well as exploring the possibility of using such systems for mental rehabilitation. It has also been identified that there is a potential to develop AR applications for healthcare workers, to train and assist them through simulations. In addition to reviewing the AR applications, this work proposes a taxonomy of AR applications in neurology posing different dimensions including scope of AR, type of neurological disorder, and impact of AR assistance on patient’s life. Furthermore, this article also highlights challenges of using AR applications in neurology. Based on the identified challenges, guidelines to design AR systems are suggested for application designers and developers to help them design and develop more useful AR systems. Lastly, this work proposes an architectural model to design the AR systems for patients suffering specifically from degenerative or movement neuro-disorders in order to guide the future research of AR usage in neuroscience.