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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.

Software Development based on Scrum Agile in a distributed development environment plays a pivotal role in the contemporary software industry by facilitating software development across geographic boundaries. However, in the past different frameworks utilized to address the challenges like communication and collaboration in scrum agile distributed software development (SADSD) were notably inadequate in transparency, security, traceability, geographically dispersed location work agreements, geographically dispersed teamwork effectiveness, and trust. These deficiencies frequently resulted in delays in software development and deployment, customer dissatisfaction, canceled agreements, project failures, and disputes over payments between customers and development teams. To address these challenges of SADSD, this paper proposes a new framework called ChainAgile, which leverages blockchain technology. ChainAgile employs a private Ethereum blockchain to facilitate the execution of smart contracts. These smart contracts cover a range of functions, including acceptance testing, secure payments, requirement verification, task prioritization, sprint backlog, user story design and development and payments with the automated distribution of payments via digital wallets to development teams. Moreover, in the ChainAgile framework, smart contracts also play a pivotal role in automatically imposing penalties on customers for making late payments or for no payments and penalties on developers for completing the tasks that exceed their deadlines. Furthermore, ChainAgile effectively addresses the scalability limitations intrinsic in blockchain technology by incorporating the Interplanetary File System (IPFS) is used for storage solutions as an off-chain mechanism. The experimental results conclusively show that this innovative approach substantially improves transparency, traceability, coordination, communication, security, and trust for both customers and developers engaged in scrum agile distributed software development (SADSD).

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.

Distributed Agile Software Development (DASD) is the most important approach for the modern software industry that allows geographically independent software development. In the past, different tools and frameworks were proposed to solve communication and collaboration issues in DASD but they lacked transparency, trust, traceability, and security. These shortcomings resulted in project failure or overdue, customer dissatisfaction, project deal cancellations, and payment clashes between the customers and development teams. This paper addresses and overcomes the major issues of transparency, trust, security, traceability, coordination, and communication in DASD by embedding blockchain technology. We have proposed a novel blockchain-based framework named as AgilePlus which executes the smart contracts on a private ethereum blockchain for acceptance testing, secure payment, verification of developer’s payment requirements, and automatic payment distribution into the digital wallets of development teams. The execution of these smart contracts automatically assign penalties to the customers for late or non-payments and also to the developers for overdue tasks. Moreover, we have also solved the blockchain’s scalability challenge in AgilePlus by utilizing Interplanetary File System (IPFS) as off-chain storage. Lastly, experimental results prove that the proposed framework enhances transparency, communication, coordination, traceability, security and solves trust issues of both customers and developers in DASD.

Deep learning methods have huge success in task specific feature representation. Transfer learning algorithms are very much effective when large training data is scarce. It has been significantly used for diagnosis of diseases in medical imaging. This article presents a systematic literature review (SLR) by conducting a comparison of a variety of transfer learning approaches with healthcare experts in diagnosing diseases from medical imaging. This study has been compiled by reviewing research studies published in renowned venues between 2014 and 2019. Moreover, the data for the diagnosis performed by health care experts has also been acquired to perform a detailed comparative analysis for a wide range of diseases. The analysis has been performed on the basis of diseases, transfer learning approaches, type of medical imaging used. The comparative analysis is based on performance indices reported in studies which include diagnostic accuracy, true-positive (TP), false-positive (FP), true-negative (TN), false-negative (FN) sensitivity, specificity, and the area under the receiver operating characteristic curve (AUROC). A total of5,188articles were identified out of which 63 studies were included. Among them 21 research studies contain sufficient data to construct the evaluation tables that enable process of test accuracy of transfer learning having sensitivity ranged from 71% to 100% (mean 85.25%) and specificity ranged from 64% to 100% (mean 81.92%). Furthermore, health experts having sensitivity ranged from 33% to 100% (mean 85.27%) and specificity ranged from 82% to 100% (mean 91.63%).This SLR found that diagnostic accuracy of transfer learning is approximately equivalent to the diagnosis of health experts. The results also revealed that convolutional neural networks (CNN) have been extensively used for disease diagnosis from medical imaging. Finally, inappropriate exposure of diseases in transfer learning studies restricts reliable elucidation of the outcomes of diagnostic accuracy.

Asthma is a deadly disease that affects the lungs and air supply of the human body. Coronavirus and its variants also affect the airways of the lungs. Asthma patients approach hospitals mostly in a critical condition and require emergency treatment, which creates a burden on health institutions during pandemics. The similar symptoms of asthma and coronavirus create confusion for health workers during patient handling and treatment of disease. The unavailability of patient history to physicians causes complications in proper diagnostics and treatments. Many asthma patient deaths have been reported especially during pandemics, which necessitates an efficient framework for asthma patients. In this article, we have proposed a blockchain consortium healthcare framework for asthma patients. The proposed framework helps in managing asthma healthcare units, coronavirus patient records and vaccination centers, insurance companies, and government agencies, which are connected through the secure blockchain network. The proposed framework increases data security and scalability as it stores encrypted patient data on the Interplanetary File System (IPFS) and keeps data hash values on the blockchain. The patient data are traceable and accessible to physicians and stakeholders, which helps in accurate diagnostics, timely treatment, and the management of patients. The smart contract ensures the execution of all business rules. The patient profile generation mechanism is also discussed. The experiment results revealed that the proposed framework has better transaction throughput, query delay, and security than existing solutions.

Software Development based on Scrum Agile in a distributed development environment plays a pivotal role in the contemporary software industry by facilitating software development across geographic boundaries. However, in the past different frameworks utilized to address the challenges like communication and collaboration in scrum agile distributed software development (SADSD) were notably inadequate in transparency, security, traceability, geographically dispersed location work agreements, geographically dispersed teamwork effectiveness, and trust. These deficiencies frequently resulted in delays in software development and deployment, customer dissatisfaction, canceled agreements, project failures, and disputes over payments between customers and development teams. To address these challenges of SADSD, this paper proposes a new framework called ChainAgile, which leverages blockchain technology. ChainAgile employs a private Ethereum blockchain to facilitate the execution of smart contracts. These smart contracts cover a range of functions, including acceptance testing, secure payments, requirement verification, task prioritization, sprint backlog, user story design and development and payments with the automated distribution of payments via digital wallets to development teams. Moreover, in the ChainAgile framework, smart contracts also play a pivotal role in automatically imposing penalties on customers for making late payments or for no payments and penalties on developers for completing the tasks that exceed their deadlines. Furthermore, ChainAgile effectively addresses the scalability limitations intrinsic in blockchain technology by incorporating the Interplanetary File System (IPFS) is used for storage solutions as an off-chain mechanism. The experimental results conclusively show that this innovative approach substantially improves transparency, traceability, coordination, communication, security, and trust for both customers and developers engaged in scrum agile distributed software development (SADSD). [ABSTRACT FROM AUTHOR]