Significant modifications have been seen in healthcare facilities over the past two decades. With the use of IoT-enabled devices, the monitoring and analysis of patient diagnostic parameters is made considerably easy. The new technology shift for medical field is IoMT. However, the problem of privacy for patient data and the security of information still a point to ponder. This research proposed a prototype model to integrate the blockchain and IoMT for providing better analysis of patient health factors. The authors suggested IoMT data to be collected over Edge Computing gateway devices and forward to Cloud Gateway. The three-layered decision making structure ensures the integrity of the data. The further analysis of information collected over sensor-based devices is done in the Cloud IoT Central Hub service. To ensure the secrecy and compliance of the patient data, Smart Contracts are integrated. After the exchange of smart contracts, a block of information is broadcast over the health blockchain. The P2P network makes it viable to retain all health statistics and further processing of information. The paper describes the scenario and experimental setup for a COVID-19 data-set analyzed in the proposed prototype mode. After the collection of information and decision making, the block of data is sent across all peer nodes. Thus, the power of IoMT and blockchain makes it easy for the healthcare worker to diagnose and handle patient data with privacy. The IoMT is integrated with artificial intelligence to enable decision making based on the classification of data. The results are saved as transactions in the blockchain hyperledger. This study demonstrates the prototype model with test data in a testing network with two peer nodes.

Validation of software requirements is a primary phase in requirements engineering that ensures requirements match the target system with the intended needs of the acquirer. It aims to detect and correct errors that prevail in the specified requirements. Although there are tremendous requirements validation approaches, some software may fail because of limited or ineffective requirements validation techniques and unreliable requirements’ quality characteristics. In this study, a systematic literature review of requirements validation is performed. The study analyzes the most adopted validation techniques, reports requirements quality characteristics, and discovers significant challenges of validation techniques. The review identified 66 relevant primary studies analyzed to derive deep insights into the following aspects of requirements validation: trends of requirements validation methods, including their subtechnique strengths and weaknesses, requirements quality characteristics categories, and adopted tools and datasets in these techniques. We grouped validation techniques into categories: prototyping, inspection, knowledge-oriented, test-oriented, modeling and assessment, and formal models. The analysis reported 19 validation techniques, 27 tools, new requirements validation characteristics, and several challenges that prevailed through validation techniques. The trend of validation techniques is to those methods that apply machine learning techniques with knowledge from dictionaries and ontologies. Most challenges are about how to express the requirements and how to revert clients’ feedback. There is a strong relationship between validation techniques, software application domain, and requirements validation quality attributes. Thus, there is an immense need to unify the quality characteristics and domain-specific validation methods.

Social media platforms such as Twitter, YouTube, Instagram and Facebook are leading sources of large datasets nowadays. Twitter’s data is one of the most reliable due to its privacy policy. Tweets have been used for sentiment analysis and to identify meaningful information within the dataset. Our study focused on the distance learning domain in Saudi Arabia by analyzing Arabic tweets about distance learning. This work proposes a model for analyzing people’s feedback using a Twitter dataset in the distance learning domain. The proposed model is based on the Apache Spark product to manage the large dataset. The proposed model uses the Twitter API to get the tweets as raw data. These tweets were stored in the Apache Spark server. A regex-based technique for preprocessing removed retweets, links, hashtags, English words and numbers, usernames, and emojis from the dataset. After that, a Logistic-based Regression model was trained on the pre-processed data. This Logistic Regression model, from the field of machine learning, was used to predict the sentiment inside the tweets. Finally, a Flask application was built for sentiment analysis of the Arabic tweets. The proposed model gives better results when compared to various applied techniques. The proposed model is evaluated on test data to calculate Accuracy, F1 Score, Precision, and Recall, obtaining scores of 91%, 90%, 90%, and 89%, respectively.

The COVID-19 pandemic affects individuals in many ways and has spread worldwide. Current methods of COVID-19 detection are based on physicians analyzing the patient’s symptoms. Machine learning with deep learning approaches applied to image processing techniques also plays a role in identifying COVID-19 from minor symptoms. The problem is that such models do not provide high performance, which impacts timely decision-making. Early disease detection in many places is limited due to the lack of expensive resources. This study employed pre-implemented instances of a convolutional neural network and Darknet to process CT scans and X-ray images. Results show that the proposed new models outperformed the state-of-the-art methods by approximately 10% in accuracy. The results will help physicians and the health care system make preemptive decisions regarding patient health. The current approach might be used jointly with existing health care systems to detect and monitor cases of COVID-19 disease quickly.

The COVID-19 epidemic is affecting individuals in many ways and continues to spread all over the world. Vaccines and traditional medical techniques are still being researched. In diagnosis and therapy, biological and digital technology is used to overcome the fear of this disease. Despite recovery in many patients, COVID-19 does not have a definite cure or a vaccine that provides permanent protection for a large number of people. Current methods focus on prevention, monitoring, and management of the spread of the disease. As a result, new technologies for combating COVID-19 are being developed. Though unreliable due to a lack of sufficient COVID-19 datasets, inconsistencies in the datasets availability, non-aggregation of the database because of conflicting data formats, incomplete information, and distortion, they are a step in the right direction. Furthermore, the privacy and confidentiality of people’s medical data are only partially ensured. As a result, this research study proposes a novel, cooperative approach that combines big data analytics with relevant Artificial Intelligence (AI) techniques and blockchain to create a system for analyzing and detecting COVID-19 instances. Based on these technologies, the reliability, affordability, and prominence of dealing with the above problems required time. The architecture of the proposed model will analyze different data sources for preliminary diagnosis, detect the affected area, and localize the abnormalities. Furthermore, the blockchain approach supports the decentralization of the central repository so that it is accessible to every stakeholder. The model proposed in this study describes the four-layered architecture. The purpose of the proposed architecture is to utilize the latest technologies to provide a reliable solution during the pandemic; the proposed architecture was sufficient to cover all the current issues, including data security. The layers are unique and individually responsible for handling steps required for data acquisition, storage, analysis, and reporting using blockchain principles in a decentralized P2P network. A systematic review of the technologies to use in the pandemic covers all possible solutions that can cover the issue best and provide a secure solution to the pandemic.