Coronaviruses (CoVs) are a large family of viruses that are common in many animal species, including camels, cattle, cats and bats. Animal CoVs, such as Middle East respiratory syndrome-CoV, severe acute respiratory syndrome (SARS)-CoV, and the new virus named SARS-CoV-2, rarely infect and spread among humans. On January 30, 2020, the International Health Regulations Emergency Committee of the World Health Organisation declared the outbreak of the resulting disease from this new CoV called 'COVID-19', as a 'public health emergency of international concern'. This global pandemic has affected almost the whole planet and caused the death of more than 315,131 patients as of the date of this article. In this context, publishers, journals and researchers are urged to research different domains and stop the spread of this deadly virus. The increasing interest in developing artificial intelligence (AI) applications has addressed several medical problems. However, such applications remain insufficient given the high potential threat posed by this virus to global public health. This systematic review addresses automated AI applications based on data mining and machine learning (ML) algorithms for detecting and diagnosing COVID-19. We aimed to obtain an overview of this critical virus, address the limitations of utilising data mining and ML algorithms, and provide the health sector with the benefits of this technique. We used five databases, namely, IEEE Xplore, Web of Science, PubMed, ScienceDirect and Scopus and performed three sequences of search queries between 2010 and 2020. Accurate exclusion criteria This article is part of the Topical Collection on Systems-Level Quality Improvement * A. A.

As coronavirus disease 2019 (COVID-19) spreads across the world, the transfusion of efficient convalescent plasma (CP) to the most critical patients can be the primary approach to preventing the virus spread and treating the disease, and this strategy is considered as an intelligent computing concern. In providing an automated intelligent computing solution to select the appropriate CP for the most critical patients with COVID-19, two challenges aspects are bound to be faced: (1) distributed hospital management aspects (including scalability and management issues for prioritising COVID-19 patients and donors simultaneously), and (2) technical aspects (including the lack of COVID-19 dataset availability of patients and donors and an accurate matching process amongst them considering all blood types). Based on previous reports, no study has provided a solution for CP-transfusion-rescue intelligent framework during this pandemic that has addressed said challenges and issues. This study aimed to propose a novel CPtransfusion intelligent framework for rescuing COVID-19 patients across centralised/decentralised telemedicine hospitals based on the matching component process to provide an efficient CP from eligible donors to the most critical patients using multicriteria decision-making (MCDM) methods. A dataset, including COVID-19 patients/donors that have met the important criteria in the virology field, must be augmented to improve the developed framework. Four consecutive phases conclude the methodology. In the first phase, a new COVID-19 dataset is generated on the basis of medical-reference ranges by specialised experts in the virology field. The simulation data are classified into 80 patients and 80 donors on the basis of the five biomarker criteria with four blood types (i.e., A, B, AB, and O) and produced for COVID-19 case study. In the second phase, the identification scenario of patient/donor distributions across four centralised/decentralised telemedicine hospitals is identified 'as a proof of concept'. In the third phase, three stages are conducted to develop a CP-transfusion-rescue framework. In the first stage, two decision matrices are adopted and developed on the basis of the five 'serological/protein biomarker' criteria for the prioritisation of patient/donor lists. In the second stage, MCDM techniques are analysed to adopt individual and group decision making based on integrated AHP-TOPSIS as suitable methods. In the third stage, the intelligent matching components amongst patients/donors are developed on the basis of four distinct rules. In the final phase, the guideline of the objective validation steps is reported. The intelligent framework implies the benefits and strength weights of biomarker criteria to the priority configuration results and can obtain efficient CPs for the most critical patients. The execution of matching components possesses the scalability and balancing presentation within centralised/decentralised hospitals. The objective validation results indicate that the ranking is valid.

Voting is one of the most fundamental components of a democratic society. In 2021 Iraq held the Council of Representatives (CoR) elections in 83 electoral constituencies in 19 governorates. Nonetheless, several significant issues arose during this election, including the problem of logistics distribution, the excessively long period of ballot counting, voters can't know if their votes were counted or if their ballots were tampered with, and the inconsistent regulation of vote counting. Blockchain technology, which was just invented, may offer a solution to these problems. This paper introduces an electronic voting system for the Iraq Council of Representatives elections that is based on a prototype of the permission hyperledger fabric blockchain. An immutable, distributed ledger maintained by all members of a network is what blockchain technology is all about. By authenticating each voter, the system can prevent voting fraud by making votes traceable and verifiable, hence decreasing the chance of unlawful activities and fraudulent ballots. This work investigates the influence of E-voting, specifically the voting phase workload, on the performance of the hyperledger fabric blockchain platform in terms of latency and throughput by altering transaction send rates (tps), block size, and block timeout.

This study presents a novel benchmarking methodology for Data Acquisition System (DAS) types to support industrial community characteristics in designing and implementing the advanced driver assistance systems within vehicles, which is considered multicriteria decision-making (MCDM) problems. Four issues support this claim. Multiple criteria need to be considered in the evaluation, data variation, trade-off and conflict. Thus, an MCDM solution is essential to overcome problem complexity. In the last years, MCDM developed methods have been studied and criticised from different theoretical aspects. The most recent method, fuzzy decision by opinion score method (FDOSM), has proven its power in solving other methods challenges. However, the FDOSM technique and its extension were based on traditional fuzzy set theory, which is limited and unable to deal with the membership and non-membership hesitation simultaneously and that affect the accuracy of final decision especial among the group of decision-makers. Therefore, this study extended FDOSM into an intuitionistic fuzzy environment that considers the hesitation index in the membership definition, then discuss the power of such membership in evaluating and benchmarking the DAS systems. The proposed methodology comprises two consecutive phases. In the first phase, a decision matrix is formulated based on the crossover of the ‘DAS systems’ and ‘multiple evaluation criteria’. In the second phase, the new method (the intuitionistic FDOSM method) has two main stages (i.e. data transformation unit and data processing). The dataset was used to prove the concept. A total of 39 DASs were evaluated based on 14 DASs criteria, involving seven sub-criteria for “comprehensive complexity assessment” purpose and eight sub-criteria for “design and implementation” purpose, which highly affected the design of DAS when implantation occurred by industrial communities. The results of this study are as follows: (1) Individual results of benchmarking, which used three decision-makers are broad, with consensus on the DAS#1 system ranked as the best. (2) The results of the proposed GDMs proved quality in DASs benchmarking, and the DAS#1 system is also the best. (3) Intuitionistic FDOSM can deal with hesitation and uncertainty problems properly. (4) Significant differences were indicated among the groups’ scores, which proves the validity of the intuitionistic FDOSM results.

The significant increase in the volume of fake (spam) messages has led to an urgent need to develop and implement a robust anti‐spam method. Several of the current anti‐spam systems depend mainly on the word order of the message in determining the spam message, which results in the system's inability to predict the correct type of message when the word order changes. In this paper, a new framework is proposed for anti‐spam filtering that does not depend on the word's position in the message, called the Trigonometric Words Ranking Model (TWRM). The proposed TWRM is based on restricting spammers over the network by measuring a theta angle, which is a relationship between message weight and spam. TWRM classifies messages by calculating the rank of each word that places the corresponding message in the correct class. The rank of words is derived from their frequency in the entire data category. The proposed method is applied to three datasets of spam messages: UCI spam email, Enron spam, and TREC spam data. The proposed model is proven as more efficient than the Minhash and vector space models. Moreover, the TWRM performance provided better retrieval time and defence, which is reflected in the accuracy of (99.64%), which is higher than that of Minhash (88.79%) and vector space (92.59%).