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  1. Al-Waisy AS, Al-Fahdawi S, Mohammed MA, Abdulkareem KH, Mostafa SA, Maashi MS, et al.
    Soft comput, 2023;27(5):2657-2672.
    PMID: 33250662 DOI: 10.1007/s00500-020-05424-3
    The outbreaks of Coronavirus (COVID-19) epidemic have increased the pressure on healthcare and medical systems worldwide. The timely diagnosis of infected patients is a critical step to limit the spread of the COVID-19 epidemic. The chest radiography imaging has shown to be an effective screening technique in diagnosing the COVID-19 epidemic. To reduce the pressure on radiologists and control of the epidemic, fast and accurate a hybrid deep learning framework for diagnosing COVID-19 virus in chest X-ray images is developed and termed as the COVID-CheXNet system. First, the contrast of the X-ray image was enhanced and the noise level was reduced using the contrast-limited adaptive histogram equalization and Butterworth bandpass filter, respectively. This was followed by fusing the results obtained from two different pre-trained deep learning models based on the incorporation of a ResNet34 and high-resolution network model trained using a large-scale dataset. Herein, the parallel architecture was considered, which provides radiologists with a high degree of confidence to discriminate between the healthy and COVID-19 infected people. The proposed COVID-CheXNet system has managed to correctly and accurately diagnose the COVID-19 patients with a detection accuracy rate of 99.99%, sensitivity of 99.98%, specificity of 100%, precision of 100%, F1-score of 99.99%, MSE of 0.011%, and RMSE of 0.012% using the weighted sum rule at the score-level. The efficiency and usefulness of the proposed COVID-CheXNet system are established along with the possibility of using it in real clinical centers for fast diagnosis and treatment supplement, with less than 2 s per image to get the prediction result.
  2. Lai H, Khan YA, Thaljaoui A, Chammam W, Abbas SZ
    Soft comput, 2021 May 19.
    PMID: 34025212 DOI: 10.1007/s00500-021-05871-6
    Unemployment remains a serious issue for both developed and developing countries and a driving force to lose their monetary and financial impact. The estimation of the unemployment rate has drawn researchers' attention in recent years. This investigation's key objective is to inquire about the impact of COVID-19 on the unemployment rate in selected, developed and developing countries of Asia. For experts and policymakers, effective prediction of the unemployment rate is an influential test that assumes an important role in planning the monetary and financial development of a country. Numerous researchers have recently utilized conventional analysis tools for unemployment rate prediction. Notably, unemployment data sets are nonstationary. Therefore, modeling these time series by conventional methods can produce an arbitrary mistake. To overcome the accuracy problem associated with conventional approaches, this investigation assumes intelligent-based prediction approaches to deal with the unemployment data and to predict the unemployment rate for the upcoming years more precisely. These intelligent-based unemployment rate strategies will force their implications by repeating diversity in the unemployment rate. For illustration purposes, unemployment data sets of five advanced and five developing countries of Asia, essentially Japan, South Korea, Malaysia, Singapore, Hong Kong, and five agricultural countries (i.e., Pakistan, China, India, Bangladesh and Indonesia) are selected. The hybrid ARIMA-ARNN model performed well among all hybrid models for advanced countries of Asia, while the hybrid ARIMA-ANN outperformed for developing countries aside from China, and hybrid ARIMA-SVM performed well for China. Furthermore, for future unemployment rate prediction, these selected models are utilized. The result displays that in developing countries of Asia, the unemployment rate will be three times higher as compared to advanced countries in the coming years, and it will take double the time to address the impacts of Coronavirus in developing countries than in developed countries of Asia.
  3. Quek SG, Garg H, Selvachandran G, Palanikumar M, Arulmozhi K, Smarandache F
    Soft comput, 2023 May 22.
    PMID: 37362303 DOI: 10.1007/s00500-023-08338-y
    This article introduces the structure of the (t,s)-regulated interval-valued neutrosophic soft set (abbr. (t,s)-INSS). The structure of (t,s)-INSS is shown to be capable of handling the sheer heterogeneity and complexity of real-life situations, i.e. multiple inputs with various natures (hence neutrosophic), uncertainties over the input strength (hence interval-valued), the existence of different opinions (hence soft), and the perception at different strictness levels (hence (t,s)-regulated). Besides, a novel distance measure for the (t,s)-INSS model is proposed, which is truthful to the nature of each of the three membership (truth, indeterminacy, falsity) values present in a neutrosophic system. Finally, a Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and a Viekriterijumsko Kompromisno Rangiranje (VIKOR) algorithm that works on the (t,s)-INSS are introduced. The design of the proposed algorithms consists of TOPSIS and VIKOR frameworks that deploy a novel distance measure truthful to its intuitive meaning. The conventional method of TOPSIS and VIKOR will be generalized for the structure of (t,s)-INSS. The parameters t and s in the (t,s)-INSS model take the role of strictness in accepting a collection of data subject to the amount of mutually contradicting information present in that collection of data. The proposed algorithm will then be subjected to rigorous testing to justify its consistency with human intuition, using numerous examples which are specifically made to tally with the various human intuitions. Both the proposed algorithms are shown to be consistent with human intuitions through all the tests that were conducted. In comparison, all other works in the previous literature failed to comply with all the tests for consistency with human intuition. The (t,s)-INSS model is designed to be a conclusive generalization of Pythagorean fuzzy sets, interval neutrosophic sets, and fuzzy soft sets. This combines the advantages of all the three previously established structures, as well as having user-customizable parameters t and s, thereby enabling the (t,s)-INSS model to handle data of an unprecedentedly heterogeneous nature. The distance measure is a significant improvement over the current disputable distance measures, which handles the three types of membership values in a neutrosophic system as independent components, as if from a Euclidean vector. Lastly, the proposed algorithms were applied to data relevant to the ongoing COVID-19 pandemic which proves indispensable for the practical implementation of artificial intelligence.
  4. Chakrabortty R, Pal SC, Ghosh M, Arabameri A, Saha A, Roy P, et al.
    Soft comput, 2023 May 29.
    PMID: 37362259 DOI: 10.1007/s00500-023-08596-w
    [This retracts the article DOI: 10.1007/s00500-021-06012-9.].
  5. Chakrabortty R, Pal SC, Ghosh M, Arabameri A, Saha A, Roy P, et al.
    Soft comput, 2023;27(6):3367-3388.
    PMID: 34276248 DOI: 10.1007/s00500-021-06012-9
    The COVID-19 pandemic enforced nationwide lockdown, which has restricted human activities from March 24 to May 3, 2020, resulted in an improved air quality across India. The present research investigates the connection between COVID-19 pandemic-imposed lockdown and its relation to the present air quality in India; besides, relationship between climate variables and daily new affected cases of Coronavirus and mortality in India during the this period has also been examined. The selected seven air quality pollutant parameters (PM10, PM2.5, CO, NO2, SO2, NH3, and O3) at 223 monitoring stations and temperature recorded in New Delhi were used to investigate the spatial pattern of air quality throughout the lockdown. The results showed that the air quality has improved across the country and average temperature and maximum temperature were connected to the outbreak of the COVID-19 pandemic. This outcomes indicates that there is no such relation between climatic parameters and outbreak and its associated mortality. This study will assist the policy maker, researcher, urban planner, and health expert to make suitable strategies against the spreading of COVID-19 in India and abroad.

    SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00500-021-06012-9.

  6. Lin Y, Alshehri Y, Alnazzawi N, Abid M, Khan SA, Jabeen F, et al.
    Soft comput, 2023 Mar 20.
    PMID: 37362268 DOI: 10.1007/s00500-023-08004-3
    The usage of social media is increasing by leaps and bounds in our day-to-day lives. It affects daily routines and brings a lot of change in different departments like health and education systems during the COVID-19 pandemic. Healthcare research and practice have been significantly impacted by the astounding growth of social media. Social media are changing health information management in several ways, from offering appropriate ways to enhance healthcare professional contact and share health-related knowledge and experience to facilitating the development of innovative medical research and wisdom. Social media analytics (SMAs) are efficient and effective interaction instruments that can be useful for both patients and clinicians in health interventions. Moreover, a significant portion of those involved in clinical practices (such as clinicians, professional colleges, and departments of health) are unaware of the importance of social media, its potential applications in their daily lives, as well as the possible consequences and how these will be handled. In the presented study, we proposed MCDM-based approaches known as "Criteria Importance Through Inter Correlation" (CRITIC) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) in order to identify the effective alternative among several options and make a better decision. After extracting features from the literature review, we choose six significant and relevant features and assign weights to them using CRITIC techniques while utilizing the TOPSIS technique to rank the alternatives based on their performance values. After the implementation of both methods and evaluation procedure, it is determined that the alternative with the highest score is placed at the top and called the "best alternative," while the alternative with the lowest score is placed at the bottom and called the worst alternative. Finally, we suggest a variety of research initiatives and new research areas where the aforementioned procedures become extremely useful in evaluating SMAs and their uses in online health interventions.
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