A Proposed Framework to Improve Diagnosis of Covid-19 Based on Patient’s Symptoms using Feature Selection Optimization

Recently, an epidemic called COVID-19 appeared, and it was one of the largest epidemics that affected the world in all economic, educational, health, and other aspects due to its rapid spread worldwide. The surge in infection rates made traditional diagnostic methods ineffective. Systems for automatic diagnosis and detection are crucial for controlling the outbreak. Other than PCR-RT, further diagnostic and detection techniques are needed. Individuals who receive positive test results often experience a range of symptoms, ranging from mild to severe, including coughing, fever, sore throats, and body pains. In more extreme cases, infected individuals may exhibit severe symptoms that make breathing challenging, ultimately leading to catastrophic organ failure. A hybrid approach called SDO-NMR-Hill has been developed for diagnosing COVID-19 based on a patient’s initial symptoms. This approach incorporates traits from three models, including two distinct feature selection optimization methods and a local search. Supply-demand optimization and the naked mole rat were preferred among metaheuristic methods because they have fewer parameters and a lower computing overhead, which can help you find superfluous and uninformative characteristics. Hill climbing was preferred among local search methods to maximize a criterion among several candidate solutions. We used decision trees, random forests, and adaptive boosting machine-learning classifiers in various experiments on three COVID-19 datasets. We carried out a natural selection of the classifier’s hyper-parameters to optimize outcomes. The optimal performance was attained using the adaptive boosting classifier, with an accuracy of 88.88% and 98.98% for the first and third datasets, respectively. The optimal performance for the second dataset was attained using the random forest classifier, with an accuracy of 97.97%. The suggested SDO-NMR-Hill model is evaluated using nine benchmark UCI datasets, and 15 different optimization techniques are contrasted

Leo: Lagrange Elementary Optimization

Global optimization problems are frequently solved using the practical and efficient method of evolutionary sophistication. But as the original problem becomes more complex, so does its efficacy and expandability. Thus, the purpose of this research is to introduce the Lagrange Elementary Optimization (Leo) as an evolutionary method, which is self-adaptive inspired by the remarkable accuracy of vaccinations using the albumin quotient of human blood. They develop intelligent agents using their fitness function value after gene crossing. These genes direct the search agents during both exploration and exploitation. The main objective of the Leo algorithm is presented in this paper along with the inspiration and motivation for the concept. To demonstrate its precision, the proposed algorithm is validated against a variety of test functions, including 19 traditional benchmark functions and the CECC06 2019 test functions. The results of Leo for 19 classic benchmark test functions are evaluated against DA, PSO, and GA separately, and then two other recent algorithms such as FDO and LPB are also included in the evaluation. In addition, the Leo is tested by ten functions on CECC06 2019 with DA, WOA, SSA, FDO, LPB, and FOX algorithms distinctly. The cumulative outcomes demonstrate Leo's capacity to increase the starting population and move toward the global optimum. Different standard measurements are used to verify and prove the stability of Leo in both the exploration and exploitation phases. Moreover, Statistical analysis supports the findings results of the proposed research. Finally, novel applications in the real world are introduced to demonstrate the practicality of Leo.Comment: 28 page

Optimization of capacitated vehicle routing problem using initial route with same size K-means and greedy algorithm for vaccines distribution

Vaccines are the solution that is currently underway to tackle COVID-19. In this paper, vaccine distribution for hospitals in Central Java is developed. The problem case in this paper is classified as a Capacitated Vehicle Routing Problem (CVRP). The method proposed is using an initial route that follows the cluster-first route-second method (CFRS). The same size K-means is used for the clustering phase and the Greedy algorithm is used for the routing phase. The result of the initial route is a clustered route for each vehicle with a balanced capacity. Then, each cluster was re-optimized using metaheuristics Guided Local Search from Google OR-tools. Our experiment results have proven that using the initial route has the effect of reducing runtime by 97.37% - 99.17% when compared to without the initial route. This is because using initial routes with the same size K-means means breaking the problem into parts, then using the Greedy algorithm can reduce the number of possible routes. However, the total distance increased by 8.22% - 16.69% because no cluster member is allowed to move to another cluster

Decision trees for early prediction of inadequate immune response to coronavirus infections: a pilot study on COVID-19

Introduction: Few artificial intelligence models exist to predict severe forms of COVID-19. Most rely on post-infection laboratory data, hindering early treatment for high-risk individuals. Methods: This study developed a machine learning model to predict inherent risk of severe symptoms after contracting SARS-CoV-2. Using a Decision Tree trained on 153 Alpha variant patients, demographic, clinical and immunogenetic markers were considered. Model performance was assessed on Alpha and Delta variant datasets. Key risk factors included age, gender, absence of KIR2DS2 gene (alone or with HLA-C C1 group alleles), presence of 14-bp polymorphism in HLA-G gene, presence of KIR2DS5 gene, and presence of KIR telomeric region A/A. Results: The model achieved 83.01% accuracy for Alpha variant and 78.57% for Delta variant, with True Positive Rates of 80.82 and 77.78%, and True Negative Rates of 85.00% and 79.17%, respectively. The model showed high sensitivity in identifying individuals at risk. Discussion: The present study demonstrates the potential of AI algorithms, combined with demographic, epidemiologic, and immunogenetic data, in identifying individuals at high risk of severe COVID-19 and facilitating early treatment. Further studies are required for routine clinical integration

Questioning the impact of AI and interdisciplinarity in science: Lessons from COVID-19

Artificial intelligence (AI) has emerged as one of the most promising technologies to support COVID-19 research, with interdisciplinary collaborations between medical professionals and AI specialists being actively encouraged since the early stages of the pandemic. Yet, our analysis of more than 10,000 papers at the intersection of COVID-19 and AI suggest that these collaborations have largely resulted in science of low visibility and impact. We show that scientific impact was not determined by the overall interdisciplinarity of author teams, but rather by the diversity of knowledge they actually harnessed in their research. Our results provide insights into the ways in which team and knowledge structure may influence the successful integration of new computational technologies in the sciences

A bi-objective optimization model to plan vaccination campaigns

Vaccination campaigns have saved thousands of lives, reaching the farthest places in the world. These campaigns have required substantial investments and accurate coordination between several actors within the vaccine supply chain. Despite these successful strategies, the outbreak of COVID-19 has altered the objectives and rules of undertaking vaccine campaigns. Then, it is essential to consider two new facts in planning vaccination campaigns. First, some groups of infected people by the virus are more vulnerable to severe illness. Second, the virus is exceptionally contagious; sometimes, no symptoms are apparent. Accordingly, we propose a bi-objective optimization model that allows healthcare decision-makers to design effective vaccination campaigns by considering these COVID-19 characteristics and controlling the associated costs. Careful utilization of temporary and traditional vaccination centers is crucial to creating a more robust strategy. Located in suitable places, temporary centers help increase the probability of reaching groups difficult to be vaccinated while simultaneously avoiding crowd congestion and reducing the risk of spreading infections in dispensing vaccination centers. Experiments were conducted using data from an area in Santiago, Chile. The results show the model allows us to manage the resources by providing a variety of vaccination plans according to the prioritization level of each objective

Deep Learning and parallelization of Meta-heuristic Methods for IoT Cloud

Healthcare 4.0 is one of the Fourth Industrial Revolution’s outcomes that make a big revolution in the medical field. Healthcare 4.0 came with more facilities advantages that improved the average life expectancy and reduced population mortality. This paradigm depends on intelligent medical devices (wearable devices, sensors), which are supposed to generate a massive amount of data that need to be analyzed and treated with appropriate data-driven algorithms powered by Artificial Intelligence such as machine learning and deep learning (DL). However, one of the most significant limits of DL techniques is the long time required for the training process. Meanwhile, the realtime application of DL techniques, especially in sensitive domains such as healthcare, is still an open question that needs to be treated. On the other hand, meta-heuristic achieved good results in optimizing machine learning models. The Internet of Things (IoT) integrates billions of smart devices that can communicate with one another with minimal human intervention. IoT technologies are crucial in enhancing several real-life smart applications that can improve life quality. Cloud Computing has emerged as a key enabler for IoT applications because it provides scalable and on-demand, anytime, anywhere access to the computing resources. In this thesis, we are interested in improving the efficacity and performance of Computer-aided diagnosis systems in the medical field by decreasing the complexity of the model and increasing the quality of data. To accomplish this, three contributions have been proposed. First, we proposed a computer aid diagnosis system for neonatal seizures detection using metaheuristics and convolutional neural network (CNN) model to enhance the system’s performance by optimizing the CNN model. Secondly, we focused our interest on the covid-19 pandemic and proposed a computer-aided diagnosis system for its detection. In this contribution, we investigate Marine Predator Algorithm to optimize the configuration of the CNN model that will improve the system’s performance. In the third contribution, we aimed to improve the performance of the computer aid diagnosis system for covid-19. This contribution aims to discover the power of optimizing the data using different AI methods such as Principal Component Analysis (PCA), Discrete wavelet transform (DWT), and Teager Kaiser Energy Operator (TKEO). The proposed methods and the obtained results were validated with comparative studies using benchmark and public medical data

Resilient Strategies and Sustainability in Agri-Food Supply Chains in the Face of High-Risk Events

[EN] Agri-food supply chains (AFSCs) are very vulnerable to high risks such as pandemics, causing economic and social impacts mainly on the most vulnerable population. Thus, it is a priority to implement resilient strategies that enable AFSCs to resist, respond and adapt to new market challenges. At the same time, implementing resilient strategies impact on the social, economic and environmental dimensions of sustainability. The objective of this paper is twofold: analyze resilient strategies on AFSCs in the literature and identify how these resilient strategies applied in the face of high risks affect the achievement of sustainability dimensions. The analysis of the articles is carried out in three points: consequences faced by agri-food supply chains due to high risks, strategies applicable in AFSCs, and relationship between resilient strategies and the achievement of sustainability dimensions.Authors of this publication acknowledge the contribution of the Project 691249, RUC-APS "Enhancing and implementing Knowledge based ICT solutions within high Risk and Uncertain Conditions for Agriculture Production Systems" (www.ruc-aps.eu), funded by the European Union under their funding scheme H2020-MSCA-RISE-2015.Zavala-Alcívar, A.; Verdecho Sáez, MJ.; Alfaro Saiz, JJ. (2020). Resilient Strategies and Sustainability in Agri-Food Supply Chains in the Face of High-Risk Events. IFIP Advances in Information and Communication Technology. 598:560-570. https://doi.org/10.1007/978-3-030-62412-5_46S560570598Gray, R.: Agriculture, transportation, and the COVID-19 crisis. Can. J. Agric. Econ. 68, 239–243 (2020)Queiroz, M.M., Ivanov, D., Dolgui, A., Fosso Wamba, S.: Impacts of epidemic outbreaks on supply chains: mapping a research agenda amid the COVID-19 pandemic through a structured literature review. Ann. Oper. Res. (2020). https://doi.org/10.1007/s10479-020-03685-7Hobbs, J.: Food supply chains during the COVID-19 pandemic. Can. J. Agric. Econ. 68, 171–176 (2020)Shashi, P., Centobelli, P., Cerchione, R., Ertz, M.: Managing supply chain resilience to pursue business and environmental strategies. Bus. Strateg. Environ. 29(3), 1215–1246 (2019)Ivanov, D.: Predicting the impacts of epidemic outbreaks on global supply chains: a simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case. Transp. Res. Part E Logist. Transp. Rev. 136, 101922 (2020)Mamani, H., Chick, S.E., Simchi-Levi, D.: A game-theoretic model of international influenza vaccination coordination. Manage. Sci. 59(7), 1650–1670 (2013)Liu, M., Zhang, D.: A dynamic logistics model for medical resources allocation in an epidemic control with demand forecast updating. J. Oper. Res. Soc. 67, 841–852 (2016)Hessel, L.: Pandemic influenza vaccines: meeting the supply, distribution and deployment challenges. Influenza Other Respir. Viruses 3, 165–170 (2009)Orenstein, W., Schaffner, W.: Lessons learned: role of influenza vaccine production, distribution, supply, and demand—what it means for the provider. Am. J. Med. 121, S22–S27 (2008)Büyüktahtakın, I., Des-Bordes, E., Kıbış, E.: A new epidemics–logistics model: Insights into controlling the Ebola virus disease in West Africa. Eur. J. Oper. Res. 26, 1046–1063 (2018)Anparasan, A., Lejeune, M.: Analyzing the response to epidemics: concept of evidence-based Haddon matrix. J. Humanit. Logist. Supply Chain Manag. 7, 266–283 (2017)Anparasan, A.A., Lejeune, M.A.: Data laboratory for supply chain response models during epidemic outbreaks. Ann. Oper. Res. 270, 53–64 (2018). https://doi.org/10.1007/s10479-017-2462-yAnparasan, A., Lejeune, M.: Resource deployment and donation allocation for epidemic outbreaks. Ann. Oper. Res. 283, 9–32 (2019). https://doi.org/10.1007/s10479-016-2392-0Ivanov, D., Dolgui, A.: Viability of intertwined supply networks: extending the supply chain resilience angles towards survivability. A position paper motivated by COVID-19 outbreak. Int. J. Prod. Res. 58, 2904–2915 (2020)Ivanov, D.: Viable supply chain model: integrating agility, resilience and sustainability perspectives—lessons from and thinking beyond the COVID-19 pandemic. Ann. Oper. Res. (2020). https://doi.org/10.1007/s10479-020-03640-6Ekici, A., Keskinocak, P., Swann, J.: Modeling influenza pandemic and planning food distribution. Manuf. Serv. Oper. Manag. 16, 11–27 (2014)Miranda, R., Schaffner, D.: Virus risk in the food supply chain. Curr. Op. Food Sci. 30, 43–48 (2019)Magalhães, A., Rossi, A., Zattar, I., Marques, M., Seleme, R.: Food traceability technologies and foodborne outbreak occurrences. Br. Food J. 121, 3362–3379 (2019)Denyer, D., Tranfield, D.: Producing a systematic review. In: Buchanan, D., Bryman, A. (eds.) The Sage Handbook of Organizational Research Methods, pp. 671–689. SAGE Publications Ltd., London (2009)Christopher, M., Peck, H.: Building the resilient supply chain. Int. J. Logist. Manag. 15, 1–14 (2004)Dolgui, A., Ivanov, D., Sokolov, B.: Ripple effect in the supply chain: an analysis and recent literature. Int. J. Prod. Res. 56, 414–430 (2018)Jüttner, U., Peck, H., Christopher, M.: Supply chain risk management: outlining an agenda for future research. Int. J. Logist. Res. 6, 197–210 (2003)Behzadi, G., O’Sullivan, M., Olsen, T., Zhang, A.: Agribusiness supply chain risk management: a review of quantitative decision models. Omega (United Kingdom) 79, 21–42 (2018)Kleindorfer, P., Saad, G.: Managing disruption risks in supply chains. Pr. Op. Man. 14, 53–68 (2005)Vishnu, C., Sridharan, R., Gunasekaran, A., Ram Kumar, P.: Strategic capabilities for managing risks in supply chains: current state and research futurities. J. Adv. Manag. Res. 17(2), 173–211 (2019)Deaton, B., Deaton, B.: Food security and Canada’s agricultural system challenged by COVID-19. Can. J. Agric. Econ. 68(2), 143–149 (2020)Richards, T., Rickard, B.: COVID-19 impact on fruit and vegetable markets. C. J. Ag. Ec. 68(2), 189–194 (2020)Larue, B.: Labor issues and COVID-19. Can. J. Agric. Econ. Can. d’agroeconomie (2020). https://doi.org/10.1111/cjag.12233Hollnagel, E.: Epilogue: RAG: the resilience analysis grid. In: Hollnagel, E., Paries, J., Woods, D., Wreathall, J. (eds.) Resilience Engineering in Practice: A Guidebook. Ashgate Pr., pp. 275–296 (2011)Ponomarov, S., Holcomb, M.: Understanding the concept of supply chain resilience. Int. J. Logist. Manag. 20, 124–143 (2009)Wu, T., Huang, S., Blackhurst, J., Zhang, X., Wang, S.: Supply chain risk management: an agent-based simulation to study the impact of retail stockouts. IEEE Trans. Eng. Manag. 60, 676–686 (2013)Schmitt, A., Singh, M.: A quantitative analysis of disruption risk in a multi-echelon supply chain. Int. J. Prod. Econ. 139, 22–32 (2012)Vroegindewey, R., Hodbod, J.: Resilience of agricultural value chains in developing country contexts: a framework and assessment approach. Sustainability 10, 916 (2018)Behzadi, G., O’Sullivan, M., Olsen, T., Scrimgeour, F., Zhang, A.: Robust and resilient strategies for managing supply disruptions in an agribusiness supply chain. Int. J. Prod. Econ. 191, 207–220 (2017)Bottani, E., Murino, T., Schiavo, M., Akkerman, R.: Resilient food supply chain design: modelling framework and metaheuristic solution approach. Comput. Ind. Eng. 135, 177–198 (2019)Meuwissen, M., et al.: A framework to assess the resilience of farming systems. Agric. Syst. 176, 102656 (2019)Dutta, P., Shrivastava, H.: The design and planning of an integrated supply chain for perishable products under uncertainties: a case study in milk industry. J. Model. Manag. (2020). https://doi.org/10.1108/JM2-03-2019-0071Aboah, J., Wilson, M., Rich, M., Lyne, M.: Operationalising resilience in tropical agricultural value chains. Supply Chain Manag. 24, 271–300 (2019)Ravulakollu, A., Urciuoli, L., Rukanova, B., Tan, Y., Hakvoort, R.: Risk based framework for assessing resilience in a complex multi-actor supply chain domain. Supply Chain Forum 19, 266–281 (2018)Das, K.: Integrating lean, green, and resilience criteria in designing a sustainable food supply chain. Proc. Int. Conf. Ind. Eng. Oper. Manag. 2018, 462–473 (2018)Zhu, Q., Krikke, H.: Managing a sustainable and resilient perishable food supply chain (PFSC) after an outbreak. Sustainability 12, 5004 (2020)Rozhkov, M., Ivanov, D.: Contingency production-inventory control policy for capacity disruptions in the retail supply chain with perishable products. IFAC-PapersOnLine 51, 1448–1452 (2018)Yavari, M., Zaker, H.: Designing a resilient-green closed loop supply chain network for perishable products by considering disruption in both supply chain and power networks. Comput. Chem. Eng. 134, 106680 (2020)Ye, F., Hou, G., Li, Y., Fu, S.: Managing bioethanol supply chain resiliency: a risk-sharing model to mitigate yield uncertainty risk. Ind. Manag. Data Syst. 118, 1510–1527 (2018)Jabbarzadeh, A., Fahimnia, B., Sheu, J., Moghadam, H.: Designing a supply chain resilient to major disruptions and supply/demand interruptions. Transp. Res. Part B Methodol. 94, 121–149 (2016)O’Leary, D.: Evolving information systems and technology research issues for COVID-19 and other pandemics. J. Organ. Comput. Electron. Commer. 30, 1–8 (2020)Zavala-Alcívar, A., Verdecho, M.-J., Alfaro-Saiz, J.-J.: A conceptual framework to manage resilience and increase sustainability in the supply chain. Sustainability 12(16), 6300 (2020)Fahimni, B., Jabbarzadeh, A.: Marrying supply chain sustainability and resilience: a match made in heaven. Transp. Res. Part E Logist. Transp. Rev. 91, 306–324 (2016)Verdecho, M.-J., Alarcón-Valero, F., Pérez-Perales, D., Alfaro-Saiz, J.-J., Rodríguez-Rodríguez, R.: A methodology to select suppliers to increase sustainability within supply chains. CEJOR (2020). https://doi.org/10.1007/s10100-019-00668-3Bai, C., Sarkis, J.: Integrating sustainability into supplier selection with grey system and rough set methodologies. Int. J. Prod. Econ. 124(1), 252–264 (2010)Bai, C., Sarkis, J.: Green supplier development: analytical evaluation using rough set theory. J. Clean. Prod. 18, 1200–1210 (2010)Valipour, S., Safaei, A., Fallah, H.: Resilient supplier selection and segmentation in grey environment. J. Clean. Prod. 207, 1123–1137 (2019)Zimmer, K., Fröhling, M., Schultmann, F.: Sustainable supplier management – a review of models supporting sustainable supplier selection, monitoring and development. Int. J. Prod. Res. 54, 1412–1442 (2016)Yang, S., Xiao, Y., Kuo, Y.: The supply chain design for perishable food with stochastic demand. Sustainability 9, 1195 (2017)Zahiri, B., Zhuang, J., Mohammadi, M.: Toward an integrated sustainable-resilient supply chain: a pharmaceutical case study. Transp. Res. Part E Logist. Transp. Rev. 103, 109–142 (2017)Duong, L., Chong, J.: Supply chain collaboration in the presence of disruptions: a literature review. Int. J. Prod. Res. 58, 3488–3507 (2020