Endüstri Mühendisliği Bölümü Koleksiyonu

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https://hdl.handle.net/20.500.14034/37

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Quasi-dynamic opposite learning enhanced Runge-Kutta optimizer for solving complex optimization problems
(Springer Heidelberg, 2024) Turgut, Oğuz Emrah; Turgut, Mert Sinan
The Runge-Kutta Optimization (RUNGE) algorithm is a recently proposed metaphor-free metaheuristic optimizer borrowing practical mathematical foundations of the famous Runge-Kutta differential equation solver. Despite its relatively new emergence, this algorithm has several applications in various branches of scientific fields. However, there is still much room for improvement as it suffers from premature convergence resulting from inefficient search space exploration. To overcome this algorithmic drawback, this research study proposes a brand-new quasi-dynamic opposition-based learning (QDOPP) mechanism to be implemented in a standard Runge-Kutta optimizer to eliminate the local minimum points over the search space. Enhancing the asymmetric search hyperspace by taking advantage of various positions of the current solution within the domain is the critical novelty to enrich general diversity in the population, significantly improving the algorithm's overall exploration capability. To validate the effectivity of the proposed RUNGE-QDOPP method, thirty-four multidimensional optimization benchmark problems comprised of unimodal and multimodal test functions with various dimensionalities have been solved, and the corresponding results are compared against the predictions obtained from the other opposition-based learning variants as well as some state-of-art literature optimizers. Furthermore, six constrained engineering design problems with different functional characteristics have been solved, and the respective results are benchmarked against those obtained for the well-known optimizers. Comparison of the solution outcomes with literature optimizers for constrained and unconstrained test problems reveals that the proposed QDOPP has significant advantages over its counterparts regarding solution accuracy and efficiency.
Developing a comprehensive framework for municipal solid waste management alternatives: a case study in Turkey
(Springer, 2024) Konyalioglu, A. K.; Oturakçı, Murat
Effective waste management techniques are required due to the serious environmental risks posed by the increase in the production of solid waste. This study addresses two important research questions regarding Turkey's management of municipal solid waste (MSW). First, it fills a gap in the literature by identifying the optimal MSW treatment option using combined Pythagorean fuzzy Analytic Hierarchy Process (AHP) and Weighted Aggregated Sum Product Assessment (WASPAS) approach. Second, it applies a novel method called Environmental Failure Mode and Effect Analysis (E-FMEA), designed for practical application, to conduct a comprehensive environmental impact assessment based on ISO 14001 standards. The results demonstrate the effectiveness of the integrated AHP-WASPAS approach in decision-making, highlighting the optimal MSW treatment option. Additionally, E-FMEA systematically examines environmental risks associated with MSW management in Turkey, offering crucial insights for practice and policy formation. At the core of this study lies the integration of all methods as robust and holistic approach, comprehensively addressing key aspects of MSW management. Landfills emerge as the preferred option for waste management due to their versatility, affordability, long-term storage capacity, and integration of modern technologies to reduce environmental impacts. Numerical results reveal that both landfills and incinerators exhibit moderate to high Risk Priority Number (RPN) values across various environmental components, highlighting their significant potential environmental impacts. Furthermore, a risk analysis through sensitivity testing reveals that landfilling is the most favorable municipal solid waste management option in Turkey. These findings highlight the need for policies that prioritize sustainable waste treatment while addressing environmental risks.
A machine learning-based two-stage approach for the location of undesirable facilities in the biomass-to-bioenergy supply chain
(Elsevier Sci Ltd, 2024) Yunusoğlu, Pınar; Ozsoydan, Fehmi Burcin; Bilgen, Bilge
Biomass-to-bioenergy supply chain management is an integral part of the sustainable industrialization of energy conversion through biomass to bioenergy by managing economic, environmental, and social challenges encountered in each supply chain stage. Motivated by a real-world biomass-to-bioenergy supply chain network design (BSCND) problem, this study addresses the location of undesirable facilities for the first time in the BSCND literature. The motivation of this study is to develop a machine learning-based two-stage approach for solving the BSCND problem with undesirable facilities that have a negative impact on surrounding communities. The first stage employs the k-means clustering algorithm to alleviate the complexity of the problem, and the second stage utilizes a novel pre-emptive goal programming (PGP) approach to optimize two distinct objectives hierarchically. The first objective maximizes the sum of the distances between all clients and the open facilities, which is the well-known objective of the obnoxious p-median (OpM) problem. The second objective maximizes the total profit of the entire supply chain. The applicability of the proposed solution approach is shown through the case problem, and performance of the two-stage approach is validated using randomly generated test problems. The computational results indicate the effectiveness of the clustering methodology in reducing the complexity of the problem while the PGP achieves the optimal configuration of the biomass-to-bioenergy supply chain handling the hierarchical objectives. The optimal solution of the case problem was achieved within 25,239.36 s execution time, and the total profit of the supply chain is $6,776,870.22 with 735 km total distance to clients. The average optimality gap for the first phase of the PGP is 4.97%, and the average optimality gap for the second phase of the PGP is 0.01% for the generated test problems.
The wildfire suppression problem with multiple types of resources
(Elsevier, 2024) Avci, Mualla Gonca; Avcı, Mustafa; Battarra, Maria; Erdogan, Gunes
The frequency and impact of wildfires have considerably increased in the past decade, due to the extreme weather conditions as well as the increased population density. The aim of this study is to introduce, model, and solve a wildfire suppression problem that involves multiple types of fire suppression resources and their operational characteristics. Two integer programming (IP) formulations, a basic IP and its reformulation with combinatorial Benders' cuts, are presented. The performances of the proposed formulations are evaluated on a set of randomly generated instances. The results indicate that the proposed formulations are able to obtain high quality upper and lower bounds. Extensive numerical experiments are performed to analyse the effects of several operational constraints on the computational performance of the models. A case study arising in Yata & gbreve;an district of Mu & gbreve;la province of T & uuml;rkiye is presented.
Fuzzy best-worst method-based approach for warehouse location selection and a case study in Izmir
(Emerald Group Publishing Ltd, 2024) Cergibozan, Cagla; Gölcük, İlker
PurposeThe study aims to propose a decision-support system to determine the location of a regional disaster logistics warehouse. Emphasizing the importance of disaster logistics, it considers the criteria to be evaluated for warehouse location selection. It is aimed to determine a warehouse location that will serve the disaster victims most efficiently in case of a disaster by making an application for the province of Izmir, where a massive earthquake hit in 2020.Design/methodology/approachThe paper proposes a fuzzy best-worst method to evaluate the alternative locations for the warehouse. The method considers the linguistic evaluations of the decision-makers and provides an advantage in terms of comparison consistency. The alternatives were identified through interviews and discussions with a group of experts in the fields of humanitarian aid and disaster relief operations. The group consists of academics and a vice-governor, who had worked in Izmir. The results of a previously conducted questionnaire were also used in determining these locations.FindingsIt is shown how the method will be applied to this problem, and the most effective location for the disaster logistics warehouse in Izmir has been determined.Originality/valueThis study contributes to disaster preparedness and brings a solution to the organization of the logistics services in Izmir.
Deep learning-based classification of breast tumors using raw microwave imaging data
(Gazi Univ, 2024) Bicer, Mustafa Berkan; Eliiyi, Uğur; Türsel Eliiyi, Deniz
Breast cancer is the leading type of malignant neoplasm disease among women worldwide. Breast screening makes extensive use of powerful techniques such as x-ray mammography, magnetic resonance imaging, and ultrasonography. While these technologies have numerous benefits, certain drawbacks such as the use of low-energy ionizing x-rays, a lack of specificity for malignant tissues, and cost, have motivated researchers to investigate novel imaging and detection modalities. Microwave imaging (MWI) has been extensively studied due to its low-cost structure and ability to perform measurements using non-ionizing electromagnetic waves. This study proposes a novel convolutional neural network (CNN) model for detecting and classifying tumor scatterers in MWI simulation data. To accomplish this, 10001 different numerical breast models with tumor scatterers of varying numbers and positions were developed, and the simulation results were derived using the synthetic aperture radar (SAR) technique. The presented CNN structure was trained using 8000 pieces of simulation data, and the remaining data were used for testing, achieving accuracy rates of 99.61% and 99.75%, respectively. The proposed model is compared to three state-of-the-art models on the same dataset in terms of classification performance. The results demonstrate that the proposed model effectively performs effectively well in detecting and classifying tumor scatterers.
A novel approach for predicting global innovation index scores
(Inderscience Publishers, 2024) Yildirim, Rabia Sultan; Ukelge, Mülayim Ongun; Essiz, Esra Sarac; Oturakçı, Murat
Innovation has great importance in growth models in today’s economy. In the globalising world, countries that renew their product and service range are at the forefront. The way to manage innovation is to measure it. Therefore, to have measurable information, the Global Innovation Index (GII) identifies inputs and outputs that are indicators of innovation. The GII provides a global ranking for countries according to their innovation capacity. In this study, GII scores of 125 countries between the years 2013 and 2020 were estimated using the artificial neural network (ANN). Before the estimation, feature selection was performed from 61 common indicator parameters. 27 parameters that best explain the GII score were selected and used in the ANN. According to the estimated GII scores, the selected 27 parameters are sufficient to calculate the GII score and has been observed that the ANN model is sufficient to determine the approximate GII score of the countries. Copyright © 2024 Inderscience Enterprises Ltd.
Analysis of industry 4.0 applications for Turkish agriculture
(EDP Sciences, 2024) Temimhan, Damla; Ekinci, Esra; Kazancoglu, Yigit
This article presents analysis of industry 4.0 applications for the Turkish agriculture sector. Applications were picked and explained. However industry 4.0 applications were explained in the literature, there are not adequate studies indicate the relationship and impact of the technologies on the agriculture sector. Also to adapt these applications, most related and solution oriented applications needs to be picked by decision makers. Study aims to evaluate and scale alternatives using interpretive structural modelling method of Multicriteria Decision Aiding. Experts are reviewed the correlation between technologies to consider and apply ISM method. The By applying this method ordering and convenient applications will be ranked and review could be interpreted in simple way. © The Authors, published by EDP Sciences, 2024.
Prioritizing hazardous areas for sustainable industrial tape production: An integrated FMEA - MCDM approach
(EDP Sciences, 2024) Yapar, Gizem; Ekinci, Esra; Türsel Eliiyi, Deniz
Industrial adhesive tape production is a critical sector that must meet high quality standards and customer expectations. This study presents a comprehensive approach to identify, prioritize and prevent failures in the industrial adhesive tape production process using FMEA and VIKOR methods. With FMEA analysis, possible failure types, causes and effects in the production process were identified. Then, using the VIKOR method, failure types were prioritized according to criteria such as severity, probability of occurrence and detectability. In this way, critical failure types were identified and preventive actions were developed. The results of the study make significant contributions to reducing production failures and improving product quality in the industrial adhesive tape industry. The integration of FMEA and VIKOR methods allows failures to be systematically analyzed and effective preventive measures to be taken. The proposed approach provides a model that can also be applied in similar industrial sectors. © The Authors, published by EDP Sciences, 2024.
Building sustainable resilient supply chain in retail sector under disruption
(Elsevier Sci Ltd, 2024) Ekinci, Esra; Sezer, Muruvvet Deniz; Mangla, Sachin Kumar; Kazancoglu, Yigit
Blockchain technologies have played a crucial role in transforming the retail industry, leading to remarkable advancements in recent times. Its pivotal role in managing risky environments by offering preventive and proactive measures cannot be overstated. The research contribution lies in introducing a set of criteria for assessing the adoption of Blockchain technology, specifically designed to evaluate the resilience of the retail sector. This study aims to ensure the establishment of a sustainable, resilient supply chain across diverse retail categories, particularly in the face of uncertain circumstances. A hybrid decision-making approach that combines the BestWorst Method (BWM) and Fuzzy TODIM has been employed to achieve these objectives. This study encompasses various types of retail companies to assess and compare their resilience levels by adopting Blockchain technology. The results of this study robustly suggest that speciality retailers with well-established, long-term partnerships are more predisposed to embrace and leverage the capabilities of Blockchain technologies. Conversely, discount retailers in Turkey face various challenges that impede their effective integration of Blockchain technologies. These challenges encompass collaborating with suppliers on short-term agreements and the unavailability of product tracking, among other factors. As a result, the outcomes of this study offer valuable insights for retailers in the sector, suggesting that they should consider modifying their operational strategies to better align with the adoption and integration of Blockchain technologies in the future.
Chaotic aquila optimization algorithm for solving phase equilibrium problems and parameter estimation of semi-empirical models
(Springer Singapore Pte Ltd, 2024) Turgut, Oğuz Emrah; Turgut, Mert Sinan; Kirtepe, Erhan
This research study aims to enhance the optimization performance of a newly emerged Aquila Optimization algorithm by incorporating chaotic sequences rather than using uniformly generated Gaussian random numbers. This work employs 25 different chaotic maps under the framework of Aquila Optimizer. It considers the ten best chaotic variants for performance evaluation on multidimensional test functions composed of unimodal and multimodal problems, which have yet to be studied in past literature works. It was found that Ikeda chaotic map enhanced Aquila Optimization algorithm yields the best predictions and becomes the leading method in most of the cases. To test the effectivity of this chaotic variant on real-world optimization problems, it is employed on two constrained engineering design problems, and its effectiveness has been verified. Finally, phase equilibrium and semi-empirical parameter estimation problems have been solved by the proposed method, and respective solutions have been compared with those obtained from state-of-art optimizers. It is observed that CH01 can successfully cope with the restrictive nonlinearities and nonconvexities of parameter estimation and phase equilibrium problems, showing the capabilities of yielding minimum prediction error values of no more than 0.05 compared to the remaining algorithms utilized in the performance benchmarking process.
Chaotic gradient based optimizer for solving multidimensional unconstrained and constrained optimization problems
(Springer Heidelberg, 2023) Turgut, Oğuz Emrah; Turgut, Mert Sinan
Gradient-based optimizer (GRAD) belongs to the recently developed population-based metaheuristic algorithms inspired by the development of Newton-type methods. Despite its new emergence, there are many successful applications of this optimizer in the existing literature; however, chaos integrated version of this algorithm has not been extensively studied yet. In his study, twenty-one different chaotic maps have been incorporated into the standard GRAD algorithm to maintain a reliable balance between exploration and exploitation mechanisms, which is not robustly constructed within the original algorithm. First ninety-nine thirty dimensional artificially generated optimization benchmark problems comprised of sixty-eight multimodal and thirty-one unimodal functions have been solved by these chaotic variants of the GRAD algorithm to determine the five best performing methods between them. Clear dominancy of the chaotic algorithms is clearly observed over the entire range of benchmark cases in terms of solution accuracy and robustness. Then, to validate the optimization capability of the chaos integrated GRAD algorithms, the best method among them is tested on fourteen constrained real world engineering problems, and its respective feasible results are benchmarked against those obtained from cutting edge metaheuristic optimizer. It is seen that the chaotic GRAD algorithm is able to effectively compete with other state-of-art algorithms on both solving unconstrained and constrained engineering problems. Moreover, it is observed that the Chebyshev chaotic map improved GRAD algorithm outperforms its contemporaries in both unconstrained and constrained cases.
İki amaçlı tesis yerleşimi problemi için bir yinelemeli yerel arama algoritması
(2021) Avcı, Mustafa
Tesis yerleşimi, modern üretim sistemlerinde karşılaşılan en önemli sorunlardan biridir. Bu çalışmada hem nicel hem de nitel hedeflerin birleştirildiği iki amaçlı bir tesis yerleşimiproblemi(İA-TYP) ele alınmaktadır. Burada nicel amaç toplam malzeme taşıma maliyetinin en aza indirgenmesidir. Nitel amacımız ise toplam yakınlık derecelendirme puanlarının maksimize edilmesidir. Problemi çözmek için Değişken Komşuluklu İniş (DKİ) algoritmasının bir saptırma mekanizması ile birleştirildiği bir Yinelemeli Yerel Arama (YYA) algoritması önerilmiştir. Önerilen algoritmanın performansı önceki çalışmalarda sunulan çözüm algoritmalarının elde ettikleri çözümler baz alınarakdeğerlendirilmiştir. Hesaplama sonuçları, önerilen algoritmanın İA-TYP örneklerine yüksek kaliteli çözümler üretebildiğini göstermektedir.
A comparative analysis of constraint-handling mechanisms for solving engineering design problems
(2021) Gölcük, İlker
Optimization problems have numerous real-life applications in science and engineering. The engineering design problems are usually subject to various constraints. Although many state-of-the-art metaheuristic optimization algorithms have been developed during the last decades, these algorithms require additional constraint-handling mechanisms to cope with constrained optimization problems. Therefore, selecting a suitable constraint-handling mechanism requires extensive trial-and-error experiments, which is time-consuming and demanding. In this study, a comparative analysis of the eight constraint handling mechanisms is carried out, guiding decision-makers in their optimization practices. The constraint-handling techniques are used along with the Whale Optimization Algorithm (WOA), and 19 real-life mechanical design problems, which are also part of the CEC2020 benchmark suite, are tested in the experimental analysis. The nonparametric statistical analysis incorporating Nemenyi and Holm post-hoc procedures shows that the inverse tangent constraint-handling and eclectic penalty methods exhibit high performance in real-life mechanical design problems.
Categorization of customer complaints in food industry using machine learning approaches
(2022) Kılınç, Deniz; Doğan, Onur; Bozyiğit, Fatma
Customer feedback is one of the most critical parameters that determine the market dynamics of product development. In this direction, analyzing product-related complaints helps sellers to identify the quality characteristics and consumer focus. There have been many studies conducted on the design of Machine Learning (ML) systems to address the causes of customer dissatisfaction. However, most of the research has been particularly performed on English. This paper contributes to developing an accurate categorization of customer complaints about package food products, written in Turkish. Accordingly, various ML algorithms using TF-IDF and word2vec feature representation strategies were performed to determine the category of complaints. Corresponding results of Linear Regression (LR), Naive Bayes (NB), k Nearest Neighbour (kNN), Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) classifiers were provided in related sections. Experimental results show that the best-performing method is XGBoost with TF-IDF weighting scheme and it achieves %86 F-measure score. The other considerable point is word2vec based ML classifiers show poor performance in terms of F-measure compared to the TF-IDF term weighting scheme. It is also observed that each experimented TF-IDF based ML algorithm gives a more successful prediction performance on the optimal subsets of features selected by the Chi Square (CH2) method. Performing CH2 on TF-IDF features increases the F-measure score from 86% to 88% in XGBoost.
Process mining methodology for digital processes under smart campus concept
(2022) Cengiz, Esra; Doğan, Onur
Digital transformation affects universities as well as many industries. Universities are increasingly using various digital resources and systems to manage their knowledge. The smart campus, on the other hand, supports informed decision-making by integrating these resources and systems. Process mining provides real insights for digital transformation, allowing processes to be examined more transparently. This study aims to examine the proposed project implementation processes related to the smart university with the process mining methodology. For this purpose, 32 completed projects submitted to İzmir Bakırçay University Scientific Research Projects Coordinatorship (BAPK) with the proposed methodology adapted from Deming's continuous improvement cycle were examined. The data are taken from two different pages in the project automation system. According to the research findings, Projects are grouped into three categories: Guided Projects (GDM, 5 projects), Graduate Thesis Projects (TEZ, 5 projects), and Career Start Support Projects (KBP, 22 projects). 40.6% (13 projects) of the applications went directly to the project review stage, while 19 (59.4%) needed procedural correction. Considering the time from the creation of the application of 32 projects to the signing of the contract, it is seen that the arithmetic average of the cycle time is 15.1 weeks, and the median average is 52.5 days. The notable difference between arithmetic and median mean is that very few projects are of long duration. Procedural adjustments affect project evaluation cycle time by an additional 14 days. The carelessness or lack of knowledge of the applicants extends the cycle time of the process from 15 days to 53 days. The total duration of unnecessary waiting time in the process is 17 days. This study primarily proposes that non-digital processes should be digitized as soon as possible.
Müşteri memnuniyetinin süreç odaklı değerlendirilmesi: Bir çağrı merkezinde süreç madenciliği uygulaması
(2021) Doğan, Onur; Ayyar, Başak; Çağıl, Gültekin
Firmaların varlıklarını sürdürebilmeleri için müşteri odaklı bir yaklaşım sergilemeleri büyük avantaj sağlamaktadır. Müşteriler artık kendisine sunulanı değil, beklentilerini karşılayan ürün veya hizmeti satın almaktadır. Bu sebepten dolayı firmalar müşterileri dinlemek, onların ihtiyaç ve beklentilerini karşılamak amacıyla çeşitli stratejiler izlemektedirler. Bu stratejik birimlerden birisi de firma ile müşteri arasında iletişimi sağlayan çağrı merkezleridir. Firmalar çağrı merkezleri ile müşterilerin sorunlarını çözerek memnuniyet düzeyini arttırmaya çalışırlar. Bununla birlikte, müşteri memnuniyetinin yönetilebilir, ölçülebilir ve karşılaştırılabilir olması için memnuniyet anketi uygulamaktadırlar. Bu çalışmanın amacı, müşteri memnuniyet anketlerinin de göz önüne alındığı süreç madenciliği yardımıyla müşteri memnuniyetinin değerlendirilmesidir. Firmanın IT sisteminde tutulan çağrı merkezi bölümüne ait 11 günlük (18 – 28 Mart 2019) tarihleri arasındaki 42185 olay günlüğü (event log) süreç madenciliği için kullanılmıştır. Öncelikle memnuniyet anketine katılan ve katılmayan müşterilerin sistem içindeki akışı çıkarılmıştır. Ardından ankete katılan müşteriler arasında 5 (çok memnunum) puan verenlerin ve diğer müşterilerin süreçleri incelenmiştir. Çalışmada ulaşılan sonuçlardan biri, çağrı sırasında bekletilen müşterilerin daha yüksek puan vermesidir. Bu sonuç müşterilerin bekletilmekten ziyade ihtiyacının karşılanmasına daha çok önem verdiğini göstermektedir. Bu açıdan çalışma, firmanın süreç odaklı yönetimine, süreç iyileştirme çalışmalarına, strateji çalışmalarına yol gösterebilecek özelliktedir.
Enterprise resource planning selection using fuzzy entropy-based fuzzy MOORA method: case study in a bearing company
(2022) Durmuş, Beyzanur; Akpınar, Muhammet Enes
Seçim problemleri işletmeler açısından sıklıkla karşılaşılan ve karar vermesi zor olan problem tiplerindendir. Zor problem olmasının sebebi birçok kriter ve alternatifin aynı anda dikkate alınması gerektiği içindir. Bu problemlerin çözümü için genellikle çok kriterli karar verme yaklaşımları kullanılmaktadır. Seçim problemleri hayatın her aşamasında karşılaştığı için çok fazla çeşitlilik gösterebilmektedir. Bu çal ışmada bir işletmenin kurumsal kaynak planlaması (KKP) seçim süreci ele al ınmıştır. Yeni bir yazılım satın almak isteyen i şletmenin satın alma departmanı birçok kriter ve alternatif yazılım belirlemiştir. Bu kriterlerin en uygun düzeyde karşılandığı alternatif yazılımın seçilmesi planlanmıştır. Bu problemin çözümü için kriter ağırlıkların belirlenmesi aşamasında bulanık Entropi yöntemi kullanılmıştır. Yazılım alternatiflerinin değerlendirilmesi sürecinde bulanık Oran Analiziyle Çok Amaçlı Optimizasyon (MOORA) yöntemi kullanılmış ve yazılımlardan en uygun olanına karar verilmiştir. Çalışma sonucunda belirlenen üç yazılım sisteminden en uygun olanın üçüncü yazılım sistemi olduğu görülmüştür.
The effect of brand-relationship quality on positive word-of-mouth intention
(IEOM Society, 2021) Akın Mahmut Selami; Doğan Onur
Measurement and management of consumer relationships with brands are critical issue that marketers have focused on for years. The quality of the relationship between the consumer and brand affects behavioral intention in favor of the brand. This research aims to reveal the effect of consumer-brand relationship quality (BRQ) on positive word-of-mouth (WoM) intention. The BRQ components were applied on the sample of private university students in Istanbul, Turkey. The questionnaire form with 5-point Likert scale was distributed and 199 valid surveys were obtained. Cronbach's Alfa was examined to internal consistency of the scales, and exploratory factor analysis was conducted to assess constructive validity. Multiple regression analysis resulted in love/passion, commitment, and self-connection, which are the dimensions of BRQ, have a positive WoM intention. On the other hand, the intimacy factor has no significant effect. © IEOM Society International.
Applications of machine learning approaches to combat COVID-19: A survey
(Elsevier, 2022) Tiwari Sanju Mishra; Doğan Onur; Jabbar M. Akhil; Shandilya Shishir K; Ortiz-Rodríguez Fernando; Bajpai Sailesh; Banerjee Sourav
Machine learning (ML) and artificial intelligence (AI) approaches are prominent and well established in the field of health-care informatics. Because they have a more productive ability to predict, they are successfully applied in several health-care applications. ML approaches are needed thanks to the unsatisfactory experience of the novel virus, considerable ambiguity, complicated social circumstances, and inadequate accessible data. Several approaches have been applied as a tool to combat and protect against the new diseases. The COVID-19 outbreak has rapid growth, so it is not easy to predict the patients and resources within a specified time. ML is a strong approach in the fighting against the pandemic such as COVID-19. It is found significant to predict the susceptible, infected, recovered, or exposed persons and can assist the control strategies to block the spread of infections. This study critically examines the appropriateness and contribution of AI/ML methods on COVID-19 datasets, enhancing the understanding to apply these methods for quick analysis and verification of pandemic databases. © 2022 Elsevier Inc. All rights reserved.

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