Particle swarm optimization (PSO) algorithm is an optimization technique with remarkable performance for problem solving. The convergence analysis of the method is still in research. This article proposes a mechanism for controlling the velocity by applying a method involving constriction factor in standard swarm optimization algorithm, that is called CSPSO. In addition, the mathematical CSPSO model with the time step attractor is presented to study the convergence condition and the corresponding stability. As a result, constriction standard particle swarm optimization that we consider has a higher potential to balance exploration and exploitation. To avoid the PSO premature convergence, CSPSO modifies all terms of the PSO velocity equation. We test the effectiveness of the CSPSO algorithm based on constriction coefficient with some benchmark functions and compare it with other basic PSO variant algorithms. The theoretical convergence and experimental analyses results are also demonstrated in tables and graphically.
{"title":"Convergence analysis of particle swarm optimization algorithms for different constriction factors","authors":"Dereje Tarekegn Nigatu, Tekle Gemechu Dinka, Surafel Luleseged Tilahun","doi":"10.3389/fams.2024.1304268","DOIUrl":"https://doi.org/10.3389/fams.2024.1304268","url":null,"abstract":"Particle swarm optimization (PSO) algorithm is an optimization technique with remarkable performance for problem solving. The convergence analysis of the method is still in research. This article proposes a mechanism for controlling the velocity by applying a method involving constriction factor in standard swarm optimization algorithm, that is called CSPSO. In addition, the mathematical CSPSO model with the time step attractor is presented to study the convergence condition and the corresponding stability. As a result, constriction standard particle swarm optimization that we consider has a higher potential to balance exploration and exploitation. To avoid the PSO premature convergence, CSPSO modifies all terms of the PSO velocity equation. We test the effectiveness of the CSPSO algorithm based on constriction coefficient with some benchmark functions and compare it with other basic PSO variant algorithms. The theoretical convergence and experimental analyses results are also demonstrated in tables and graphically.","PeriodicalId":507585,"journal":{"name":"Frontiers in Applied Mathematics and Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139779497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Particle swarm optimization (PSO) algorithm is an optimization technique with remarkable performance for problem solving. The convergence analysis of the method is still in research. This article proposes a mechanism for controlling the velocity by applying a method involving constriction factor in standard swarm optimization algorithm, that is called CSPSO. In addition, the mathematical CSPSO model with the time step attractor is presented to study the convergence condition and the corresponding stability. As a result, constriction standard particle swarm optimization that we consider has a higher potential to balance exploration and exploitation. To avoid the PSO premature convergence, CSPSO modifies all terms of the PSO velocity equation. We test the effectiveness of the CSPSO algorithm based on constriction coefficient with some benchmark functions and compare it with other basic PSO variant algorithms. The theoretical convergence and experimental analyses results are also demonstrated in tables and graphically.
{"title":"Convergence analysis of particle swarm optimization algorithms for different constriction factors","authors":"Dereje Tarekegn Nigatu, Tekle Gemechu Dinka, Surafel Luleseged Tilahun","doi":"10.3389/fams.2024.1304268","DOIUrl":"https://doi.org/10.3389/fams.2024.1304268","url":null,"abstract":"Particle swarm optimization (PSO) algorithm is an optimization technique with remarkable performance for problem solving. The convergence analysis of the method is still in research. This article proposes a mechanism for controlling the velocity by applying a method involving constriction factor in standard swarm optimization algorithm, that is called CSPSO. In addition, the mathematical CSPSO model with the time step attractor is presented to study the convergence condition and the corresponding stability. As a result, constriction standard particle swarm optimization that we consider has a higher potential to balance exploration and exploitation. To avoid the PSO premature convergence, CSPSO modifies all terms of the PSO velocity equation. We test the effectiveness of the CSPSO algorithm based on constriction coefficient with some benchmark functions and compare it with other basic PSO variant algorithms. The theoretical convergence and experimental analyses results are also demonstrated in tables and graphically.","PeriodicalId":507585,"journal":{"name":"Frontiers in Applied Mathematics and Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139839373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-13DOI: 10.3389/fams.2024.1327376
Abdulsalam Elnaeem Balila, A. Shabri
Predicting property prices is a crucial task in the real estate market, and machine learning algorithms offer valuable tools for accurate predictions. In this study, we introduce a comprehensive comparison of eight well-known machine learning algorithms, namely, ensemble empirical mode decomposition (EEMD)–stochastic (S) + deterministic (D)–support vector machine (EEMD-SD-SVM), support vector machine (SVM), gradient boosting, random forest, K-nearest neighbors (KNN), linear regression, artificial neural networks (ANN), and decision trees. The focus is on predicting property prices in Dubai, with the primary objective of assessing the predictive performance of these algorithms within this specific market context.The evaluation is based on four key performance metrics: R-squared (R2), mean squared error (MSE), root mean squared error (RMSE), and mean absolute percentage error (MAPE). These metrics provide insights into prediction errors, accuracy in percentage terms, and the proportion of variance in property prices explained by independent variables. The study compares the strengths and limitations of each algorithm for predicting property prices in Dubai, highlighting scenarios where certain algorithms excel based on the nature of decision boundaries, handling complex data, capturing localized patterns, and offering interpretability.Findings from the comparative analysis shed light on the performance of each algorithm in predicting property prices in Dubai. EEMD-SD-SVM and SVM excel in scenarios requiring precise decision boundaries, while gradient boosting and random forests demonstrate robust performance with complex and noisy property price data. KNN captures localized patterns effectively, linear regression is suitable for straightforward regression tasks, ANN excels with extensive datasets, and decision trees offer interpretability in understanding factors influencing property prices.The study emphasizes the significance of model tuning, feature selection, and data pre-processing to enhance predictive power. Additionally, practical aspects such as computational efficiency, model interpretability, and scalability in real-world applications are discussed. The comparative analysis provides valuable guidance for stakeholders, including real estate professionals, data scientists, and stakeholders interested in selecting the most suitable machine learning algorithm for predicting property prices in Dubai, with a focus on the essential evaluation metrics of MSE, RMSE, MAPE, and R2. This study offers insights into the applicability and performance of different machine learning algorithms for predicting property prices in Dubai. Stakeholders such as real estate agents, buyers, sellers, or investors can leverage these insights to make informed decisions in the Dubai real estate market.
{"title":"Comparative analysis of machine learning algorithms for predicting Dubai property prices","authors":"Abdulsalam Elnaeem Balila, A. Shabri","doi":"10.3389/fams.2024.1327376","DOIUrl":"https://doi.org/10.3389/fams.2024.1327376","url":null,"abstract":"Predicting property prices is a crucial task in the real estate market, and machine learning algorithms offer valuable tools for accurate predictions. In this study, we introduce a comprehensive comparison of eight well-known machine learning algorithms, namely, ensemble empirical mode decomposition (EEMD)–stochastic (S) + deterministic (D)–support vector machine (EEMD-SD-SVM), support vector machine (SVM), gradient boosting, random forest, K-nearest neighbors (KNN), linear regression, artificial neural networks (ANN), and decision trees. The focus is on predicting property prices in Dubai, with the primary objective of assessing the predictive performance of these algorithms within this specific market context.The evaluation is based on four key performance metrics: R-squared (R2), mean squared error (MSE), root mean squared error (RMSE), and mean absolute percentage error (MAPE). These metrics provide insights into prediction errors, accuracy in percentage terms, and the proportion of variance in property prices explained by independent variables. The study compares the strengths and limitations of each algorithm for predicting property prices in Dubai, highlighting scenarios where certain algorithms excel based on the nature of decision boundaries, handling complex data, capturing localized patterns, and offering interpretability.Findings from the comparative analysis shed light on the performance of each algorithm in predicting property prices in Dubai. EEMD-SD-SVM and SVM excel in scenarios requiring precise decision boundaries, while gradient boosting and random forests demonstrate robust performance with complex and noisy property price data. KNN captures localized patterns effectively, linear regression is suitable for straightforward regression tasks, ANN excels with extensive datasets, and decision trees offer interpretability in understanding factors influencing property prices.The study emphasizes the significance of model tuning, feature selection, and data pre-processing to enhance predictive power. Additionally, practical aspects such as computational efficiency, model interpretability, and scalability in real-world applications are discussed. The comparative analysis provides valuable guidance for stakeholders, including real estate professionals, data scientists, and stakeholders interested in selecting the most suitable machine learning algorithm for predicting property prices in Dubai, with a focus on the essential evaluation metrics of MSE, RMSE, MAPE, and R2. This study offers insights into the applicability and performance of different machine learning algorithms for predicting property prices in Dubai. Stakeholders such as real estate agents, buyers, sellers, or investors can leverage these insights to make informed decisions in the Dubai real estate market.","PeriodicalId":507585,"journal":{"name":"Frontiers in Applied Mathematics and Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139780477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-13DOI: 10.3389/fams.2024.1327376
Abdulsalam Elnaeem Balila, A. Shabri
Predicting property prices is a crucial task in the real estate market, and machine learning algorithms offer valuable tools for accurate predictions. In this study, we introduce a comprehensive comparison of eight well-known machine learning algorithms, namely, ensemble empirical mode decomposition (EEMD)–stochastic (S) + deterministic (D)–support vector machine (EEMD-SD-SVM), support vector machine (SVM), gradient boosting, random forest, K-nearest neighbors (KNN), linear regression, artificial neural networks (ANN), and decision trees. The focus is on predicting property prices in Dubai, with the primary objective of assessing the predictive performance of these algorithms within this specific market context.The evaluation is based on four key performance metrics: R-squared (R2), mean squared error (MSE), root mean squared error (RMSE), and mean absolute percentage error (MAPE). These metrics provide insights into prediction errors, accuracy in percentage terms, and the proportion of variance in property prices explained by independent variables. The study compares the strengths and limitations of each algorithm for predicting property prices in Dubai, highlighting scenarios where certain algorithms excel based on the nature of decision boundaries, handling complex data, capturing localized patterns, and offering interpretability.Findings from the comparative analysis shed light on the performance of each algorithm in predicting property prices in Dubai. EEMD-SD-SVM and SVM excel in scenarios requiring precise decision boundaries, while gradient boosting and random forests demonstrate robust performance with complex and noisy property price data. KNN captures localized patterns effectively, linear regression is suitable for straightforward regression tasks, ANN excels with extensive datasets, and decision trees offer interpretability in understanding factors influencing property prices.The study emphasizes the significance of model tuning, feature selection, and data pre-processing to enhance predictive power. Additionally, practical aspects such as computational efficiency, model interpretability, and scalability in real-world applications are discussed. The comparative analysis provides valuable guidance for stakeholders, including real estate professionals, data scientists, and stakeholders interested in selecting the most suitable machine learning algorithm for predicting property prices in Dubai, with a focus on the essential evaluation metrics of MSE, RMSE, MAPE, and R2. This study offers insights into the applicability and performance of different machine learning algorithms for predicting property prices in Dubai. Stakeholders such as real estate agents, buyers, sellers, or investors can leverage these insights to make informed decisions in the Dubai real estate market.
{"title":"Comparative analysis of machine learning algorithms for predicting Dubai property prices","authors":"Abdulsalam Elnaeem Balila, A. Shabri","doi":"10.3389/fams.2024.1327376","DOIUrl":"https://doi.org/10.3389/fams.2024.1327376","url":null,"abstract":"Predicting property prices is a crucial task in the real estate market, and machine learning algorithms offer valuable tools for accurate predictions. In this study, we introduce a comprehensive comparison of eight well-known machine learning algorithms, namely, ensemble empirical mode decomposition (EEMD)–stochastic (S) + deterministic (D)–support vector machine (EEMD-SD-SVM), support vector machine (SVM), gradient boosting, random forest, K-nearest neighbors (KNN), linear regression, artificial neural networks (ANN), and decision trees. The focus is on predicting property prices in Dubai, with the primary objective of assessing the predictive performance of these algorithms within this specific market context.The evaluation is based on four key performance metrics: R-squared (R2), mean squared error (MSE), root mean squared error (RMSE), and mean absolute percentage error (MAPE). These metrics provide insights into prediction errors, accuracy in percentage terms, and the proportion of variance in property prices explained by independent variables. The study compares the strengths and limitations of each algorithm for predicting property prices in Dubai, highlighting scenarios where certain algorithms excel based on the nature of decision boundaries, handling complex data, capturing localized patterns, and offering interpretability.Findings from the comparative analysis shed light on the performance of each algorithm in predicting property prices in Dubai. EEMD-SD-SVM and SVM excel in scenarios requiring precise decision boundaries, while gradient boosting and random forests demonstrate robust performance with complex and noisy property price data. KNN captures localized patterns effectively, linear regression is suitable for straightforward regression tasks, ANN excels with extensive datasets, and decision trees offer interpretability in understanding factors influencing property prices.The study emphasizes the significance of model tuning, feature selection, and data pre-processing to enhance predictive power. Additionally, practical aspects such as computational efficiency, model interpretability, and scalability in real-world applications are discussed. The comparative analysis provides valuable guidance for stakeholders, including real estate professionals, data scientists, and stakeholders interested in selecting the most suitable machine learning algorithm for predicting property prices in Dubai, with a focus on the essential evaluation metrics of MSE, RMSE, MAPE, and R2. This study offers insights into the applicability and performance of different machine learning algorithms for predicting property prices in Dubai. Stakeholders such as real estate agents, buyers, sellers, or investors can leverage these insights to make informed decisions in the Dubai real estate market.","PeriodicalId":507585,"journal":{"name":"Frontiers in Applied Mathematics and Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139840409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-06DOI: 10.3389/fams.2024.1351526
Nikita Kumawat, Akanksha Shukla, M. Mishra, Rahul Sharma, Ravi Shanker Dubey
The paper aims to utilize an integral transform, specifically the Khalouta transform, an abstraction of various integral transforms, to address fractional differential equations using both Riemann-Liouville and Caputo fractional derivative. We discuss some results and the existence of this integral transform. In addition, we also discuss the duality between Shehu transform and Khalouta transform. The numerical examples are provided to confirm the applicability and correctness of the proposed method for solving fractional differential equations.Primary 92B05, 92C60; Secondary 26A33.
{"title":"Khalouta transform and applications to Caputo-fractional differential equations","authors":"Nikita Kumawat, Akanksha Shukla, M. Mishra, Rahul Sharma, Ravi Shanker Dubey","doi":"10.3389/fams.2024.1351526","DOIUrl":"https://doi.org/10.3389/fams.2024.1351526","url":null,"abstract":"The paper aims to utilize an integral transform, specifically the Khalouta transform, an abstraction of various integral transforms, to address fractional differential equations using both Riemann-Liouville and Caputo fractional derivative. We discuss some results and the existence of this integral transform. In addition, we also discuss the duality between Shehu transform and Khalouta transform. The numerical examples are provided to confirm the applicability and correctness of the proposed method for solving fractional differential equations.Primary 92B05, 92C60; Secondary 26A33.","PeriodicalId":507585,"journal":{"name":"Frontiers in Applied Mathematics and Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139798395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-06DOI: 10.3389/fams.2024.1351526
Nikita Kumawat, Akanksha Shukla, M. Mishra, Rahul Sharma, Ravi Shanker Dubey
The paper aims to utilize an integral transform, specifically the Khalouta transform, an abstraction of various integral transforms, to address fractional differential equations using both Riemann-Liouville and Caputo fractional derivative. We discuss some results and the existence of this integral transform. In addition, we also discuss the duality between Shehu transform and Khalouta transform. The numerical examples are provided to confirm the applicability and correctness of the proposed method for solving fractional differential equations.Primary 92B05, 92C60; Secondary 26A33.
{"title":"Khalouta transform and applications to Caputo-fractional differential equations","authors":"Nikita Kumawat, Akanksha Shukla, M. Mishra, Rahul Sharma, Ravi Shanker Dubey","doi":"10.3389/fams.2024.1351526","DOIUrl":"https://doi.org/10.3389/fams.2024.1351526","url":null,"abstract":"The paper aims to utilize an integral transform, specifically the Khalouta transform, an abstraction of various integral transforms, to address fractional differential equations using both Riemann-Liouville and Caputo fractional derivative. We discuss some results and the existence of this integral transform. In addition, we also discuss the duality between Shehu transform and Khalouta transform. The numerical examples are provided to confirm the applicability and correctness of the proposed method for solving fractional differential equations.Primary 92B05, 92C60; Secondary 26A33.","PeriodicalId":507585,"journal":{"name":"Frontiers in Applied Mathematics and Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139858505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-27DOI: 10.3389/fams.2023.1285023
J. J. Muñoz, Muhammad Aslam, Manuel J. Campuzano
When initial sample information falls short of enabling industrial engineers to confidently make decisions about lot quality assessment, repetitive sampling emerges as a solution. In this study, we present an optimized repetitive sampling control chart for X-bar values. Through meticulous analysis, we determined the optimal control chart coefficients. Additionally, we established the control chart parameters for scenarios where the sample size equals the average sample number, encompassing both in-control and out-of-control processes. To underscore the effectiveness of our proposed chart compared to the traditional Shewhart control chart, we provide comprehensive tables across various sample sizes. By meticulously examining these tables alongside the corresponding control charts, the chart's efficacy in relation to the Shewhart alternative becomes evident.
当初始样本信息不足以让工业工程师对批量质量评估做出有把握的决策时，重复取样就成为了一种解决方案。在本研究中，我们提出了一种针对 X 柱值的优化重复抽样控制图。通过细致的分析，我们确定了最佳控制图系数。此外，我们还为样本量等于平均样本数的情况建立了控制图参数，包括控制内和控制外流程。为了强调我们提出的控制图与传统 Shewhart 控制图相比的有效性，我们提供了各种样本量的综合表格。通过仔细研究这些表格和相应的控制图，该控制图相对于 Shewhart 控制图的功效就显而易见了。
{"title":"Optimized repetitive sampling X-bar control chart: performance evaluation and comparison with Shewhart control chart","authors":"J. J. Muñoz, Muhammad Aslam, Manuel J. Campuzano","doi":"10.3389/fams.2023.1285023","DOIUrl":"https://doi.org/10.3389/fams.2023.1285023","url":null,"abstract":"When initial sample information falls short of enabling industrial engineers to confidently make decisions about lot quality assessment, repetitive sampling emerges as a solution. In this study, we present an optimized repetitive sampling control chart for X-bar values. Through meticulous analysis, we determined the optimal control chart coefficients. Additionally, we established the control chart parameters for scenarios where the sample size equals the average sample number, encompassing both in-control and out-of-control processes. To underscore the effectiveness of our proposed chart compared to the traditional Shewhart control chart, we provide comprehensive tables across various sample sizes. By meticulously examining these tables alongside the corresponding control charts, the chart's efficacy in relation to the Shewhart alternative becomes evident.","PeriodicalId":507585,"journal":{"name":"Frontiers in Applied Mathematics and Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139235088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-22DOI: 10.3389/fams.2023.1257066
A. Kovtanyuk, A. Chebotarev, Reneé Lampe
A non-linear model of oxygen transport from a capillary to tissue is considered. The model takes into account the convection of oxygen in the blood, its diffusion transfer through the capillary wall, and the diffusion and consumption of oxygen in tissue. In the current work, a boundary value problem for the oxygen transport model is studied. The existence theorem is proved and a numerical algorithm is constructed and implemented. The numerical experiments studying the effect of low hematocrit and reduced blood flow rate on cerebral hypoxia in preterm infants are conducted.
{"title":"Mathematical modeling of cerebral oxygen transport from capillaries to tissue","authors":"A. Kovtanyuk, A. Chebotarev, Reneé Lampe","doi":"10.3389/fams.2023.1257066","DOIUrl":"https://doi.org/10.3389/fams.2023.1257066","url":null,"abstract":"A non-linear model of oxygen transport from a capillary to tissue is considered. The model takes into account the convection of oxygen in the blood, its diffusion transfer through the capillary wall, and the diffusion and consumption of oxygen in tissue. In the current work, a boundary value problem for the oxygen transport model is studied. The existence theorem is proved and a numerical algorithm is constructed and implemented. The numerical experiments studying the effect of low hematocrit and reduced blood flow rate on cerebral hypoxia in preterm infants are conducted.","PeriodicalId":507585,"journal":{"name":"Frontiers in Applied Mathematics and Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139248070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The early detection and preliminary diagnosis of COVID-19 play a crucial role in effectively managing the pandemic. Radiographic images have emerged as valuable tool in achieving this objective. Deep learning techniques, a subset of artificial intelligence, have been extensively employed for the processing and analysis of these radiographic images. Notably, their ability to identify and detect patterns within radiographic images can be extended beyond COVID-19 and can be applied to recognize patterns associated with other pandemics or diseases. This paper seeks to provide an overview of the deep learning techniques developed for detection of corona-virus (COVID-19) based on radiological data (X-Ray and CT images). It also sheds some information on the methods utilized for feature extraction and data preprocessing in this field. The purpose of this study is to make it easier for researchers to comprehend various deep learning techniques that are used to detect COVID-19 and to introduce or ensemble those approaches to prevent the spread of corona virus in future.
{"title":"Deep learning models/techniques for COVID-19 detection: a survey","authors":"Kumari Archana, Amandeep Kaur, Yonis Gulzar, Yasir Hamid, Mohammad Shuaib Mir, Arjumand Bano Soomro","doi":"10.3389/fams.2023.1303714","DOIUrl":"https://doi.org/10.3389/fams.2023.1303714","url":null,"abstract":"The early detection and preliminary diagnosis of COVID-19 play a crucial role in effectively managing the pandemic. Radiographic images have emerged as valuable tool in achieving this objective. Deep learning techniques, a subset of artificial intelligence, have been extensively employed for the processing and analysis of these radiographic images. Notably, their ability to identify and detect patterns within radiographic images can be extended beyond COVID-19 and can be applied to recognize patterns associated with other pandemics or diseases. This paper seeks to provide an overview of the deep learning techniques developed for detection of corona-virus (COVID-19) based on radiological data (X-Ray and CT images). It also sheds some information on the methods utilized for feature extraction and data preprocessing in this field. The purpose of this study is to make it easier for researchers to comprehend various deep learning techniques that are used to detect COVID-19 and to introduce or ensemble those approaches to prevent the spread of corona virus in future.","PeriodicalId":507585,"journal":{"name":"Frontiers in Applied Mathematics and Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139264004","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-17DOI: 10.3389/fams.2023.1274530
Deepak Singh, Pradip Basak, Raju Kumar, Tauqueer Ahmad
Indices are created by consolidating multidimensional data into a single representative measure known as an index, using a fundamental mathematical model. Most present indices are essentially the averages or weighted averages of the variables under study, ignoring multicollinearity among the variables, with the exception of the existing Ordinary Least Squares (OLS) estimator based OLS-PCA index methodology. Many existing surveys adopt survey designs that incorporate survey weights, aiming to obtain a representative sample of the population while minimizing costs. Survey weights play a crucial role in addressing the unequal probabilities of selection inherent in complex survey designs, ensuring accurate and representative estimates of population parameters. However, the existing OLS-PCA based index methodology is designed for simple random sampling and is incapable of incorporating survey weights, leading to biased estimates and erroneous rankings that can result in flawed inferences and conclusions for survey data. To address this limitation, we propose a novel Survey Weighted PCA (SW-PCA) based Index methodology, tailored for survey-weighted data. SW-PCA incorporates survey weights, facilitating the development of unbiased and efficient composite indices, improving the quality and validity of survey-based research. Simulation studies demonstrate that the SW-PCA based index outperforms the OLS-PCA based index that neglects survey weights, indicating its higher efficiency. To validate the methodology, we applied it to a Household Consumer Expenditure Survey (HCES), NSS 68th Round survey data to construct a Food Consumption Index for different states of India. The result was significant improvements in state rankings when survey weights were considered. In conclusion, this study highlights the crucial importance of incorporating survey weights in index construction from complex survey data. The SW-PCA based Index provides a valuable solution, enhancing the accuracy and reliability of survey-based research, ultimately contributing to more informed decision-making.
{"title":"On the methodological framework of composite index under complex surveys and its application in development of food consumption index for India","authors":"Deepak Singh, Pradip Basak, Raju Kumar, Tauqueer Ahmad","doi":"10.3389/fams.2023.1274530","DOIUrl":"https://doi.org/10.3389/fams.2023.1274530","url":null,"abstract":"Indices are created by consolidating multidimensional data into a single representative measure known as an index, using a fundamental mathematical model. Most present indices are essentially the averages or weighted averages of the variables under study, ignoring multicollinearity among the variables, with the exception of the existing Ordinary Least Squares (OLS) estimator based OLS-PCA index methodology. Many existing surveys adopt survey designs that incorporate survey weights, aiming to obtain a representative sample of the population while minimizing costs. Survey weights play a crucial role in addressing the unequal probabilities of selection inherent in complex survey designs, ensuring accurate and representative estimates of population parameters. However, the existing OLS-PCA based index methodology is designed for simple random sampling and is incapable of incorporating survey weights, leading to biased estimates and erroneous rankings that can result in flawed inferences and conclusions for survey data. To address this limitation, we propose a novel Survey Weighted PCA (SW-PCA) based Index methodology, tailored for survey-weighted data. SW-PCA incorporates survey weights, facilitating the development of unbiased and efficient composite indices, improving the quality and validity of survey-based research. Simulation studies demonstrate that the SW-PCA based index outperforms the OLS-PCA based index that neglects survey weights, indicating its higher efficiency. To validate the methodology, we applied it to a Household Consumer Expenditure Survey (HCES), NSS 68th Round survey data to construct a Food Consumption Index for different states of India. The result was significant improvements in state rankings when survey weights were considered. In conclusion, this study highlights the crucial importance of incorporating survey weights in index construction from complex survey data. The SW-PCA based Index provides a valuable solution, enhancing the accuracy and reliability of survey-based research, ultimately contributing to more informed decision-making.","PeriodicalId":507585,"journal":{"name":"Frontiers in Applied Mathematics and Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139266033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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