These days, sugar glucose monitoring is very important for both diabetic and non-diabetic patients while they are eating and doing different activities in practice. There are different ways to monitor body glucose levels such as blood-based glucose monitoring and smart watches-based glucose monitoring. However, continuous glucose monitoring (CGM) is an emerging non-invasive method for different subjects (e.g., patients and customers). However, smartwatches have limitations. In this paper, we present a new smartwatch framework that monitors the body's glucose level with new features such as nutrition, and activities. We present the modified dataset with an additional feature such as sugar glucose level with different activities (e.g., running, sitting, sleeping, and walking) while eating different nutrition in different time intervals. We present empirical machine learning such as an activity glucose monitoring algorithm (ASA) which executes all datasets with more optimal results. Simulation results show that our proposed framework is more optimal and shows glucose monitoring with different activities with more features as compared to existing smartwatches and obtained an accuracy of 78% as compared to existing machine learning methods.
{"title":"Smart-Watches Assisted Sugar Level Monitoring with Different Activities and Nutrition based on Machine Learning Approaches","authors":"Sajida Memon","doi":"10.31181/jopi21202419","DOIUrl":"https://doi.org/10.31181/jopi21202419","url":null,"abstract":"These days, sugar glucose monitoring is very important for both diabetic and non-diabetic patients while they are eating and doing different activities in practice. There are different ways to monitor body glucose levels such as blood-based glucose monitoring and smart watches-based glucose monitoring. However, continuous glucose monitoring (CGM) is an emerging non-invasive method for different subjects (e.g., patients and customers). However, smartwatches have limitations. In this paper, we present a new smartwatch framework that monitors the body's glucose level with new features such as nutrition, and activities. We present the modified dataset with an additional feature such as sugar glucose level with different activities (e.g., running, sitting, sleeping, and walking) while eating different nutrition in different time intervals. We present empirical machine learning such as an activity glucose monitoring algorithm (ASA) which executes all datasets with more optimal results. Simulation results show that our proposed framework is more optimal and shows glucose monitoring with different activities with more features as compared to existing smartwatches and obtained an accuracy of 78% as compared to existing machine learning methods.","PeriodicalId":489110,"journal":{"name":"Journal of Operations Intelligence","volume":"117 19","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140678280","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 existing sustainable supplier selection methods are not sufficient to deal with the problem of sustainable food supplier selection with the interaction of criteria under uncertainty. Therefore, this paper proposes a method of sustainable food supplier selection based on an extended decision model. Firstly, a processing method for supplier evaluation information is constructed using the Pythagorean fuzzy set has the function of processing complex uncertain information. Secondly, to obtain the objective weights of decision experts, a Pythagorean fuzzy weighted distance measure model is constructed, and an expert information fusion method based on a weighted power mean operator is proposed to construct the group decision matrix. Then, the decision experiment and evaluation experiment methods are integrated with the traditional MARCOS method, to construct a sustainable food supplier selection method considering the interaction of factors. This method can effectively deal with the complicated and uncertain problem of sustainable food supplier selection with interactive factors. Finally, the feasibility of the proposed method is verified by an example of sustainable food supplier selection. In addition, parameter sensitivity analysis and multi-method comparative analysis verified the rationality of the proposed selection method for sustainable food supplier selection.
{"title":"Study on the Method of Selecting Sustainable Food Suppliers Considering Interactive Factors","authors":"Yi Wang, Huizhi Yang, Xiao Han","doi":"10.31181/jopi21202420","DOIUrl":"https://doi.org/10.31181/jopi21202420","url":null,"abstract":"The existing sustainable supplier selection methods are not sufficient to deal with the problem of sustainable food supplier selection with the interaction of criteria under uncertainty. Therefore, this paper proposes a method of sustainable food supplier selection based on an extended decision model. Firstly, a processing method for supplier evaluation information is constructed using the Pythagorean fuzzy set has the function of processing complex uncertain information. Secondly, to obtain the objective weights of decision experts, a Pythagorean fuzzy weighted distance measure model is constructed, and an expert information fusion method based on a weighted power mean operator is proposed to construct the group decision matrix. Then, the decision experiment and evaluation experiment methods are integrated with the traditional MARCOS method, to construct a sustainable food supplier selection method considering the interaction of factors. This method can effectively deal with the complicated and uncertain problem of sustainable food supplier selection with interactive factors. Finally, the feasibility of the proposed method is verified by an example of sustainable food supplier selection. In addition, parameter sensitivity analysis and multi-method comparative analysis verified the rationality of the proposed selection method for sustainable food supplier selection.","PeriodicalId":489110,"journal":{"name":"Journal of Operations Intelligence","volume":"111 38","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140678195","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}
E. A. Alp, Selçuk Alp, Mefule Findikci Erdogan, Ahmet Oğuz Demir
The purpose of this study is to investigate the initial impacts of the COVID-19 pandemic on enterprises and identify differences in the effects of COVID-19 across scales and sectors. This study employed AHP to prioritize solution proposals, factor analysis to determine problem components by sectors and scales, and machine learning methods to estimate enterprise sector and scale based on survey data. The study included 255 statistically reliable samples collected between July to October 2020. Survey and comparison questions were used to determine the impact level of enterprise problems. Open-ended questions categorized pandemic-related commercial activity problems and solution proposals by enterprise scale and sector. The AHP analysis prioritized the same three problems across different scales and sectors, but machine learning-based classification analysis revealed varying criteria for determining sector and scale. Due to the fragility of developing markets public authorities expanding their economic activities during crises need to design appropriate different policies especially to protect SMEs s and keep enterprises standing. This paper presents a unique and high-quality dataset collected through a survey, examining similar issues from a historical perspective, and providing insight into the initial impacts of COVID-19 on enterprises for policymakers. The study stands out for its analysis of COVID-19 from both scale and sector perspectives, with Istanbul providing a representative sample of all sectors and scales due to Istanbul having the highest diversity among the regions in Turkey in terms of enterprises.
{"title":"Determining The Impact of the COVID-19 Pandemic on Commercial Activities in Istanbul","authors":"E. A. Alp, Selçuk Alp, Mefule Findikci Erdogan, Ahmet Oğuz Demir","doi":"10.31181/jopi21202414","DOIUrl":"https://doi.org/10.31181/jopi21202414","url":null,"abstract":"The purpose of this study is to investigate the initial impacts of the COVID-19 pandemic on enterprises and identify differences in the effects of COVID-19 across scales and sectors. This study employed AHP to prioritize solution proposals, factor analysis to determine problem components by sectors and scales, and machine learning methods to estimate enterprise sector and scale based on survey data. The study included 255 statistically reliable samples collected between July to October 2020. Survey and comparison questions were used to determine the impact level of enterprise problems. Open-ended questions categorized pandemic-related commercial activity problems and solution proposals by enterprise scale and sector. The AHP analysis prioritized the same three problems across different scales and sectors, but machine learning-based classification analysis revealed varying criteria for determining sector and scale. Due to the fragility of developing markets public authorities expanding their economic activities during crises need to design appropriate different policies especially to protect SMEs s and keep enterprises standing. This paper presents a unique and high-quality dataset collected through a survey, examining similar issues from a historical perspective, and providing insight into the initial impacts of COVID-19 on enterprises for policymakers. The study stands out for its analysis of COVID-19 from both scale and sector perspectives, with Istanbul providing a representative sample of all sectors and scales due to Istanbul having the highest diversity among the regions in Turkey in terms of enterprises.","PeriodicalId":489110,"journal":{"name":"Journal of Operations Intelligence","volume":"81 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139440463","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 purpose of this study is to determine depot locations where expired ammunition will be controlled before being sent to recycling facilities. Expiration of ammunition means that using, transporting and even storing that ammunition where it is located poses a greater risk. For this reason, it is important to determine facility locations so that ammunition is stored in places that will least harm the environment and human health. The criteria to be used for ammunition depot location selection were determined through literature review, various researches and expert opinions. The proposed model is based on the combined use of Geographic information system (GIS) and multi-criteria decision making. For an example application of the model, a generic study on a district basis in Turkey is presented. Candidate depot locations were determined using GIS with the help of 6 main criteria and 18 sub-criteria. Then, candidate depot locations were ranked by the Pythagorean Fuzzy Set-based WASPAS (Weighted Aggregated Sum Product Assessing) method, taking into account the opinions of military experts for the main criteria. WASPAS method selected location A1 as the most suitable ammunition depot location. The results show that the proposed methodology can be practically applied.
{"title":"Facility Location Selection for Ammunition Depots based on GIS and Pythagorean Fuzzy WASPAS","authors":"Hakan Ayhan Dagistanli, Kemal Gürol Kurtay","doi":"10.31181/jopi2120247","DOIUrl":"https://doi.org/10.31181/jopi2120247","url":null,"abstract":"The purpose of this study is to determine depot locations where expired ammunition will be controlled before being sent to recycling facilities. Expiration of ammunition means that using, transporting and even storing that ammunition where it is located poses a greater risk. For this reason, it is important to determine facility locations so that ammunition is stored in places that will least harm the environment and human health. The criteria to be used for ammunition depot location selection were determined through literature review, various researches and expert opinions. The proposed model is based on the combined use of Geographic information system (GIS) and multi-criteria decision making. For an example application of the model, a generic study on a district basis in Turkey is presented. Candidate depot locations were determined using GIS with the help of 6 main criteria and 18 sub-criteria. Then, candidate depot locations were ranked by the Pythagorean Fuzzy Set-based WASPAS (Weighted Aggregated Sum Product Assessing) method, taking into account the opinions of military experts for the main criteria. WASPAS method selected location A1 as the most suitable ammunition depot location. The results show that the proposed methodology can be practically applied.","PeriodicalId":489110,"journal":{"name":"Journal of Operations Intelligence","volume":"52 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139441242","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}
Saalam Ali, Hamza Naveed, Imran Siddique, R. M. Zulqarnain
A recently emerged area of research named intuitionistic fuzzy hypersoft set (IFHSS) attempts to describe the internal limitations of intuitionistic fuzzy soft sets on multiparameter functions. A computation of such a type connects a power set of the universe with a tuple of sub-parameters. The strategy shows the allocation of attributes to their respective sub-attribute values in distinct groupings. The above features make it a unique methodical tool for handling obstacles of hesitation. The aggregation operators have an important role in the assessment of both types of potential and in identifying problems from their assessment. This research extends the use of the interaction aggregation operator to the interval-valued intuitionistic fuzzy hypersoft set (IVIFHSS), which is an entirely new structure generated through the interval-valued intuitionistic fuzzy soft set (IVIFSS). The IVIFHSS significantly condenses information that is inaccurate and imprecise compared to the frequently utilized IFSS and IVIFSS. Fuzzy reasoning is recognized as the prevalent strategy for improving imperfect data in decision-making processes. The core objective of the research is to develop operational rules for interval-valued intuitionistic fuzzy hypersoft numbers (IVIFHSNs), which promote interactions. This research is designed to broaden the utilization of the interaction geometric aggregation operator in the framework of IVIFHSS. In particular, we propose a novel operator known as the Interval-Valued Intuitionistic Fuzzy Hypersoft Interactive Weighted Geometric (IVIFHSIWG) operator. The aggregation operator indicates industry professional support for the implementation of a robust MCGDM material selection technique in order to address this need. The practical application of the intended MCGDM technique has been introduced in selecting materials (MS) for cryogenic storage containers. The influence advocates that the anticipated model is more operational and stable in demonstrating anxious facts based on IVIFHSS.
{"title":"Extension of Interaction Geometric Aggregation Operator for Material Selection Using Interval-Valued Intuitionistic Fuzzy Hypersoft Set","authors":"Saalam Ali, Hamza Naveed, Imran Siddique, R. M. Zulqarnain","doi":"10.31181/jopi21202410","DOIUrl":"https://doi.org/10.31181/jopi21202410","url":null,"abstract":"A recently emerged area of research named intuitionistic fuzzy hypersoft set (IFHSS) attempts to describe the internal limitations of intuitionistic fuzzy soft sets on multiparameter functions. A computation of such a type connects a power set of the universe with a tuple of sub-parameters. The strategy shows the allocation of attributes to their respective sub-attribute values in distinct groupings. The above features make it a unique methodical tool for handling obstacles of hesitation. The aggregation operators have an important role in the assessment of both types of potential and in identifying problems from their assessment. This research extends the use of the interaction aggregation operator to the interval-valued intuitionistic fuzzy hypersoft set (IVIFHSS), which is an entirely new structure generated through the interval-valued intuitionistic fuzzy soft set (IVIFSS). The IVIFHSS significantly condenses information that is inaccurate and imprecise compared to the frequently utilized IFSS and IVIFSS. Fuzzy reasoning is recognized as the prevalent strategy for improving imperfect data in decision-making processes. The core objective of the research is to develop operational rules for interval-valued intuitionistic fuzzy hypersoft numbers (IVIFHSNs), which promote interactions. This research is designed to broaden the utilization of the interaction geometric aggregation operator in the framework of IVIFHSS. In particular, we propose a novel operator known as the Interval-Valued Intuitionistic Fuzzy Hypersoft Interactive Weighted Geometric (IVIFHSIWG) operator. The aggregation operator indicates industry professional support for the implementation of a robust MCGDM material selection technique in order to address this need. The practical application of the intended MCGDM technique has been introduced in selecting materials (MS) for cryogenic storage containers. The influence advocates that the anticipated model is more operational and stable in demonstrating anxious facts based on IVIFHSS.","PeriodicalId":489110,"journal":{"name":"Journal of Operations Intelligence","volume":"29 33","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139442695","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}
Yasar Gökalp, H. Di̇nçer, Serkan Eti, Serhat Yüksel
Improving public health affects society in many ways. Improved public health can lead to a longer and healthier life. Policymakers cannot address all these criteria at the same time due to both time and budget constraints. Therefore, priority strategies need to be formulated by determining the importance weights of these criteria. Accordingly, the purpose of this study is to evaluate the significance of the strategies determined for the development of public health. For this purpose, analytic hierarchy process (AHP) method is considered to define the importance of the strategies. Within this scope, artificial intelligence methodology is integrated with the Spherical fuzzy sets. In this framework, the decision matrix of AHP is obtained by artificial intelligence system. Next, the steps of Spherical fuzzy sets are implemented. The main contribution of this manuscript is considering artificial intelligence methodology to create decision matrix. Hence, the weights of the experts can be computed based on their qualifications. By the help of this condition, it may be possible for the opinion of experts with better qualifications to be taken into consideration with a higher coefficient. This situation has a positive contribution to increase the accuracy of the findings. The findings indicate that accessibility is the most important strategy to improve public health. Similarly, vaccination and preventive services also play a significant role for this situation.
{"title":"Generating a Novel Artificial Intelligence-Based Decision-Making Model for Determining Priority Strategies for Improving Community Health","authors":"Yasar Gökalp, H. Di̇nçer, Serkan Eti, Serhat Yüksel","doi":"10.31181/jopi21202413","DOIUrl":"https://doi.org/10.31181/jopi21202413","url":null,"abstract":"Improving public health affects society in many ways. Improved public health can lead to a longer and healthier life. Policymakers cannot address all these criteria at the same time due to both time and budget constraints. Therefore, priority strategies need to be formulated by determining the importance weights of these criteria. Accordingly, the purpose of this study is to evaluate the significance of the strategies determined for the development of public health. For this purpose, analytic hierarchy process (AHP) method is considered to define the importance of the strategies. Within this scope, artificial intelligence methodology is integrated with the Spherical fuzzy sets. In this framework, the decision matrix of AHP is obtained by artificial intelligence system. Next, the steps of Spherical fuzzy sets are implemented. The main contribution of this manuscript is considering artificial intelligence methodology to create decision matrix. Hence, the weights of the experts can be computed based on their qualifications. By the help of this condition, it may be possible for the opinion of experts with better qualifications to be taken into consideration with a higher coefficient. This situation has a positive contribution to increase the accuracy of the findings. The findings indicate that accessibility is the most important strategy to improve public health. Similarly, vaccination and preventive services also play a significant role for this situation.","PeriodicalId":489110,"journal":{"name":"Journal of Operations Intelligence","volume":"2 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139380209","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 analytic The Analytic Hierarchy Process (AHP) is a well-established methodology for tackling complex multi-criteria decision problems in practical contexts. However, like many decision-making approaches, AHP confronts certain limitations, particularly in scenarios where evaluations are fraught with uncertainty and imprecision. This study sets out to enhance the capabilities of the AHP method and provide a comprehensive evaluation of public bus transport service quality within Budapest, Hungary. To address the inherent uncertainties in real-world decision-making, the study leverages the Fuzzy Analytic Hierarchy Process (FAHP), a fusion of Fuzzy Set Theory with the traditional AHP. This novel approach equips decision-makers with a more robust framework to handle the multifaceted nature of real-world decision problems. The study is grounded in empirical data obtained through dynamic surveys, ensuring its relevance to the actual conditions experienced in Budapest. Expert evaluators, well-versed in the field, contribute their assessments to enrich the analysis. This novel FAHP approach doesn't just promise improved decision-making outcomes; it also champions simplicity and comprehensibility. Its computational efficiency streamlines the decision-making process, providing a powerful tool for evaluating public bus transport service quality, thereby offering a significant contribution to the sustainable development of Budapest's transportation system.
{"title":"Fuzzy Analytic Hierarchal Process for Sustainable Public Transport System","authors":"Havraz Khedhir Younis Al-Zibaree, Mine Konur","doi":"10.31181/jopi1120234","DOIUrl":"https://doi.org/10.31181/jopi1120234","url":null,"abstract":"The analytic The Analytic Hierarchy Process (AHP) is a well-established methodology for tackling complex multi-criteria decision problems in practical contexts. However, like many decision-making approaches, AHP confronts certain limitations, particularly in scenarios where evaluations are fraught with uncertainty and imprecision. This study sets out to enhance the capabilities of the AHP method and provide a comprehensive evaluation of public bus transport service quality within Budapest, Hungary. To address the inherent uncertainties in real-world decision-making, the study leverages the Fuzzy Analytic Hierarchy Process (FAHP), a fusion of Fuzzy Set Theory with the traditional AHP. This novel approach equips decision-makers with a more robust framework to handle the multifaceted nature of real-world decision problems. The study is grounded in empirical data obtained through dynamic surveys, ensuring its relevance to the actual conditions experienced in Budapest. Expert evaluators, well-versed in the field, contribute their assessments to enrich the analysis. This novel FAHP approach doesn't just promise improved decision-making outcomes; it also champions simplicity and comprehensibility. Its computational efficiency streamlines the decision-making process, providing a powerful tool for evaluating public bus transport service quality, thereby offering a significant contribution to the sustainable development of Budapest's transportation system.","PeriodicalId":489110,"journal":{"name":"Journal of Operations Intelligence","volume":"5 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134972578","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}
Mohammed Rashad Baker, A.H. Alamoodi, O.S. Albahri, A.S. Albahri, Salem Garfan, Amneh Alamleh, Moceheb Lazam Shuwandy, Ibrahim Alshakhatreh
Ever since COVID-19 was declared a pandemic, governments around the world have implemented numerous phases of lockdown measures to curb the spread of the virus. These lockdown tactics manifest themselves in the form of widespread fear and panic driven by social media discussions. Given that individuals hold diverse opinions about these lockdown measures during and after their completion, positive and negative lockdown-related discussions should be differentiated to further understand the major related issues and to make appropriate messaging and policy choices in the future. We conduct a sentiment analysis (SA) of COVID-19-lockdown-related tweets by using different machine learning (ML) classifiers and then evaluate their performance before and after using the synthetic minority oversampling technique (SMOTE). This research is performed in five phases, starting with data collection and followed by pre-processing the dataset, preparing the dataset by annotation, applying SMOTE and using ML classifiers. We observe an improvement in accuracy ( ) as confirmed by the Matthew correlation coefficient ( ) across most classifiers, except for the k-nearest neighbour (KNN), whose Acc decreased from 0.82 to 0.59 and MCC decreased from 0.544 to 0.279 before and after SMOTE was applied. Despite the potential of SMOTE with some classifiers, this technique cannot be considered an ultimate solution, especially with other classifiers and datasets. The study provides insights into the need to evaluate and benchmark the integration of data balancing approaches with ML classifiers in addition to considering additional metrics, such as MCC, for binary classification problems, especially in SA.
{"title":"Comparison of Machine Learning Approaches for Detecting COVID-19-Lockdown-Related Discussions During Recovery and Lockdown Periods","authors":"Mohammed Rashad Baker, A.H. Alamoodi, O.S. Albahri, A.S. Albahri, Salem Garfan, Amneh Alamleh, Moceheb Lazam Shuwandy, Ibrahim Alshakhatreh","doi":"10.31181/jopi1120233","DOIUrl":"https://doi.org/10.31181/jopi1120233","url":null,"abstract":"Ever since COVID-19 was declared a pandemic, governments around the world have implemented numerous phases of lockdown measures to curb the spread of the virus. These lockdown tactics manifest themselves in the form of widespread fear and panic driven by social media discussions. Given that individuals hold diverse opinions about these lockdown measures during and after their completion, positive and negative lockdown-related discussions should be differentiated to further understand the major related issues and to make appropriate messaging and policy choices in the future. We conduct a sentiment analysis (SA) of COVID-19-lockdown-related tweets by using different machine learning (ML) classifiers and then evaluate their performance before and after using the synthetic minority oversampling technique (SMOTE). This research is performed in five phases, starting with data collection and followed by pre-processing the dataset, preparing the dataset by annotation, applying SMOTE and using ML classifiers. We observe an improvement in accuracy ( ) as confirmed by the Matthew correlation coefficient ( ) across most classifiers, except for the k-nearest neighbour (KNN), whose Acc decreased from 0.82 to 0.59 and MCC decreased from 0.544 to 0.279 before and after SMOTE was applied. Despite the potential of SMOTE with some classifiers, this technique cannot be considered an ultimate solution, especially with other classifiers and datasets. The study provides insights into the need to evaluate and benchmark the integration of data balancing approaches with ML classifiers in addition to considering additional metrics, such as MCC, for binary classification problems, especially in SA.","PeriodicalId":489110,"journal":{"name":"Journal of Operations Intelligence","volume":"23 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134973240","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}
In the present-day, within the highly competitive and ever-evolving global market landscape, exporting companies are presented with numerous opportunities to enhance their performance relative to their rivals by introducing innovative products and implementing appropriate pricing strategies. Within this context, product innovation and pricing proficiency stand out as pivotal factors exerting significant influence on the international performance of exporting enterprises. Consequently, the primary aim of this research endeavor was to explore the repercussions of product innovation and pricing competency on the international performance of exporting firms, considering the mediating role played by competitive advantage the research sample encompassed a statistical population comprising 51 exporting companies. The participants in this study included CEOs, as well as financial and marketing managers, and sales experts from these organizations. Employing Morgan's table and taking into account the total pool of exporting firms in the study, a sample size of 36 companies was selected, and a total of 108 questionnaires were gathered. The principal data collection instrument employed was a questionnaire. Rigorous measures were taken to validate the content of the questionnaire through expert assessments, and its structural validity was confirmed via factor analysis. Moreover, the reliability of the questionnaire's variables was verified using Cronbach's alpha coefficient. The data analysis phase entailed the application of correlation and linear regression methods, employing SPSS 26 software. The outcomes of the analysis demonstrated that both product innovation and pricing capability wield a positive and substantial influence on the competitive advantage enjoyed by exporting firms, as well as on their overall international performance. Furthermore, the findings indicated that, when considering the mediating role of competitive advantage, product innovation and pricing capability do not significantly impact the performance of exporting companies.
{"title":"Examining the Impact of Product Innovation and Pricing Capability on the International Performance of Exporting Companies with the Mediating Role of Competitive Advantage for Analysis and decision making","authors":"Javad Rezazadeh, Ruhollah Bagheri, Shabana Karimi, Javad Nazarian-Jashnabadi, Mahmoud Zahedian Nezhad","doi":"10.31181/jopi1120232","DOIUrl":"https://doi.org/10.31181/jopi1120232","url":null,"abstract":"In the present-day, within the highly competitive and ever-evolving global market landscape, exporting companies are presented with numerous opportunities to enhance their performance relative to their rivals by introducing innovative products and implementing appropriate pricing strategies. Within this context, product innovation and pricing proficiency stand out as pivotal factors exerting significant influence on the international performance of exporting enterprises. Consequently, the primary aim of this research endeavor was to explore the repercussions of product innovation and pricing competency on the international performance of exporting firms, considering the mediating role played by competitive advantage the research sample encompassed a statistical population comprising 51 exporting companies. The participants in this study included CEOs, as well as financial and marketing managers, and sales experts from these organizations. Employing Morgan's table and taking into account the total pool of exporting firms in the study, a sample size of 36 companies was selected, and a total of 108 questionnaires were gathered. The principal data collection instrument employed was a questionnaire. Rigorous measures were taken to validate the content of the questionnaire through expert assessments, and its structural validity was confirmed via factor analysis. Moreover, the reliability of the questionnaire's variables was verified using Cronbach's alpha coefficient. The data analysis phase entailed the application of correlation and linear regression methods, employing SPSS 26 software. The outcomes of the analysis demonstrated that both product innovation and pricing capability wield a positive and substantial influence on the competitive advantage enjoyed by exporting firms, as well as on their overall international performance. Furthermore, the findings indicated that, when considering the mediating role of competitive advantage, product innovation and pricing capability do not significantly impact the performance of exporting companies.","PeriodicalId":489110,"journal":{"name":"Journal of Operations Intelligence","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134972580","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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