Pub Date : 2024-01-01DOI: 10.1142/s0219622024020012
J. Tien, Yong Shi, Jianping Li
{"title":"Guest Editors' Introduction for the Special Issue on The Role of Decision Making to Overcome COVID-19","authors":"J. Tien, Yong Shi, Jianping Li","doi":"10.1142/s0219622024020012","DOIUrl":"https://doi.org/10.1142/s0219622024020012","url":null,"abstract":"","PeriodicalId":13527,"journal":{"name":"Int. J. Inf. Technol. Decis. Mak.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140524367","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 : 2023-01-01DOI: 10.1142/S0219622022500778
Xinwang Liu, Yuyao Yang, Jing Jiang
{"title":"The Behavioral TOPSIS Based on Prospect Theory and Regret Theory","authors":"Xinwang Liu, Yuyao Yang, Jing Jiang","doi":"10.1142/S0219622022500778","DOIUrl":"https://doi.org/10.1142/S0219622022500778","url":null,"abstract":"","PeriodicalId":13527,"journal":{"name":"Int. J. Inf. Technol. Decis. Mak.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81743716","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 : 2023-01-01DOI: 10.1142/S0219622022500754
U. Khaire, R. Dhanalakshmi, K. Balakrishnan, M. Akila
{"title":"Instigating the Sailfish Optimization Algorithm Based on Opposition-Based Learning to Determine the Salient Features From a High-Dimensional Dataset","authors":"U. Khaire, R. Dhanalakshmi, K. Balakrishnan, M. Akila","doi":"10.1142/S0219622022500754","DOIUrl":"https://doi.org/10.1142/S0219622022500754","url":null,"abstract":"","PeriodicalId":13527,"journal":{"name":"Int. J. Inf. Technol. Decis. Mak.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84633585","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 : 2022-12-03DOI: 10.1142/s0219622022500869
Anusha Ampavathi, Pradeepini Gera, T. V. Saradhi
Background: In recent times, medical technology has generated massive reports such as scanned medical images and electronic patient accounts. These reports are necessary to be stored in the highly secured platform for further reference. Traditional storage systems are infeasible for storing massive data. In addition, it suffers to provide secure storage and privacy protection at the time of medical services. It is necessary to provide secure storage and full utilization of personal medical records for the common people in practice. The healthcare system based on IoT enhances the support for the patients and doctors in diagnosing the sufferers at an accurate time using the monitored health data. Yet, doctors make an inappropriate decision regarding the sufferer’s sickness when the information regarding health data saved in the cloud gets lost or hacked owing to an external attack or also power failure. Hence, it is highly essential for verifying the truthfulness of the sufferer’s information regarding health data saved on the cloud.Hypothesis: The major intention of this task is to adopt a new chaotic-based healthcare medical data storage system for storing medical data (medical images) with high protection. Methodology: Initially, the input medical images are gathered from the benchmark datasets concerning different modalities. The collected medical images are enciphered by developing Hybrid Chaotic Map by adapting the 2D-Logistic Chaotic Map (2DLCM), and Piece-Wise Linear Chaotic Map (PWLCM) referred to as Hybrid Logistic Piece-Wise Chaotic Map (HLPWCM). An Optimized Recurrent Neural Network (O-RNN) is proposed for key generation using Best Fitness-based Coefficient vector improved Spotted Hyena Optimizer (BF-CSHO). The O-RNN-based key generation utilizes the extracted image features like first and second-order statistical features and the targets are acquired as a unique encrypted key, which is used for securing the medical data. The same BF-CSHO is used for improving the training algorithm (weight optimization) of RNN to minimize the Mean Absolute Error (MAE) between the cipher (encrypted) images and original images. Results: From the result analysis, the suggested BF-CSHO-RNN-HLPWCM, by considering the image size at [Formula: see text] shows 10.4%, 8.5%, 3.97%, 0.62%, 3.88%, 2.40%, and 7.82% provides better computational efficiency than LCM, PWLCM, LPWCM, PSO-RNN-HLPWCM, JA-RNN-HLPWCM, GWO-RNN-HLPWCM, and SHO-RNN-HLPWCM, respectively. Conclusion: Thus, the simulation findings show the effective efficiency of the offered method owing to the security of the stored medical data.
{"title":"Optimized Deep Learning-Enabled Hybrid Logistic Piece-Wise Chaotic Map for Secured Medical Data Storage System","authors":"Anusha Ampavathi, Pradeepini Gera, T. V. Saradhi","doi":"10.1142/s0219622022500869","DOIUrl":"https://doi.org/10.1142/s0219622022500869","url":null,"abstract":"Background: In recent times, medical technology has generated massive reports such as scanned medical images and electronic patient accounts. These reports are necessary to be stored in the highly secured platform for further reference. Traditional storage systems are infeasible for storing massive data. In addition, it suffers to provide secure storage and privacy protection at the time of medical services. It is necessary to provide secure storage and full utilization of personal medical records for the common people in practice. The healthcare system based on IoT enhances the support for the patients and doctors in diagnosing the sufferers at an accurate time using the monitored health data. Yet, doctors make an inappropriate decision regarding the sufferer’s sickness when the information regarding health data saved in the cloud gets lost or hacked owing to an external attack or also power failure. Hence, it is highly essential for verifying the truthfulness of the sufferer’s information regarding health data saved on the cloud.Hypothesis: The major intention of this task is to adopt a new chaotic-based healthcare medical data storage system for storing medical data (medical images) with high protection. Methodology: Initially, the input medical images are gathered from the benchmark datasets concerning different modalities. The collected medical images are enciphered by developing Hybrid Chaotic Map by adapting the 2D-Logistic Chaotic Map (2DLCM), and Piece-Wise Linear Chaotic Map (PWLCM) referred to as Hybrid Logistic Piece-Wise Chaotic Map (HLPWCM). An Optimized Recurrent Neural Network (O-RNN) is proposed for key generation using Best Fitness-based Coefficient vector improved Spotted Hyena Optimizer (BF-CSHO). The O-RNN-based key generation utilizes the extracted image features like first and second-order statistical features and the targets are acquired as a unique encrypted key, which is used for securing the medical data. The same BF-CSHO is used for improving the training algorithm (weight optimization) of RNN to minimize the Mean Absolute Error (MAE) between the cipher (encrypted) images and original images. Results: From the result analysis, the suggested BF-CSHO-RNN-HLPWCM, by considering the image size at [Formula: see text] shows 10.4%, 8.5%, 3.97%, 0.62%, 3.88%, 2.40%, and 7.82% provides better computational efficiency than LCM, PWLCM, LPWCM, PSO-RNN-HLPWCM, JA-RNN-HLPWCM, GWO-RNN-HLPWCM, and SHO-RNN-HLPWCM, respectively. Conclusion: Thus, the simulation findings show the effective efficiency of the offered method owing to the security of the stored medical data.","PeriodicalId":13527,"journal":{"name":"Int. J. Inf. Technol. Decis. Mak.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91068019","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 : 2022-11-16DOI: 10.1142/s0219622022500985
M. Hatefi
{"title":"A Typology Scheme for the Criteria Weighting Methods in MADM","authors":"M. Hatefi","doi":"10.1142/s0219622022500985","DOIUrl":"https://doi.org/10.1142/s0219622022500985","url":null,"abstract":"","PeriodicalId":13527,"journal":{"name":"Int. J. Inf. Technol. Decis. Mak.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83694679","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 : 2022-11-11DOI: 10.1142/s021962202250078x
Kalyana C. Chejarla, O. Vaidya
The ubiquity of data, and in particular in MCDM situations, makes it challenging for the Decision Makers (DM) to figure out a way of making proper use of data. This paper presents a three-stage decision framework for DMs to consider the performance range of alternatives holistically. The framework consists of (i) data preparation, (ii) two distance-based Gray Multi-Criteria Decision-Making (MCDM-G) methods using gray interval data to rank the alternatives and (iii) a decision analysis template. For comparison, gray Evaluation based on Distance from Average Solution (EDAS) and gray Multi-Attributive Border Approximation area Comparison (MABAC) methods that rely on arithmetic and geometric mean respectively are used to generate the ranks. The mean-based ranking methods produce stable and efficient ranks in comparison to extremum-based comparison methods, due to their innate nature. The correlation of ranks is analyzed to conclude that the stability of ranks is better when gray interval data is considered. As an example, this paper considers performance range of the 10 criteria used in computing Ease of Doing Business (EDB) index as the gray interval. The sample performance of the G20 countries during the period 2004 to 2020 was used to illustrate the calculations. Further, a general analytic template based on the rank deviation on account of differences in upper and lower bounds of performance helped in classifying the economies as stable leaders, predictable middle and volatile followers. The paper contributes a suitable MCDM and analysis approach when the DM is presented with a gray interval as the alternatives’ performance.
{"title":"Ease of Doing Business: Performance Comparison of G20 Countries Using Gray MCDM","authors":"Kalyana C. Chejarla, O. Vaidya","doi":"10.1142/s021962202250078x","DOIUrl":"https://doi.org/10.1142/s021962202250078x","url":null,"abstract":"The ubiquity of data, and in particular in MCDM situations, makes it challenging for the Decision Makers (DM) to figure out a way of making proper use of data. This paper presents a three-stage decision framework for DMs to consider the performance range of alternatives holistically. The framework consists of (i) data preparation, (ii) two distance-based Gray Multi-Criteria Decision-Making (MCDM-G) methods using gray interval data to rank the alternatives and (iii) a decision analysis template. For comparison, gray Evaluation based on Distance from Average Solution (EDAS) and gray Multi-Attributive Border Approximation area Comparison (MABAC) methods that rely on arithmetic and geometric mean respectively are used to generate the ranks. The mean-based ranking methods produce stable and efficient ranks in comparison to extremum-based comparison methods, due to their innate nature. The correlation of ranks is analyzed to conclude that the stability of ranks is better when gray interval data is considered. As an example, this paper considers performance range of the 10 criteria used in computing Ease of Doing Business (EDB) index as the gray interval. The sample performance of the G20 countries during the period 2004 to 2020 was used to illustrate the calculations. Further, a general analytic template based on the rank deviation on account of differences in upper and lower bounds of performance helped in classifying the economies as stable leaders, predictable middle and volatile followers. The paper contributes a suitable MCDM and analysis approach when the DM is presented with a gray interval as the alternatives’ performance.","PeriodicalId":13527,"journal":{"name":"Int. J. Inf. Technol. Decis. Mak.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74203732","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 : 2022-11-02DOI: 10.1142/s0219622022500766
Marta Płonka, Jerzy Grobelny, Rafał Michalski
Product packaging has a great influence on customers’ decision-making and shapes purchase intentions. The graphic message is the crucial component of this impact. Digital presentations of goods are ubiquitous, therefore understanding how graphical features influence customer decisions is of enormous theoretical and practical importance. Despite the interest, the role of specific factors and their combinations is still unclear, especially if medium-involvement products are concerned. Since only a few studies have considered this context, this research examines how eight variants of a digital presentation of cordless kettle packaging influence purchase willingness, which was derived from pairwise comparisons using eigenvectors. The experimental conditions differed in three factors: the existence of a product graphical context, a brief or extended product description, and white or black packaging background color. Results of analyses of variance and conjoint analyses revealed a significant role of all examined effects, with the background color being the least influential. The best-rated designs included graphical context and extended textual information. There were also some meaningful gender-related differences revealed by conjoint analyses. The black background color was much more important for females than males. The outcomes broaden our knowledge on people’s perception of packaging design graphical factors, and their impact on purchase decisions.
{"title":"Conjoint Analysis Models of Digital Packaging Information Features in Customer Decision-Making","authors":"Marta Płonka, Jerzy Grobelny, Rafał Michalski","doi":"10.1142/s0219622022500766","DOIUrl":"https://doi.org/10.1142/s0219622022500766","url":null,"abstract":"Product packaging has a great influence on customers’ decision-making and shapes purchase intentions. The graphic message is the crucial component of this impact. Digital presentations of goods are ubiquitous, therefore understanding how graphical features influence customer decisions is of enormous theoretical and practical importance. Despite the interest, the role of specific factors and their combinations is still unclear, especially if medium-involvement products are concerned. Since only a few studies have considered this context, this research examines how eight variants of a digital presentation of cordless kettle packaging influence purchase willingness, which was derived from pairwise comparisons using eigenvectors. The experimental conditions differed in three factors: the existence of a product graphical context, a brief or extended product description, and white or black packaging background color. Results of analyses of variance and conjoint analyses revealed a significant role of all examined effects, with the background color being the least influential. The best-rated designs included graphical context and extended textual information. There were also some meaningful gender-related differences revealed by conjoint analyses. The black background color was much more important for females than males. The outcomes broaden our knowledge on people’s perception of packaging design graphical factors, and their impact on purchase decisions.","PeriodicalId":13527,"journal":{"name":"Int. J. Inf. Technol. Decis. Mak.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74288345","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 : 2022-10-28DOI: 10.1142/s0219622022500912
B. Kaya, M. Dağdeviren
{"title":"Optimal Solution Accoutrement for Crew Scheduling Problem: An Innovative Solution Approach Predicating on a Tailor-Made DSS","authors":"B. Kaya, M. Dağdeviren","doi":"10.1142/s0219622022500912","DOIUrl":"https://doi.org/10.1142/s0219622022500912","url":null,"abstract":"","PeriodicalId":13527,"journal":{"name":"Int. J. Inf. Technol. Decis. Mak.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76910552","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 : 2022-10-21DOI: 10.1142/s0219622022500742
Abdelghani Tafsast, A. Khelalef, K. Ferroudji, M. Hadjili, A. Bouakaz, N. Benoudjit
Classification of microemboli is important in predicting clinical complications. In this study, we suggest a deep learning-based approach using convolutional neural network (CNN) and backscattered radio-frequency (RF) signals for classifying microemboli. The RF signals are converted into two-dimensional (2D) spectrograms which are exploited as inputs for the CNN. To confirm the usefulness of RF ultrasound signals in the classification of microemboli, two in vitro setups are developed. For the two setups, a contrast agent consisting of microbubbles is used to imitate the acoustic behavior of gaseous microemboli. In order to imitate the acoustic behavior of solid microemboli, the tissue mimicking material surrounding the tube is used for the first setup. However, for the second setup, a Doppler fluid containing particles with scattering characteristics comparable to the red blood cells is used. Results have shown that the suggested approach achieved better classification rates compared to the results obtained in previous studies.
{"title":"Enhanced Ultrasound Classification of Microemboli Using Convolutional Neural Network","authors":"Abdelghani Tafsast, A. Khelalef, K. Ferroudji, M. Hadjili, A. Bouakaz, N. Benoudjit","doi":"10.1142/s0219622022500742","DOIUrl":"https://doi.org/10.1142/s0219622022500742","url":null,"abstract":"Classification of microemboli is important in predicting clinical complications. In this study, we suggest a deep learning-based approach using convolutional neural network (CNN) and backscattered radio-frequency (RF) signals for classifying microemboli. The RF signals are converted into two-dimensional (2D) spectrograms which are exploited as inputs for the CNN. To confirm the usefulness of RF ultrasound signals in the classification of microemboli, two in vitro setups are developed. For the two setups, a contrast agent consisting of microbubbles is used to imitate the acoustic behavior of gaseous microemboli. In order to imitate the acoustic behavior of solid microemboli, the tissue mimicking material surrounding the tube is used for the first setup. However, for the second setup, a Doppler fluid containing particles with scattering characteristics comparable to the red blood cells is used. Results have shown that the suggested approach achieved better classification rates compared to the results obtained in previous studies.","PeriodicalId":13527,"journal":{"name":"Int. J. Inf. Technol. Decis. Mak.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87183655","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 : 2022-10-19DOI: 10.1142/s0219622022500729
A. Hadi-Vencheh, P. Wanke, Ali Jamshidi, J. Antunes
In this paper, we propose a robust ABC classification for inventories using a hybrid technique for order of preference by similarity to ideal solution-alternative factor extraction approach (TOPSIS-AFEA) as the cornerstone method to calculate and rank importance scores for each item in stock. This is done to mitigate multicollinearity that may exist among different inventory criteria, which artificially inflates total data variance. Besides, and differently from previous research, information reliability techniques such as information entropy and gray relational analysis (GRA) are used as an auxiliary tool to differentiate alternative ABC methods proposed in the literature in terms of the principle of maximal entropy. This principle states that the probability distribution that best represents the current state of knowledge given prior data is the one with largest entropy. Results suggest that the proposed robust TOPSIS-AFEA provides an adequate representation of score ranks that may be computed on different datasets by using existing alternative ABC inventory classification models.
{"title":"Robust ABC Inventory Classification Using Hybrid TOPSIS-Alternative Factor Extraction Approaches","authors":"A. Hadi-Vencheh, P. Wanke, Ali Jamshidi, J. Antunes","doi":"10.1142/s0219622022500729","DOIUrl":"https://doi.org/10.1142/s0219622022500729","url":null,"abstract":"In this paper, we propose a robust ABC classification for inventories using a hybrid technique for order of preference by similarity to ideal solution-alternative factor extraction approach (TOPSIS-AFEA) as the cornerstone method to calculate and rank importance scores for each item in stock. This is done to mitigate multicollinearity that may exist among different inventory criteria, which artificially inflates total data variance. Besides, and differently from previous research, information reliability techniques such as information entropy and gray relational analysis (GRA) are used as an auxiliary tool to differentiate alternative ABC methods proposed in the literature in terms of the principle of maximal entropy. This principle states that the probability distribution that best represents the current state of knowledge given prior data is the one with largest entropy. Results suggest that the proposed robust TOPSIS-AFEA provides an adequate representation of score ranks that may be computed on different datasets by using existing alternative ABC inventory classification models.","PeriodicalId":13527,"journal":{"name":"Int. J. Inf. Technol. Decis. Mak.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77055792","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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