Identifying chronic obstructive pulmonary disease (COPD) severity stages is of great importance to control the related mortality rates and reduce the associated costs. This study aims to build prediction models for COPD stages and, to compare the relative performance of five machine learning algorithms to determine the optimal prediction algorithm. This research is based on data collected from a private hospital in Egypt for the two calendar years 2018 and 2019. Five machine learning algorithms were used for the comparison. The F1 score, specificity, sensitivity, accuracy, positive predictive value and negative predictive value were the performance measures used for algorithms comparison. Analysis included 211 patients’ records. Our results show that the best performing algorithm in most of the disease stages is the PNN with the optimal prediction accuracy and hence it can be considered as a powerful prediction tool used by decision makers in predicting severity stages of COPD.
{"title":"Prediction of Chronic Obstructive Pulmonary Disease Stages Using Machine Learning Algorithms","authors":"I. Mohamed","doi":"10.4018/ijdsst.286693","DOIUrl":"https://doi.org/10.4018/ijdsst.286693","url":null,"abstract":"Identifying chronic obstructive pulmonary disease (COPD) severity stages is of great importance to control the related mortality rates and reduce the associated costs. This study aims to build prediction models for COPD stages and, to compare the relative performance of five machine learning algorithms to determine the optimal prediction algorithm. This research is based on data collected from a private hospital in Egypt for the two calendar years 2018 and 2019. Five machine learning algorithms were used for the comparison. The F1 score, specificity, sensitivity, accuracy, positive predictive value and negative predictive value were the performance measures used for algorithms comparison. Analysis included 211 patients’ records. Our results show that the best performing algorithm in most of the disease stages is the PNN with the optimal prediction accuracy and hence it can be considered as a powerful prediction tool used by decision makers in predicting severity stages of COPD.","PeriodicalId":42414,"journal":{"name":"International Journal of Decision Support System Technology","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85286746","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}
Plant detection forms an integral part of the life of the forest guards, researchers, and students in the field of Botany and for common people also who are curious about knowing a plant. But detecting plants suffer a major drawback that the true identifier is only the flower and in certain species flowering occurs at major time period gaps spanning from few months to over 100 years (in certain types of bamboos). Machine Learning-based systems could be used in developing models where the experience of researchers in the field of plant sciences can be incorporated into the model. In this paper, we present a machine learning-based approach based upon other quantifiable parameters for the detection of the plant presented. The system takes plant parameters as the inputs and will detect the plant family as the output.
{"title":"Parametric Model for Flora Detection in Middle Himalayas","authors":"Aviral Sharma, S. Nigam","doi":"10.4018/ijdsst.286698","DOIUrl":"https://doi.org/10.4018/ijdsst.286698","url":null,"abstract":"Plant detection forms an integral part of the life of the forest guards, researchers, and students in the field of Botany and for common people also who are curious about knowing a plant. But detecting plants suffer a major drawback that the true identifier is only the flower and in certain species flowering occurs at major time period gaps spanning from few months to over 100 years (in certain types of bamboos). Machine Learning-based systems could be used in developing models where the experience of researchers in the field of plant sciences can be incorporated into the model. In this paper, we present a machine learning-based approach based upon other quantifiable parameters for the detection of the plant presented. The system takes plant parameters as the inputs and will detect the plant family as the output.","PeriodicalId":42414,"journal":{"name":"International Journal of Decision Support System Technology","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82583082","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 this article, we treat the problem of container storage in the export direction, exactly in the containership loading process. We propose an approach to the problem of container placement in a containership by describing a decision model to help decision-makers (handling operators) to minimize the total containers shifting. This is obtained by using a multicriteria decision method named Electre III (Elimination and Choice Expressing Reality) to identify the best location of any container. Here, we consider four criteria: the container destination, the container weight, the departure date of the container and the container type. This method has as input a matrix of performance and the subjective parameters and gives a ranking of alternatives as an output.
{"title":"An Approach to Optimize Container Locations in a Containership With Electre III","authors":"Hocine Tahiri, K. Bouamrane, Khadidja Yachba","doi":"10.4018/ijdsst.286681","DOIUrl":"https://doi.org/10.4018/ijdsst.286681","url":null,"abstract":"In this article, we treat the problem of container storage in the export direction, exactly in the containership loading process. We propose an approach to the problem of container placement in a containership by describing a decision model to help decision-makers (handling operators) to minimize the total containers shifting. This is obtained by using a multicriteria decision method named Electre III (Elimination and Choice Expressing Reality) to identify the best location of any container. Here, we consider four criteria: the container destination, the container weight, the departure date of the container and the container type. This method has as input a matrix of performance and the subjective parameters and gives a ranking of alternatives as an output.","PeriodicalId":42414,"journal":{"name":"International Journal of Decision Support System Technology","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87804804","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}
Cloud computing enables on-demand access to a public resource pool. Many businesses are migrating to the cloud due to its popularity and financial benefits. As a result, finding a suitable and best Cloud Service Provider is a difficult task for all cloud users. Many ranking systems, such as ANP, AHP and TOPSIS, have been proposed in the literature .However, many of the studies concentrated on quantitative data. But qualitative attributes are equally significant in many applications where the user is more concerned with the qualitative features.The implementation of MCDM approach for the ranking and the selection of the best player in the market as per the qualitative need of the cloud users like business organization or cloud brokers is the aim of this article. An ISO approved standard SMI framework is available for the evaluation of the CSPs.The authors have considered SMI attributes like accountability and security as the criteria for evaluation of the CSPs. The MCDM approach called IVF-TOPSIS that can handle the inherent vagueness in the cloud dataset is implemented in this work
{"title":"Cloud Provider Selection Based on Accountability and Security Using Interval-Valued Fuzzy TOPSIS","authors":"T. Thasni, C. Kalaiarasan, K. Venkatesh","doi":"10.4018/ijdsst.286684","DOIUrl":"https://doi.org/10.4018/ijdsst.286684","url":null,"abstract":"Cloud computing enables on-demand access to a public resource pool. Many businesses are migrating to the cloud due to its popularity and financial benefits. As a result, finding a suitable and best Cloud Service Provider is a difficult task for all cloud users. Many ranking systems, such as ANP, AHP and TOPSIS, have been proposed in the literature .However, many of the studies concentrated on quantitative data. But qualitative attributes are equally significant in many applications where the user is more concerned with the qualitative features.The implementation of MCDM approach for the ranking and the selection of the best player in the market as per the qualitative need of the cloud users like business organization or cloud brokers is the aim of this article. An ISO approved standard SMI framework is available for the evaluation of the CSPs.The authors have considered SMI attributes like accountability and security as the criteria for evaluation of the CSPs. The MCDM approach called IVF-TOPSIS that can handle the inherent vagueness in the cloud dataset is implemented in this work","PeriodicalId":42414,"journal":{"name":"International Journal of Decision Support System Technology","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88554251","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}
Software maintenance is an element-key of the life cycle of software. However,the techniques of software maintenance do not consider the diversity and the complexity of decisions which do not stop increasing. So, there are at present a few tools, susceptible to insure the relevance and the efficiency of the decision-making in this phase. The work presented in this paper aims to eliminate or at least to reduce the effect to fall in an expensive change by reducing the time to find a compromise on the adequate change. The development of the decision support system for software maintenance is an answer to the problem. The developed tool allows:-to make a fast diagnosis on the software by using the coupling metrics;-to help the decision-makers of the maintenance, according to their preferences often conflicting, to adopt a change among several proposed. To answer this group decision where various points of view are considered, we propose a negotiation protocol. This protocol try to find a compromise that suits best all the decision-makers.
{"title":"Towards Group Decision Support in the Software Maintenance Process","authors":"Dinedane Mohammed Zoheir, A. Kamel","doi":"10.4018/ijdsst.286677","DOIUrl":"https://doi.org/10.4018/ijdsst.286677","url":null,"abstract":"Software maintenance is an element-key of the life cycle of software. However,the techniques of software maintenance do not consider the diversity and the complexity of decisions which do not stop increasing. So, there are at present a few tools, susceptible to insure the relevance and the efficiency of the decision-making in this phase. The work presented in this paper aims to eliminate or at least to reduce the effect to fall in an expensive change by reducing the time to find a compromise on the adequate change. The development of the decision support system for software maintenance is an answer to the problem. The developed tool allows:-to make a fast diagnosis on the software by using the coupling metrics;-to help the decision-makers of the maintenance, according to their preferences often conflicting, to adopt a change among several proposed. To answer this group decision where various points of view are considered, we propose a negotiation protocol. This protocol try to find a compromise that suits best all the decision-makers.","PeriodicalId":42414,"journal":{"name":"International Journal of Decision Support System Technology","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88888997","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}
This paper proposes an optimization strategy for the best selection process of suppliers. Based on recent literature reviews, the paper assumes a selection of commonly used variables for selecting suppliers, and using Logistic regression algorithm technique, to build a model of optimization that learns from customer’s requirements and supplier’s data, and then make predictions and recommendations for best suppliers. The supplier selection process can quickly at times, turn into a complex task for decision-makers, to dealing with the growing number of supplier base list. But Logistics regression technique makes the process easier in the ability to efficiently fetch customer’s requirements with the entire supplier base list and determine by predicting a list of potential suppliers meeting the actual requirements. The selected suppliers make up the recommendation list for the best suppliers for the requirements. And finally, graphical representations are given to showcase the framework analysis, variable selection, and other illustrations about the model analysis
{"title":"Optimal Strategy for Supplier Selection in a Global Supply Chain Using Machine Learning Technique","authors":"Itoua Wanck Eyika Gaida, M. Mittal, A. S. Yadav","doi":"10.4018/ijdsst.292449","DOIUrl":"https://doi.org/10.4018/ijdsst.292449","url":null,"abstract":"This paper proposes an optimization strategy for the best selection process of suppliers. Based on recent literature reviews, the paper assumes a selection of commonly used variables for selecting suppliers, and using Logistic regression algorithm technique, to build a model of optimization that learns from customer’s requirements and supplier’s data, and then make predictions and recommendations for best suppliers. The supplier selection process can quickly at times, turn into a complex task for decision-makers, to dealing with the growing number of supplier base list. But Logistics regression technique makes the process easier in the ability to efficiently fetch customer’s requirements with the entire supplier base list and determine by predicting a list of potential suppliers meeting the actual requirements. The selected suppliers make up the recommendation list for the best suppliers for the requirements. And finally, graphical representations are given to showcase the framework analysis, variable selection, and other illustrations about the model analysis","PeriodicalId":42414,"journal":{"name":"International Journal of Decision Support System Technology","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86020032","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}
Present day applications process large amount of data that is being produced at brisk rate and is heterogeneous with levels of trustworthiness. This Big data largely consists of semi-structured and unstructured data, which needs to be processed in admissible time so that timely decisions are taken that benefit the organization and society. Such real time processing would require Big data view materialization that would enable faster and timely processing of decision making queries. Several algorithms exist for Big data view materialization. These algorithms aim to select Big data views that minimize the total query processing cost for the query workload. In literature, this problem has been articulated as a bi-objective optimization problem, which minimizes the query evaluation cost along with the update processing cost. This paper proposes to adapt the reference point based non-dominated sorting genetic algorithm, to design an NSGA-III based Big data view selection algorithm (BDVSANSGA-III) to address this bi-objective Big data view selection problem. Experimental results revealed that the proposed BDVSANSGA-III was able to compute diverse non-dominated Big data views and performed better than the existing algorithms..
{"title":"Multi-Objective Big Data View Materialization Using NSGA-III","authors":"Akshay Kumar, T. Kumar","doi":"10.4018/ijdsst.311066","DOIUrl":"https://doi.org/10.4018/ijdsst.311066","url":null,"abstract":"Present day applications process large amount of data that is being produced at brisk rate and is heterogeneous with levels of trustworthiness. This Big data largely consists of semi-structured and unstructured data, which needs to be processed in admissible time so that timely decisions are taken that benefit the organization and society. Such real time processing would require Big data view materialization that would enable faster and timely processing of decision making queries. Several algorithms exist for Big data view materialization. These algorithms aim to select Big data views that minimize the total query processing cost for the query workload. In literature, this problem has been articulated as a bi-objective optimization problem, which minimizes the query evaluation cost along with the update processing cost. This paper proposes to adapt the reference point based non-dominated sorting genetic algorithm, to design an NSGA-III based Big data view selection algorithm (BDVSANSGA-III) to address this bi-objective Big data view selection problem. Experimental results revealed that the proposed BDVSANSGA-III was able to compute diverse non-dominated Big data views and performed better than the existing algorithms..","PeriodicalId":42414,"journal":{"name":"International Journal of Decision Support System Technology","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80964094","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}
S. Sivakumar, D. Haritha, N. S. Ram, Naveen Kumar, G. RamaKrishna, A. DineshKumar
Customer conveys their opinion in natural language about an entity. Applying sentiment analysis to those reviews is a very complex task. The significance terms that are influencing the polarity of a review are not examined. The terms that are having contextual meaning are not recognized which are present across multiple sentences in a review. To address the above two issues, we have proposed an Attention-based Convolution Bi-directional Recurrent Neural Network (ACBRNN). In this model, two convolution layer captures phrase-level feature, while Self-Attention in the middle assigns high weight to the significant terms and Bi-directional GRU performs a conceptual scanning of review through forward and backward direction. We have conducted four different experiments viz., Unidirectional, Bidirectional, Hybrid and Proposed model on IMDB dataset to show the significance of the proposed model. The proposed model has obtained an F1 score of 87.94% on IMDB dataset which is 5.41% higher than CNN. Thus the proposed architecture performs well while comparing with all other baseline models.
{"title":"Attention-Based Convolution Bidirectional Recurrent Neural Network for Sentiment Analysis","authors":"S. Sivakumar, D. Haritha, N. S. Ram, Naveen Kumar, G. RamaKrishna, A. DineshKumar","doi":"10.4018/ijdsst.300368","DOIUrl":"https://doi.org/10.4018/ijdsst.300368","url":null,"abstract":"Customer conveys their opinion in natural language about an entity. Applying sentiment analysis to those reviews is a very complex task. The significance terms that are influencing the polarity of a review are not examined. The terms that are having contextual meaning are not recognized which are present across multiple sentences in a review. To address the above two issues, we have proposed an Attention-based Convolution Bi-directional Recurrent Neural Network (ACBRNN). In this model, two convolution layer captures phrase-level feature, while Self-Attention in the middle assigns high weight to the significant terms and Bi-directional GRU performs a conceptual scanning of review through forward and backward direction. We have conducted four different experiments viz., Unidirectional, Bidirectional, Hybrid and Proposed model on IMDB dataset to show the significance of the proposed model. The proposed model has obtained an F1 score of 87.94% on IMDB dataset which is 5.41% higher than CNN. Thus the proposed architecture performs well while comparing with all other baseline models.","PeriodicalId":42414,"journal":{"name":"International Journal of Decision Support System Technology","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79320970","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}
Intuitionistic Fuzzy Sets(1986) invented by Atanassov(Atanassov, 1986) has gained the wide popularity among various researchers because of its applications in various fields such as image processing, edge detection, medical diagnosis, pattern recognition etc. One of the significant tool by which the decision can be made is Intuitionistic Fuzzy Similarity Measure. In this communication, the authors have introduced two new Intuitionistic fuzzy similarity measures based on the trigonometric functions and its validity is proved. The proposed similarity measure is applied to medical diagnosis and pattern recognition.
{"title":"Two Trigonometric Intuitionistic Fuzzy Similarity Measures","authors":"Rozy Boora, V. P. Tomar","doi":"10.4018/ijdsst.286694","DOIUrl":"https://doi.org/10.4018/ijdsst.286694","url":null,"abstract":"Intuitionistic Fuzzy Sets(1986) invented by Atanassov(Atanassov, 1986) has gained the wide popularity among various researchers because of its applications in various fields such as image processing, edge detection, medical diagnosis, pattern recognition etc. One of the significant tool by which the decision can be made is Intuitionistic Fuzzy Similarity Measure. In this communication, the authors have introduced two new Intuitionistic fuzzy similarity measures based on the trigonometric functions and its validity is proved. The proposed similarity measure is applied to medical diagnosis and pattern recognition.","PeriodicalId":42414,"journal":{"name":"International Journal of Decision Support System Technology","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74743109","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 this paper, a two-stage method has been proposed for solving Fuzzy Multi-objective Linear Programming Problem (FMOLPP) with Interval Type-2 Triangular Fuzzy Numbers (IT2TFNs) as its coefficients. In the first stage of problem solving, the imprecise nature of the problem has been handled. All technological coefficients given by IT2TFNs are first converted to a closed interval and then the objectives are made crisp by reducing a closed interval into a crisp number and constraints are made crisp by using the concept of acceptability index. The amount by which a specific constraint can be relaxed is decided by the decision maker and thus the problem reduces to a crisp multi-objective linear programming problem (MOLPP). In the second stage of problem solving, the multi-objective nature of the problem is handled by using fuzzy mathematical programming approach. In order to explain the methodology, two numerical examples of the proposed methodology in Production planning and Diet planning problems have also been worked out in this paper.
{"title":"Fuzzy Multi-Objective Linear Programming Problem Using DM's Perspective","authors":"Vishnu Pratap Singh, M. Deshmukh, K. Sharma","doi":"10.4018/ijdsst.286695","DOIUrl":"https://doi.org/10.4018/ijdsst.286695","url":null,"abstract":"In this paper, a two-stage method has been proposed for solving Fuzzy Multi-objective Linear Programming Problem (FMOLPP) with Interval Type-2 Triangular Fuzzy Numbers (IT2TFNs) as its coefficients. In the first stage of problem solving, the imprecise nature of the problem has been handled. All technological coefficients given by IT2TFNs are first converted to a closed interval and then the objectives are made crisp by reducing a closed interval into a crisp number and constraints are made crisp by using the concept of acceptability index. The amount by which a specific constraint can be relaxed is decided by the decision maker and thus the problem reduces to a crisp multi-objective linear programming problem (MOLPP). In the second stage of problem solving, the multi-objective nature of the problem is handled by using fuzzy mathematical programming approach. In order to explain the methodology, two numerical examples of the proposed methodology in Production planning and Diet planning problems have also been worked out in this paper.","PeriodicalId":42414,"journal":{"name":"International Journal of Decision Support System Technology","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72431335","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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