Dingju Zhu, Jianbin Tan, Guangbo Luo, Haoxiang Gu, Zhanhao Ye, Renfeng Deng, Keyi He, Kai-Leung Yung, Andrew W. H. Ip
So far, no research has used the partial order algorithm for the mining of hospital medical technology. This paper proposed a novel knowledge discovery method of hospital medical technology based on partial ordered structure diagrams, constructed attribute partial ordered structure diagram and object partial ordered structure diagram for the formal context constructed by hospital set and medical technology set, and finally analyzed them using the knowledge discovery method. The experiments show that the partial ordered structure diagram can effectively visualize the structural relationships between hospital sets and medical technology sets, and the distribution characteristics of medical technology sets in hospital sets and the rules of medical technology sets owned by hospital sets can be obtained based on the node, branch, and group structure relationships of the partial ordered structure diagram.
{"title":"Knowledge Discovery of Hospital Medical Technology Based on Partial Ordered Structure Diagrams","authors":"Dingju Zhu, Jianbin Tan, Guangbo Luo, Haoxiang Gu, Zhanhao Ye, Renfeng Deng, Keyi He, Kai-Leung Yung, Andrew W. H. Ip","doi":"10.4018/ijssci.320499","DOIUrl":"https://doi.org/10.4018/ijssci.320499","url":null,"abstract":"So far, no research has used the partial order algorithm for the mining of hospital medical technology. This paper proposed a novel knowledge discovery method of hospital medical technology based on partial ordered structure diagrams, constructed attribute partial ordered structure diagram and object partial ordered structure diagram for the formal context constructed by hospital set and medical technology set, and finally analyzed them using the knowledge discovery method. The experiments show that the partial ordered structure diagram can effectively visualize the structural relationships between hospital sets and medical technology sets, and the distribution characteristics of medical technology sets in hospital sets and the rules of medical technology sets owned by hospital sets can be obtained based on the node, branch, and group structure relationships of the partial ordered structure diagram.","PeriodicalId":432255,"journal":{"name":"Int. J. Softw. Sci. Comput. Intell.","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115556866","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}
Individuals with cognitive impairment survive mental challenges; they hardly perform daily life assignments. The individuals with down syndrome face mild to severe cognitive challenges that affect daily life activities and learning. A goal is to reduce the social and economic burden of their family and to make their lives productive. Achieving these goals requires improvement in limited mental challenge. Most of the work has been done on facial expression, prediction of inhibitory capacity, and prediction of mental deficiency. The review highlights the usefulness of machine learning-techniques, including convolution neural network and artificial neural network, applied to address mental challenge. Based on the gaps of existing AI techniques, the authors provide a recommendation for the identification of mental challenges using a survey-based Software approach, which is focused on analyzing and improving mental challenges from severe-moderate to moderate-mild; and to enhance the academics, social collaboration, and employment capability to the Down syndrome individuals.
{"title":"Artificial Intelligence Techniques to improve cognitive traits of Down Syndrome Individuals: An Analysis","authors":"Irfan M. Leghari, Syed Asif Ali","doi":"10.4018/ijssci.318677","DOIUrl":"https://doi.org/10.4018/ijssci.318677","url":null,"abstract":"Individuals with cognitive impairment survive mental challenges; they hardly perform daily life assignments. The individuals with down syndrome face mild to severe cognitive challenges that affect daily life activities and learning. A goal is to reduce the social and economic burden of their family and to make their lives productive. Achieving these goals requires improvement in limited mental challenge. Most of the work has been done on facial expression, prediction of inhibitory capacity, and prediction of mental deficiency. The review highlights the usefulness of machine learning-techniques, including convolution neural network and artificial neural network, applied to address mental challenge. Based on the gaps of existing AI techniques, the authors provide a recommendation for the identification of mental challenges using a survey-based Software approach, which is focused on analyzing and improving mental challenges from severe-moderate to moderate-mild; and to enhance the academics, social collaboration, and employment capability to the Down syndrome individuals.","PeriodicalId":432255,"journal":{"name":"Int. J. Softw. Sci. Comput. Intell.","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128372317","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 healthcare data available on social media has exploded in recent years. The cures and treatments suggested by non-medical experts can lead to more damage than expected. Assuring the credibility of the information conveyed is an enormous challenge. This study aims to categorize the credibility of online health information into multiple classes. This paper proposes a model named Text Analysis of Web-based Health Information (TA-WHI), based on an algorithm designed for this. It categorizes health-related social media feeds into five categories: sufficient, fabricated, meaningful, advertisement, and misleading. The authors have created their own labeled dataset for this model. For data cleaning, they have designed a dictionary having nouns, adverbs, adjectives, negative words, positive words, and medical terms named MeDF. Using polarity and conditional procedure, the data is ranked and classified into multiple classes. The authors evaluate the performance of the model using deep-learning classifiers such as CNN, LSTM, and CatBoost. The suggested model has attained an accuracy of 98% with CatBoost.
{"title":"TA-WHI: Text Analysis of Web-Based Health Information","authors":"P. Bagla, Kuldeep Kumar","doi":"10.4018/ijssci.316972","DOIUrl":"https://doi.org/10.4018/ijssci.316972","url":null,"abstract":"The healthcare data available on social media has exploded in recent years. The cures and treatments suggested by non-medical experts can lead to more damage than expected. Assuring the credibility of the information conveyed is an enormous challenge. This study aims to categorize the credibility of online health information into multiple classes. This paper proposes a model named Text Analysis of Web-based Health Information (TA-WHI), based on an algorithm designed for this. It categorizes health-related social media feeds into five categories: sufficient, fabricated, meaningful, advertisement, and misleading. The authors have created their own labeled dataset for this model. For data cleaning, they have designed a dictionary having nouns, adverbs, adjectives, negative words, positive words, and medical terms named MeDF. Using polarity and conditional procedure, the data is ranked and classified into multiple classes. The authors evaluate the performance of the model using deep-learning classifiers such as CNN, LSTM, and CatBoost. The suggested model has attained an accuracy of 98% with CatBoost.","PeriodicalId":432255,"journal":{"name":"Int. J. Softw. Sci. Comput. Intell.","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125941732","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}
Integrating the internet of things (IoT) and healthcare monitoring systems is one of the most dynamic innovations in the research area. Since the tremendous number of IoT devices in smart healthcare systems is increasing exponentially, privacy and security issues related to the patient's data are significant concerns. The authors propose an access control for a healthcare monitoring system using blockchain-based smart contracts. They created four smart contract forms for user registration, authentication, access control including misbehavior detection and access revocation. The sensor automatically measures the patient's health data and filters the data before determining whether to write the data into the blockchain or not. The sensor detects abnormal data and alerts doctors and hospitals for immediate treatment. The efficiency of the proposed framework is verified by performance evaluation based on the Ethereum test environment. The proposed system outperforms existing approaches by reducing deployment and execution latency and average response latency in the real-time smart healthcare system.
{"title":"A Privacy-Preserving Authentic Healthcare Monitoring System Using Blockchain","authors":"A. Raj, S. Prakash","doi":"10.4018/ijssci.310942","DOIUrl":"https://doi.org/10.4018/ijssci.310942","url":null,"abstract":"Integrating the internet of things (IoT) and healthcare monitoring systems is one of the most dynamic innovations in the research area. Since the tremendous number of IoT devices in smart healthcare systems is increasing exponentially, privacy and security issues related to the patient's data are significant concerns. The authors propose an access control for a healthcare monitoring system using blockchain-based smart contracts. They created four smart contract forms for user registration, authentication, access control including misbehavior detection and access revocation. The sensor automatically measures the patient's health data and filters the data before determining whether to write the data into the blockchain or not. The sensor detects abnormal data and alerts doctors and hospitals for immediate treatment. The efficiency of the proposed framework is verified by performance evaluation based on the Ethereum test environment. The proposed system outperforms existing approaches by reducing deployment and execution latency and average response latency in the real-time smart healthcare system.","PeriodicalId":432255,"journal":{"name":"Int. J. Softw. Sci. Comput. Intell.","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122712245","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}
F. García-Peñalvo, Tamanna Maan, Sunil K. Singh, Sudhakar Kumar, Varsha Arya, Kwok Tai Chui, Gaurav Pratap Singh
Prediction of stock prices is a challenging task owing to its volatile and constantly fluctuating nature. Stock price prediction has sparked the interest of various investors, data analysists, and researchers because of high returns on their investments. A sustainable framework for stock price prediction is proposed to quantify the factors affecting the stock price and impact of technology on the ever-changing business world. The proposed framework also helps to understand how technology can be used to predict the future price of stocks by using some historical dataset to produce desirable results using machine learning algorithms. The aim of this research paper is to learn about stock price prediction by using different machine learning algorithms and comparing their performance. The results reveal that Fb-prophet should be preferred for more precise prediction among different ML algorithms.
{"title":"Sustainable Stock Market Prediction Framework Using Machine Learning Models","authors":"F. García-Peñalvo, Tamanna Maan, Sunil K. Singh, Sudhakar Kumar, Varsha Arya, Kwok Tai Chui, Gaurav Pratap Singh","doi":"10.4018/ijssci.313593","DOIUrl":"https://doi.org/10.4018/ijssci.313593","url":null,"abstract":"Prediction of stock prices is a challenging task owing to its volatile and constantly fluctuating nature. Stock price prediction has sparked the interest of various investors, data analysists, and researchers because of high returns on their investments. A sustainable framework for stock price prediction is proposed to quantify the factors affecting the stock price and impact of technology on the ever-changing business world. The proposed framework also helps to understand how technology can be used to predict the future price of stocks by using some historical dataset to produce desirable results using machine learning algorithms. The aim of this research paper is to learn about stock price prediction by using different machine learning algorithms and comparing their performance. The results reveal that Fb-prophet should be preferred for more precise prediction among different ML algorithms.","PeriodicalId":432255,"journal":{"name":"Int. J. Softw. Sci. Comput. Intell.","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134252630","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 fight against SARS-CoV-2, Pfizer BioNTech based on synthetic messenger RNA (mRNA) proved to be quicker and more effective even with a small dose of micrograms per injection. Unfortunately, such a vaccine requires very low temperatures to prevent degradation of mRNA. In this paper, we have developed three new models of recurrent neural network (1- simple LSTM 2-BDLSTM 3-BERT) using n-gram-codon technique for the codification of mRNA. The primary aim is to analyse the mRNA sequence and predict the stability/reactivity rates at various codon positions. The results of the predictions will be presented in the form of recommendations to support laboratories in updating Pfizer's BioNTech vaccine. The obtained results were validated by the Stanford OpenVaccine dataset and the evaluation measures recall, precision, f1-score, accuracy and loss.
{"title":"N-Gram-Codon and Recurrent Neural Network (RNN) to Update Pfizer-BioNTech mRNA Vaccine","authors":"Hadj Ahmed Bouarara","doi":"10.4018/ijssci.305838","DOIUrl":"https://doi.org/10.4018/ijssci.305838","url":null,"abstract":"In the fight against SARS-CoV-2, Pfizer BioNTech based on synthetic messenger RNA (mRNA) proved to be quicker and more effective even with a small dose of micrograms per injection. Unfortunately, such a vaccine requires very low temperatures to prevent degradation of mRNA. In this paper, we have developed three new models of recurrent neural network (1- simple LSTM 2-BDLSTM 3-BERT) using n-gram-codon technique for the codification of mRNA. The primary aim is to analyse the mRNA sequence and predict the stability/reactivity rates at various codon positions. The results of the predictions will be presented in the form of recommendations to support laboratories in updating Pfizer's BioNTech vaccine. The obtained results were validated by the Stanford OpenVaccine dataset and the evaluation measures recall, precision, f1-score, accuracy and loss.","PeriodicalId":432255,"journal":{"name":"Int. J. Softw. Sci. Comput. Intell.","volume":"56 2-3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131491829","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}
Anupama Mishra, B. Joshi, Varsha Arya, A. Gupta, Kwok Tai Chui
The term “distributed denial of service” (DDoS) refers to one of the most common types of attacks. Sending a huge volume of data packets to the server machine is the target of a DDoS attack. This results in the majority of the consumption of network bandwidth and server, which ultimately leads to an issue with denial of service. In this paper, a majority vote-based ensemble of classifiers is utilized in the Sever technique, which results in improved accuracy and reduced computational overhead, when detecting attacks. For the experiment, the authors have used the CICDDOS2019 dataset. According to the findings of the experiment, a high level of accuracy of 99.98% was attained. In this paper, the classifiers use random forest, decision tree, and naïve bayes for majority voting classifiers, and from the results and performance, it can be seen that majority vote classifiers performed better.
{"title":"Detection of Distributed Denial of Service (DDoS) Attacks Using Computational Intelligence and Majority Vote-Based Ensemble Approach","authors":"Anupama Mishra, B. Joshi, Varsha Arya, A. Gupta, Kwok Tai Chui","doi":"10.4018/ijssci.309707","DOIUrl":"https://doi.org/10.4018/ijssci.309707","url":null,"abstract":"The term “distributed denial of service” (DDoS) refers to one of the most common types of attacks. Sending a huge volume of data packets to the server machine is the target of a DDoS attack. This results in the majority of the consumption of network bandwidth and server, which ultimately leads to an issue with denial of service. In this paper, a majority vote-based ensemble of classifiers is utilized in the Sever technique, which results in improved accuracy and reduced computational overhead, when detecting attacks. For the experiment, the authors have used the CICDDOS2019 dataset. According to the findings of the experiment, a high level of accuracy of 99.98% was attained. In this paper, the classifiers use random forest, decision tree, and naïve bayes for majority voting classifiers, and from the results and performance, it can be seen that majority vote classifiers performed better.","PeriodicalId":432255,"journal":{"name":"Int. J. Softw. Sci. Comput. Intell.","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114740785","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}
Deepak Srivastava, Kwok Tai Chui, Varsha Arya, F. G. Peñalvo, Pramod Kumar, Ashutosh Kumar Singh
Proteins are fundamental compounds in biological processes during the analysis of drug target indication for drug repurposing. The identification of relevant features is a necessary step in determining protein structure. A classification technique is used to identify the most important features in a dataset, which is why feature selection is so important. For protein structure prediction, recent research has developed a wide range of new methods to improve accuracy. The authors use principal component analysis (PCA) with correlation-matrix-based feature selection to analyse breast cancer data. In this paper, they discussed a therapeutic agent that is used to reduce the dataset by reduction-based algorithm and after that applied reduced dataset labelled as Standard Gold Dataset on machine learning model to analyze drug target indication. They get the higher accuracy of 92.8%, 93.9%, and 95.3%, each of the three datasets with 200, 500, and 1000 features with SVM with RBF kernel function. Also they found the best result, 97.8%, with the same classifier.
{"title":"Analysis of Protein Structure for Drug Repurposing Using Computational Intelligence and ML Algorithm","authors":"Deepak Srivastava, Kwok Tai Chui, Varsha Arya, F. G. Peñalvo, Pramod Kumar, Ashutosh Kumar Singh","doi":"10.4018/ijssci.312562","DOIUrl":"https://doi.org/10.4018/ijssci.312562","url":null,"abstract":"Proteins are fundamental compounds in biological processes during the analysis of drug target indication for drug repurposing. The identification of relevant features is a necessary step in determining protein structure. A classification technique is used to identify the most important features in a dataset, which is why feature selection is so important. For protein structure prediction, recent research has developed a wide range of new methods to improve accuracy. The authors use principal component analysis (PCA) with correlation-matrix-based feature selection to analyse breast cancer data. In this paper, they discussed a therapeutic agent that is used to reduce the dataset by reduction-based algorithm and after that applied reduced dataset labelled as Standard Gold Dataset on machine learning model to analyze drug target indication. They get the higher accuracy of 92.8%, 93.9%, and 95.3%, each of the three datasets with 200, 500, and 1000 features with SVM with RBF kernel function. Also they found the best result, 97.8%, with the same classifier.","PeriodicalId":432255,"journal":{"name":"Int. J. Softw. Sci. Comput. Intell.","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128198432","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}
Marketing requires an understanding of relationships and current research has progressed much beyond the simple dyadic relationships to look at how social media networks influence the behavior of customers. Social media's power is fascinating as a seemingly inconsequential figure emerges from the ruins and attracts tens of thousands, if not millions, of followers and thus providing an average individual a huge platform to interact with the rest of the world. Academics have used Network theory and formal network analysis approaches to harvest the large pool of social media influencers available on the internet. The goal of this paper is to use various graph theory algorithms to portray the impact of social network analysis on internet marketing, with a primary focus on social media influencers, and to illustrate a variety of network measurements ideas that may be employed in social media management research that takes into account the enormous social media communication network.
{"title":"To Study the Impact of Social Network Analysis on Social Media Marketing Using Graph Theory","authors":"Rupsha Kar","doi":"10.4018/ijssci.304437","DOIUrl":"https://doi.org/10.4018/ijssci.304437","url":null,"abstract":"Marketing requires an understanding of relationships and current research has progressed much beyond the simple dyadic relationships to look at how social media networks influence the behavior of customers. Social media's power is fascinating as a seemingly inconsequential figure emerges from the ruins and attracts tens of thousands, if not millions, of followers and thus providing an average individual a huge platform to interact with the rest of the world. Academics have used Network theory and formal network analysis approaches to harvest the large pool of social media influencers available on the internet. The goal of this paper is to use various graph theory algorithms to portray the impact of social network analysis on internet marketing, with a primary focus on social media influencers, and to illustrate a variety of network measurements ideas that may be employed in social media management research that takes into account the enormous social media communication network.","PeriodicalId":432255,"journal":{"name":"Int. J. Softw. Sci. Comput. Intell.","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133646609","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 article addresses the 3-dimensional mixed intuitionistic fuzzy assignment problems (3D-MIFAPs). In this article, firstly, the author formulates an assignment problem (AP) and assumes the parameters are in uncertainty with hesitation. Secondly, based on the nature of the parameter the author defines various types of solid assignment problem (SAP) in uncertain environment. Thirdly, to solve 3D-MIFAP the PSK method for finding an optimal solution of fully intuitionistic fuzzy assignment problem (FIFAP) is extended by the author. Fourthly, the author presents the proofs of the proposed theorems and corollary. Fifthly, the proposed approach is illustrated with three numerical examples and the optimal objective value of 3D-MIFAP is obtained in the form of intuitionistic fuzzy number and the solution is checked with MATLAB and their coding are also given by the author. Sixthly, the author presents the comparison results and their graphical representation, merits and demerits of the proposed and existing methods and finally the author presents conclusion and future research directions.
{"title":"Computationally Simple and Efficient Method for Solving Real-Life Mixed Intuitionistic Fuzzy 3D Assignment Problems","authors":"P. Senthil Kumar","doi":"10.4018/ijssci.291715","DOIUrl":"https://doi.org/10.4018/ijssci.291715","url":null,"abstract":"This article addresses the 3-dimensional mixed intuitionistic fuzzy assignment problems (3D-MIFAPs). In this article, firstly, the author formulates an assignment problem (AP) and assumes the parameters are in uncertainty with hesitation. Secondly, based on the nature of the parameter the author defines various types of solid assignment problem (SAP) in uncertain environment. Thirdly, to solve 3D-MIFAP the PSK method for finding an optimal solution of fully intuitionistic fuzzy assignment problem (FIFAP) is extended by the author. Fourthly, the author presents the proofs of the proposed theorems and corollary. Fifthly, the proposed approach is illustrated with three numerical examples and the optimal objective value of 3D-MIFAP is obtained in the form of intuitionistic fuzzy number and the solution is checked with MATLAB and their coding are also given by the author. Sixthly, the author presents the comparison results and their graphical representation, merits and demerits of the proposed and existing methods and finally the author presents conclusion and future research directions.","PeriodicalId":432255,"journal":{"name":"Int. J. Softw. Sci. Comput. Intell.","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125366769","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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