Epidemiological data is the data obtained based on disease, injury or environmental hazard occurrence using the previous data on the epidemic situation. We can use it for analysis and find the trends and patterns. We can use different machine learning models to create a platform that can be used for different time series data. We can rely on the properties of time series data like trends and seasonality and use this for future prediction. Acquiring the dataset is the first step in data preprocessing in machine learning. We have collected the dataset from ourWorldIndia website which is a real-life dataset of covid-19. This paper presents the idea of a dedicated machine learning model to forecast the future using epidemiological data. We have taken a data-set of covid-19 for the prediction of the number of daily cases infected by the coronavirus. Our machine learning model can be applied on the dataset of any country in the world. We have applied it on the dataset of India in the experimentation. Our goal behind this research paper is to give the ML model which can be easily used on any epidemiological data for prediction by analysing the seasonality.
{"title":"Analytics of Epidemiological Data using Machine Learning Models","authors":"Harshita Barapatre, Jatin Jangir, Sudhanshu Bajpai, Bhavesh Chawla, Gunjan Keswani","doi":"10.47164/ijngc.v14i1.1014","DOIUrl":"https://doi.org/10.47164/ijngc.v14i1.1014","url":null,"abstract":"Epidemiological data is the data obtained based on disease, injury or environmental hazard occurrence using the previous data on the epidemic situation. We can use it for analysis and find the trends and patterns. We can use different machine learning models to create a platform that can be used for different time series data. We can rely on the properties of time series data like trends and seasonality and use this for future prediction. Acquiring the dataset is the first step in data preprocessing in machine learning. We have collected the dataset from ourWorldIndia website which is a real-life dataset of covid-19. This paper presents the idea of a dedicated machine learning model to forecast the future using epidemiological data. We have taken a data-set of covid-19 for the prediction of the number of daily cases infected by the coronavirus. Our machine learning model can be applied on the dataset of any country in the world. We have applied it on the dataset of India in the experimentation. Our goal behind this research paper is to give the ML model which can be easily used on any epidemiological data for prediction by analysing the seasonality.","PeriodicalId":42021,"journal":{"name":"International Journal of Next-Generation Computing","volume":"57 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2023-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83367070","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 study presents a non-orthogonal multiple access (NOMA) solution for 5G that unifies communication betweenmacro-cells via the S-R NOMA link and communication between small cells via the R-D NOMA link. S-R NOMA isused to decode own signal by respective relay. Separate studies of outage performance in S-R and R-D connectionsmay formerly be used to get an accurate definition of system outage likelihood. Our mathematical analysis issupported by simulation findings, which indicate that NOMA-assisted relaying systems outperform OrthogonalMultiple Access systems in terms of lower outage probability and better cumulative capacities (OMA).
{"title":"Outage Probability and Capacity Analysis for NOMA based 5G and B5G Cellular Communication","authors":"Aditi Agrawal, Ishant Kohad, Mrunmayi Kinhikar, Dolly Tiwari, Prasheel Thakre, Sanjay Pokle","doi":"10.47164/ijngc.v13i5.912","DOIUrl":"https://doi.org/10.47164/ijngc.v13i5.912","url":null,"abstract":"This study presents a non-orthogonal multiple access (NOMA) solution for 5G that unifies communication betweenmacro-cells via the S-R NOMA link and communication between small cells via the R-D NOMA link. S-R NOMA isused to decode own signal by respective relay. Separate studies of outage performance in S-R and R-D connectionsmay formerly be used to get an accurate definition of system outage likelihood. Our mathematical analysis issupported by simulation findings, which indicate that NOMA-assisted relaying systems outperform OrthogonalMultiple Access systems in terms of lower outage probability and better cumulative capacities (OMA).","PeriodicalId":42021,"journal":{"name":"International Journal of Next-Generation Computing","volume":"1 6","pages":""},"PeriodicalIF":0.3,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72427282","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 : 2022-11-26DOI: 10.47164/ijngc.v13i5.968
Suruchi Dive, Gopal Sakarkar
Glucose metabolism disorder known as Diabetes Mellitus is a state created by uncontrolled blood sugar that may lead to serious damage to multiple organs in patients. Identifying and predicting this disease will save human life. While designing medical diagnosis software, disease prediction is said to be one of the capricious tasks. In the current scenario, many researchers have provided their ideas on using machine learning and artificial intelligence for automated prediction of Diabetes Mellitus. A set of five popular Naïve Bayes, Random Forest, SVM, KNN and Decision Tree have been identified as well as a set of four rarely used GPC, QDA, LDA and AdaBoost have been identified from literature survey. The study is an effort to make a comparative report of the accuracy of two sets and identify the best performer. In conclusion, Support Vector Machine achieved highest accuracy with 81.00% in popular classifiers whereas Linear Discriminant Analysis achieved highest accuracy with 82.00% in less frequently used classifiers. Hence, more such rarely used classifiers should be explored for the realistic health management of diabetes.
{"title":"Machine Learning For Non- Invasive Diagnostics Of Glucose Metabolism Disorder","authors":"Suruchi Dive, Gopal Sakarkar","doi":"10.47164/ijngc.v13i5.968","DOIUrl":"https://doi.org/10.47164/ijngc.v13i5.968","url":null,"abstract":"Glucose metabolism disorder known as Diabetes Mellitus is a state created by uncontrolled blood sugar that may lead to serious damage to multiple organs in patients. Identifying and predicting this disease will save human life. While designing medical diagnosis software, disease prediction is said to be one of the capricious tasks. In the current scenario, many researchers have provided their ideas on using machine learning and artificial intelligence for automated prediction of Diabetes Mellitus. A set of five popular Naïve Bayes, Random Forest, SVM, KNN and Decision Tree have been identified as well as a set of four rarely used GPC, QDA, LDA and AdaBoost have been identified from literature survey. The study is an effort to make a comparative report of the accuracy of two sets and identify the best performer. In conclusion, Support Vector Machine achieved highest accuracy with 81.00% in popular classifiers whereas Linear Discriminant Analysis achieved highest accuracy with 82.00% in less frequently used classifiers. Hence, more such rarely used classifiers should be explored for the realistic health management of diabetes.\u0000 ","PeriodicalId":42021,"journal":{"name":"International Journal of Next-Generation Computing","volume":"52 8","pages":""},"PeriodicalIF":0.3,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72629249","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 : 2022-11-26DOI: 10.47164/ijngc.v13i5.909
Prasheel Thakre, Sanjay Pokle
As a result of continued expansion of 5G technology, the density of IoT devices has increased dramatically.Increasing the throughput of 5G systems is now extremely important. Non-orthogonal multiple access technologiesand Ultra-dense networks have lately attracted a lot of attention in the context of Internet of Things networksbecause to their capacity to multiplex from the space domain and power domain. In order to boost systemthroughput, this article integrates non-orthogonal multiple access technology with ultra-dense network technology,taking into consideration orthogonal frequency division multiplexing non-orthogonal multiple access-based ultradensenetworks with several base stations. The network model and the channel model were created first. As aresult, under the condition of total power, the downlink transmission rate maximization problem is formulated.Then, the problem is divided into two subproblems to solve: device grouping and sub-band power distributionand built the best power allocation strategies by using convex optimization theory to these subproblems. Finally,numerical simulations are undertaken to validate the efficiency of proposed optimal downlink power distributionapproach and the total throughput of the system has substantially enhanced as compared to orthogonal Multipleaccess.
{"title":"Optimal power allocation for NOMA-based Internet of things over OFDM sub bands","authors":"Prasheel Thakre, Sanjay Pokle","doi":"10.47164/ijngc.v13i5.909","DOIUrl":"https://doi.org/10.47164/ijngc.v13i5.909","url":null,"abstract":"As a result of continued expansion of 5G technology, the density of IoT devices has increased dramatically.Increasing the throughput of 5G systems is now extremely important. Non-orthogonal multiple access technologiesand Ultra-dense networks have lately attracted a lot of attention in the context of Internet of Things networksbecause to their capacity to multiplex from the space domain and power domain. In order to boost systemthroughput, this article integrates non-orthogonal multiple access technology with ultra-dense network technology,taking into consideration orthogonal frequency division multiplexing non-orthogonal multiple access-based ultradensenetworks with several base stations. The network model and the channel model were created first. As aresult, under the condition of total power, the downlink transmission rate maximization problem is formulated.Then, the problem is divided into two subproblems to solve: device grouping and sub-band power distributionand built the best power allocation strategies by using convex optimization theory to these subproblems. Finally,numerical simulations are undertaken to validate the efficiency of proposed optimal downlink power distributionapproach and the total throughput of the system has substantially enhanced as compared to orthogonal Multipleaccess.","PeriodicalId":42021,"journal":{"name":"International Journal of Next-Generation Computing","volume":"17 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80177286","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}
Agriculture plays a very important role in strengthening the economy of a country. Disease in plants is the majorcause of production and economy loss which also reduced the quality and quantity of agriculture products. Farmersface a lot of difficulty in detecting the diseases with naked eye which is the traditional and most used way. It isan important and tedious task to detect disease on crops. It requires a lot of skilled labour and huge amount oftime. This paper compares the benefits and limitations of existing techniques for disease detections. Finally, itwill talk about a method for disease detection in plants using convolutional neural network (CNN).
{"title":"Plant leaves disease detection using Image Processing and Machine learning techniques","authors":"Pratibha Kokardekar, Aman Shah, Arjun Thakur, Prachi Shahu, Rohan Raggad, Sudhanshu Keshaowar, Vineet Pashine","doi":"10.47164/ijngc.v13i5.926","DOIUrl":"https://doi.org/10.47164/ijngc.v13i5.926","url":null,"abstract":"Agriculture plays a very important role in strengthening the economy of a country. Disease in plants is the majorcause of production and economy loss which also reduced the quality and quantity of agriculture products. Farmersface a lot of difficulty in detecting the diseases with naked eye which is the traditional and most used way. It isan important and tedious task to detect disease on crops. It requires a lot of skilled labour and huge amount oftime. This paper compares the benefits and limitations of existing techniques for disease detections. Finally, itwill talk about a method for disease detection in plants using convolutional neural network (CNN).","PeriodicalId":42021,"journal":{"name":"International Journal of Next-Generation Computing","volume":"37 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81415012","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}
Machine learning (ML] helps with the future prediction of action and take decision. A variety of prediction techniques are used for the future prediction of risks and effectively dealing it. This work shows how ML models can predict death rates of COVID-19 patients so that we can do effective treatment and try to minimize the effect of the causes. Coronavirus 2019, COVID-19 is a member of the Coronaviridae genus. A virus without a cure causes unpredictable devastation to people's lives as well as the financial and economic systems of every nation on earth. We have taken certain features from the COVID-19 dataset to study and comprehend the future circumstance using machine learning algorithms, various prediction models are created, and their performances are calculated and assessed. We have compared machine learning algorithms viz. Random Forest and Linear Regression, Decision Tree to predict a number of cases.
{"title":"A Covid Outbreak Prediction using Machine Learning","authors":"Sakshi Saklani, Ashish Chandak, Purshottam J Assudani, Amrusha Rahangdale, Achal Loya","doi":"10.47164/ijngc.v13i5.925","DOIUrl":"https://doi.org/10.47164/ijngc.v13i5.925","url":null,"abstract":"Machine learning (ML] helps with the future prediction of action and take decision. A variety of prediction techniques are used for the future prediction of risks and effectively dealing it. This work shows how ML models can predict death rates of COVID-19 patients so that we can do effective treatment and try to minimize the effect of the causes. Coronavirus 2019, COVID-19 is a member of the Coronaviridae genus. A virus without a cure causes unpredictable devastation to people's lives as well as the financial and economic systems of every nation on earth. We have taken certain features from the COVID-19 dataset to study and comprehend the future circumstance using machine learning algorithms, various prediction models are created, and their performances are calculated and assessed. We have compared machine learning algorithms viz. Random Forest and Linear Regression, Decision Tree to predict a number of cases.","PeriodicalId":42021,"journal":{"name":"International Journal of Next-Generation Computing","volume":"22 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76442083","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 : 2022-11-26DOI: 10.47164/ijngc.v13i5.958
Sushilkumar Chavhan, Dr. R. C. Dharmik
Machine Learning based ranking is done every filed. Ranking is also solved by using (LTR i. e. learning to Rank)techniques. In this work, we propose a Heuristics LTR based models for information retrieval. Different newalgorithms are tackling the problem feature selection in ranking. In this proposed model try to makes use of thesimulated annealing and Principal Component analysis for document retrieval using learning to rank. A use ofsimulated annealing heuristics method used for the feature Selection to test the results improvement. The featureextraction technique helps to find the minimal subsets of features for better results. The core idea of the proposedframework is to make use of k-fold cross validation of training queries in the SA as well as the training queriesin the any feature selection method to extract features and only using training quires make use of validationand test quires to create a learning model with LTR. The standard evaluation measures are used to verify thesignificant improvement in the proposed model. Performance of proposed model are measured based on predictionon some selected benchmark datasets, Improvement in the results are compare on recent high performed pairwisealgorithms.
{"title":"Feature Selection for Ranking using Heuristics based Learning to Rank using Machine Learning","authors":"Sushilkumar Chavhan, Dr. R. C. Dharmik","doi":"10.47164/ijngc.v13i5.958","DOIUrl":"https://doi.org/10.47164/ijngc.v13i5.958","url":null,"abstract":"Machine Learning based ranking is done every filed. Ranking is also solved by using (LTR i. e. learning to Rank)techniques. In this work, we propose a Heuristics LTR based models for information retrieval. Different newalgorithms are tackling the problem feature selection in ranking. In this proposed model try to makes use of thesimulated annealing and Principal Component analysis for document retrieval using learning to rank. A use ofsimulated annealing heuristics method used for the feature Selection to test the results improvement. The featureextraction technique helps to find the minimal subsets of features for better results. The core idea of the proposedframework is to make use of k-fold cross validation of training queries in the SA as well as the training queriesin the any feature selection method to extract features and only using training quires make use of validationand test quires to create a learning model with LTR. The standard evaluation measures are used to verify thesignificant improvement in the proposed model. Performance of proposed model are measured based on predictionon some selected benchmark datasets, Improvement in the results are compare on recent high performed pairwisealgorithms.","PeriodicalId":42021,"journal":{"name":"International Journal of Next-Generation Computing","volume":"22 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83954806","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 : 2022-11-26DOI: 10.47164/ijngc.v13i5.949
Sachin Upadhye, Lalit Agrawal
As Agriculture is the pivotal point of survival, rainfall is the important source of its cultivation. Rainfall prophecy has always been a major problem as a prophecy of downfall gives awareness to people and to know in advance about rain to take necessary precautions to cover their crops from rain. A particular dataset is taken from the Kaggle community and this design predicts whether it will rain henceforth or not by using the rainfall in the dataset. Cat Boost model is executed in this design as it’s an open-sourced machine knowledge algorithm, and features great quality without parameter tuning, categorical point support, bettered delicacy, and fast prophecy. Cat Boost model is a Grade boosting toolkit and two critical algorithms classical and innovative are introduced to produce a fight in prophecy shift present in presently being prosecutions of grade boosting algorithms. Cat Boostperformed truly well giving an AUC (Area under wind) score0.8 and a ROC (Receiver operating characteristic wind) score of 89. ROC is called an assessing wind whereas AUC presents a degree or measure of separability as the model is professed enough to distinguish between classes. An Exploratory data analysis is done to examine data distribution, and outliers and provides tools for imaging and understanding the data through graphical representation.
{"title":"A Model for Rainfall Forecasting using Distinct Machine Learning Algorithm","authors":"Sachin Upadhye, Lalit Agrawal","doi":"10.47164/ijngc.v13i5.949","DOIUrl":"https://doi.org/10.47164/ijngc.v13i5.949","url":null,"abstract":"As Agriculture is the pivotal point of survival, rainfall is the important source of its cultivation. Rainfall prophecy has always been a major problem as a prophecy of downfall gives awareness to people and to know in advance about rain to take necessary precautions to cover their crops from rain. A particular dataset is taken from the Kaggle community and this design predicts whether it will rain henceforth or not by using the rainfall in the dataset. Cat Boost model is executed in this design as it’s an open-sourced machine knowledge algorithm, and features great quality without parameter tuning, categorical point support, bettered delicacy, and fast prophecy. Cat Boost model is a Grade boosting toolkit and two critical algorithms classical and innovative are introduced to produce a fight in prophecy shift present in presently being prosecutions of grade boosting algorithms. Cat Boostperformed truly well giving an AUC (Area under wind) score0.8 and a ROC (Receiver operating characteristic wind) score of 89. ROC is called an assessing wind whereas AUC presents a degree or measure of separability as the model is professed enough to distinguish between classes. An Exploratory data analysis is done to examine data distribution, and outliers and provides tools for imaging and understanding the data through graphical representation.","PeriodicalId":42021,"journal":{"name":"International Journal of Next-Generation Computing","volume":"15 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82428858","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 : 2022-11-26DOI: 10.47164/ijngc.v13i5.939
Monal R.Torney, Dr.K.H.Walse, Dr.V.M.Thakare
The rapid expansion of internet usage and related services like social media and blogs has increased people's level of expressiveness in day-to-day life. Social media platforms like Twitter and Facebook facilitate people to interact and exchange opinions about people, products, and services. As a result, a vast amount of data is available online in the form of views, tweets, messages, audio, and videos. An interface is needed to collect knowledge and insights from the various tweets, ideas, and comments. Thus we have proposed the Twitter API-based Interface, able to perform Hashtag searches and extract tweets from Twitter along with the ample number of fields related to the Twitter object. Using the interface, the 55 properties of each tweet are collected and used for further investigations. The python-based library called Tweepy is used to interact with the Twitter API. Due to the availability of real-worlddata, various issues related to text analysis can be addressed. The problems such as Sentiment Analysis, Opinion Mining, Implicit and Explicit detection, genuineness of views, and Opinion Spam detection can be addressed using the dataset availability.
{"title":"An Effective Framework for design of Dataset Using Twitter","authors":"Monal R.Torney, Dr.K.H.Walse, Dr.V.M.Thakare","doi":"10.47164/ijngc.v13i5.939","DOIUrl":"https://doi.org/10.47164/ijngc.v13i5.939","url":null,"abstract":"The rapid expansion of internet usage and related services like social media and blogs has increased people's level of expressiveness in day-to-day life. Social media platforms like Twitter and Facebook facilitate people to interact and exchange opinions about people, products, and services. As a result, a vast amount of data is available online in the form of views, tweets, messages, audio, and videos. An interface is needed to collect knowledge and insights from the various tweets, ideas, and comments. Thus we have proposed the Twitter API-based Interface, able to perform Hashtag searches and extract tweets from Twitter along with the ample number of fields related to the Twitter object. Using the interface, the 55 properties of each tweet are collected and used for further investigations. The python-based library called Tweepy is used to interact with the Twitter API. Due to the availability of real-worlddata, various issues related to text analysis can be addressed. The problems such as Sentiment Analysis, Opinion Mining, Implicit and Explicit detection, genuineness of views, and Opinion Spam detection can be addressed using the dataset availability.","PeriodicalId":42021,"journal":{"name":"International Journal of Next-Generation Computing","volume":"45 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78627531","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 : 2022-11-26DOI: 10.47164/ijngc.v13i5.938
Mangesh Joshi, R. Mankar, Himanshu M. Shukla
One of the recognized techniques for making decisions in ambiguous environment is the fuzzy Decision Making Trialand Evaluation Laboratory [DEMATEL] method. The fuzzy set [FS] and intuitionistic fuzzy set [IFS] conceptsare generalized in the Pythagorean fuzzy set [PFS]. This study focuses on the software quality evaluation problemin software management using the DEMATEL approach with PFS. It is suitable for addressing ambiguous humanjudgments and unclear and inadequate information when choosing the criteria for a software quality review. Themethod discovers cause-and-effect system components while taking into account the independence of the criteriaand provides mutual links among the criteria. Based on information gathered from a group of professionals, theimplemented method is illustrated. Originality: Software quality evaluation is handled first time with Pythagoreanfuzzy set-based DEMATEL approach.
{"title":"Identification and Evaluation of Factors Influencing Software Quality using Pythagorean Fuzzy DEMATEL Approach","authors":"Mangesh Joshi, R. Mankar, Himanshu M. Shukla","doi":"10.47164/ijngc.v13i5.938","DOIUrl":"https://doi.org/10.47164/ijngc.v13i5.938","url":null,"abstract":"One of the recognized techniques for making decisions in ambiguous environment is the fuzzy Decision Making Trialand Evaluation Laboratory [DEMATEL] method. The fuzzy set [FS] and intuitionistic fuzzy set [IFS] conceptsare generalized in the Pythagorean fuzzy set [PFS]. This study focuses on the software quality evaluation problemin software management using the DEMATEL approach with PFS. It is suitable for addressing ambiguous humanjudgments and unclear and inadequate information when choosing the criteria for a software quality review. Themethod discovers cause-and-effect system components while taking into account the independence of the criteriaand provides mutual links among the criteria. Based on information gathered from a group of professionals, theimplemented method is illustrated. Originality: Software quality evaluation is handled first time with Pythagoreanfuzzy set-based DEMATEL approach.","PeriodicalId":42021,"journal":{"name":"International Journal of Next-Generation Computing","volume":"111 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81252930","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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