Husam Salah Sameen Al Rubaye, Abdul Saboor Mohammad, A. Khan, Md Firoz Alam, Mohd Kashif Afzal
The purpose of this study is to identify the relationship of assets and liabilities of newness with the variables of consumer behavior, customer satisfaction and loyalty. The evidence has been drawn from the field survey and thus identifies factors of customer’s evaluation for a new venture. The survey is done with a sample of 260 customers who have visited restaurants which have been at least for one year in the market and not more than three years. The scale used in the study has been adopted from the study of Nagy’s newness scale. The statistical design includes regression to identify the relationship. A positive relationship has been found among the variables suggesting the importance of assets and liabilities in developing customer’s criteria of evaluation and thus affects customer satisfaction and loyalty. Manager’s insight is necessary to understand the factors of customer’s evaluation of any new product or new firm and this study aims to add theoretical as well as practical dimensions to existing theories.
{"title":"Managing newness of SME startups for increasing customer satisfaction and loyalty","authors":"Husam Salah Sameen Al Rubaye, Abdul Saboor Mohammad, A. Khan, Md Firoz Alam, Mohd Kashif Afzal","doi":"10.47974/jios-1311","DOIUrl":"https://doi.org/10.47974/jios-1311","url":null,"abstract":"The purpose of this study is to identify the relationship of assets and liabilities of newness with the variables of consumer behavior, customer satisfaction and loyalty. The evidence has been drawn from the field survey and thus identifies factors of customer’s evaluation for a new venture. The survey is done with a sample of 260 customers who have visited restaurants which have been at least for one year in the market and not more than three years. The scale used in the study has been adopted from the study of Nagy’s newness scale. The statistical design includes regression to identify the relationship. A positive relationship has been found among the variables suggesting the importance of assets and liabilities in developing customer’s criteria of evaluation and thus affects customer satisfaction and loyalty. Manager’s insight is necessary to understand the factors of customer’s evaluation of any new product or new firm and this study aims to add theoretical as well as practical dimensions to existing theories.","PeriodicalId":46518,"journal":{"name":"JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70469859","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}
Reeta Madan, Soni Pathak, R. Muneshwar, K. L. Bondar
In the recent paper, R. A. Muneshwar et al., introduced a graph structure called open subset intersection graph g(t) on a topological space (X, t). In this paper, we study some important results of a graph g(t) of a product topological space (X × Y, t). We also determine relationship between diameter, girth, clique number, chromatic number, domination number etc. of an open subset intersection graph of a topological space (X × Y, t), (X, tX) and (Y, tY). Moreover, we proved that, if (X, tX) and (Y, tY) are discrete topological space then w(g(tX × tY)) = w(g(tX)) * w(g(tY)) – 2 and c(g(tX × tY)) = c(g(tX)) * c(g(tY)) – 2 and domination number of g(tX × tY) is 2. We also determine diameter and girth of intersection Graph of Product Topology on X × Y for different values of m and n.
{"title":"Some results on the open subset intersection graph of a product topological space","authors":"Reeta Madan, Soni Pathak, R. Muneshwar, K. L. Bondar","doi":"10.47974/jios-1229","DOIUrl":"https://doi.org/10.47974/jios-1229","url":null,"abstract":"In the recent paper, R. A. Muneshwar et al., introduced a graph structure called open subset intersection graph g(t) on a topological space (X, t). In this paper, we study some important results of a graph g(t) of a product topological space (X × Y, t). We also determine relationship between diameter, girth, clique number, chromatic number, domination number etc. of an open subset intersection graph of a topological space (X × Y, t), (X, tX) and (Y, tY). Moreover, we proved that, if (X, tX) and (Y, tY) are discrete topological space then w(g(tX × tY)) = w(g(tX)) * w(g(tY)) – 2 and c(g(tX × tY)) = c(g(tX)) * c(g(tY)) – 2 and domination number of g(tX × tY) is 2. We also determine diameter and girth of intersection Graph of Product Topology on X × Y for different values of m and n.","PeriodicalId":46518,"journal":{"name":"JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70469228","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 term phishing refers to an attack that pretends to be the website of a large corporation, typically one dealing with money, such as a bank or other financial institution or an online retailer. Its primary objective is to acquire personally identifiable information from users, such as their social security numbers, credit card information, and passwords. Due to the rise of phishing attacks, various techniques have been developed in order to combat these threats. One of these is deep learning algorithms, which are capable of learning and analyzing massive datasets. Due to their capabilities, these algorithms are very useful in identifying and preventing phishing attacks. Due to the complexity of the phishing websites, many development systems have been created to detect them. Unfortunately, the output that was desired cannot be achieved by these systems, and they have a number of other flaws as well. The purpose of this paper is to propose a hybrid deep learning-based phishing detection system that is easy to put into practice. The quality of the input dataset is improved through the process of preprocessing the dataset. After that, the procedures of clustering and feature selection are carried out in order to improve the accuracy and decrease the amount of time required for the processing. The resulting features are then fed into the CNN_LSTM, which is a classification system that classifies websites that are phishing and legitimate. Proposed Hybrid deep learning models are proposed to combine the features of natural language processing (NLP) and character embedding. They can then reveal high-level connections between characters. In terms of the metric that is being used for the evaluation, the performance of the models that have been proposed is better than that of the other models.
{"title":"Deep learning based phishing website identification system using CNN-LSTM classifier","authors":"Vinod Sapkal, Praveen Gupta, Aboo Bakar Khan","doi":"10.47974/jios-1343","DOIUrl":"https://doi.org/10.47974/jios-1343","url":null,"abstract":"The term phishing refers to an attack that pretends to be the website of a large corporation, typically one dealing with money, such as a bank or other financial institution or an online retailer. Its primary objective is to acquire personally identifiable information from users, such as their social security numbers, credit card information, and passwords. Due to the rise of phishing attacks, various techniques have been developed in order to combat these threats. One of these is deep learning algorithms, which are capable of learning and analyzing massive datasets. Due to their capabilities, these algorithms are very useful in identifying and preventing phishing attacks. Due to the complexity of the phishing websites, many development systems have been created to detect them. Unfortunately, the output that was desired cannot be achieved by these systems, and they have a number of other flaws as well. The purpose of this paper is to propose a hybrid deep learning-based phishing detection system that is easy to put into practice. The quality of the input dataset is improved through the process of preprocessing the dataset. After that, the procedures of clustering and feature selection are carried out in order to improve the accuracy and decrease the amount of time required for the processing. The resulting features are then fed into the CNN_LSTM, which is a classification system that classifies websites that are phishing and legitimate. Proposed Hybrid deep learning models are proposed to combine the features of natural language processing (NLP) and character embedding. They can then reveal high-level connections between characters. In terms of the metric that is being used for the evaluation, the performance of the models that have been proposed is better than that of the other models.","PeriodicalId":46518,"journal":{"name":"JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70470091","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 study is focused on certain personality traits that are significantly associated with investors’ biases. The study attempts to develop a more comprehensive mathematical model that covers a wider range of behavioral aspects related to individual investors The proposed Mathematical model can be used to better understand the major behavioral dimensions that need to be considered for investment decisions in the stock market In this study, a Partial Least Square Structural Equation Modelling (PLS-SEM) is used to quantify the association between major personality traits i.e. Agreeableness (AG), Conscientiousness (CO), Extroversion (EX) Neuroticism (NE), and Openness (OP) and major psychological biases such as Herding (HE), Overconfidence (OC), Representativeness (RP), and Anchoring (AN) in the stock market. The model is based on a survey of 467 individual investors, who provided information on their personality traits and psychological biases. The regression analysis was done to examine the relationship between personality traits, and psychological biases. Further, the explanatory power and predictive relevance of the model are tested using R2, Q2, and RMSE.
{"title":"Mathematical model for analysis of the relationship between personality traits and psychological biases of individual investors","authors":"A. Kumari, Ruchi Goyal, Sunil Kumar","doi":"10.47974/jios-1408","DOIUrl":"https://doi.org/10.47974/jios-1408","url":null,"abstract":"The study is focused on certain personality traits that are significantly associated with investors’ biases. The study attempts to develop a more comprehensive mathematical model that covers a wider range of behavioral aspects related to individual investors The proposed Mathematical model can be used to better understand the major behavioral dimensions that need to be considered for investment decisions in the stock market In this study, a Partial Least Square Structural Equation Modelling (PLS-SEM) is used to quantify the association between major personality traits i.e. Agreeableness (AG), Conscientiousness (CO), Extroversion (EX) Neuroticism (NE), and Openness (OP) and major psychological biases such as Herding (HE), Overconfidence (OC), Representativeness (RP), and Anchoring (AN) in the stock market. The model is based on a survey of 467 individual investors, who provided information on their personality traits and psychological biases. The regression analysis was done to examine the relationship between personality traits, and psychological biases. Further, the explanatory power and predictive relevance of the model are tested using R2, Q2, and RMSE.","PeriodicalId":46518,"journal":{"name":"JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70470314","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}
Sakshi Kalra, Y. Bansal, Yashvardhan Sharma, G. S. Chauhan
Social media encourages information sharing without a physical barrier making it the perfect platform for learning and communication. In the meantime, it acts as a means of quickly disseminating misleading information. Researchers are battling fake news using strategies like detection, verification, mitigation, and analysis because of significant social concerns. It can be hard to tell the difference between true and false information. In the area of knowledge verification, various machine and deep learning-based approaches have been used to identify false data. However, there are some drawbacks of using AI-powered technologies, including data dependency, security concerns when applying AI-powered methods in the real world, and gaining user trust. In order to address the issues with AI-powered technologies, a blockchain-based idea (FakeSpotter) is put forth in this work. We offer an idea i.e.based on blockchain that utilizes crowdsourcing to determine whether or not content is fake. We attempt to use Blockchain technology’s features correctly and completely to create a secure system with no authoritative control over information dissemination. In this attempt, we aim to build a system that is not reliant on pre-defined datasets and discuss the initiatives taken in the fight against disinformation.
{"title":"FakeSpotter: A blockchain-based trustworthy idea for fake news detection in social media","authors":"Sakshi Kalra, Y. Bansal, Yashvardhan Sharma, G. S. Chauhan","doi":"10.47974/jios-1411","DOIUrl":"https://doi.org/10.47974/jios-1411","url":null,"abstract":"Social media encourages information sharing without a physical barrier making it the perfect platform for learning and communication. In the meantime, it acts as a means of quickly disseminating misleading information. Researchers are battling fake news using strategies like detection, verification, mitigation, and analysis because of significant social concerns. It can be hard to tell the difference between true and false information. In the area of knowledge verification, various machine and deep learning-based approaches have been used to identify false data. However, there are some drawbacks of using AI-powered technologies, including data dependency, security concerns when applying AI-powered methods in the real world, and gaining user trust. In order to address the issues with AI-powered technologies, a blockchain-based idea (FakeSpotter) is put forth in this work. We offer an idea i.e.based on blockchain that utilizes crowdsourcing to determine whether or not content is fake. We attempt to use Blockchain technology’s features correctly and completely to create a secure system with no authoritative control over information dissemination. In this attempt, we aim to build a system that is not reliant on pre-defined datasets and discuss the initiatives taken in the fight against disinformation.","PeriodicalId":46518,"journal":{"name":"JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70470471","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, we will discuss an analytical solution and numerical simulation of fractional order mathematical model on COVID-19 under Caputo and conformable sense with the help of fractional differential transform method for different values of q, where q ∈ (0, 1). The underlying mathematical model on COVID-19 consists of four compartments, like, the susceptible class, the healthy class,the infected class and the quarantine class. We show the reliability and simplicity of the methods by comparing the solution of given model obtained by FDTM with the solution obtained by CFDTM graphically and numerically. Further, we analyse the stability of model using Lyapunov direct method under Caputo sense. We conclude that the use of fractional epidemic model provides better understanding and biologically deeper insights about the disease dynamics.
{"title":"Analytical solutions and numerical simulation of COVID-19 fractional order mathematical model by Caputo and conformable fractional differential transform method","authors":"A. D. Nagargoje, V. C. Borkar, R. Muneshwar","doi":"10.47974/jios-1219","DOIUrl":"https://doi.org/10.47974/jios-1219","url":null,"abstract":"In this paper, we will discuss an analytical solution and numerical simulation of fractional order mathematical model on COVID-19 under Caputo and conformable sense with the help of fractional differential transform method for different values of q, where q ∈ (0, 1). The underlying mathematical model on COVID-19 consists of four compartments, like, the susceptible class, the healthy class,the infected class and the quarantine class. We show the reliability and simplicity of the methods by comparing the solution of given model obtained by FDTM with the solution obtained by CFDTM graphically and numerically. Further, we analyse the stability of model using Lyapunov direct method under Caputo sense. We conclude that the use of fractional epidemic model provides better understanding and biologically deeper insights about the disease dynamics.","PeriodicalId":46518,"journal":{"name":"JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70469399","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}
Recommendation systems are a very popular service whose accuracy and sophistication keeps increasing every day. Yet current systems pose a limitation on personalized user recommendation, which we wish to improve. We are developing Content-Based, Collaborative Filtering and Knowledge-Based models and we wish to find the most appropriate approach to build restaurant recommendation systems. We followed steps that involved a pipeline to process reviews of restaurants obtained from a widely used online network of zomato users (India’s largest restaurant service) and calculate ratings of restaurants from reviews. Using a machine learning technique, it continuously analyses user restaurant visit patterns.
{"title":"A recommendation system for online social semantic network using knowledge based, content based and collaborative filtering","authors":"Monika Chhikara, S. K. Malik","doi":"10.47974/jios-1356","DOIUrl":"https://doi.org/10.47974/jios-1356","url":null,"abstract":"Recommendation systems are a very popular service whose accuracy and sophistication keeps increasing every day. Yet current systems pose a limitation on personalized user recommendation, which we wish to improve. We are developing Content-Based, Collaborative Filtering and Knowledge-Based models and we wish to find the most appropriate approach to build restaurant recommendation systems. We followed steps that involved a pipeline to process reviews of restaurants obtained from a widely used online network of zomato users (India’s largest restaurant service) and calculate ratings of restaurants from reviews. Using a machine learning technique, it continuously analyses user restaurant visit patterns.","PeriodicalId":46518,"journal":{"name":"JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70470359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The purpose of this research study is to explore the behavioral dimensions specifically, representativeness, availability, anchoring, overconfidence, loss aversion, regret aversion, and herding biases of retail investors towards investment decision-making and their performance in the stock market. To achieve the research objectives, a survey questionnaire method was adopted to collect data from 467 retail investors using the convenience sampling technique. The study has used Structural Equation Modeling (SEM) approach to analyze the relationship between the predictor variables and investors’ behavior. The findings of the study suggest that Overconfidence Bias is the most significant predictor variable for individual investors’ behavior, followed by loss aversion, and then anchoring bias. In contrast, availability bias contributes the least to investors’ behavior. This study will be helpful in identifying and unraveling the irrational factors that affect investors’ decision-making processes, leading to better investment decisions and improved performance in the stock market.
{"title":"Analytical study of behavioral dimensions of retail investors towards investment decision making & performance in stock market","authors":"A. Kumari, Ruchi Goyal, S. Sushanth Kumar","doi":"10.47974/jios-1358","DOIUrl":"https://doi.org/10.47974/jios-1358","url":null,"abstract":"The purpose of this research study is to explore the behavioral dimensions specifically, representativeness, availability, anchoring, overconfidence, loss aversion, regret aversion, and herding biases of retail investors towards investment decision-making and their performance in the stock market. To achieve the research objectives, a survey questionnaire method was adopted to collect data from 467 retail investors using the convenience sampling technique. The study has used Structural Equation Modeling (SEM) approach to analyze the relationship between the predictor variables and investors’ behavior. The findings of the study suggest that Overconfidence Bias is the most significant predictor variable for individual investors’ behavior, followed by loss aversion, and then anchoring bias. In contrast, availability bias contributes the least to investors’ behavior. This study will be helpful in identifying and unraveling the irrational factors that affect investors’ decision-making processes, leading to better investment decisions and improved performance in the stock market.","PeriodicalId":46518,"journal":{"name":"JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70470381","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}
P. Ajitha, T. Tamilvizhi, K. Sowjanya, R. Surendran, B. Bala
Time-series forecasting is an approach that uses historical and current data to project future values over time or at a given point in time, while forecasting and prediction are often synonymous, there is one interesting detail. In some professions, forecasting may refer to data at a specific future point in time, whereas prediction refers to future data in general. Most widely used to determine the nature of stock prices. A series of analyses and modeling by a finance committee is to guide investors, professors of legal sciences, and processes. And that is why he proposes that this series argument not include a sliding window; they were wise to back then, and they gave up everything, anticipating stock values relative to her. The system presents the (GUI) Graphical User Interface as a stand-alone application. The proposed findings demonstrate a highly predicted accurate approach for nonlinear time series models that are difficult to obtain from traditional models.
{"title":"Consumer product prediction using machine learning","authors":"P. Ajitha, T. Tamilvizhi, K. Sowjanya, R. Surendran, B. Bala","doi":"10.47974/jios-1415","DOIUrl":"https://doi.org/10.47974/jios-1415","url":null,"abstract":"Time-series forecasting is an approach that uses historical and current data to project future values over time or at a given point in time, while forecasting and prediction are often synonymous, there is one interesting detail. In some professions, forecasting may refer to data at a specific future point in time, whereas prediction refers to future data in general. Most widely used to determine the nature of stock prices. A series of analyses and modeling by a finance committee is to guide investors, professors of legal sciences, and processes. And that is why he proposes that this series argument not include a sliding window; they were wise to back then, and they gave up everything, anticipating stock values relative to her. The system presents the (GUI) Graphical User Interface as a stand-alone application. The proposed findings demonstrate a highly predicted accurate approach for nonlinear time series models that are difficult to obtain from traditional models.","PeriodicalId":46518,"journal":{"name":"JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70470658","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}
For those who can uncover the knowledge hidden inside this data, this offers enormous opportunity, but it also creates new difficulties. In this study, we explore how the contemporary discipline of data mining might be used to glean usable information from the data that surrounds us. k Machine learning techniques include genetic algorithms, Bayesian approaches, and nearest neighbor. By combining these approaches and algorithms, a hybrid method is created in this study. The goal is to successfully categorize data by removing any information that makes it harder to learn. According to solid facts at hand, a novel data set formation strategy is suggested. Five datasets for machine learning from UCI are used in the testing procedure. These data sets pertain to the iris, breast cancer, glass, yeast, and wine. The success of the research is taken into consideration when test findings are analyzed in conjunction with prior efforts.
{"title":"Optimized data analysis through hybrid approach over trusted security environment","authors":"Satyajeet Sharma, Bhavna Sharma","doi":"10.47974/jios-1344","DOIUrl":"https://doi.org/10.47974/jios-1344","url":null,"abstract":"For those who can uncover the knowledge hidden inside this data, this offers enormous opportunity, but it also creates new difficulties. In this study, we explore how the contemporary discipline of data mining might be used to glean usable information from the data that surrounds us. k Machine learning techniques include genetic algorithms, Bayesian approaches, and nearest neighbor. By combining these approaches and algorithms, a hybrid method is created in this study. The goal is to successfully categorize data by removing any information that makes it harder to learn. According to solid facts at hand, a novel data set formation strategy is suggested. Five datasets for machine learning from UCI are used in the testing procedure. These data sets pertain to the iris, breast cancer, glass, yeast, and wine. The success of the research is taken into consideration when test findings are analyzed in conjunction with prior efforts.","PeriodicalId":46518,"journal":{"name":"JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70469719","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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