{"title":"Intellectual method for business location selection in smart cities","authors":"Khrystyna Lipianina-Honcharenko","doi":"10.34185/1562-9945-4-147-2023-12","DOIUrl":null,"url":null,"abstract":"The relevance of the topic lies in the complexity of selecting a location for starting a business in smart cities, as it requires analyzing a large amount of data and considering vari-ous factors such as population, competition, infrastructure, and other parameters. The use of an intelligent method based on machine learning enables the collection, processing, and analysis of large volumes of data for accurate location assessment and providing recommen-dations to entrepreneurs. This enhances the decision-making process, ensures more informed choices, and increases the chances of business success in a smart city. The problem statement involves the need to expedite the process of selecting an optimal location for business placement in a smart city. This task is challenging and long-term, re-quiring the analysis of extensive data and consideration of various factors that impact busi-ness success, such as geographical position, competition, potential customer base, and other relevant aspects. It is also crucial to provide entrepreneurs with fast access to information and precise recommendations to make informed decisions regarding their business location. Solving this problem will facilitate efficient resource utilization and ensure business success in a smart city. The purpose of the study is to develop an intelligent method for choosing a location for starting a business in a smart city. This method aims to use large amounts of data collected from various sources to determine the most optimal locations for starting a new business. The method is based on existing machine learning techniques such as image recognition, data preprocessing, classification, and clustering of numerical data. Results and key conclusions. A method has been developed, the implementation of which will allow recommending optimal locations for business in smart cities. This will help to increase customer satisfaction, improve the quality of life and increase the profit of entre-preneurs. The intelligent method is a powerful tool for solving the problems of choosing a lo-cation for starting a business in smart cities.","PeriodicalId":493145,"journal":{"name":"Sistemnì tehnologìï","volume":"128 15","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Sistemnì tehnologìï","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.34185/1562-9945-4-147-2023-12","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The relevance of the topic lies in the complexity of selecting a location for starting a business in smart cities, as it requires analyzing a large amount of data and considering vari-ous factors such as population, competition, infrastructure, and other parameters. The use of an intelligent method based on machine learning enables the collection, processing, and analysis of large volumes of data for accurate location assessment and providing recommen-dations to entrepreneurs. This enhances the decision-making process, ensures more informed choices, and increases the chances of business success in a smart city. The problem statement involves the need to expedite the process of selecting an optimal location for business placement in a smart city. This task is challenging and long-term, re-quiring the analysis of extensive data and consideration of various factors that impact busi-ness success, such as geographical position, competition, potential customer base, and other relevant aspects. It is also crucial to provide entrepreneurs with fast access to information and precise recommendations to make informed decisions regarding their business location. Solving this problem will facilitate efficient resource utilization and ensure business success in a smart city. The purpose of the study is to develop an intelligent method for choosing a location for starting a business in a smart city. This method aims to use large amounts of data collected from various sources to determine the most optimal locations for starting a new business. The method is based on existing machine learning techniques such as image recognition, data preprocessing, classification, and clustering of numerical data. Results and key conclusions. A method has been developed, the implementation of which will allow recommending optimal locations for business in smart cities. This will help to increase customer satisfaction, improve the quality of life and increase the profit of entre-preneurs. The intelligent method is a powerful tool for solving the problems of choosing a lo-cation for starting a business in smart cities.