{"title":"Analytics Using Big Data in Healthcare Systems","authors":"","doi":"10.46632/daai/2/6/1","DOIUrl":"https://doi.org/10.46632/daai/2/6/1","url":null,"abstract":"","PeriodicalId":226827,"journal":{"name":"Data Analytics and Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122205735","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}
Nowadays, banking industry is generating huge amount of data. Previously, most banks have failed to utilize this data. However, nowadays, banks have starts using this data to reach their main objectives of marketing. By using this data, many secrets can be revealed like money movements, thefts, disasters. This paper aims to find out how big data analytics can be used in banking sector to find out spending patterns of customer, sentiment and feedback analysis etc.
{"title":"Impact of Big Data Analytics on Banking Sector","authors":"Abhinav Kathuria","doi":"10.46632/daai/2/6/7","DOIUrl":"https://doi.org/10.46632/daai/2/6/7","url":null,"abstract":"Nowadays, banking industry is generating huge amount of data. Previously, most banks have failed to utilize this data. However, nowadays, banks have starts using this data to reach their main objectives of marketing. By using this data, many secrets can be revealed like money movements, thefts, disasters. This paper aims to find out how big data analytics can be used in banking sector to find out spending patterns of customer, sentiment and feedback analysis etc.","PeriodicalId":226827,"journal":{"name":"Data Analytics and Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128372319","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}
{"title":"Implementation of artificial intelligence in agriculture for optimisation- Literature review","authors":"","doi":"10.46632/daai/2/6/2","DOIUrl":"https://doi.org/10.46632/daai/2/6/2","url":null,"abstract":"","PeriodicalId":226827,"journal":{"name":"Data Analytics and Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115943879","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}
{"title":"Proceeding of 1st National Conference on Recent Advancements in Civil, Mechanical and Management Studies","authors":"","doi":"10.46632/daai/2/2/9","DOIUrl":"https://doi.org/10.46632/daai/2/2/9","url":null,"abstract":"","PeriodicalId":226827,"journal":{"name":"Data Analytics and Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127427296","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}
Vidhya Prasanth, M. Ramachandran, Kurinjimalar Ramu
It is to define its relationship with the partners during the formation and registration process of S Company Is a legal document prepared and also refers to the minute of the angle corresponding to the MOA 360 minute mark. Each minute represents 1/60 of a degree, just like the minutes of an hour. When shooting, even a small angle can cause you to miss the mark, so it is important to adjust your MOA to a precise angle or fine for a minute. Stands for Memorandum of Association, which refers to articles of association. They help protect and build your business and help establish the company's identity, work ethic and goals. The MOA's first duty is to obtain the patient's personal information before proceeding with the medical journey. Once the MOA collects the patient's information, he will begin transferring the patient to the doctor's office. At this point, the doctor may begin to perform medical procedures. Memorandum of Association (MOA) with its partners defines a company relationship. Is a Memorandum of Association (MOA) To define its relationship with partners Formation of limited liability company And is a legal document prepared during the registration process. The Military Operation Area (MOA) is a Class A aircraft designated to distinguish or differentiate certain hazardous military operations from IFR traffic and to identify VFR traffic carrying these operations. A company is also involved in a business or industrial organization is In order to operate a law firm Created by a group of individuals. They vary between private and public companies. Both have different ownership structures, Terms and conditions include financial statement requirements. The document containing Rules governing the internal management of a company and the regulations are called the article of the association. Select the document type as the consolidation document and select the year the attachment was filed. Pay the fee and request a certified copy. Memorandum of Association (MoA) Memorandum of Association articles there are the following subcategories: This subdivision refers to the name of the company. Company name should not be synonymous with any existing company. Also, if it is a private company, the last word should be the private company.
{"title":"A Study on Mayfly Algorithm and Its Recent Developments","authors":"Vidhya Prasanth, M. Ramachandran, Kurinjimalar Ramu","doi":"10.46632/daai/2/2/6","DOIUrl":"https://doi.org/10.46632/daai/2/2/6","url":null,"abstract":"It is to define its relationship with the partners during the formation and registration process of S Company Is a legal document prepared and also refers to the minute of the angle corresponding to the MOA 360 minute mark. Each minute represents 1/60 of a degree, just like the minutes of an hour. When shooting, even a small angle can cause you to miss the mark, so it is important to adjust your MOA to a precise angle or fine for a minute. Stands for Memorandum of Association, which refers to articles of association. They help protect and build your business and help establish the company's identity, work ethic and goals. The MOA's first duty is to obtain the patient's personal information before proceeding with the medical journey. Once the MOA collects the patient's information, he will begin transferring the patient to the doctor's office. At this point, the doctor may begin to perform medical procedures. Memorandum of Association (MOA) with its partners defines a company relationship. Is a Memorandum of Association (MOA) To define its relationship with partners Formation of limited liability company And is a legal document prepared during the registration process. The Military Operation Area (MOA) is a Class A aircraft designated to distinguish or differentiate certain hazardous military operations from IFR traffic and to identify VFR traffic carrying these operations. A company is also involved in a business or industrial organization is In order to operate a law firm Created by a group of individuals. They vary between private and public companies. Both have different ownership structures, Terms and conditions include financial statement requirements. The document containing Rules governing the internal management of a company and the regulations are called the article of the association. Select the document type as the consolidation document and select the year the attachment was filed. Pay the fee and request a certified copy. Memorandum of Association (MoA) Memorandum of Association articles there are the following subcategories: This subdivision refers to the name of the company. Company name should not be synonymous with any existing company. Also, if it is a private company, the last word should be the private company.","PeriodicalId":226827,"journal":{"name":"Data Analytics and Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127415330","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}
Nelder-Mead in n dimensions facilitates the set of n + 1 test points. It finds a new test point, Makes one of the old test points new, and so the technique progresses into objective behavior process is measured at each test point. The Nelder-Mead Simplex system uses Simplex to find the minimum space. The algorithm operates using a design framework with n + 1 points (called simplex), Where n is for simplex based operation Number of input dimensions. The Nelder-Mead method is one of the most popular non-derivative methods, using only the values of f to search. Only in the simplex formation of n + 1 will the points / moving / contraction of this simplex be in a positive direction. Strictly speaking, Nelder-Mead is not a truly universal optimization algorithm; however, in it works reasonably well for many non-local problems. Direct search is the gradient of the objective process Optimization is a method for solving problems that require no information. All of the points approaching an optimal point Pattern search algorithms that calculate the sequence. The existence of local trust is a key factor in defining the difficulty of the global optimization problem because it is relatively easy to improve locally and relatively difficult to improve locally. Slope Descent is an optimal method Machine learning models and to train neurological networks commonly used. Training data these models allow learning over time, and pricing function is particularly active in gradient descent. The barometer is an optimization algorithm that measures its accuracy at each parameter update and can be repeated by comparing optimal or different solutions. A satisfactory the solution will be found. With the advent of computers, optimization has become of computer aided design activities has become a part of Gradient Decent (GT) is a functional first-order upgrade algorithm Local minimum of the given function and Used to determine the maximum. This method is commonly used to reduce cost / loss performance in machine learning (ML) and deep learning (DL). The problem with finding optimal points in such situations is referred to as derivative-free optimization, and algorithms that do not use derivatives or defined variants are called derivative-free algorithms.
{"title":"Nelder–Mead Simplex Search Method - A Study","authors":"M. Selvam, M. Ramachandran, Vimala Saravanan","doi":"10.46632/daai/2/2/7","DOIUrl":"https://doi.org/10.46632/daai/2/2/7","url":null,"abstract":"Nelder-Mead in n dimensions facilitates the set of n + 1 test points. It finds a new test point, Makes one of the old test points new, and so the technique progresses into objective behavior process is measured at each test point. The Nelder-Mead Simplex system uses Simplex to find the minimum space. The algorithm operates using a design framework with n + 1 points (called simplex), Where n is for simplex based operation Number of input dimensions. The Nelder-Mead method is one of the most popular non-derivative methods, using only the values of f to search. Only in the simplex formation of n + 1 will the points / moving / contraction of this simplex be in a positive direction. Strictly speaking, Nelder-Mead is not a truly universal optimization algorithm; however, in it works reasonably well for many non-local problems. Direct search is the gradient of the objective process Optimization is a method for solving problems that require no information. All of the points approaching an optimal point Pattern search algorithms that calculate the sequence. The existence of local trust is a key factor in defining the difficulty of the global optimization problem because it is relatively easy to improve locally and relatively difficult to improve locally. Slope Descent is an optimal method Machine learning models and to train neurological networks commonly used. Training data these models allow learning over time, and pricing function is particularly active in gradient descent. The barometer is an optimization algorithm that measures its accuracy at each parameter update and can be repeated by comparing optimal or different solutions. A satisfactory the solution will be found. With the advent of computers, optimization has become of computer aided design activities has become a part of Gradient Decent (GT) is a functional first-order upgrade algorithm Local minimum of the given function and Used to determine the maximum. This method is commonly used to reduce cost / loss performance in machine learning (ML) and deep learning (DL). The problem with finding optimal points in such situations is referred to as derivative-free optimization, and algorithms that do not use derivatives or defined variants are called derivative-free algorithms.","PeriodicalId":226827,"journal":{"name":"Data Analytics and Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124798968","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}
A Regular busy server crashes due to negative customer traffic, and holiday interruption is being considered. If the orbit empties at the end of a positive customer service, the server worked Going on vacation. A working vacation (WV) server at a low service rate works. If there are clients on the computer at the end of each holiday, the server the probability that a new visitor is inactive and on vacation is p (single WV) or with probability q (multiple WVs). Substantial variable technique, constant state probability for the system and its orbit we found the generating function. System performance measures, reliability measures and random decay law are discussed. Finally, Some numerical examples and cost optimization analysis provided. Alternative: Single-Server Review G- Sequence, Incredible Review G-Series, Volume Visit Review G-Series. Evaluation Preference: Working vacation, Bernoulli feedback, Random vacations, single vacation. Unreliable retrial G-queue, Batch arrival Retrial G-queue, single server iteration is taken as a G-sequence alternative and working vacations, random vacations, single vacation, and Bernoulli vacation is taken evaluation parameters. In this from analysis Fuzzy ARAS method the best solution determines the solution with the shortest distance and the longest distance from the negative-best solution, but comparison of these distances is not considered significant. As a result it seems unreliable retrial G-queue got the first rank where as is the Batch arrival Retrial G-queue is having the lowest rank.
{"title":"Evaluation of Unreliable Retrial G-queue Using Fuzzy ARAS Method","authors":"S. Suresh, M Ramachandran, Sathiyaraj Chinnasamy","doi":"10.46632/daai/2/2/5","DOIUrl":"https://doi.org/10.46632/daai/2/2/5","url":null,"abstract":"A Regular busy server crashes due to negative customer traffic, and holiday interruption is being considered. If the orbit empties at the end of a positive customer service, the server worked Going on vacation. A working vacation (WV) server at a low service rate works. If there are clients on the computer at the end of each holiday, the server the probability that a new visitor is inactive and on vacation is p (single WV) or with probability q (multiple WVs). Substantial variable technique, constant state probability for the system and its orbit we found the generating function. System performance measures, reliability measures and random decay law are discussed. Finally, Some numerical examples and cost optimization analysis provided. Alternative: Single-Server Review G- Sequence, Incredible Review G-Series, Volume Visit Review G-Series. Evaluation Preference: Working vacation, Bernoulli feedback, Random vacations, single vacation. Unreliable retrial G-queue, Batch arrival Retrial G-queue, single server iteration is taken as a G-sequence alternative and working vacations, random vacations, single vacation, and Bernoulli vacation is taken evaluation parameters. In this from analysis Fuzzy ARAS method the best solution determines the solution with the shortest distance and the longest distance from the negative-best solution, but comparison of these distances is not considered significant. As a result it seems unreliable retrial G-queue got the first rank where as is the Batch arrival Retrial G-queue is having the lowest rank.","PeriodicalId":226827,"journal":{"name":"Data Analytics and Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126781520","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 form of analysis the WASPAS method is the most ideal solution Short-distance and negative-best The solution with the longest distance from the solution Determines, but the comparison of these distances Does not consider importance. From the result it is seen that Reliability is the first rank whereas is the Safety is having the lowest rank. Sustainable transportation systems are the need of modern times. There has been an unexpected growth in the number of transportation activities over years and the trend is expected to continue in the coming years. This has obviously associated environmental costs like air pollution, noise, etc. which is degrading the quality of life in modern cities. To cope with this crisis, municipal administrations are investing in sustainable transportation systems that are not only efficient, robust and economical but also friendly towards the environment. Sustainability has become an overarching concern for transportation policy and planning around the world. This article presents an approach for urban transport sustainability performance evaluation using fuzzy logic. This article presents a model for transport sustainability performance evaluation. Appropriate transport sustainability indicators were identified based on literature. Recently, sustainability has become a very important research area in transportation because of the dependencies between transportation, economic, and environmental systems. Alternative: Safety, Security, Reliability, Air pollutants, Noise. .Evaluation Preference: Cost benefits analysis optimization models, Life cycle analysis, Data analysis. WASPAS-Weighted Aggregated Sum Product Assessment. In this method from WASPAS in Sustainable Transportation Systems. From the result it is seen that Reliability and is the first rank whereas the Safety got is having the lowest rank.
{"title":"Sustainable Transportation Systems Analysis using WASPAS MCDM Method","authors":"C. Venkateswaran, M. Ramachandran, M. Selvam","doi":"10.46632/daai/2/2/8","DOIUrl":"https://doi.org/10.46632/daai/2/2/8","url":null,"abstract":"In this form of analysis the WASPAS method is the most ideal solution Short-distance and negative-best The solution with the longest distance from the solution Determines, but the comparison of these distances Does not consider importance. From the result it is seen that Reliability is the first rank whereas is the Safety is having the lowest rank. Sustainable transportation systems are the need of modern times. There has been an unexpected growth in the number of transportation activities over years and the trend is expected to continue in the coming years. This has obviously associated environmental costs like air pollution, noise, etc. which is degrading the quality of life in modern cities. To cope with this crisis, municipal administrations are investing in sustainable transportation systems that are not only efficient, robust and economical but also friendly towards the environment. Sustainability has become an overarching concern for transportation policy and planning around the world. This article presents an approach for urban transport sustainability performance evaluation using fuzzy logic. This article presents a model for transport sustainability performance evaluation. Appropriate transport sustainability indicators were identified based on literature. Recently, sustainability has become a very important research area in transportation because of the dependencies between transportation, economic, and environmental systems. Alternative: Safety, Security, Reliability, Air pollutants, Noise. .Evaluation Preference: Cost benefits analysis optimization models, Life cycle analysis, Data analysis. WASPAS-Weighted Aggregated Sum Product Assessment. In this method from WASPAS in Sustainable Transportation Systems. From the result it is seen that Reliability and is the first rank whereas the Safety got is having the lowest rank.","PeriodicalId":226827,"journal":{"name":"Data Analytics and Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130169458","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}
Big Data is a collection of technologies developed to store, analyze and manage this data. It is a macro tool. Today it is used in fields as diverse as medicine, agriculture, gambling and environmental protection. Machine learning, forecasting Companies use big data to streamline their marketing campaigns and techniques. Modeling and other advanced analytics applications enable big data organizations to generate valuable insights. Companies use it in machine learning. Programs cannot balance large data in any particular database. When datasets are large in size, velocity, and volume, the following three distinct dimensions constitute "big data." Examples of big data analytics include stock markets, social media platforms, and jet engines. But it is growing exponentially with time. Traditional data management tools cannot store or process it efficiently. It is a large scale technology developed by Story, Analysis and Manoj, Macro-Tool. Find patterns of exploding confusion in information about smart design, however, implying that various big data structures are structured, whether this includes the amount of information, the speed with which it is generated and collected, or the variety or scope of related data points. In the past, data was collected only from spreadsheets and databases of large, disparate information that grew at an ever-increasing rate. Very large, complex data refers to large amounts of data for sets that are impossible to analyze with traditional data processing applications. Is software configuration used to handle the problem? Apache is an advanced application for storing and processing large, complex data sets and analyzing big data. Analytical techniques against very large, heterogeneous databases containing varying amounts of data, big data analytics help businesses gain insights from today's massive data sources. Defined as software tools for processing and extraction. People, companies and more and more machines are now developing technologies to analyze more complex and large databases that cannot be handled by traditional management tools. It is designed to discover patterns of chaos that explode in information in order to design smart solutions.
{"title":"Exploring the recent trends in Big Data Analysis","authors":"S. Suresh, M. Ramachandran, Chinnasami Sivaji","doi":"10.46632/daai/2/2/4","DOIUrl":"https://doi.org/10.46632/daai/2/2/4","url":null,"abstract":"Big Data is a collection of technologies developed to store, analyze and manage this data. It is a macro tool. Today it is used in fields as diverse as medicine, agriculture, gambling and environmental protection. Machine learning, forecasting Companies use big data to streamline their marketing campaigns and techniques. Modeling and other advanced analytics applications enable big data organizations to generate valuable insights. Companies use it in machine learning. Programs cannot balance large data in any particular database. When datasets are large in size, velocity, and volume, the following three distinct dimensions constitute \"big data.\" Examples of big data analytics include stock markets, social media platforms, and jet engines. But it is growing exponentially with time. Traditional data management tools cannot store or process it efficiently. It is a large scale technology developed by Story, Analysis and Manoj, Macro-Tool. Find patterns of exploding confusion in information about smart design, however, implying that various big data structures are structured, whether this includes the amount of information, the speed with which it is generated and collected, or the variety or scope of related data points. In the past, data was collected only from spreadsheets and databases of large, disparate information that grew at an ever-increasing rate. Very large, complex data refers to large amounts of data for sets that are impossible to analyze with traditional data processing applications. Is software configuration used to handle the problem? Apache is an advanced application for storing and processing large, complex data sets and analyzing big data. Analytical techniques against very large, heterogeneous databases containing varying amounts of data, big data analytics help businesses gain insights from today's massive data sources. Defined as software tools for processing and extraction. People, companies and more and more machines are now developing technologies to analyze more complex and large databases that cannot be handled by traditional management tools. It is designed to discover patterns of chaos that explode in information in order to design smart solutions.","PeriodicalId":226827,"journal":{"name":"Data Analytics and Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125626923","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}
{"title":"A Study on Prediction Using Machine Learning","authors":"","doi":"10.46632/daai/2/2/3","DOIUrl":"https://doi.org/10.46632/daai/2/2/3","url":null,"abstract":"","PeriodicalId":226827,"journal":{"name":"Data Analytics and Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126141536","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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