Cloud document search

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There are many different sources of searches
Insight engine is based on the data available in companies. It includes a broader spectrum
of technologies that increase the quality of the search. All the searches should be in the
simple language which results in relevant search. Data should be connected with
connectors. It should also add some extra information to the search which is not asked. It
should also contain videos, images, voice messages of the search.
Cloud document search is one of the largest
document searches which store private
documents and it is still increasing. It is most
important for them who have tool for cloud
document search. It is a source which shares
all the knowledge using tool to the single
technology of document. It captures all the
information’s related to a single topic. Many
users search via cloud document search.
Information retrieval is based on full text or other contents. It is a science of search for
information in a document and also searches for metadata which describes the data. It
obtains information system resources which are relevant to the information search. It was
popularised with the article As We May Thing written by Vannevar Bush in 1945.

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Er. Saurabh Singh

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Search engine is the popular term for an information retrieval (IR) system. Typically, search engine can be based on full-text indexing. Changing the presentation from the text data to multimedia data types make an information retrieval process more complex such as a retrieval of image or sounds in large databases. This paper introduces the use of language and text independent speech as input queries in a large sound database by using Speaker identification algorithm. The method consists of 2 main processing first steps, we separate vocal and non-vocal identification after that vocal be used to speaker identification for audio query by speaker voice. For the speaker identification and audio query by process, we estimate the similarity of the example signal and the samples in the queried database by calculating the Euclidian distance between the Mel frequency cepstral coefficients (MFCC) and Energy spectrum of acoustic features. The simulations show that the good performance with a sustainable computational cost and obtained the average accuracy rate more than 90%.

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4 posts Re: Topic 2 DQ 1 Qualitative research produces a variety of data, from a variety of sources. Data sources may be personal interviews (written or recorded), surveys, questionnaires, official documents or observation notes. To complicate matters, more often than not, there are numerous respondents or participants and multiple researchers. To extricate and code data from multiple data sources can be difficult, but made much easier if the data is organized appropriately. (Katherine B.2017) The vast majority of qualitative data is "Unstructured Data," which includes documents, photographs, audio, and video. The simplest things we can do to improve the usability of unstructured data for analysis are: Convert it to a structured schema that can be evaluated with quantitative methods. Make it simple to find. On the first point, we can feed documents to full-text search engines such as Lucene, which make data retrieval simple. We can also design full text search engines to execute faceted searches, allowing us to attach Metadata facets (e.g., Author, Media Type, Creation Date, etc.) to enhance our quantitative research. The same search engine was used. (Bensal P and others…. 2010) On the second point, there are a variety of methods for converting qualitative Unstructured Data into Structured Data (which may be quantitatively examined). But it all relies on what you want to do with the Structured Data and how you get it. You can, for example, create n-grams (continuous sequences of words) and then analyze those n-grams to identify what the most common terms are within a subset of texts. You might wish to have someone manually transcribe all consumer references of a product when evaluating footage. There are already Machine Learning algorithms that can transcribe and recognize speech. Machine Learning and Deep Learning programs that can extract usable and reliable quantitative data from qualitative data will be extremely important in the future of analytics. However, manual methods such as employing Amazon Mechanical Turk, or a combination of both, are equally viable options for extracting Quantitative Structured Data from Qualitative Unstructured Data. Using 200-300 APA FORMAT with references to support this discussion, Qualitative data has been described as voluminous and sometimes overwhelming to the researcher. Discuss two strategies that would help a researcher manage and organize the data. .

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บริการต่างๆบนอินเทอร์เน็ต

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Information and communication technology has the capability to improve the process by which governments involve citizens in formulating public policy and public projects. Even though much of government regulations may now be in digital form (and often available online), due to their complexity and diversity, identifying the ones relevant to a particular context is a non-trivial task. Similarly, with the advent of a number of electronic online forums, social networking sites and blogs, the opportunity of gathering citizens’ petitions and stakeholders’ views on government policy and proposals has increased greatly, but the volume and the complexity of analyzing unstructured data makes this difficult. On the other hand, text mining has come a long way from simple keyword search, and matured into a discipline capable of dealing with much more complex tasks. In this paper we discuss how text-mining techniques can help in retrieval of information and relationships from textual data sources, thereby assisting policy makers in discovering associations between policies and citizens’ opinions expressed in electronic public forums and blogs etc. We also present here, an integrated text mining based architecture for e-governance decision support along with a discussion on the Indian scenario.

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Post 1: What is text analytics? How does it differ from text mining? Text Analytics is applying of statistical and machine learning techniques to be able to predict /prescribe or infer any information from the text-mined data. Text mining is a tool that helps in getting the data cleaned up.Text analytics and text mining approaches have essentially equivalent performance. Text analytics requires an expert linguist to produce complex rule sets, whereas text mining requires the analyst to hand-label cases with outcomes or classes to create training data. Differences between Text Mining and Text Analytics: • Text Mining and Text Analytics solve the same problems, but use different techniques and are complementary ways to automatically extract meaning from text. • Text Analytics is developed within the field of computational linguistics. It has the ability to encode human understanding into a series of linguistic rules which are generated by humans are high in precision, but they do not automatically adapt and are usually fragile when tried in new situations. • Text mining is a newer discipline arising out of the fields of statistics, data mining, and machine learning. Its strength is the ability to inductively create models from collections of historical data. Because statistical models are learned from training data they are adaptive and can identify “unknown unknowns”, leading to the better recall. Still, they can be prone to missing something that would seem obvious to a human. • Text analytics and text mining approaches have essentially equivalent performance. Text analytics requires an expert linguist to produce complex rule sets, whereas text mining requires the analyst to hand-label cases with outcomes or classes to create training data. • Due to their different perspectives and strengths, combining text analytics with text mining often leads to better performance than either approach alone. 2. What technologies were used in building Watson (both hardware and software)? Watson is an extraordinary computer system (a novel combination of advanced hardware an software) designed at answering questions posed in natural human language.Watson is an artificially intelligent computer system capable of answering questions posed in natural language, developed in IBM's DeepQA project by a research team led by principal investigator David Ferrucci. Watson was named after IBM's first CEO and industrialist Thomas J. Watson. The computer system was specifically developed to answer questions on the quiz show Jeopardy! In 2011, Watson competed on Jeopardy! against former winners Brad Rutter and Ken Jennings. Watson received the first prize of $1 million.The goal was to advance computer science by exploring new ways for computer technology to affect science, business, and society.IBM undertook a challenge to build a computer system that could compete at the human champion level in real time on the American TV quiz show Jeopardy!The extent of the challenge in ...

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Post 1What is text analytics How does it differ from text mini.docx

Post 1What is text analytics How does it differ from text mini.docx

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Post 1: What is text analytics? How does it differ from text mining? Text Analytics is applying of statistical and machine learning techniques to be able to predict /prescribe or infer any information from the text-mined data. Text mining is a tool that helps in getting the data cleaned up.Text analytics and text mining approaches have essentially equivalent performance. Text analytics requires an expert linguist to produce complex rule sets, whereas text mining requires the analyst to hand-label cases with outcomes or classes to create training data. Differences between Text Mining and Text Analytics: • Text Mining and Text Analytics solve the same problems, but use different techniques and are complementary ways to automatically extract meaning from text. • Text Analytics is developed within the field of computational linguistics. It has the ability to encode human understanding into a series of linguistic rules which are generated by humans are high in precision, but they do not automatically adapt and are usually fragile when tried in new situations. • Text mining is a newer discipline arising out of the fields of statistics, data mining, and machine learning. Its strength is the ability to inductively create models from collections of historical data. Because statistical models are learned from training data they are adaptive and can identify “unknown unknowns”, leading to the better recall. Still, they can be prone to missing something that would seem obvious to a human. • Text analytics and text mining approaches have essentially equivalent performance. Text analytics requires an expert linguist to produce complex rule sets, whereas text mining requires the analyst to hand-label cases with outcomes or classes to create training data. • Due to their different perspectives and strengths, combining text analytics with text mining often leads to better performance than either approach alone. 2. What technologies were used in building Watson (both hardware and software)? Watson is an extraordinary computer system (a novel combination of advanced hardware an software) designed at answering questions posed in natural human language.Watson is an artificially intelligent computer system capable of answering questions posed in natural language, developed in IBM's DeepQA project by a research team led by principal investigator David Ferrucci. Watson was named after IBM's first CEO and industrialist Thomas J. Watson. The computer system was specifically developed to answer questions on the quiz show Jeopardy! In 2011, Watson competed on Jeopardy! against former winners Brad Rutter and Ken Jennings. Watson received the first prize of $1 million.The goal was to advance computer science by exploring new ways for computer technology to affect science, business, and society.IBM undertook a challenge to build a computer system that could compete at the human champion level in real time on the American TV quiz show Jeopardy!The extent of the challenge in ...

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Data Science is a broad term that encompasses multiple disciplines. It is a rapidly growing field of study that uses scientific methods to extract meaningful insights from given input data. The rapid growth in the field of data science has opened the eyes of researchers interested in this field to explore more into the multiple disciplines that encompass data science. Let's see a few of these broad areas that are fundamental aspects to be covered for mastering Data science.

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Post 1What is text analytics How does it differ from text mini.docx

Post 1What is text analytics How does it differ from text mini.docx

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Post 1What is text analytics How does it differ from text mini

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In my capstone project, I investigated the impact of COVID-19 on education. Using data analysis and statistical methods, I explored various aspects such as enrollment trends, access to resources, and socioeconomic disparities. I found a significant association between children missing classes and a lack of internet connection at home, as well as between household financial situations and children's enrollment in school. These findings highlight the importance of addressing disparities in internet access, household finances, and geographical location to ensure equal educational opportunities for all students during and beyond the pandemic.

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原版定制【微信:153539019】《(Dalhousie毕业证书）达尔豪斯大学毕业证》【微信:153539019】成绩单、雅思、外壳、留信学历认证永久存档查询，采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。【业务选择办理准则】一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信153539019】文凭即可二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信153539019】即可三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。留信网认证的作用: 1:该专业认证可证明留学生真实身份 2:同时对留学生所学专业登记给予评定 3:国家专业人才认证中心颁发入库证书 4:这个认证书并且可以归档倒地方 5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息 6:个人职称评审加20分 7:个人信誉贷款加10分 8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才【关于价格问题（保证一手价格）】我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子我给客户的都是第一手的代理价格，因为我想坦诚对待大家不想跟大家在价格方面浪费时间对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。

如何办理(Dalhousie毕业证书）达尔豪斯大学毕业证成绩单留信学历认证

如何办理(Dalhousie毕业证书）达尔豪斯大学毕业证成绩单留信学历认证

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Harnessing the Power of GenAI for BI and Reporting.pptx

Harnessing the Power of GenAI for BI and Reporting.pptx

Harnessing the Power of GenAI for BI and Reporting.pptx

Context 1. Make Insight-informed Decisions: Clinic collected data on heart disease diagnosis and other patient information, and wants to use the data to make insight-informed decisions. Objective 2. Predict Patient’s Well-being: To identify the rules that will predict whether a patient will have heart disease in the future, based on the data collected on him/her. Strategy 3. Deploy Decision Tree Model: Create a Decision Tree Model, with rules, to predict whether a patient will have a heart disease in the future based on collected data. 3.1 To train and evaluate the model 3.2 Boost the model’s performance 3.3 Conduct predictions Author: Anthony Mok Date: 18 Nov 2023 Email: xxiaohao@yahoo.com

Identify Rules that Predict Patient’s Heart Disease - An Application of Decis...

Identify Rules that Predict Patient’s Heart Disease - An Application of Decis...

Identify Rules that Predict Patient’s Heart Disease - An Application of Decis...

ThinkInnovation

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Context 1. Social Enterprise collected data on customers & wants to make insight-informed decisions. Objective 2. To identify customer segments to customised offers for each segment. Strategy 3. Explore & Clean data for analysis. 4. Perform K-Means Clustering, in Orange, to find possible segments in the customer data. 5. Tune the model to improve its performance. 6. Visualise the findings, share conclusions, and give insight-driven recommendations. Author: Anthony mok date: 18 Nov 2023 Email: xxiaohao@yahoo.com

Identify Customer Segments to Create Customer Offers for Each Segment - Appli...

Identify Customer Segments to Create Customer Offers for Each Segment - Appli...

Identify Customer Segments to Create Customer Offers for Each Segment - Appli...

ThinkInnovation

Simplify hybrid data integration at an enterprise scale. Integrate all your d...

Simplify hybrid data integration at an enterprise scale. Integrate all your d...

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varanasisatyanvesh

原版定制【微信:153539019】《(UCLA毕业证书）加州大学洛杉矶分校毕业证》【微信:153539019】成绩单、雅思、外壳、留信学历认证永久存档查询，采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。【业务选择办理准则】一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信153539019】文凭即可二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信153539019】即可三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。留信网认证的作用: 1:该专业认证可证明留学生真实身份 2:同时对留学生所学专业登记给予评定 3:国家专业人才认证中心颁发入库证书 4:这个认证书并且可以归档倒地方 5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息 6:个人职称评审加20分 7:个人信誉贷款加10分 8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才【关于价格问题（保证一手价格）】我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子我给客户的都是第一手的代理价格，因为我想坦诚对待大家不想跟大家在价格方面浪费时间对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。

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Northern New England Tableau User Group (TUG) May 2024

Northern New England Tableau User Group (TUG) May 2024

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原版定制【微信:153539019】《(曼大毕业证书）曼尼托巴大学毕业证》【微信:153539019】（留信学历认证永久存档查询）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。【业务选择办理准则】一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信153539019】文凭即可二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信153539019】即可三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。留信网认证的作用: 1:该专业认证可证明留学生真实身份 2:同时对留学生所学专业登记给予评定 3:国家专业人才认证中心颁发入库证书 4:这个认证书并且可以归档倒地方 5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息 6:个人职称评审加20分 7:个人信誉贷款加10分 8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才【关于价格问题（保证一手价格）】我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子我给客户的都是第一手的代理价格，因为我想坦诚对待大家不想跟大家在价格方面浪费时间对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。

一比一原版(曼大毕业证书）曼尼托巴大学毕业证成绩单留信学历认证一手价格

一比一原版(曼大毕业证书）曼尼托巴大学毕业证成绩单留信学历认证一手价格

一比一原版(曼大毕业证书）曼尼托巴大学毕业证成绩单留信学历认证一手价格

原版定制【微信:153539019】《澳洲拉筹伯大学毕业证（LaTrobe毕业证书）》【微信:153539019】（留信学历认证永久存档查询）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。【业务选择办理准则】一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信153539019】文凭即可二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信153539019】即可三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。留信网认证的作用: 1:该专业认证可证明留学生真实身份 2:同时对留学生所学专业登记给予评定 3:国家专业人才认证中心颁发入库证书 4:这个认证书并且可以归档倒地方 5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息 6:个人职称评审加20分 7:个人信誉贷款加10分 8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才【关于价格问题（保证一手价格）】我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子我给客户的都是第一手的代理价格，因为我想坦诚对待大家不想跟大家在价格方面浪费时间对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。

如何办理澳洲拉筹伯大学毕业证（LaTrobe毕业证书）成绩单原件一模一样

如何办理澳洲拉筹伯大学毕业证（LaTrobe毕业证书）成绩单原件一模一样

如何办理澳洲拉筹伯大学毕业证（LaTrobe毕业证书）成绩单原件一模一样

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如何办理澳洲拉筹伯大学毕业证（LaTrobe毕业证书）成绩单原件一模一样

如何办理澳洲拉筹伯大学毕业证（LaTrobe毕业证书）成绩单原件一模一样

社内勉強会資料_Object Recognition as Next Token Prediction

社内勉強会資料_Object Recognition as Next Token Prediction

社内勉強会資料_Object Recognition as Next Token Prediction

Cloud document search

1. There are many different sources of searches Insight engine is based on the data available in companies. It includes a broader spectrum of technologies that increase the quality of the search. All the searches should be in the simple language which results in relevant search. Data should be connected with connectors. It should also add some extra information to the search which is not asked. It should also contain videos, images, voice messages of the search. Cloud document search is one of the largest document searches which store private documents and it is still increasing. It is most important for them who have tool for cloud document search. It is a source which shares all the knowledge using tool to the single technology of document. It captures all the information’s related to a single topic. Many users search via cloud document search. Information retrieval is based on full text or other contents. It is a science of search for information in a document and also searches for metadata which describes the data. It obtains information system resources which are relevant to the information search. It was popularised with the article As We May Thing written by Vannevar Bush in 1945.