Sentiment Analysis

•Télécharger en tant que PPT, PDF•

4 j'aime•2,022 vues

RexNige

Technologie Business

Definition of Sentiment Analysis : ,[object Object],[object Object],[object Object],[object Object]

The demand for sentiment Analysis : ,[object Object]

Sentiment Analysis & Web 2.0 ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Applications : ,[object Object],[object Object],[object Object]

Sentiment Analysis Benefits : ,[object Object],[object Object],[object Object],[object Object]

Sentiment Analysis Issues : ,[object Object],[object Object],[object Object],[object Object]

Examples : ,[object Object],[object Object]

Contenu connexe

Tendances

I created this presentation to present my research work to the committee. My research was on extracting tweets and analyzing it with an previously created ontology model. The results of the ontology model will help in identifying the domain area of the problem for which use had shared negative sentiments on tweeter. This system along with the ontology model developed for Postal service domain. The next step in research will be to generate automated responses on twitter to the user who shares negative sentiments.

Ontology based sentiment analysis

prathako

One fundamental problem in sentiment analysis is categorization of sentiment polarity. Given a piece of written text, the problem is to categorize the text into one specific sentiment polarity, positive or negative (or neutral). Based on the scope of the text, there are three distinctions of sentiment polarity categorization, namely the document level, the sentence level, and the entity and aspect level. Consider a review Ã¢â‚¬Å“I like multimedia features but the battery life sucks.Ã¢â‚¬Â This sentence has a mixed emotion. The emotion regarding multimedia is positive whereas that regarding battery life is negative. Hence, it is required to extract only those opinions relevant to a particular feature (like battery life or multimedia) and classify them, instead of taking the complete sentence and the overall sentiment. In this paper, we present a novel approach to identify pattern specific expressions of opinion in text.

SENTIMENT ANALYSIS-AN OBJECTIVE VIEW

Journal For Research

An informative session on Amazon Mechanical Turk where you will learn how your company can leverage the human crowd for human sentiment analysis of content such as tweets, articles, RSS feeds and blog posts. This session digs into the details of getting started and provides information on how to be successful so you get accurate results. Additionally, FreedomOSS will share their experiences designing and managing sentiment tasks and demo's their CrowdControl crowdsourcing platform that is built on top of Mechanical Turk.

Best Practices for Sentiment Analysis Webinar

Mechanical Turk

Sentiment analysis - Our approach and use cases

Karol Chlasta

Sentiment mining- The Design and Implementation of an Internet PublicOpinion...

Prateek Singh

SentiCheNews - Sentiment Analysis on Newspapers and Tweets

🧑‍💻 Manuel Coppotelli

Amazon seniment

Subhadeep Chakraborty

A review of sentiment analysis approaches in big

Nurfadhlina Mohd Sharef

Project report

Utkarsh Soni

Application Of Python in Medical Science

Aditya Nag

How Does Customer Feedback Sentiment Analysis Work in Search Marketing?

Countants

Machine Learning PPT BY RAVINDRA SINGH KUSHWAHA B.TECH(IT) CHAUDHARY CHARAN S...

RavindraSinghKushwah1

Sentimental analysis is a context based mining of text, which extracts and identify subjective information from a text or sentence provided. Here the main concept is extracting the sentiment of the text using machine learning techniques such as LSTM Long short term memory . This text classification method analyses the incoming text and determines whether the underlined emotion is positive or negative along with probability associated with that positive or negative statements. Probability depicts the strength of a positive or negative statement, if the probability is close to zero, it implies that the sentiment is strongly negative and if probability is close to1, it means that the statement is strongly positive. Here a web application is created to deploy this model using a Python based micro framework called flask. Many other methods, such as RNN and CNN, are inefficient when compared to LSTM. Dirash A R | Dr. S K Manju Bargavi "LSTM Based Sentiment Analysis" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd42345.pdf Paper URL: https://www.ijtsrd.comcomputer-science/data-processing/42345/lstm-based-sentiment-analysis/dirash-a-r

LSTM Based Sentiment Analysis

ijtsrd

Introduction to Sentiment Analysis

Makrand Patil

Sentiment analysis in Twitter on Big Data

Iswarya M

sentiment analysis text extraction from social media

Ravindra Chaudhary

Social Data Sentiment Analysis

Seth Grimes

Practical sentiment analysis

Diana Maynard

Social media & sentiment analysis splunk conf2012

Michael Wilde

Sentiment Analysis of Twitter Data

Sumit Raj

Tendances (20)

Ontology based sentiment analysis

SENTIMENT ANALYSIS-AN OBJECTIVE VIEW

Best Practices for Sentiment Analysis Webinar

Sentiment analysis - Our approach and use cases

Sentiment mining- The Design and Implementation of an Internet PublicOpinion...

SentiCheNews - Sentiment Analysis on Newspapers and Tweets

Amazon seniment

A review of sentiment analysis approaches in big

Project report

Application Of Python in Medical Science

How Does Customer Feedback Sentiment Analysis Work in Search Marketing?

Machine Learning PPT BY RAVINDRA SINGH KUSHWAHA B.TECH(IT) CHAUDHARY CHARAN S...

LSTM Based Sentiment Analysis

Introduction to Sentiment Analysis

Sentiment analysis in Twitter on Big Data

sentiment analysis text extraction from social media

Social Data Sentiment Analysis

Practical sentiment analysis

Social media & sentiment analysis splunk conf2012

Sentiment Analysis of Twitter Data

En vedette

Mike davies sentiment_analysis_presentation_backup

m1ked

Ujian selaras f4 2015

roslini

Tobi Timeline

ttakata

Social Media Best Practices OMMA Global Case Study

Context Optional

Winter p4

luciamontoliu

Islandscapes By Day

Patricia_Waldygo

Redesigning & integrating bluetooth 3

Mahesh Gupta

Peggy and mummy

luciamontoliu

Ppt01 the balloon 2010

luciamontoliu

Background Check & Due Diligence Services - Arch360 Group

Gabriela Coldea, CFE

ad:tech New York: Marketing to Millennials

Context Optional

What i do in my house

luciamontoliu

Open Source Presentation

RexNige

Web Application Cache (APC, Memcache)

Vijay Kumbhar

Refueling

pkryan

Islandscapes By Night

Survey Results 2010

Tree story

Australia

ad:tech Expo Theater: Why Social is Worth More

Context Optional

En vedette (20)

Mike davies sentiment_analysis_presentation_backup

Ujian selaras f4 2015

Tobi Timeline

Social Media Best Practices OMMA Global Case Study

Winter p4

Islandscapes By Day

Redesigning & integrating bluetooth 3

Peggy and mummy

Ppt01 the balloon 2010

Background Check & Due Diligence Services - Arch360 Group

ad:tech New York: Marketing to Millennials

What i do in my house

Open Source Presentation

Web Application Cache (APC, Memcache)

Refueling

Islandscapes By Night

Survey Results 2010

Tree story

Australia

ad:tech Expo Theater: Why Social is Worth More

Similaire à Sentiment Analysis

Sentiment Analysis involves determining the evaluative nature of a piece of text. A product review can express a positive, negative, or neutral sentiment or polarity . Automatically identifying sentiment expressed in text has a number of applications, including tracking sentiment towards Movie reviews and Automobile reviews improving customer relation models, detecting happiness and well being, and improving automatic dialogue systems. The evaluative intensity for both positive and negative terms changes in a negated context, and the amount of change varies from term to term. To adequately capture the impact of negation on individual terms, here proposed to empirically estimate the sentiment scores of terms in negated context from movie review and auto mobile review, and built two lexicons, one for terms in negated contexts and one for terms in affirmative non negated contexts. By using these Affirmative Context Lexicons and Negated Context Lexicons were able to significantly improve the performance of the overall sentiment analysis system on both tasks. This thesis have proposed a sentiment analysis system that detects the sentiment of corpus dataset using movie review and Automobile review as well as the sentiment of a term a word or a phrase within a message term level task using R language. B. Nagajothi | Dr. R. Jemima Priyadarsini "Sentiment Analysis on Twitter Dataset using R Language" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-6 , October 2019, URL: https://www.ijtsrd.com/papers/ijtsrd28071.pdf Paper URL: https://www.ijtsrd.com/computer-science/data-miining/28071/sentiment-analysis-on-twitter-dataset-using-r-language/b-nagajothi

Sentiment Analysis on Twitter Dataset using R Language

ijtsrd

Determine the sentiment of sentence that is positive or negative based on the presence of part of speech tag, the emoticons present in the sentences. For this research we use the most popular microblogging sit twitter for sentiment orientation. In this paper we want to extract tweets form the twitter related to the product like mobile phones, home appliances, vehicle etc. After retrieving tweets we perform some preprocessing on it like remove retweets, remove tweets containing few words with minimum threshold of length five, remove tweets containing only urls. After this the remaining tweets are pre-processed like that transform all letters of the tweets to the lower case then remove punctuation from the tweets because it reduces the accuracy of result. After this remove extra white spaces from the tweets, then we apply a pos tagger to tag each word. The tuple after the applying above steps contain (word, pos tag, English-word, stop-word). We are interested in only tweets that contain opinion and eliminate the remaining non-opinion tweets from the data set. For this we use the Naïve Bays classification algorithm. After this we use short text classification on tweets i.e., the word having different meaning in different domain. In order to solve this problem we use two different feature selection algorithms the mutual information (MI) and the X2 feature selection. At final stage predicting the orientation of an opinion sentence that is positive or negative as we mentioned above. For this we use two model like unigram model and opinion miner.

Sentiment of Sentence in Tweets: A Review

iosrjce

W01761157162

IOSR Journals

How could a product or service is reasonably evaluated by anyone in the shortest time? A million dollar question but it is having a simple answer: Sentiment analysis. Sentiment analysis is consumers review on products and services which helps both the producers and consumers (stakeholders) to take effective and efficient decision within a shortest period of time. Producers can have better knowledge of their products and services through the sentiment analysis (ex. positive and negative comments or consumers likes and dislikes) which will help them to know their products status (ex. product limitations or market status). Consumers can have better knowledge of their interested products and services through the sentiment analysis (ex. positive and negative comments or consumers likes and dislikes) which will help them to know their deserving products status (ex. product limitations or market status). For more specification of the sentiment values, fuzzy logic could be introduced. Therefore, sentiment analysis with the help of fuzzy logic (deals with reasoning and gives closer views to the exact sentiment values) will help the producers or consumers or any interested person for taking the effective decision according to their product or service interest.

Sentiment analysis by using fuzzy logic

ijcseit

International Journal of Computer Science, Engineering and Information Techno...

ijcseit

Sentiment Analysis using Fuzzy logic

Vinay Sawant

SENTIMENT ANALYSIS BY USING FUZZY LOGIC

ijcseit

Anu paper(IJARCCE)

Anu Maheshwari

Humans communication is generally under the control of emotions and full of opinions. Emotions an d their opinions plays an important role in thinking process of mind, influences the human actions too. Sentiment analysis is one of the ways to explore user’s opinion made on any social media and networking site for various commercial applications in number of fields. This paper takes into account the basis requirements of opinion mining to explore the present techniques used to develop a fully fledged system. Is highlights the opportunities or deployment and research of such systems. The available tools used for building such applications have even presented with their merits and limitations.

Review on Opinion Mining for Fully Fledged System

ijeei-iaes

Monitoring opinion on esop through social media and clustering its polarity

International Journal of Advance Research and Innovative Ideas in Education

Humans communication is generally under the control of emotions and full of opinions. Emotions and their opinions plays an important role in thinking process of mind, influences the human actions too. Sentiment analysis is one of the ways to explore user’s opinion made on any social media and networking site for various commercial applications in number of fields. This paper takes into account the basis requirements of opinion mining to explore the present techniques used to developed an full fledge system. Is highlights the opportunities or deployment and research of such systems. The available tools used for building such applications have even presented with their merits and limitations.

Ijcatr04061001

Editor IJCATR

IRJET- Real Time Sentiment Analysis of Political Twitter Data using Machi...

IRJET Journal

Due to the fast growth of World Wide Web the online communication has increased. In recent times the communication focus has shifted to social networking. In order to enhance the text methods of communication such as tweets, blogs and chats, it is necessary to examine the emotion of user by studying the input text. Online reviews are posted by customers for the products and services on offer at a website portal. This has provided impetus to substantial growth of online purchasing making opinion analysis a vital factor for business development. To analyze such text and reviews sentiment analysis is used. Sentiment analysis is a sub domain of Natural Language Processing which acquires writer’s feelings about several products which are placed on the internet through various comments or posts. It is used to find the opinion or response of the user. Opinion may be positive, negative or neutral. In this paper a review on sentiment analysis is done and the challenges and issues involved in the process are discussed. The approaches to sentiment analysis using dictionaries such as SenticNet, SentiFul, SentiWordNet, and WordNet are studied. Dictionary-based approaches are efficient over a domain of study. Although a generalized dictionary like WordNet may be used, the accuracy of the classifier get affected due to issues like negation, synonyms, sarcasm, etc. w

Dictionary Based Approach to Sentiment Analysis - A Review

INFOGAIN PUBLICATION

Sub1557

International Journal of Science and Research (IJSR)

ppt rubric_1.pptx

ujjwalsingh414879

Opinion Mining also called as Sentiment Analysis is a process that provides with the subjective informationfor the text provided. In other words we can say that it analyzes person’s opinion, evaluations, emotions,appraisals, etc. towards a particular product, event, issue, service, topic, etc. This paper focuses on the machine learning techniques used for sentiment analysis and opinion mining. These methods are furthercompared on the basis of their accuracy, advantages and limitations.

A SURVEY OF MACHINE LEARNING TECHNIQUES FOR SENTIMENT CLASSIFICATION

ijcsa

SENTIMENT ANALYSIS OF TWITTER DATA

anargha gangadharan

With the rise of social networking epoch, there has been a surge of user generated content. Micro blogging sites have millions of people sharing their thoughts daily because of its characteristic short and simple manner of expression. We propose and investigate a paradigm to mine the sentiment from a popular real-time micro blogging service, Twitter, where users post real time reactions to and opinions about “everything”. In this paper, we expound a hybrid approach using both corpus based and dictionary based methods to determine the semantic orientation of the opinion words in tweets. A case study is presented to illustrate the use and effectiveness of the proposed system.

REAL TIME SENTIMENT ANALYSIS OF TWITTER DATA

Mary Lis Joseph

SENTIMENT ANALYSIS OF TWITTER DATA

Parvathy Devaraj

Sentiment Analysis is the process of finding the sentiments from different classes of words. Generally speaking, sentiment analysis aims to determine the attitude of a speaker or a writer with respect to some topic or the overall contextual polarity of a document. The attitude may be his or her judgment or evaluation, affective state, or the intended emotional communication. In this case, ‘tweets’! Given a micro-blogging platform where official, verified tweets are available to us, we need to identify the sentiments of those tweets. A model must be constructed where the sentiments are scored, for each product individually and then they are compared with, diagrammatically, portraying users’ feedback from the producers stand point. There are many websites that offer a comparison between various products or services based on certain features of the article such as its predominant traits, price, and its welcome in the market and so on. However not many provide a juxtaposing of commodities with user review as the focal point. Those few that do work with Naïve Bayes Machine Learning Algorithms, that poses a disadvantage as it mandatorily assumes that the features, in our project, words, are independent of each other. This is a comparatively inefficient method of performing Sentiment Analysis on bulk text, for official purposes, since sentences will not give the meaning they are supposed to convey, if each word is considered a separate entity. Maximum Entropy Classifier overcomes this draw back by limiting the assumptions it makes of the input data feed, which is what we use in the proposed system.

[IJET V2I4P9] Authors: Praveen Jayasankar , Prashanth Jayaraman ,Rachel Hannah

IJET - International Journal of Engineering and Techniques

Similaire à Sentiment Analysis (20)

Sentiment Analysis on Twitter Dataset using R Language

Sentiment of Sentence in Tweets: A Review

W01761157162

Sentiment analysis by using fuzzy logic

International Journal of Computer Science, Engineering and Information Techno...

Sentiment Analysis using Fuzzy logic

SENTIMENT ANALYSIS BY USING FUZZY LOGIC

Anu paper(IJARCCE)

Review on Opinion Mining for Fully Fledged System

Monitoring opinion on esop through social media and clustering its polarity

Ijcatr04061001

IRJET- Real Time Sentiment Analysis of Political Twitter Data using Machi...

Dictionary Based Approach to Sentiment Analysis - A Review

Sub1557

ppt rubric_1.pptx

A SURVEY OF MACHINE LEARNING TECHNIQUES FOR SENTIMENT CLASSIFICATION

SENTIMENT ANALYSIS OF TWITTER DATA

REAL TIME SENTIMENT ANALYSIS OF TWITTER DATA

SENTIMENT ANALYSIS OF TWITTER DATA

[IJET V2I4P9] Authors: Praveen Jayasankar , Prashanth Jayaraman ,Rachel Hannah

Dernier

Created by Mozilla Research in 2012 and now part of Linux Foundation Europe, the Servo project is an experimental rendering engine written in Rust. It combines memory safety and concurrency to create an independent, modular, and embeddable rendering engine that adheres to web standards. Stewardship of Servo moved from Mozilla Research to the Linux Foundation in 2020, where its mission remains unchanged. After some slow years, in 2023 there has been renewed activity on the project, with a roadmap now focused on improving the engine’s CSS 2 conformance, exploring Android support, and making Servo a practical embeddable rendering engine. In this presentation, Rakhi Sharma reviews the status of the project, our recent developments in 2023, our collaboration with Tauri to make Servo an easy-to-use embeddable rendering engine, and our plans for the future to make Servo an alternative web rendering engine for the embedded devices industry. (c) Embedded Open Source Summit 2024 April 16-18, 2024 Seattle, Washington (US) https://events.linuxfoundation.org/embedded-open-source-summit/ https://ossna2024.sched.com/event/1aBNF/a-year-of-servo-reboot-where-are-we-now-rakhi-sharma-igalia

A Year of the Servo Reboot: Where Are We Now?

Igalia

Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...

Neo4j

Data Cloud, More than a CDP by Matt Robison

Anna Loughnan Colquhoun

Following the popularity of "Cloud Revolution: Exploring the New Wave of Serverless Spatial Data," we're thrilled to announce this much-anticipated encore webinar. In this sequel, we'll dive deeper into the Cloud-Native realm by uncovering practical applications and FME support for these new formats, including COGs, COPC, FlatGeoBuf, GeoParquet, STAC, and ZARR. Building on the foundation laid by industry leaders Michelle Roby of Radiant Earth and Chris Holmes of Planet in the first webinar, this second part offers an in-depth look at the real-world application and behind-the-scenes dynamics of these cutting-edge formats. We will spotlight specific use-cases and workflows, showcasing their efficiency and relevance in practical scenarios. Discover the vast possibilities each format holds, highlighted through detailed discussions and demonstrations. Our expert speakers will dissect the key aspects and provide critical takeaways for effective use, ensuring attendees leave with a thorough understanding of how to apply these formats in their own projects. Elevate your understanding of how FME supports these cutting-edge technologies, enhancing your ability to manage, share, and analyze spatial data. Whether you're building on knowledge from our initial session or are new to the serverless spatial data landscape, this webinar is your gateway to mastering cloud-native formats in your workflows.

Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME

Safe Software

Increase engagement and revenue with Muvi Live Paywall! In this presentation, we will explore the five key benefits of using Muvi Live Paywall to monetize your live streams. You'll learn how Muvi Live Paywall can help you: Monetize your live content easily: Set up pay-per-view access to your live streams and start generating revenue from your content. Increase audience engagement: Provide exclusive, premium content behind the paywall to keep your viewers engaged. Gain valuable viewer insights: Track viewer data and analytics to better understand your audience and tailor your content accordingly. Reduce content piracy: Muvi Live Paywall's security features help protect your content from unauthorized distribution. Streamline your workflow: The all-in-one platform simplifies the process of managing and monetizing your live streams. With Muvi Live Paywall, you can take control of your live stream monetization and create a sustainable business model for your content. Learn more about Muvi Live Paywall and start generating revenue from your live streams today!

Top 5 Benefits OF Using Muvi Live Paywall For Live Streams

Roshan Dwivedi

Three things you will take away from the session: • How to run an effective tenant-to-tenant migration • Best practices for before, during, and after migration • Tips for using migration as a springboard to prepare for Copilot in Microsoft 365 Main ideas: Migration Overview: The presentation covers the current reality of cross-tenant migrations, the triggers, phases, best practices, and benefits of a successful tenant migration Considerations: When considering a migration, it is important to consider the migration scope, performance, customization, flexibility, user-friendly interface, automation, monitoring, support, training, scalability, data integrity, data security, cost, and licensing structure Next Wave: The next wave of change includes the launch of Copilot, which requires businesses to be prepared for upcoming changes related to Copilot and the cloud, and to consolidate data and tighten governance ShareGate: ShareGate can help with pre-migration analysis, configurable migration tool, and automated, end-user driven collaborative governance

Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff

sammart93

AWS Community Day CPH - Three problems of Terraform

Andrey Devyatkin

Discord is a free app offering voice, video, and text chat functionalities, primarily catering to the gaming community. It serves as a hub for users to create and join servers tailored to their interests. Discord’s ecosystem comprises servers, each functioning as a distinct online community with its own channels dedicated to specific topics or activities. Users can engage in text-based discussions, voice calls, or video chats within these channels. Understanding Discord Servers Discord servers are virtual spaces where users congregate to interact, share content, and build communities. Servers may revolve around gaming, hobbies, interests, or fandoms, providing a platform for like-minded individuals to connect. Communication Features Discord offers a range of communication tools, including text channels for messaging, voice channels for real-time audio conversations, and video channels for face-to-face interactions. These features facilitate seamless communication and collaboration. What Does NSFW Mean? The acronym NSFW stands for “Not Safe For Work,” indicating content that may be inappropriate for professional or public settings. NSFW Content NSFW content encompasses material that is sexually explicit, violent, or otherwise graphic in nature. It often includes nudity, profanity, or depictions of sensitive topics.

Understanding Discord NSFW Servers A Guide for Responsible Users.pdf

UK Journal

Real Time Object Detection Using Open CV

Khem

Artificial Intelligence Chap.5 : Uncertainty

Khushali Kathiriya

💉💊+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHABI}}+971581248768 +971581248768 Mtp-Kit (500MG) Prices » Dubai [(+971581248768**)] Abortion Pills For Sale In Dubai, UAE, Mifepristone and Misoprostol Tablets Available In Dubai, UAE CONTACT DR.Maya Whatsapp +971581248768 We Have Abortion Pills / Cytotec Tablets /Mifegest Kit Available in Dubai, Sharjah, Abudhabi, Ajman, Alain, Fujairah, Ras Al Khaimah, Umm Al Quwain, UAE, Buy cytotec in Dubai +971581248768''''Abortion Pills near me DUBAI | ABU DHABI|UAE. Price of Misoprostol, Cytotec” +971581248768' Dr.DEEM ''BUY ABORTION PILLS MIFEGEST KIT, MISOPROTONE, CYTOTEC PILLS IN DUBAI, ABU DHABI,UAE'' Contact me now via What's App…… abortion Pills Cytotec also available Oman Qatar Doha Saudi Arabia Bahrain Above all, Cytotec Abortion Pills are Available In Dubai / UAE, you will be very happy to do abortion in Dubai we are providing cytotec 200mg abortion pill in Dubai, UAE. Medication abortion offers an alternative to Surgical Abortion for women in the early weeks of pregnancy. We only offer abortion pills from 1 week-6 Months. We then advise you to use surgery if its beyond 6 months. Our Abu Dhabi, Ajman, Al Ain, Dubai, Fujairah, Ras Al Khaimah (RAK), Sharjah, Umm Al Quwain (UAQ) United Arab Emirates Abortion Clinic provides the safest and most advanced techniques for providing non-surgical, medical and surgical abortion methods for early through late second trimester, including the Abortion By Pill Procedure (RU 486, Mifeprex, Mifepristone, early options French Abortion Pill), Tamoxifen, Methotrexate and Cytotec (Misoprostol). The Abu Dhabi, United Arab Emirates Abortion Clinic performs Same Day Abortion Procedure using medications that are taken on the first day of the office visit and will cause the abortion to occur generally within 4 to 6 hours (as early as 30 minutes) for patients who are 3 to 12 weeks pregnant. When Mifepristone and Misoprostol are used, 50% of patients complete in 4 to 6 hours; 75% to 80% in 12 hours; and 90% in 24 hours. We use a regimen that allows for completion without the need for surgery 99% of the time. All advanced second trimester and late term pregnancies at our Tampa clinic (17 to 24 weeks or greater) can be completed within 24 hours or less 99% of the time without the need surgery. The procedure is completed with minimal to no complications. Our Women's Health Center located in Abu Dhabi, United Arab Emirates, uses the latest medications for medical abortions (RU-486, Mifeprex, Mifegyne, Mifepristone, early options French abortion pill), Methotrexate and Cytotec (Misoprostol). The safety standards of our Abu Dhabi, United Arab Emirates Abortion Doctors remain unparalleled. They consistently maintain the lowest complication rates throughout the nation. Our Physicians and staff are always available to answer questions and care for women in one of the most difficult times in their lives. The decision to have an abortion at the Abortion Cl

+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...

?#DUbAI#??##{{(☎️+971_581248768%)**%*]'#abortion pills for sale in dubai@

In this session, we will delve into strategic approaches for optimizing knowledge management within Microsoft 365, amidst the evolving landscape of Copilot. From leveraging automatic metadata classification and permission governance with SharePoint Premium, to unlocking Viva Engage for the cultivation of knowledge and communities, you will gain actionable insights to bolster your organization's knowledge-sharing initiatives. In this session, we will also explore how to facilitate solutions to enable your employees to find answers and expertise within Microsoft 365. You will leave equipped with practical techniques and a deeper understanding of how there is more to effective knowledge management than just enabling Copilot, but building actual solutions to prepare the knowledge that Copilot and your employees can use.

Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...

Drew Madelung

A Principled Technologies deployment guide Conclusion Deploying VMware Cloud Foundation 5.1 on next gen Dell PowerEdge servers brings together critical virtualization capabilities and high-performing hardware infrastructure. Relying on our hands-on experience, this deployment guide offers a comprehensive roadmap that can guide your organization through the seamless integration of advanced VMware cloud solutions with the performance and reliability of Dell PowerEdge servers. In addition to the deployment efficiency, the Cloud Foundation 5.1 and PowerEdge solution delivered strong performance while running a MySQL database workload. By leveraging VMware Cloud Foundation 5.1 and PowerEdge servers, you could help your organization embrace cloud computing with confidence, potentially unlocking a new level of agility, scalability, and efficiency in your data center operations.

Deploy with confidence: VMware Cloud Foundation 5.1 on next gen Dell PowerEdg...

Principled Technologies

Automating Google Workspace (GWS) & more with Apps Script

wesley chun

Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving

Edi Saputra

How to Troubleshoot Apps for the Modern Connected Worker

ThousandEyes

Join our latest Connector Corner webinar to discover how UiPath Integration Service revolutionizes API-centric automation in a 'Quote to Cash' process—and how that automation empowers businesses to accelerate revenue generation. A comprehensive demo will explore connecting systems, GenAI, and people, through powerful pre-built connectors designed to speed process cycle times. Speakers: James Dickson, Senior Software Engineer Charlie Greenberg, Host, Product Marketing Manager

Connector Corner: Accelerate revenue generation using UiPath API-centric busi...

DianaGray10

Abhishek Deb(1), Mr Abdul Kalam(2) M. Des (UX) , School of Design, DIT University , Dehradun. This paper explores the future potential of AI-enabled smartphone processors, aiming to investigate the advancements, capabilities, and implications of integrating artificial intelligence (AI) into smartphone technology. The research study goals consist of evaluating the development of AI in mobile phone processors, analyzing the existing state as well as abilities of AI-enabled cpus determining future patterns as well as chances together with reviewing obstacles as well as factors to consider for more growth.

Exploring the Future Potential of AI-Enabled Smartphone Processors

debabhi2

Boost Fertility New Invention Ups Success Rates.pdf

sudhanshuwaghmare1

🐬 The future of MySQL is Postgres 🐘

RTylerCroy

Dernier (20)

A Year of the Servo Reboot: Where Are We Now?

Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...

Data Cloud, More than a CDP by Matt Robison

Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME

Top 5 Benefits OF Using Muvi Live Paywall For Live Streams

Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff

AWS Community Day CPH - Three problems of Terraform

Understanding Discord NSFW Servers A Guide for Responsible Users.pdf

Real Time Object Detection Using Open CV

Artificial Intelligence Chap.5 : Uncertainty

+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...

Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...

Deploy with confidence: VMware Cloud Foundation 5.1 on next gen Dell PowerEdg...

Automating Google Workspace (GWS) & more with Apps Script

Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving

How to Troubleshoot Apps for the Modern Connected Worker

Connector Corner: Accelerate revenue generation using UiPath API-centric busi...

Exploring the Future Potential of AI-Enabled Smartphone Processors

Boost Fertility New Invention Ups Success Rates.pdf

🐬 The future of MySQL is Postgres 🐘