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Search results “Opinion mining project management”
Opinion Mining Project For Sale
 
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This is a graduation project for sale. Its idea based on opinion mining and review analysis. Contact me for more inforation and previewing the whole project if you want to buy it. Gmail: [email protected] Skype: mohamed.hana11 Egypt Mobile: 01020442063
Views: 81 Mohamed Hana
Opinion Mining For Social Networking Sites Java Project
 
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Opinion Mining For Social Networking Sites Java Project Download Project Code, Report and PPT :+91 7702177291, +91 9052016340 Email : [email protected] Website : www.1000projects.org
Views: 318 1000 Projects
Website Evaluation Using Opinion Mining
 
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Get this project at http://nevonprojects.com/website-evaluation-using-opinion-mining/ Here we propose an advanced Website Evaluation system that rates the website based on the opinions mined from users comments on respective sites
Views: 5440 Nevon Projects
Complementary Aspect-based Opinion Mining
 
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Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/dhBA4M Chat Now @ http://goo.gl/snglrO Visit Our Channel: https://www.youtube.com/user/clickmyproject Mail Us: [email protected]
Views: 155 Clickmyproject
Working in Mining with Mining Plus - Rosie Allen - Senior Mining Consultant
 
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Mining Plus Senior Mining Consultant - Rosie Allen gives her raw opinion on why you should seek a career in mining and working at Mining Plus. If you are looking for a career in Mining then Unearth your potential with Mining Plus. To join our team of Awesome! Please visit www.mining-plus.com and look through our many opportunities available throughout our global locations. #MiningPlus #Mining #MiningIsAwesome #MiningCareers
Views: 2634 Mining Plus
Fake Product Review Monitoring
 
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Get this project at http://nevonprojects.com/fake-product-review-monitoring-and-removal-for-genuine-online-product-reviews-using-opinion-mining/ System allows admin to detect fake reviews posted online to ensure genuine product rating system
Views: 6621 Nevon Projects
Barbara Plank | Keynote - Natural Language Processing: Challenges and Next Frontiers
 
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Barbara Plank is tenured Assistant Professor in Natural Language Processing at the University of Groningen, The Netherlands. Her research focuses on cross-domain and cross-language NLP. She is interested in robust language technology, learning under sample selection bias (domain adaptation, transfer learning), annotation bias (embracing annotator disagreements in learning), and generally, semi-supervised and weakly-supervised machine learning for a variety of NLP tasks and applications, including syntactic processing, opinion mining, information and relation extraction and personality prediction. Natural Language Processing: Challenges and Next Frontiers Despite many advances of Natural Language Processing (NLP) in recent years, largely due to the advent of deep learning approaches, there are still many challenges ahead to build successful NLP models. In this talk I will outline what makes NLP so challenging. Besides ambiguity, one major challenges is variability. In NLP, we typically deal with data from a variety of sources, like data from different domains, languages and media, while assuming that our models work well on a range of tasks, from classification to structured prediction. Data variability is an issue that affects all NLP models. I will then delineate one possible way to go about it, by combining recent success in deep multi-task learning with fortuitous data sources, which allows learning from distinct views and distinct sources. This will be one step towards one of the next frontiers: learning under limited (or absence) of annotated resources, for a variety of NLP tasks. Link to Q&A: https://youtu.be/JtiCdsESuT0 www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.
Views: 2638 PyData
How data mining works
 
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In this video we describe data mining, in the context of knowledge discovery in databases. More videos on classification algorithms can be found at https://www.youtube.com/playlist?list=PLXMKI02h3_qjYoX-f8uKrcGqYmaqdAtq5 Please subscribe to my channel, and share this video with your peers!
Views: 229554 Thales Sehn Körting
Interview with a Data Scientist
 
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This video is part of the Udacity course "Intro to Programming". Watch the full course at https://www.udacity.com/course/ud000
Views: 297523 Udacity
Sentiment analysis and opinion mining, Franco Tuveri
 
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L'Opinion Mining, o Sentiment Analysis, indica il processo di estrazione di informazioni legate alle opinioni espresse in rete da fruitori di servizi, prodotti ed eventi. Il seminario tratta le tematiche legate all'Opinion Mining secondo un approccio linguistico. Si parla di strutture linguistiche, del loro ruolo nell'interpretazione semantica dei testi e dei diversi campi di applicazione dell'Opinion Mining spaziando dalla "brand reputation" al "voice of consumers", o "opinion monitoring", sino al "real marketing".
Views: 495 CRS4video
Fake product review detection and removal using opinion mining
 
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In our final year project, we have used VADER for sentiment analysis first, and then we have used our own classification method using basic neural network to first classify suspicious-clear-hazy reviews. Then we have annotated the review with the same along with the polarity of it for user information. Thus user knows if it is positive spam or negative spam.
Views: 67 Aishwerya Kapoor
Data Mining using R | Data Mining Tutorial for Beginners | R Tutorial for Beginners | Edureka
 
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( R Training : https://www.edureka.co/r-for-analytics ) This Edureka R tutorial on "Data Mining using R" will help you understand the core concepts of Data Mining comprehensively. This tutorial will also comprise of a case study using R, where you'll apply data mining operations on a real life data-set and extract information from it. Following are the topics which will be covered in the session: 1. Why Data Mining? 2. What is Data Mining 3. Knowledge Discovery in Database 4. Data Mining Tasks 5. Programming Languages for Data Mining 6. Case study using R Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS #LogisticRegression #Datasciencetutorial #Datasciencecourse #datascience How it Works? 1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. You will get Lifetime Access to the recordings in the LMS. 4. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities. - - - - - - - - - - - - - - Why Learn Data Science? Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework. After the completion of the Data Science course, you should be able to: 1. Gain insight into the 'Roles' played by a Data Scientist 2. Analyse Big Data using R, Hadoop and Machine Learning 3. Understand the Data Analysis Life Cycle 4. Work with different data formats like XML, CSV and SAS, SPSS, etc. 5. Learn tools and techniques for data transformation 6. Understand Data Mining techniques and their implementation 7. Analyse data using machine learning algorithms in R 8. Work with Hadoop Mappers and Reducers to analyze data 9. Implement various Machine Learning Algorithms in Apache Mahout 10. Gain insight into data visualization and optimization techniques 11. Explore the parallel processing feature in R - - - - - - - - - - - - - - Who should go for this course? The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course: 1. Developers aspiring to be a 'Data Scientist' 2. Analytics Managers who are leading a team of analysts 3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics 4. Business Analysts who want to understand Machine Learning (ML) Techniques 5. Information Architects who want to gain expertise in Predictive Analytics 6. 'R' professionals who want to captivate and analyze Big Data 7. Hadoop Professionals who want to learn R and ML techniques 8. Analysts wanting to understand Data Science methodologies For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free). Website: https://www.edureka.co/data-science Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Customer Reviews: Gnana Sekhar Vangara, Technology Lead at WellsFargo.com, says, "Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best. " Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 70115 edureka!
Twitter Sentiment Analysis Opinion Mining Projects
 
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Contact Best Phd Projects Visit us: http://www.phdprojects.org/
Views: 38 PHD PROJECTS
Monitoring Fake Product Reviews Java Project
 
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Monitoring Fake Product Reviews Java & MySQL Project
Views: 306 1000 Projects
Social Media Marketing and Management - Data Mining - Text Mining - Sentimental Analysis
 
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-Explanation: A Social Media Marketing and Management Project -Lesson: Data Mining -Subject: Sentimental Analysis ( Emotional Analysis ) of Text Mining ----------- -Açıklama: Sosyal Medya ve Pazarlama Uygulaması Projesi -Ders: Veri Madenciliği -Konu: Duygusal Metin Analizi
Views: 187 Egemen Kayalidere
Sentiment analysis: from opinion mining to human-agent interaction | Final Year Projects 2016
 
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Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://myprojectbazaar.com Get Discount @ https://goo.gl/dhBA4M Chat Now @ http://goo.gl/snglrO Visit Our Channel: https://www.youtube.com/user/myprojectbazaar Mail Us: [email protected]
Views: 54 myproject bazaar
Cancer Identification Data Mining Java Project
 
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Cancer Identification System Data Mining Java Project Download Project Code, Report and PPT :+91 7702177291, +91 9052016340 Email : [email protected] Website : www.1000projects.org
Views: 1919 1000 Projects
SAGA Talk - Frank Karg - The 4th Industrial Revolution & Mining 4.0: A business perspective
 
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23rd November 2018 Check out an opinion piece on how the South African Labour Departments is positioned to deal with Mining 4.0 that was published after this talk: https://www.dailymaverick.co.za/article/2018-12-07-labour-department-is-out-of-its-depth-on-the-fourth-industrial-revolution/ The ppt for this talk can be downloaded at: https://drive.google.com/open?id=15mZlgtAl30ln25NmN7yDHj3QC_Epdw-1 Frank Karg's CV: Frank’s background is as an independent management consultant with extensive corporate experience in finance, marketing and sales, logistics and technical functions, from supervisory up to executive positions in the IT and telecommunications industry. He spent the first 15 years of his career in technical services management and then became involved in managing the financial and business operations in these companies for a further 15 years at senior management and executive level. From 2003 until 2010 he worked in the training and human capital development fields for clients in government, parastatal and NGO environments. Frank has worked on projects for clients such as the Department of Labour, several SETAs, a bargaining council and two major banks. He has also written and facilitated training programmes in areas such as Project Management, Financial Management and Financial Intelligence. In 2009 he began coaching MBA students at Wits Business School and became involved with activities at the Leadership Development Centre (LDC) in the Executive Education department as well. He has been a sessional staff member since 2013, working as a Programme Director on development programmes.
How to Read a Research Paper
 
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Ever wondered how I consume research so fast? I'm going to describe the process i use to read lots of machine learning research papers fast and efficiently. It's basically a 3-pass approach, i'll go over the details and show you the extra resources I use to learn these advanced topics. You don't have to be a PhD, anyone can read research papers. It just takes practice and patience. Please Subscribe! And like. And comment. That's what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology instagram: https://www.instagram.com/sirajraval More learning resources: http://www.arxiv-sanity.com/ https://www.reddit.com/r/MachineLearning/ https://www.elsevier.com/connect/infographic-how-to-read-a-scientific-paper https://www.quora.com/How-do-I-start-reading-research-papers-on-Machine-Learning https://www.reddit.com/r/MachineLearning/comments/6rj9r4/d_how_do_you_read_mathheavy_machine_learning/ https://machinelearningmastery.com/how-to-research-a-machine-learning-algorithm/ http://www.sciencemag.org/careers/2016/03/how-seriously-read-scientific-paper Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 206261 Siraj Raval
unstructured datamining of hotel reviews
 
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this a project on dataming for opinion mining analysis of the hotel reviews and generate the new rating model fo hotels.
Views: 360 subash khati
Tips, Tricks and Topics in Text Analysis - Bhargav Srinivasa Desikan
 
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PyData LA 2018 Not only is there an abundance of textual data, there is also an abundance of tools help analyse this data - and it is tough to choose the right tool for the right task. In this workshop we will be dealing with the entire text analysis process - this means we'll start with finding data, set up a pipeline to clean our text, annotate it, and then have it ready to do some more advanced analysis. Repo - https://github.com/bhargavvader/personal/tree/master/notebooks/text_analysis_tutorial --- www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.
Views: 755 PyData
YouTube for Opinion Mining Research at the USC Institute for Creative Technologies
 
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University of Southern California Institute for Creative Technologies computer scientist Louis-Philippe Morency is analyzing online videos to capture the nuances of how people communicate opinions through words and actions. For Morency, who is also research assistant professor at the USC Viterbi School of Engineering, online videos are the latest tool in the growing field of opinion mining. In his current research -- figuring out how to identify when someone is sharing a positive, negative or neutral opinion - YouTube provides a limitless library of likes and loathes. Morency and his colleagues created a proof-of-concept data set of about 50 YouTube videos that feature people expressing their opinions. The videos were input into a computer program Morency developed that zeroes in on aspects of the speaker's language, speech patterns and facial expressions to determine the type of opinion being shared. Morency's small sample has already identified several advantages to analyzing gestures and speech patterns over looking at writing alone. First, people don't always use obvious polarizing words like love and hate each time they express an opinion. So software programmed to search for these "obvious" occurrences can miss many other valuable posts. Also, Morency found that people smile and look at the camera more when sharing a positive view. Their voices become higher pitched when they have a positive or negative opinion, and they start to use a lot more pauses when they are neutral. "These early findings are promising but we still have a long way to go," said Morency. "What they tell us is that what you say, how you say it, and the gestures you make while speaking all play a role in pinpointing the correct sentiment." Morency first demonstrated his YouTube model at the International Conference on Multimodal Interaction in Spain last fall. He has since expanded the data set to include close to 500 videos and will submit results from this larger sample for publication later this year. The YouTube opinion data set is also available to other researchers by contacting Morency's Multimodal Communication and Machine Learning lab at ICT. Potential commercial uses could include for marketing or survey purposes. In the academic community, Morency foresees his research and database being resources for scientists working to understand human non-verbal and verbal communication, helping to identify conditions like autism or depression or to build more engaging educational systems. For more information go to: http://multicomp.ict.usc.edu/
Views: 2018 USCICT
How Big Data Is Used In Amazon Recommendation Systems | Big Data Application & Example | Simplilearn
 
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This Big Data Video will help you understand how Amazon is using Big Data is ued in their recommendation syatems. You will understand the importance of Big Data using case study. Recommendation systems have impacted or even redefined our lives in many ways. One example of this impact is how our online shopping experience is being redefined. As we browse through products, the Recommendation system offer recommendations of products we might be interested in. Regardless of the perspectives, business or consumer, Recommendation systems have been immensely beneficial. And big data is the driving force behind Recommendation systems. Subscribe to Simplilearn channel for more Big Data and Hadoop Tutorials - https://www.youtube.com/user/Simplilearn?sub_confirmation=1 Check our Big Data Training Video Playlist: https://www.youtube.com/playlist?list=PLEiEAq2VkUUJqp1k-g5W1mo37urJQOdCZ Big Data and Analytics Articles - https://www.simplilearn.com/resources/big-data-and-analytics?utm_campaign=Amazon-BigData-S4RL6prqtGQ&utm_medium=Tutorials&utm_source=youtube To gain in-depth knowledge of Big Data and Hadoop, check our Big Data Hadoop and Spark Developer Certification Training Course: http://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training?utm_campaign=Amazon-BigData-S4RL6prqtGQ&utm_medium=Tutorials&utm_source=youtube #bigdata #bigdatatutorialforbeginners #bigdataanalytics #bigdatahadooptutorialforbeginners #bigdatacertification #HadoopTutorial - - - - - - - - - About Simplilearn's Big Data and Hadoop Certification Training Course: The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab. Mastering real-time data processing using Spark: You will learn to do functional programming in Spark, implement Spark applications, understand parallel processing in Spark, and use Spark RDD optimization techniques. You will also learn the various interactive algorithm in Spark and use Spark SQL for creating, transforming, and querying data form. As a part of the course, you will be required to execute real-life industry-based projects using CloudLab. The projects included are in the domains of Banking, Telecommunication, Social media, Insurance, and E-commerce. This Big Data course also prepares you for the Cloudera CCA175 certification. - - - - - - - - What are the course objectives of this Big Data and Hadoop Certification Training Course? This course will enable you to: 1. Understand the different components of Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark 2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management 3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts 4. Get an overview of Sqoop and Flume and describe how to ingest data using them 5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning 6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution 7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations 8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS 9. Gain a working knowledge of Pig and its components 10. Do functional programming in Spark 11. Understand resilient distribution datasets (RDD) in detail 12. Implement and build Spark applications 13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques 14. Understand the common use-cases of Spark and the various interactive algorithms 15. Learn Spark SQL, creating, transforming, and querying Data frames - - - - - - - - - - - Who should take up this Big Data and Hadoop Certification Training Course? Big Data career opportunities are on the rise, and Hadoop is quickly becoming a must-know technology for the following professionals: 1. Software Developers and Architects 2. Analytics Professionals 3. Senior IT professionals 4. Testing and Mainframe professionals 5. Data Management Professionals 6. Business Intelligence Professionals 7. Project Managers 8. Aspiring Data Scientists - - - - - - - - For more updates on courses and tips follow us on: - Facebook : https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn - LinkedIn: https://www.linkedin.com/company/simplilearn - Website: https://www.simplilearn.com Get the android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
Views: 29818 Simplilearn
An Ontology Based Text Mining Framework for R&D Project Selection
 
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An Ontology Based Text Mining Framework for R&D Project Selection ieee project in java
Views: 361 satya narayana
AI and Opinion Mining
 
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AI and Opinion Mining Abstract: The advent of Web 2.0 and social media content has stirred much excitement and created abundant opportunities for understanding the opinions of the general public and consumers toward social events, political movements, company strategies, marketing campaigns, and product preferences. Many new and exciting social, geopolitical, and business-related research questions can be answered by analyzing the thousands, even millions, of comments and responses expressed in various blogs (such as the blogosphere), forums (such as Yahoo Forums), social media and social network sites (including YouTube, Facebook, and Flikr), virtual worlds (such as Second Life), and tweets (Twitter). Opinion mining, a subdiscipline within data mining and computational linguistics, refers to the computational techniques for extracting, classifying, understanding, and assessing the opinions expressed in various online news sources, social media comments, and other user-generated content. Sentiment analysis is often used in opinion mining to identify sentiment, affect, subjectivity, and other emotional states in online text. SIMILAR VIDEOS: https://www.youtube.com/watch?v=AZI6oHAEtU8 https://www.youtube.com/watch?v=o0mT99zKAqA https://www.youtube.com/watch?v=X7jZtTq74WU https://www.youtube.com/watch?v=EO1rgFk07kQ https://www.youtube.com/watch?v=ACtU9aaoh_8 https://www.youtube.com/watch?v=cbZFKV4A0X8 https://www.youtube.com/watch?v=AWcD3pIGJjI https://www.youtube.com/watch?v=0y5w5CbMips https://www.youtube.com/watch?v=rhCtDFPNHCE https://www.youtube.com/watch?v=t41nfgBy8pY https://www.youtube.com/watch?v=LLUlzVlIJOw https://www.youtube.com/watch?v=mSjS4IGyrW0 https://www.youtube.com/watch?v=1TnAqAkxuws https://www.youtube.com/watch?v=nxoUUe8rrtQ https://www.youtube.com/watch?v=XBzwg1EY2SI https://www.youtube.com/watch?v=RRVWWUd9NLk https://www.youtube.com/watch?v=Es0eHDHksiM https://www.youtube.com/watch?v=x5CAAPGuo3g https://www.youtube.com/watch?v=sQKIpfEpQmo https://www.youtube.com/watch?v=hcmrJkwn1T4 https://www.youtube.com/watch?v=cNw3u68a424 https://www.youtube.com/watch?v=6sKfA1vFZBA https://www.youtube.com/watch?v=cFsryGMYxIE For More Videos - https://www.youtube.com/channel/UCR5lsF-lDQu6rVYVJPqNn6Q SOCIAL HANDLES: SCOOP IT- http://www.scoop.it/u/1croreprojects FACEBOOK - https://www.facebook.com/1Croreprojectsieeeprojects/ TWITTER - https://twitter.com/1crore_projects LINKEDIN - https://www.linkedin.com/in/1-crore-projects-ba982a118/ GOOGLE+ - https://plus.google.com/u/0/105783610929019156122 PINTEREST - https://in.pinterest.com/onecroreproject/ BLOG - 1croreprojectz.blogspot.com DOMAIN PROJECTS DOTNET - http://www.1croreprojects.com/dotnet-ieee-project-centers-in-chennai.php JAVA - http://www.1croreprojects.com/java-ieee-projects-chennai.php EMBEDDED - http://www.1croreprojects.com/embedded-systems-ieee-projects-chennai.php MATLAB - http://www.1croreprojects.com/matlab-ieee-projects-chennai.php NS2 - http://www.1croreprojects.com/ns2-ieee-projects-chennai.php VLSI -http://www.1croreprojects.com/vlsi-ieee-projects-chennai.php FOR PROJECTS - http://www.1croreprojects.com/ BUSINESS CONTACT: Email - [email protected] We are always open for all business prospects. You can get in touch which us, using the above mentioned e-mail id and contact number. ABOUT 1CROREPROJECTS: 1Crore Projects is company providing outstanding, cost-effective, effective result authorized on solutions. Our objective is to create solutions that enhance company process and increase come back in most possible time. We started truly to provide solutions to the customers all over the world. We have been effectively in providing solutions for different challenges across a wide range of market and customers propagate across the globe.
Views: 60 1 Crore Projects
How to Build a Text Mining, Machine Learning Document Classification System in R!
 
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We show how to build a machine learning document classification system from scratch in less than 30 minutes using R. We use a text mining approach to identify the speaker of unmarked presidential campaign speeches. Applications in brand management, auditing, fraud detection, electronic medical records, and more.
Views: 164869 Timothy DAuria
Big Data Analytics using Python and Apache Spark | Machine Learning Tutorial
 
09:28:18
Apache Spark is the most active Apache project, and it is pushing back Map Reduce. It is fast, general purpose and supports multiple programming languages, data sources and management systems. More and more organizations are adapting Apache Spark to build big data solutions through batch, interactive and stream processing paradigms. The demand for trained professionals in Spark is going through the roof. Being a new technology, there aren't enough training sources to provide easy guidance on building end-to-end solutions. Section 1: Introduction Lecture 1 About the course 08:42 Lecture 2 About V2 Maestros 01:39 Lecture 3 Resource Bundle Article Section 2: Overview Lecture 4 Hadoop Overview 10:06 Lecture 5 HDFS Architecture 14:46 Lecture 6 Map Reduce - How it works 17:24 Lecture 7 Map Reduce - Example 16:46 Lecture 8 Hadoop Stack 06:27 Lecture 9 What is Spark? 14:03 Lecture 10 Spark Architecture - Part 1 13:23 Lecture 11 Spark Architecture - Part 2 13:25 Lecture 12 Installing Spark and Setting up for Python 12:05 Quiz 1 Hadoop and Spark Architecture 5 questions Section 3: Programming with Spark Lecture 13 Spark Transformations 11:33 Lecture 14 Spark Actions 15:04 Lecture 15 Advanced Spark Programming 10:10 Lecture 16 Python - Spark Programming examples 1 16:11 Lecture 17 Python - Spark Programming Examples 2 17:18 Quiz 2 Data Engineering with Spark 5 questions Lecture 18 PRACTICE Exercise : Spark Operations Article Section 4: Spark SQL Lecture 19 Spark SQL Overview 10:03 Lecture 20 Python - Spark SQL Examples 16:16 Quiz 3 Spark SQL 2 questions Lecture 21 PRACTICE Exercise : Spark SQL Article Section 5: Spark Streaming Lecture 22 Streaming with Apache Spark 15:53 Lecture 23 Python - Spark Streaming examples 17:47 Quiz 4 Spark Streaming 3 questions Section 6: Real time Data Science Lecture 24 Basic Elements of Data Science 11:51 Lecture 25 The Dataset 10:44 Lecture 26 Learning from relationships 12:55 Lecture 27 Modeling and Prediction 09:31 Lecture 28 Data Science Use Cases 07:47 Lecture 29 Types of Analytics 12:08 Lecture 30 Types of Learning 17:16 Lecture 31 Doing Data Science in real time with Spark 07:39 Quiz 5 Spark Data Science 5 questions Section 7: Machine Learning with Spark Lecture 32 Spark Machine Learning 12:18 Lecture 33 Analyzing Results and Errors 13:46 Lecture 34 Linear Regression 19:00 Lecture 35 Spark Use Case : Linear Regression 18:33 Lecture 36 Decision Trees 10:42 Lecture 37 Spark Use Case : Decision Trees Classification 14:58 Lecture 38 Principal Component Analysis 07:28 Lecture 39 Random Forests Classification 10:31 Lecture 40 Python Use Case : Random Forests & PCA 13:16 Lecture 41 Text Preprocessing with TF-IDF 14:53 Lecture 42 Naive Bayes Classification 19:21 Lecture 43 Spark Use Case : Naive Bayes & TF-IDF 07:26 Lecture 44 K-Means Clustering 11:53 Lecture 45 Spark Use Case : K-Means 14:26 Lecture 46 Recommendation Engines 11:55 Lecture 47 Spark Use Case : Collaborative Filtering 06:34 Lecture 48 Real Time Twitter Data Sentiment Analysis 10:11 Quiz 6 Spark Machine Learning Algorithms 4 questions Lecture 49 PRACTICE Exercise : Spark Clustering Article Lecture 50 PRACTICE Exercise : Spark Classification Article Section 8: Conclusion Lecture 51 Closing Remarks 01:56 Lecture 52 BONUS Lecture : Other courses you should check out Article
Product Review Analysis
 
10:38
Get this project at http://nevonprojects.com/product-review-analysis-for-genuine-rating/ System analyses product reviews to extract positivity or negativity & generate genuine rating of products as per reviews to help customer in buying right product
Views: 3130 Nevon Projects
Social Media Mining
 
01:11
Hundreds of millions of people spending countless hours on social media to share, communicate, connect, interact, and create user-generated data. Using data mining, machine learning, text mining, social network analysis, and information retrieval, we could mine valuable knowledge for social science researches and business marketing proposes. This project was our graduation project. we used a real data from Facebook to give a proper recommendation for users about movies and series due to the social group that our users belongs to, we also managed to recommend friends to a user due to interests similarity.
Data mining demo
 
02:36
Read more about Work Package 4 "Decision Rules and Evidence" at http://www.transformproject.eu/about/work-packages/ Read the Work Package 4 deliverables and reports at http://www.transformproject.eu/dissemination/deliverables/
Product Review Monitoring | Product Reviews Management | Amazon Review Notification Negative Alert
 
01:51
Product review monitoring and management solution. An easy way to monitor your products reviews and brand reputation across the entire internet. http://www.reviewmonitoring.com
Views: 11557 Review Monitoring
Sentimental Analysis in R
 
13:51
Sentiment analysis or opinion mining is the computational study of people’s opinions, sentiments, attitudes, and emotions expressed in written language. Also it refers to the task of natural language processing to determine whether a piece of text contains some subjective information and what subjective information it expresses, i.e., whether the attitude behind this text is positive, negative or neutral. Understanding the opinions behind user-generated content automatically is of great help for commercial and political use, among others. The task can be conducted on different levels, classifying the polarity of words or sentences. It is one of the most active research areas in natural language processing and text mining in recent years. Its popularity is mainly due to two reasons. First, it has a wide range of applications because opinions are central to almost all human activities and are key influencers of our behaviors. Whenever we need to make a decision, we want to hear others’ opinions. Second, it presents many challenging research problems, which had never been attempted before the year 2000. Part of the reason for the lack of study before was that there was little opinionated text in digital forms. It is thus no surprise that the inception and the rapid growth of the field coincide with those of the social media on the Web. In fact, the research has also spread outside of computer science to management sciences and social sciences due to its importance to business and society as a whole.
Views: 4236 Mavericks 045_049_078
Detecting Fake Reviews
 
01:16:29
Opinions in social media are increasingly used by individuals and organizations for making purchase decisions and for marketing and product design. Positive opinions can result in significant financial gains and fames for businesses and individuals. This, unfortunately, gives strong incentives for people to game the system by posting fake positive opinions/reviews to some entities (e.g. products and services) in order to promote them, and/or malicious negative reviews to damage their reputations. Such imposters are called opinion spammers and their activities are called opinion spamming. Fake reviews are rampant on the Internet and are seriously undermining the credibility and trustworthiness of online opinions. Fake reviews come from many different sources. Businesses may write for themselves and also pay individuals, middlemen, and also the so-called �reputation management� firms to write on their behalf. They may also ask their customers to write by giving the customers discounts. As more and more individuals and organizations are using reviews for their decision making, detecting fake reviews has become a pressing issue. Many high profile fake review cases have been reported in the news. I have compiled several press articles and interesting links and listed them in my research page (http://www.cs.uic.edu/~liub/FBS/fake-reviews.html). In this talk, I will first introduce the problem and discuss its major challenges. I will then describe some of our recent work on the topic.
Views: 1397 Microsoft Research
Mining Social Media Data for Understanding Students’ Learning Experiences
 
00:54
Students’ informal conversations on social media (e.g. Twitter, Facebook) shed light into their educational experiences—opinions, feelings, and concerns about the learning process. Data from such uninstrumented environments can provide valuable knowledge to inform student learning. Analyzing such data, however, can be challenging. The complexity of students’ experiences reflected from social media content requires human interpretation. However, the growing scale of data demands automatic data analysis techniques. In this paper, we developed a workflow to integrate both qualitative analysis and large-scale data mining techniques. We focused on engineering students’ Twitter posts to understand issues and problems in their educational experiences. We first conducted a qualitative analysis on samples taken from about 25,000 tweets related to engineering students’ college life. We found engineering students encounter problems such as heavy study load, lack of social engagement, and sleep deprivation. Based on these results, we implemented a multi label classification algorithm to classify tweets reflecting students’ problems. We then used the algorithm to train a detector of student problems from tweets streamed at the geo-location of Purdue University. This work, for the first time, presents a methodology and results that show how informal social media data can provide insights into students’ experiences.
Optimizing a multi product continuous review inventory model with uncertain demand,quality improveme
 
11:00
Optimizing a multi product continuous review inventory model with uncertain demand,quality improveme- IEEE PROJECTS 2018 Download projects @ www.micansinfotech.com WWW.SOFTWAREPROJECTSCODE.COM https://www.facebook.com/MICANSPROJECTS Call: +91 90036 28940 ; +91 94435 11725 IEEE PROJECTS, IEEE PROJECTS IN CHENNAI,IEEE PROJECTS IN PONDICHERRY.IEEE PROJECTS 2018,IEEE PAPERS,IEEE PROJECT CODE,FINAL YEAR PROJECTS,ENGINEERING PROJECTS,PHP PROJECTS,PYTHON PROJECTS,NS2 PROJECTS,JAVA PROJECTS,DOT NET PROJECTS,IEEE PROJECTS TAMBARAM,HADOOP PROJECTS,BIG DATA PROJECTS,Signal processing,circuits system for video technology,cybernetics system,information forensic and security,remote sensing,fuzzy and intelligent system,parallel and distributed system,biomedical and health informatics,medical image processing,CLOUD COMPUTING, NETWORK AND SERVICE MANAGEMENT,SOFTWARE ENGINEERING,DATA MINING,NETWORKING ,SECURE COMPUTING,CYBERSECURITY,MOBILE COMPUTING, NETWORK SECURITY,INTELLIGENT TRANSPORTATION SYSTEMS,NEURAL NETWORK,INFORMATION AND SECURITY SYSTEM,INFORMATION FORENSICS AND SECURITY,NETWORK,SOCIAL NETWORK,BIG DATA,CONSUMER ELECTRONICS,INDUSTRIAL ELECTRONICS,PARALLEL AND DISTRIBUTED SYSTEMS,COMPUTER-BASED MEDICAL SYSTEMS (CBMS),PATTERN ANALYSIS AND MACHINE INTELLIGENCE,SOFTWARE ENGINEERING,COMPUTER GRAPHICS, INFORMATION AND COMMUNICATION SYSTEM,SERVICES COMPUTING,INTERNET OF THINGS JOURNAL,MULTIMEDIA,WIRELESS COMMUNICATIONS,IMAGE PROCESSING,IEEE SYSTEMS JOURNAL,CYBER-PHYSICAL-SOCIAL COMPUTING AND NETWORKING,DIGITAL FORENSIC,DEPENDABLE AND SECURE COMPUTING,AI - MACHINE LEARNING (ML),AI - DEEP LEARNING ,AI - NATURAL LANGUAGE PROCESSING ( NLP ),AI - VISION (IMAGE PROCESSING),mca project NETWORK AND SERVICE MANAGEMENT 1. Bacterial foraging optimization based Radial Basis Function Neural Network (BRBFNN) for identification and classification of plant leaf diseases: An automatic approach towards Plant Pathology(12 February 2018 ) 2. Fault-Tolerant Clustering Topology Evolution Mechanism of Wireless Sensor Networks (08 June 2018) SOFTWARE ENGINEERING 1. Reviving Sequential Program Birthmarking for Multithreaded Software Plagiarism Detection 2. EVA: Visual Analytics to Identify Fraudulent Events DATA MINING 1. Opinion Aspect Relations in Cognizing Customer Feelings via Reviews(24 January 2018) 2. Optimizing a multi-product continuous-review inventory model with uncertain demand, quality improvement, setup cost reduction, and variation control in lead time (27 June 2018) 3. Evaluation of Predictive Data Mining Algorithms in Soil Data Classification for Optimized Crop Recommendation (09 April 2018) 4. Prediction of Effective Rainfall and Crop Water Needs using Data Mining Techniques (01 February 2018) 5. A Secure Client-Side Framework for Protecting the Privacy of Health DataStored on the Cloud( 04 June 2018) 6. Greedy Optimization for K-Means-Based Consensus Clustering(April 2018) 7. A Two-stage Biomedical Event Trigger Detection Method Integrating Feature Selection and Word Embeddings 8. Principal Component Analysis Based Filtering for Scalable, High Precision k-NN Search 9. Entity Linking: A Problem to Extract Corresponding Entity with Knowledge Base 10. Collective List-Only Entity Linking: A Graph-Based Approach 11. Web Media and Stock Markets : A Survey and Future Directionsfrom a Big Data Perspective 12. Selective Database Projections Based Approach for Mining High-Utility Itemsets 13. Reverse k Nearest Neighbor Search over Trajectories 14. Range-based Nearest Neighbor Queries with Complex-shaped Obstacles 15. Predicting Contextual Informativeness for Vocabulary Learning 16. Online Product Quantization 17. Highlighter: automatic highlighting of electronic learning documents 18. Fuzzy Bag-of-Words Model for Document Representation 19. Frequent Itemsets Mining with Differential Privacy over Large-scale Data 20. Fast Cosine Similarity Search in Binary Space with Angular Multi-index Hashing 21. Efficient Vertical Mining of High Average-Utility Itemsets based on Novel Upper-Bounds 22. Document Summarization for Answering Non-Factoid Queries 23. Discovering Canonical Correlations between Topical andTopological Information in Document Networks 24. Complementary Aspect-based Opinion Mining 25. An Efficient Method for High Quality and Cohesive Topical Phrase Mining 26. A Weighted Frequent Itemset Mining Algorithm for Intelligent Decision in Smart Systems 27. A Correlation-based Feature Weighting Filter for Naive Bayes 28. Comments Mining With TF-IDF: The Inherent Bias and Its Removal 29. Bayesian Nonparametric Learning for Hierarchical and Sparse Topics 30. Supervised Topic Modeling using Hierarchical Dirichlet Process-based Inverse Regression: Experiments on E-Commerce Applications 31. Emotion Recognition on Twitter: Comparative Study and Training a Unison Model 32. Search Result Diversity Evaluation based on Intent Hierarchies 33. A Two-Phase Algorithm for Differentially Private Frequent Subgraph Mining
Views: 4 Micans Infotech
Optimizing a multi product continuous review inventory model with uncertain demand,quality improveme
 
11:00
Optimizing a multi product continuous review inventory model with uncertain demand,quality improveme- IEEE PROJECTS 2018 Download projects @ www.micansinfotech.com WWW.SOFTWAREPROJECTSCODE.COM https://www.facebook.com/MICANSPROJECTS Call: +91 90036 28940 ; +91 94435 11725 IEEE PROJECTS, IEEE PROJECTS IN CHENNAI,IEEE PROJECTS IN PONDICHERRY.IEEE PROJECTS 2018,IEEE PAPERS,IEEE PROJECT CODE,FINAL YEAR PROJECTS,ENGINEERING PROJECTS,PHP PROJECTS,PYTHON PROJECTS,NS2 PROJECTS,JAVA PROJECTS,DOT NET PROJECTS,IEEE PROJECTS TAMBARAM,HADOOP PROJECTS,BIG DATA PROJECTS,Signal processing,circuits system for video technology,cybernetics system,information forensic and security,remote sensing,fuzzy and intelligent system,parallel and distributed system,biomedical and health informatics,medical image processing,CLOUD COMPUTING, NETWORK AND SERVICE MANAGEMENT,SOFTWARE ENGINEERING,DATA MINING,NETWORKING ,SECURE COMPUTING,CYBERSECURITY,MOBILE COMPUTING, NETWORK SECURITY,INTELLIGENT TRANSPORTATION SYSTEMS,NEURAL NETWORK,INFORMATION AND SECURITY SYSTEM,INFORMATION FORENSICS AND SECURITY,NETWORK,SOCIAL NETWORK,BIG DATA,CONSUMER ELECTRONICS,INDUSTRIAL ELECTRONICS,PARALLEL AND DISTRIBUTED SYSTEMS,COMPUTER-BASED MEDICAL SYSTEMS (CBMS),PATTERN ANALYSIS AND MACHINE INTELLIGENCE,SOFTWARE ENGINEERING,COMPUTER GRAPHICS, INFORMATION AND COMMUNICATION SYSTEM,SERVICES COMPUTING,INTERNET OF THINGS JOURNAL,MULTIMEDIA,WIRELESS COMMUNICATIONS,IMAGE PROCESSING,IEEE SYSTEMS JOURNAL,CYBER-PHYSICAL-SOCIAL COMPUTING AND NETWORKING,DIGITAL FORENSIC,DEPENDABLE AND SECURE COMPUTING,AI - MACHINE LEARNING (ML),AI - DEEP LEARNING ,AI - NATURAL LANGUAGE PROCESSING ( NLP ),AI - VISION (IMAGE PROCESSING),mca project NETWORK AND SERVICE MANAGEMENT 1. Bacterial foraging optimization based Radial Basis Function Neural Network (BRBFNN) for identification and classification of plant leaf diseases: An automatic approach towards Plant Pathology(12 February 2018 ) 2. Fault-Tolerant Clustering Topology Evolution Mechanism of Wireless Sensor Networks (08 June 2018) SOFTWARE ENGINEERING 1. Reviving Sequential Program Birthmarking for Multithreaded Software Plagiarism Detection 2. EVA: Visual Analytics to Identify Fraudulent Events DATA MINING 1. Opinion Aspect Relations in Cognizing Customer Feelings via Reviews(24 January 2018) 2. Optimizing a multi-product continuous-review inventory model with uncertain demand, quality improvement, setup cost reduction, and variation control in lead time (27 June 2018) 3. Evaluation of Predictive Data Mining Algorithms in Soil Data Classification for Optimized Crop Recommendation (09 April 2018) 4. Prediction of Effective Rainfall and Crop Water Needs using Data Mining Techniques (01 February 2018) 5. A Secure Client-Side Framework for Protecting the Privacy of Health DataStored on the Cloud( 04 June 2018) 6. Greedy Optimization for K-Means-Based Consensus Clustering(April 2018) 7. A Two-stage Biomedical Event Trigger Detection Method Integrating Feature Selection and Word Embeddings 8. Principal Component Analysis Based Filtering for Scalable, High Precision k-NN Search 9. Entity Linking: A Problem to Extract Corresponding Entity with Knowledge Base 10. Collective List-Only Entity Linking: A Graph-Based Approach 11. Web Media and Stock Markets : A Survey and Future Directionsfrom a Big Data Perspective 12. Selective Database Projections Based Approach for Mining High-Utility Itemsets 13. Reverse k Nearest Neighbor Search over Trajectories 14. Range-based Nearest Neighbor Queries with Complex-shaped Obstacles 15. Predicting Contextual Informativeness for Vocabulary Learning 16. Online Product Quantization 17. Highlighter: automatic highlighting of electronic learning documents 18. Fuzzy Bag-of-Words Model for Document Representation 19. Frequent Itemsets Mining with Differential Privacy over Large-scale Data 20. Fast Cosine Similarity Search in Binary Space with Angular Multi-index Hashing 21. Efficient Vertical Mining of High Average-Utility Itemsets based on Novel Upper-Bounds 22. Document Summarization for Answering Non-Factoid Queries 23. Discovering Canonical Correlations between Topical andTopological Information in Document Networks 24. Complementary Aspect-based Opinion Mining 25. An Efficient Method for High Quality and Cohesive Topical Phrase Mining 26. A Weighted Frequent Itemset Mining Algorithm for Intelligent Decision in Smart Systems 27. A Correlation-based Feature Weighting Filter for Naive Bayes 28. Comments Mining With TF-IDF: The Inherent Bias and Its Removal 29. Bayesian Nonparametric Learning for Hierarchical and Sparse Topics 30. Supervised Topic Modeling using Hierarchical Dirichlet Process-based Inverse Regression: Experiments on E-Commerce Applications
Prediction of effective rainfall and crop water needs using data- IEEE PROJECTS 2018
 
09:18
Prediction of effective rainfall and crop water needs using data mining techniques- IEEE PROJECTS 2018 Download projects @ www.micansinfotech.com WWW.SOFTWAREPROJECTSCODE.COM https://www.facebook.com/MICANSPROJECTS Call: +91 90036 28940 ; +91 94435 11725 IEEE PROJECTS, IEEE PROJECTS IN CHENNAI,IEEE PROJECTS IN PONDICHERRY.IEEE PROJECTS 2018,IEEE PAPERS,IEEE PROJECT CODE,FINAL YEAR PROJECTS,ENGINEERING PROJECTS,PHP PROJECTS,PYTHON PROJECTS,NS2 PROJECTS,JAVA PROJECTS,DOT NET PROJECTS,IEEE PROJECTS TAMBARAM,HADOOP PROJECTS,BIG DATA PROJECTS,Signal processing,circuits system for video technology,cybernetics system,information forensic and security,remote sensing,fuzzy and intelligent system,parallel and distributed system,biomedical and health informatics,medical image processing,CLOUD COMPUTING, NETWORK AND SERVICE MANAGEMENT,SOFTWARE ENGINEERING,DATA MINING,NETWORKING ,SECURE COMPUTING,CYBERSECURITY,MOBILE COMPUTING, NETWORK SECURITY,INTELLIGENT TRANSPORTATION SYSTEMS,NEURAL NETWORK,INFORMATION AND SECURITY SYSTEM,INFORMATION FORENSICS AND SECURITY,NETWORK,SOCIAL NETWORK,BIG DATA,CONSUMER ELECTRONICS,INDUSTRIAL ELECTRONICS,PARALLEL AND DISTRIBUTED SYSTEMS,COMPUTER-BASED MEDICAL SYSTEMS (CBMS),PATTERN ANALYSIS AND MACHINE INTELLIGENCE,SOFTWARE ENGINEERING,COMPUTER GRAPHICS, INFORMATION AND COMMUNICATION SYSTEM,SERVICES COMPUTING,INTERNET OF THINGS JOURNAL,MULTIMEDIA,WIRELESS COMMUNICATIONS,IMAGE PROCESSING,IEEE SYSTEMS JOURNAL,CYBER-PHYSICAL-SOCIAL COMPUTING AND NETWORKING,DIGITAL FORENSIC,DEPENDABLE AND SECURE COMPUTING,AI - MACHINE LEARNING (ML),AI - DEEP LEARNING ,AI - NATURAL LANGUAGE PROCESSING ( NLP ),AI - VISION (IMAGE PROCESSING),mca project NETWORK AND SERVICE MANAGEMENT 1. Bacterial foraging optimization based Radial Basis Function Neural Network (BRBFNN) for identification and classification of plant leaf diseases: An automatic approach towards Plant Pathology(12 February 2018 ) 2. Fault-Tolerant Clustering Topology Evolution Mechanism of Wireless Sensor Networks (08 June 2018) SOFTWARE ENGINEERING 1. Reviving Sequential Program Birthmarking for Multithreaded Software Plagiarism Detection 2. EVA: Visual Analytics to Identify Fraudulent Events DATA MINING 1. Opinion Aspect Relations in Cognizing Customer Feelings via Reviews(24 January 2018) 2. Optimizing a multi-product continuous-review inventory model with uncertain demand, quality improvement, setup cost reduction, and variation control in lead time (27 June 2018) 3. Evaluation of Predictive Data Mining Algorithms in Soil Data Classification for Optimized Crop Recommendation (09 April 2018) 4. Prediction of Effective Rainfall and Crop Water Needs using Data Mining Techniques (01 February 2018) 5. A Secure Client-Side Framework for Protecting the Privacy of Health DataStored on the Cloud( 04 June 2018) 6. Greedy Optimization for K-Means-Based Consensus Clustering(April 2018) 7. A Two-stage Biomedical Event Trigger Detection Method Integrating Feature Selection and Word Embeddings 8. Principal Component Analysis Based Filtering for Scalable, High Precision k-NN Search 9. Entity Linking: A Problem to Extract Corresponding Entity with Knowledge Base 10. Collective List-Only Entity Linking: A Graph-Based Approach 11. Web Media and Stock Markets : A Survey and Future Directionsfrom a Big Data Perspective 12. Selective Database Projections Based Approach for Mining High-Utility Itemsets 13. Reverse k Nearest Neighbor Search over Trajectories 14. Range-based Nearest Neighbor Queries with Complex-shaped Obstacles 15. Predicting Contextual Informativeness for Vocabulary Learning 16. Online Product Quantization 17. Highlighter: automatic highlighting of electronic learning documents 18. Fuzzy Bag-of-Words Model for Document Representation 19. Frequent Itemsets Mining with Differential Privacy over Large-scale Data 20. Fast Cosine Similarity Search in Binary Space with Angular Multi-index Hashing 21. Efficient Vertical Mining of High Average-Utility Itemsets based on Novel Upper-Bounds 22. Document Summarization for Answering Non-Factoid Queries 23. Discovering Canonical Correlations between Topical andTopological Information in Document Networks 24. Complementary Aspect-based Opinion Mining 25. An Efficient Method for High Quality and Cohesive Topical Phrase Mining 26. A Weighted Frequent Itemset Mining Algorithm for Intelligent Decision in Smart Systems 27. A Correlation-based Feature Weighting Filter for Naive Bayes 28. Comments Mining With TF-IDF: The Inherent Bias and Its Removal 29. Bayesian Nonparametric Learning for Hierarchical and Sparse Topics 30. Supervised Topic Modeling using Hierarchical Dirichlet Process-based Inverse Regression: Experiments on E-Commerce Applications
Social Network Analysis
 
02:06:01
An overview of social networks and social network analysis. See more on this video at https://www.microsoft.com/en-us/research/video/social-network-analysis/
Views: 4618 Microsoft Research
Wireless Cocunut Cutting Robot
 
02:30
Excellence B-Tech Projects The No.1 institute for emerging institutes TRIVANDRUM & KOTTAYAM Phn : 9447717308 A unique project training institute having its own industrial facility Final Year Projects for BE, ME, B.Tech, M.Tech, MCA, M.Sc-CS, M.Sc-IT, BCA,, CSE, IT, EEE, ECE, E and I, Mechanical, Embedded, Computer Science, Information Technology, Ph.D, Final Year Projects using IEEE | , , C#, , VB, JAVA, JSP, J2EE, J2ME, ANDROID, PHP, SQL, MY-SQL, ORACLE, PYTHON, Raspberry PI, MATLAB, NS2, NS3, Simulink, VLSI, VHDL Final Year Projects Abstracts, Synopsis, Report, Documentation, Source Code, Topics, Titles | Final Year Projects in Web Based Applications | Windows, Networking, Network Security, Data Mining, Big Data, Cloud Computing, Grid Computing, Mobile Computing, Software Engineering, Image Processing, Knowledge Mining, Artificial Intelligence, Bluetooth, GSM, GPS Applications, Electronics, Embedded Systems, Robotics, Micro-controller, Instrumentation, Mechanical, Solar Engineering, Bio- Medical, Bio- Metrics, Academic Project Training and Development | Real Time and Live Projects | Free Download Final Year Projects | Best Project Center in trivandrum |Best Project Center kottayam |
Views: 292 Excellence Academy
Meet General Manager Stephen K. Ndede, Perseus Mining Ghana Ltd,
 
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Stephen Kofi Ndede is the Mine Operations Manager of Perseus Mining Ghana Ltd., Ghana. He has worked extensively in the Mining industry across the West African sub-region and is of the opinion that we shouldn't just mine, but mine economically and safely. Stephen Ndede is currently the General Manager of Perseus Mining Ghana Ltd (PMGL). This interview was done when he was the Mine Operations manager. Interviewer Lawrence Omari - Mensah is the President of the West African Institute of Mining, Metallurgy and Petroleum (WAIMM)
Views: 356 The WAIMM
Bitcoin news today: BTC predictions. new crypto coins. gold vs cryptocurrency mining
 
11:39
Bitcoin news today: BTC predictions, new crypto coins. gold vs cryptocurrency mining with http://nakamotojedi.com Meet NakamotoJedi with the latest btc news! In today’s episode: According to Bloomberg, the current volatility of the market is caused by $2 Billion Bitcoin Whale: when it is making a transaction, the whole crypto world is wobbling. Maybe, a famous economist Mohamad El-Erian is to be suspected for this? He sticks to the idea that bitcoin price should be $5,000. And McAfee, on the contrary, forecasts that in 2020 BTC will cost $1 million. Also, McAfee states that soon centralized exchanges will die out. To believe it or no, but MarketWatch is sure – the end of the year is always a prosperous period for cryptocurrency. Just trace the historical data from 2015, 2016 and 2017 – and you’ll see. Three months are left till the end of 2018, and there is still some time for Andrew Keys’s predictions on Ether to turn into reality. The leader of ConsenSys Capital claimed that ETH would surpass BTC in price, but it doesn’t seem realistic now. In the Canadian city of Midland thousands of PCs were hacked, and the intruders demanded the ransom in BTC. The sum hasn’t been disclosed, but it’s probably lower than 850,000 BTC – the most significant robbery in the history of bitcoins. And BitConnect has been considered a Ponzi scheme – American investors were fooled and lost their money. But Indian authorities already know whom to punish for it. Now SEC has a competitor - Crypto Asset Management project which claimed to be the first crypto regulator of asset funds in US. Guys, you are trespassing on the sacred! The best way to protect crypto from fin regulators is to form a powerful community, what Crypto Harbor is actually doing. Little CryptoKitties are going to Opera – these collectibles are now available in the Opera browser. And French FC Paris Saint-Germain are launching their new crypto coins. Mining has always been considered an energy-consuming activity, and probably you remember that one year of BTC mining needs as much energy as Austria or Denmark. But the head of ECB Mario Draghi says that mining gold is not cheaper. In fact, extraction of traditional gold costs $87 billion annually, while BTC mining needs $4.3 billion per year. One more argument is that the market cap of gold amounts to $8 trillion while the market capitalization of all cryptocurrencies is a little over $200 billion. According the expert Tim Draper, the situation will change in 15 years. At the end of today’s episode NakamotoJedi is putting cards on the table. Get the details of our giveaway and take part in it – a vast sum of money at stake. The most relevant crypto news on youtube here at NakamotoJedi! Get known about btc latest news, coin news, blockchain news, interesting announcements, and sensational updates of the crypto world. With our channel it will become clear for you how to make money trading cryptocurrency. Subscribe to our social media not to miss anything: https://NakamotoJedi.com https://twitter.com/NakamotoJedi https://facebook.com/NakamotoJedi/ https://t.me/cryptoconsultinginfo https://medium.com/@NakamotoJedi NakamotoJedi - Your Jedi Master in Blockchain and Cryptocurrencies! #bitcoin #btc #cryptocurrency #crypto #nakamotojedi
Text Mining
 
13:42
trabajo final de EDA
Automated Phrase Mining from Massive Text Corpora
 
09:12
2018 IEEE Transaction on Knowledge and Data Engineering For More Details::Contact::K.Manjunath - 09535866270 http://www.tmksinfotech.com and http://www.bemtechprojects.com 2018 and 2019 IEEE [email protected] TMKS Infotech,Bangalore
Views: 394 manju nath
Hotel Review Sentiment Analysis
 
01:51
Analyze customer reviews and feedbacks to find out the best hotels for distinct locations. In this solution, customer sentiment analysis is done to gain meaningful insights and pick up the hotels which are providing the best experience to their customers.
Views: 127 Advaiya
Co-Extracting Opinion Targets and Opinion Words from Online Reviews Based on the Word Alignment
 
14:38
Co-Extracting Opinion Targets and Opinion Words from Online Reviews Based on the Word Alignment Model To get this project in ONLINE or through TRAINING Sessions, Contact: JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai -83.Landmark: Next to Kotak Mahendra Bank. Pondicherry Office: JP INFOTECH, #45, Kamaraj Salai,Thattanchavady, Puducherry -9.Landmark: Next to VVP Nagar Arch. Mobile: (0) 9952649690, Email: [email protected], web: www.jpinfotech.org Blog: www.jpinfotech.blogspot.com Mining opinion targets and opinion words from online reviews are important tasks for fine-grained opinion mining, the key component of which involves detecting opinion relations among words. To this end, this paper proposes a novel approach based on the partially-supervised alignment model, which regards identifying opinion relations as an alignment process. Then, a graph-based co-ranking algorithm is exploited to estimate the confidence of each candidate. Finally, candidates with higher confidence are extracted as opinion targets or opinion words. Compared to previous methods based on the nearest-neighbor rules, our model captures opinion relations more precisely, especially for long-span relations. Compared to syntax-based methods, our word alignment model effectively alleviates the negative effects of parsing errors when dealing with informal online texts. In particular, compared to the traditional unsupervised alignment model, the proposed model obtains better precision because of the usage of partial supervision. In addition, when estimating candidate confidence, we penalize higher-degree vertices in our graph-based co-ranking algorithm to decrease the probability of error generation. Our experimental results on three corpora with different sizes and languages show that our approach effectively outperforms state-of-the-art methods.
Views: 2358 jpinfotechprojects
Complementary Aspect based Opinion Mining- IEEE PROJECTS 2018
 
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Complementary Aspect based Opinion Mining- IEEE PROJECTS 2018 Download projects @ www.micansinfotech.com WWW.SOFTWAREPROJECTSCODE.COM https://www.facebook.com/MICANSPROJECTS Call: +91 90036 28940 ; +91 94435 11725 IEEE PROJECTS, IEEE PROJECTS IN CHENNAI,IEEE PROJECTS IN PONDICHERRY.IEEE PROJECTS 2018,IEEE PAPERS,IEEE PROJECT CODE,FINAL YEAR PROJECTS,ENGINEERING PROJECTS,PHP PROJECTS,PYTHON PROJECTS,NS2 PROJECTS,JAVA PROJECTS,DOT NET PROJECTS,IEEE PROJECTS TAMBARAM,HADOOP PROJECTS,BIG DATA PROJECTS,Signal processing,circuits system for video technology,cybernetics system,information forensic and security,remote sensing,fuzzy and intelligent system,parallel and distributed system,biomedical and health informatics,medical image processing,CLOUD COMPUTING, NETWORK AND SERVICE MANAGEMENT,SOFTWARE ENGINEERING,DATA MINING,NETWORKING ,SECURE COMPUTING,CYBERSECURITY,MOBILE COMPUTING, NETWORK SECURITY,INTELLIGENT TRANSPORTATION SYSTEMS,NEURAL NETWORK,INFORMATION AND SECURITY SYSTEM,INFORMATION FORENSICS AND SECURITY,NETWORK,SOCIAL NETWORK,BIG DATA,CONSUMER ELECTRONICS,INDUSTRIAL ELECTRONICS,PARALLEL AND DISTRIBUTED SYSTEMS,COMPUTER-BASED MEDICAL SYSTEMS (CBMS),PATTERN ANALYSIS AND MACHINE INTELLIGENCE,SOFTWARE ENGINEERING,COMPUTER GRAPHICS, INFORMATION AND COMMUNICATION SYSTEM,SERVICES COMPUTING,INTERNET OF THINGS JOURNAL,MULTIMEDIA,WIRELESS COMMUNICATIONS,IMAGE PROCESSING,IEEE SYSTEMS JOURNAL,CYBER-PHYSICAL-SOCIAL COMPUTING AND NETWORKING,DIGITAL FORENSIC,DEPENDABLE AND SECURE COMPUTING,AI - MACHINE LEARNING (ML),AI - DEEP LEARNING ,AI - NATURAL LANGUAGE PROCESSING ( NLP ),AI - VISION (IMAGE PROCESSING),mca project, IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS, ,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS, IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS, IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,IEEE PROJECTS,FINAL YEAR PROJECTS,FINAL YEAR PROJECTS,FINAL YEAR PROJECTS,FINAL YEAR PROJECTS,FINAL YEAR PROJECTS,,FINAL YEAR PROJECTS,FINAL YEAR PROJECTS,FINAL YEAR PROJECTS,FINAL YEAR PROJECTS,FINAL YEAR PROJECTS,FINAL YEAR PROJECTS,FINAL YEAR PROJECTS,FINAL YEAR PROJECTS
Secured outsourcing towards a cloud computing environment based on DNA cryptography
 
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Secured outsourcing towards a cloud computing environment based on DNA cryptography- IEEE PROJECTS 2018 Download projects @ www.micansinfotech.com WWW.SOFTWAREPROJECTSCODE.COM https://www.facebook.com/MICANSPROJECTS Call: +91 90036 28940 ; +91 94435 11725 IEEE PROJECTS, IEEE PROJECTS IN CHENNAI,IEEE PROJECTS IN PONDICHERRY.IEEE PROJECTS 2018,IEEE PAPERS,IEEE PROJECT CODE,FINAL YEAR PROJECTS,ENGINEERING PROJECTS,PHP PROJECTS,PYTHON PROJECTS,NS2 PROJECTS,JAVA PROJECTS,DOT NET PROJECTS,IEEE PROJECTS TAMBARAM,HADOOP PROJECTS,BIG DATA PROJECTS,Signal processing,circuits system for video technology,cybernetics system,information forensic and security,remote sensing,fuzzy and intelligent system,parallel and distributed system,biomedical and health informatics,medical image processing,CLOUD COMPUTING, NETWORK AND SERVICE MANAGEMENT,SOFTWARE ENGINEERING,DATA MINING,NETWORKING ,SECURE COMPUTING,CYBERSECURITY,MOBILE COMPUTING, NETWORK SECURITY,INTELLIGENT TRANSPORTATION SYSTEMS,NEURAL NETWORK,INFORMATION AND SECURITY SYSTEM,INFORMATION FORENSICS AND SECURITY,NETWORK,SOCIAL NETWORK,BIG DATA,CONSUMER ELECTRONICS,INDUSTRIAL ELECTRONICS,PARALLEL AND DISTRIBUTED SYSTEMS,COMPUTER-BASED MEDICAL SYSTEMS (CBMS),PATTERN ANALYSIS AND MACHINE INTELLIGENCE,SOFTWARE ENGINEERING,COMPUTER GRAPHICS, INFORMATION AND COMMUNICATION SYSTEM,SERVICES COMPUTING,INTERNET OF THINGS JOURNAL,MULTIMEDIA,WIRELESS COMMUNICATIONS,IMAGE PROCESSING,IEEE SYSTEMS JOURNAL,CYBER-PHYSICAL-SOCIAL COMPUTING AND NETWORKING,DIGITAL FORENSIC,DEPENDABLE AND SECURE COMPUTING,AI - MACHINE LEARNING (ML),AI - DEEP LEARNING ,AI - NATURAL LANGUAGE PROCESSING ( NLP ),AI - VISION (IMAGE PROCESSING),mca project NETWORK AND SERVICE MANAGEMENT 1. Bacterial foraging optimization based Radial Basis Function Neural Network (BRBFNN) for identification and classification of plant leaf diseases: An automatic approach towards Plant Pathology(12 February 2018 ) 2. Fault-Tolerant Clustering Topology Evolution Mechanism of Wireless Sensor Networks (08 June 2018) SOFTWARE ENGINEERING 1. Reviving Sequential Program Birthmarking for Multithreaded Software Plagiarism Detection 2. EVA: Visual Analytics to Identify Fraudulent Events DATA MINING 1. Opinion Aspect Relations in Cognizing Customer Feelings via Reviews(24 January 2018) 2. Optimizing a multi-product continuous-review inventory model with uncertain demand, quality improvement, setup cost reduction, and variation control in lead time (27 June 2018) 3. Evaluation of Predictive Data Mining Algorithms in Soil Data Classification for Optimized Crop Recommendation (09 April 2018) 4. Prediction of Effective Rainfall and Crop Water Needs using Data Mining Techniques (01 February 2018) 5. A Secure Client-Side Framework for Protecting the Privacy of Health DataStored on the Cloud( 04 June 2018) 6. Greedy Optimization for K-Means-Based Consensus Clustering(April 2018) 7. A Two-stage Biomedical Event Trigger Detection Method Integrating Feature Selection and Word Embeddings 8. Principal Component Analysis Based Filtering for Scalable, High Precision k-NN Search 9. Entity Linking: A Problem to Extract Corresponding Entity with Knowledge Base 10. Collective List-Only Entity Linking: A Graph-Based Approach 11. Web Media and Stock Markets : A Survey and Future Directionsfrom a Big Data Perspective 12. Selective Database Projections Based Approach for Mining High-Utility Itemsets 13. Reverse k Nearest Neighbor Search over Trajectories 14. Range-based Nearest Neighbor Queries with Complex-shaped Obstacles 15. Predicting Contextual Informativeness for Vocabulary Learning 16. Online Product Quantization 17. Highlighter: automatic highlighting of electronic learning documents 18. Fuzzy Bag-of-Words Model for Document Representation 19. Frequent Itemsets Mining with Differential Privacy over Large-scale Data 20. Fast Cosine Similarity Search in Binary Space with Angular Multi-index Hashing 21. Efficient Vertical Mining of High Average-Utility Itemsets based on Novel Upper-Bounds 22. Document Summarization for Answering Non-Factoid Queries 23. Discovering Canonical Correlations between Topical andTopological Information in Document Networks 24. Complementary Aspect-based Opinion Mining 25. An Efficient Method for High Quality and Cohesive Topical Phrase Mining 26. A Weighted Frequent Itemset Mining Algorithm for Intelligent Decision in Smart Systems 27. A Correlation-based Feature Weighting Filter for Naive Bayes 28. Comments Mining With TF-IDF: The Inherent Bias and Its Removal 29. Bayesian Nonparametric Learning for Hierarchical and Sparse Topics 30. Supervised Topic Modeling using Hierarchical Dirichlet Process-based Inverse Regression: Experiments on E-Commerce Applications 31. Emotion Recognition on Twitter: Comparative Study and Training a Unison Model 32. Search Result Diversity Evaluation based on Intent Hierarchies 33. A Two-Phase Algorithm for Differentially Private Frequent Subgraph Mining
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