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Search results “Web-based data-mining decision support system”
Web-Based DSS
 
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Views: 69 Andrew Palmesano
Diagnosis of Lung Cancer Prediction System Using Data Mining Classification Techniques
 
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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/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 5053 ClickMyProject
Analytica K-12 Decision Support System from EIS Education
 
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Analytica is an advanced decision support system for K-12 educators. Hosted on a district's own private Microsoft Azure cloud, it brings together data and content from disparate school systems into a central web based suite for use by teachers, campus leaders and district administrators. Visit www.EisEducation.com to learn more.
Views: 255 EIS INC
An Example Application of Data Mining
 
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Have a look at one of our decision support systems powered by our data mining algorithms.
Data mining
 
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Data mining (the analysis step of the "Knowledge Discovery in Databases" process, or KDD), an interdisciplinary subfield of computer science, is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. Aside from the raw analysis step, it involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating. The term is a misnomer, because the goal is the extraction of patterns and knowledge from large amount of data, not the extraction of data itself. It also is a buzzword, and is frequently also applied to any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) as well as any application of computer decision support system, including artificial intelligence, machine learning, and business intelligence. The popular book "Data mining: Practical machine learning tools and techniques with Java" (which covers mostly machine learning material) was originally to be named just "Practical machine learning", and the term "data mining" was only added for marketing reasons. Often the more general terms "(large scale) data analysis", or "analytics" -- or when referring to actual methods, artificial intelligence and machine learning -- are more appropriate. This video is targeted to blind users. Attribution: Article text available under CC-BY-SA Creative Commons image source in video
Views: 1589 Audiopedia
Heart Disease Prediction Project
 
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Get this project kit at http://nevonprojects.com/heart-disease-prediction-project/ System allows user to predict heart disease by users symptoms using data mining
Views: 25829 Nevon Projects
Web mining 1
 
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Views: 237 hardi rafat
Decision Support and Information Management System for Breast Cancer
 
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DESIREE project is an international initiative for breast cancer study and treatment improvement, which uses the latest available technology to help clinicians in their decision making process. This technology includes advanced clinical decision support systems (based on formalized clinical guidelines, knowledge discovery, and case based reasoning), image-based breast and tumour characterization tools and predictive modeling for 3D breast reconstruction. More info: http://www.desiree-project.eu/ [email protected] Partners: Vicomtech (Coordinator), Spain Arivis AG, Germany Hôpital Tenon, France Bilbomatica, Spain ERESA, Spain Fundación Onkologikoa, Spain INSERM-LIMICS, France Medical Innovation and Technology, Greece Sistemas Genómicos, Spain University of Houston, USA University of Ulster, UK
Views: 575 Vicomtech
Rapid Learning Health Systems  From Big Data to Decision Support
 
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Niels Peek (MSc, PhD) is Reader in Health Informatics at the Health e-Research Centre from the University of Manchester. He has a background in Computer Science and Artificial Intelligence. His research focuses on data-driven informatics methods for healthcare quality improvement, data mining for healthcare, predictive models, and clinical computerised decision support. He has co-authored more than 100 peer-reviewed, scientific publications. Previously based at the University of Amsterdam, he led the “CARDSS” initiative which secured 1.7M euro funding and introduced computerised decision support in 40 Dutch hospitals. He co-organised international workshops on intelligent data analysis in biomedicine in Aberdeen (2005), Verona (2006) and Bled (2011), and a workshop on electronic phenotyping in Washington, DC (2014). In 2013, he acted as Scientific Programme Chair of the 14th Conference on Artificial Intelligence in Medicine (AIME 2013). Currently he chairs an international working group on “Data Mining and Big Data Analytics” from the International Medical Informatics Association and is the President of the European Society of Artificial Intelligence in Medicine. Building on the concept of rapid learning health systems, Dr. Peek’s seminar will focus on the use of health information technology to address epidemiological and public health questions and to accelerate the translation of research findings to clinical practice. https://globalhealthtrainingcentre.tghn.org/e-seminars-new/ www.theglobalhealthnetwork.org
Views: 569 infoTGHN
Critical study on Data Mining with IDSS using RAPID technique for Diabetes
 
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Critical study on Data Mining with IDSS using RAPID technique for Diabetes To get this project in ONLINE or through TRAINING Sessions, Contact: JP INFOTECH, #37, Kamaraj Salai,Thattanchavady, Puducherry -9. Mobile: (0)9952649690, Email: [email protected], Website: http://www.jpinfotech.org In data mining, knowledge is extracted using key elements and concepts after identifying relevant and reliable data. But in the field of health care, researchers are finding it difficult to convert the bio-medical database into knowledge at a rapid pace. The medical data is huge, complex and heterogeneous in nature. Data Mining principles& tools are used in conjunction with health care expert systems to extract inherent relationships among data elements as knowledge. By integrating different data mining concepts with expert systems, a new system called “Integrated Decision Support System” (IDSS) is proposed, which can provide better results compared to existing ones. It converts knowledge into useful format and uses different tools for construction of its architecture. To reduce possible solutions for diabetic diagnosis, Case Based Reasoning (CBR), Rule Based Reasoning (RBR) and Web Based Portal Joint Asia Diabetes Evaluation( JADE) programs are integrated with Reliable Access and Probabilistic Inference based on clinical Data (RAPID) in the developed IDSS system to enhance existing systems for fast extraction of knowledge.
Views: 20 jpinfotechprojects
Data Mining Lecture -- Rule - Based Classification (Eng-Hindi)
 
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-~-~~-~~~-~~-~- Please watch: "PL vs FOL | Artificial Intelligence | (Eng-Hindi) | #3" https://www.youtube.com/watch?v=GS3HKR6CV8E -~-~~-~~~-~~-~-
Views: 26961 Well Academy
Using EHR Data and Clinical Decision Support Tools
 
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Speaker: Rachel Gold, Ph.D., MPH Assistant Investigator , Kaiser Permanente Center for Health Research Description: Integrated health care organizations such as Kaiser Permanente (Kaiser) have developed clinical decision support systems (CDSS) that harness EHR data to improve provider adherence to guideline-based prescribing. For example, Kaiser's diabetes quality improvement intervention, the 'ALL Initiative,' uses EHR-based CDSS tools to improve rates of guideline-based cardio-protective prescribing. To assess whether this effective intervention could be adapted for use in CHCs, we conducted a 'translational' randomized trial involving11 CHCs in Portland, OR. We adapted Kaiser's intervention elements for the CHC context, implemented the adapted CDSS tools in the study CHCs, and assessed how the tools impacted rates of guideline-based prescribing. We found that we needed to substantially adapt the EHR-based tools to provide the CHCs with functions equivalent to Kaiser's; we also identified and addressed a number of challenges to integrating the tools into the CHCs' workflows. Despite these challenges, implementing the 'ALL Initiative' CDSS tools was associated with significant improvements in care quality. Our results illustrate the challenges involved in using EHR-based tools to support practice change and quality improvement efforts in CHCs.
Views: 2181 NIHOD
Data Mining Classification and Prediction ( in Hindi)
 
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A tutorial about classification and prediction in Data Mining .
Views: 15014 Red Apple Tutorials
Intelligent Heart Disease Prediction System Using Data Mining Techniques || in Bangalore
 
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The healthcare industry collects huge amounts of healthcare data which, unfortunately, are not “mined” to discover hidden information for effective decision making. Discovery of hidden patterns and relationships often goes unexploited. Advanced data mining techniques can help remedy this situation. This research has developed a prototype Intelligent Heart Disease Prediction System (IHDPS) using data mining techniques, namely, Decision Trees, Naïve Bayes and Neural Network. Results show that each technique has its unique strength in realizing the objectives of the defined mining goals. IHDPS can answer complex “what if” queries which traditional decision support systems cannot. Using medical profiles such as age, sex, blood pressure and blood sugar it can predict the likelihood of patients getting a heart disease. It enables significant knowledge, e.g. patterns, relationships between medical factors related to heart disease, to be established. IHDPS is Web-based, user-friendly, scalable, reliable and expandable. It is implemented on the .NET platform.
A Review on Mining Students’ Data for Performance Prediction  | Final Year Projects 2016 - 2017
 
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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: 370 ClickMyProject
Introduction to data mining and architecture  in hindi
 
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Sample Notes : https://drive.google.com/file/d/19xmuQO1cprKqqbIVKcd7_-hILxF9yfx6/view?usp=sharing for notes fill the form : https://goo.gl/forms/C7EcSPmfOGleVOOA3 For full course:https://goo.gl/bYbuZ2 More videos coming soon so Subscribe karke rakho  :  https://goo.gl/85HQGm for full notes   please fill the form for notes :https://goo.gl/forms/MJD1mAOaTzyag64P2 For full hand made  notes of data warehouse and data mining  its only 200rs payment options is PAYTM :7038604912 once we get payment notification we will mail you the notes on your email id contact us at :[email protected] For full course :https://goo.gl/Y1UcLd Topic wise: Introduction to Datawarehouse:https://goo.gl/7BnSFo Meta data in 5 mins :https://goo.gl/7aectS Datamart in datawarehouse :https://goo.gl/rzE7SJ Architecture of datawarehouse:https://goo.gl/DngTu7 how to draw star schema slowflake schema and fact constelation:https://goo.gl/94HsDT what is Olap operation :https://goo.gl/RYQEuN OLAP vs OLTP:https://goo.gl/hYL2kd decision tree with solved example:https://goo.gl/nNTFJ3 K mean clustering algorithm:https://goo.gl/9gGGu5 Introduction to data mining and architecture:https://goo.gl/8dUADv Naive bayes classifier:https://goo.gl/jVUNyc Apriori Algorithm:https://goo.gl/eY6Kbx Agglomerative clustering algorithmn:https://goo.gl/8ktMss KDD in data mining :https://goo.gl/K2vvuJ ETL process:https://goo.gl/bKnac9 FP TREE Algorithm:https://goo.gl/W24ZRF Decision tree:https://goo.gl/o3xHgo more videos coming soon so channel ko subscribe karke rakho
Views: 135974 Last moment tuitions
Dataiku DSS Presentation
 
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Watch our Deployment Strategist Charlie Cohen present Dataiku Data Science Studio, the enterprise software solution for advanced analytics and machine learning that enables companies to scale, build and deliver their data products more efficiently. Its collaborative, team-based user interface works for all profiles, from data scientists to beginner analysts, and the unified framework allows for both development and deployment of data projects. Learn more at www.dataiku.com
Views: 370 Dataiku
Cloud-Based Clinical Decision Support Solutions | FDB & MEDITECH
 
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www.fdbhealth.com/fdb-medknowledge What if two of the leading healthcare IT companies joined together to reimagine how drug knowledge is delivered to clinicians? First Databank (FDB) and MEDITECH have collaborated to develop the FDB Cloud Connector, which will enable FDB customers to access drug knowledge through Web Services in the cloud. This industry-changing collaboration from two of healthcare IT’s leading companies will fundamentally change how drug knowledge is accessed, used, and delivered. FDB solutions are engineered for interoperability with most healthcare information systems, so implementation is seamless. We believe technology solutions should work for clinical and administrative health professionals, rather than vice versa. We welcome partnerships that accelerate our mission to provide configurable and targeted data to guide clinical decision making. Click the link above to see how FDB is promoting innovation in healthcare through strategic partnerships for better drug knowledge.
Views: 886 FDBFirstDatabank
Digital Intro to Webhead
 
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Knowledge Engineering We build, maintain and develop knowledge-based databases, data mining systems, expert systems, decision support systems and geographic information systems. Cyber Security We secure networks, operating systems, and coding, applications, and cloud services. New Industries We design, develop and execute innovative solutions for defense, intelligence, government, digital media, and cyber space industries. Integration of Customer Touch Points We integrate, centralize and optimize data across multi platforms of display for web, mobile, tablets, and social media technologies. Assessment and Analysis We handle strategic deterrence, operational and tactical planning, and analysis for business, science, technology and social cultural domains.
Views: 364 webhead210
Last Minute Tutorials | Data mining | Introduction | Examples
 
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NOTES:- Theory of computation : https://viden.io/knowledge/theory-of-computation?utm_campaign=creator_campaign&utm_medium=referral&utm_source=youtube&utm_term=last-minute-tutorials-1 DAA(all topics are included in this link) : https://viden.io/knowledge/design-and-analysis-of-algorithms-topic-wise-ada?utm_campaign=creator_campaign&utm_medium=referral&utm_source=youtube&utm_term=last-minute-tutorials-1 Advanced DBMS : https://viden.io/knowledge/advanced-dbms?utm_campaign=creator_campaign&utm_medium=referral&utm_source=youtube&utm_term=last-minute-tutorials-1 for QM method-https://viden.io/knowledge/quine-mccluskey-method-qm-method?utm_campaign=creator_campaign&utm_medium=referral&utm_source=youtube&utm_term=last-minute-tutorials-1 K-MAPS : https://viden.io/knowledge/k-maps-karnaugh-map?utm_campaign=creator_campaign&utm_medium=referral&utm_source=youtube&utm_term=last-minute-tutorials-1 Basics of logic gates : https://viden.io/knowledge/basics-of-logic-gates-and-more?utm_campaign=creator_campaign&utm_medium=referral&utm_source=youtube&utm_term=last-minute-tutorials-1 Website: https://lmtutorials.com/ Facebook: https://www.facebook.com/Last-Minute-Tutorials-862868223868621/ For any queries or suggestions, kindly mail at: [email protected]
Views: 27197 Last Minute Tutorials
Mining Unstructured Data in Software Repositories
 
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The amount of unstructured data available to software engineering researchers in versioning systems, issue trackers, achieved communications, etc is continuously growing over time. The mining of such data represents an unprecedented opportunity for researchers to investigate new research questions and to build a new generation of recommender systems supporting development and maintenance activities. This talk describes works on the application of Mining Unstructured Data (MUD) in software engineering. The talk briefly reviews the types of unstructured data available to researchers providing pointers to basic mining techniques to exploit them. Then, an overview of the existing applications of MUD in software engineering is provided with a specific focus on textual data present in software repositories and code components. The talk also discusses perils the "miner" should avoid while mining unstructured data and lists possible future trends for the field.
Views: 212 SANER2016 FOSE
Stanford Webinar - Using Electronic Health Records for Better Care
 
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In the era of Electronic Health Records, it’s possible to examine the decision outcomes made by doctors and identify patterns of care by generating evidence from the collective experience of patients. In this webinar, Stanford Assistant Professor Nigam Shah will show you methods that transform unstructured patient notes into a de-identified, temporally ordered, patient-feature matrix. Four use-cases will be examined, which use the resulting de-identified data matrix to illustrate the learning of practice-based evidence from unstructured data in electronic medical records.. This webinar will teach you the practical value of: •Monitoring for adverse drug events •Identifying drug-drug interactions •Profiling the safety of off-label drug usage •Generating practice-based evidence for difficult-to-test clinical hypotheses. Presented by the Stanford Center for Professional Development (http://scpd.stanford.edu)
Views: 9172 stanfordonline
International Journal of Database Management Systems ( IJDMS )
 
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International Journal of Database Management Systems ( IJDMS ) ISSN : 0975-5705 (Online); 0975-5985 (Print) http://airccse.org/journal/ijdms/index.html ******************************************************************* Scope & Topics The International Journal of Database Management Systems (IJDMS) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the database management systems & its applications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on understanding Modern developments in this filed, and establishing new collaborations in these areas. Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Database management systems. Topics of interest include, but are not limited to, the following . Constraint Modelling and Processing . Data and Information Integration & Modelling . Data and Information Networks . Data and Information Privacy and Security . Data and Information Quality . Data and Information Semantics . Data and Information Streams . Data Management in Grid and P2P Systems . Data Mining Algorithms . Data Mining Systems, Data Warehousing, OLAP . Data Structures and Data Management Algorithms . Database and Information System Architecture and Performance . DB Systems & Applications . Digital Libraries . Distributed, Parallel, P2P, and Grid-based Databases . Electronic Commerce and Web Technologies . Electronic Government & eParticipation . Expert Systems and Decision Support Systems . Expert Systems, Decision Support Systems & applications . Information Retrieval and Database Systems . Information Systems . Interoperability . Knowledge Acquisition, discovery & Management . Knowledge and information processing . Knowledge Modelling . Knowledge Processing . Metadata Management . Mobile Data and Information . Multi-databases and Database Federation . Multimedia, Object, Object Relational, and Deductive Databases . Pervasive Data and Information . Process Modelling . Process Support and Automation . Query Processing and Optimization . Semantic Web and Ontologies . Sensor Data Management . Statistical and Scientific Databases . Temporal, Spatial, and High Dimensional Databases . Trust, Privacy & Security in Digital Business . User Interfaces to Databases and Information Systems . Very Large Data Bases . Workflow Management and Databases . WWW and Databases . XML and Databases Paper Submission: Authors are invited to submit papers for this journal through e-mail [email protected] Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal. Important Dates: Submission Deadline : November 18, 2017 Acceptance Notification : December 18, 2017 Final Manuscript Due : December 26, 2017 Publication Date: Determined by the Editor-in-Chief For other details please visit http://airccse.org/journal/ijdms/index.html
Views: 2 Ijics Journal
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: 156186 Timothy DAuria
Weka Data Mining Tutorial for First Time & Beginner Users
 
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23-minute beginner-friendly introduction to data mining with WEKA. Examples of algorithms to get you started with WEKA: logistic regression, decision tree, neural network and support vector machine. Update 7/20/2018: I put data files in .ARFF here http://pastebin.com/Ea55rc3j and in .CSV here http://pastebin.com/4sG90tTu Sorry uploading the data file took so long...it was on an old laptop.
Views: 415301 Brandon Weinberg
An Internal Intrusion Detection and Protection System by Using Data Mining and Forensic Techniques
 
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An Internal Intrusion Detection and Protection System by Using Data Mining and Forensic Techniques Website: - http://cloudstechnologies.in Like us on FB https://www.facebook.com/cloudtechnologiespro?ref=hl Follow us on https://twitter.com/cloudtechpro Cloud technologies is one of the best renowned software development company In Hyderabad India. We guide and train the students based on their qualification under the guidance of vast experienced real time developers.
Views: 425 Cloud Technologies
ALT Data Mining Software - Demo Video
 
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This VIDEO demonstrates the use of the DATA MINING function in the Automatic Lead Tools software. The data extraction is highly accurate; it's cloud-based; offers unlimited Lead Extractions; uses Search Engines and Directories; Auto-Catalogs within the system, and can easily print mailing labels for Direct Mail.
International Journal of Database Management Systems ( IJDMS )
 
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ISSN : 0975-5705 (Online); 0975-5985 (Print) http://airccse.org/journal/ijdms/index.html ******************************************************************* Scope & Topics The International Journal of Database Management Systems (IJDMS) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the database management systems & its applications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on understanding Modern developments in this filed, and establishing new collaborations in these areas. Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Database management systems. Topics of interest include, but are not limited to, the following . Constraint Modelling and Processing . Data and Information Integration & Modelling . Data and Information Networks . Data and Information Privacy and Security . Data and Information Quality . Data and Information Semantics . Data and Information Streams . Data Management in Grid and P2P Systems . Data Mining Algorithms . Data Mining Systems, Data Warehousing, OLAP . Data Structures and Data Management Algorithms . Database and Information System Architecture and Performance . DB Systems & Applications . Digital Libraries . Distributed, Parallel, P2P, and Grid-based Databases . Electronic Commerce and Web Technologies . Electronic Government & eParticipation . Expert Systems and Decision Support Systems . Expert Systems, Decision Support Systems & applications . Information Retrieval and Database Systems . Information Systems . Interoperability . Knowledge Acquisition, discovery & Management . Knowledge and information processing . Knowledge Modelling . Knowledge Processing . Metadata Management . Mobile Data and Information . Multi-databases and Database Federation . Multimedia, Object, Object Relational, and Deductive Databases . Pervasive Data and Information . Process Modelling . Process Support and Automation . Query Processing and Optimization . Semantic Web and Ontologies . Sensor Data Management . Statistical and Scientific Databases . Temporal, Spatial, and High Dimensional Databases . Trust, Privacy & Security in Digital Business . User Interfaces to Databases and Information Systems . Very Large Data Bases . Workflow Management and Databases . WWW and Databases . XML and Databases Paper Submission: Authors are invited to submit papers for this journal through e-mail [email protected] Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal. Important Dates: Submission Deadline : May 06, 2018 Acceptance Notification : June 06, 2018 Final Manuscript Due : June 14, 2018 Publication Date: Determined by the Editor-in-Chief For other details please visit http://airccse.org/journal/ijdms/index.html
Views: 0 ijsc journal
Machine Learning & Artificial Intelligence: Crash Course Computer Science #34
 
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So we've talked a lot in this series about how computers fetch and display data, but how do they make decisions on this data? From spam filters and self-driving cars, to cutting edge medical diagnosis and real-time language translation, there has been an increasing need for our computers to learn from data and apply that knowledge to make predictions and decisions. This is the heart of machine learning which sits inside the more ambitious goal of artificial intelligence. We may be a long way from self-aware computers that think just like us, but with advancements in deep learning and artificial neural networks our computers are becoming more powerful than ever. Produced in collaboration with PBS Digital Studios: http://youtube.com/pbsdigitalstudios Want to know more about Carrie Anne? https://about.me/carrieannephilbin The Latest from PBS Digital Studios: https://www.youtube.com/playlist?list=PL1mtdjDVOoOqJzeaJAV15Tq0tZ1vKj7ZV Want to find Crash Course elsewhere on the internet? Facebook - https://www.facebook.com/YouTubeCrash... Twitter - http://www.twitter.com/TheCrashCourse Tumblr - http://thecrashcourse.tumblr.com Support Crash Course on Patreon: http://patreon.com/crashcourse CC Kids: http://www.youtube.com/crashcoursekids
Views: 324155 CrashCourse
What is DATA MINING? What does DATA MINING mean? DATA MINING meaning, definition & explanation
 
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What is DATA MINING? What does DATA MINING mean? DATA MINING meaning - DATA MINING definition - DATA MINING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Data mining is an interdisciplinary subfield of computer science. It is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. Aside from the raw analysis step, it involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating. Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD. The term is a misnomer, because the goal is the extraction of patterns and knowledge from large amounts of data, not the extraction (mining) of data itself. It also is a buzzword and is frequently applied to any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) as well as any application of computer decision support system, including artificial intelligence, machine learning, and business intelligence. The book Data mining: Practical machine learning tools and techniques with Java (which covers mostly machine learning material) was originally to be named just Practical machine learning, and the term data mining was only added for marketing reasons. Often the more general terms (large scale) data analysis and analytics – or, when referring to actual methods, artificial intelligence and machine learning – are more appropriate. The actual data mining task is the automatic or semi-automatic analysis of large quantities of data to extract previously unknown, interesting patterns such as groups of data records (cluster analysis), unusual records (anomaly detection), and dependencies (association rule mining). This usually involves using database techniques such as spatial indices. These patterns can then be seen as a kind of summary of the input data, and may be used in further analysis or, for example, in machine learning and predictive analytics. For example, the data mining step might identify multiple groups in the data, which can then be used to obtain more accurate prediction results by a decision support system. Neither the data collection, data preparation, nor result interpretation and reporting is part of the data mining step, but do belong to the overall KDD process as additional steps. The related terms data dredging, data fishing, and data snooping refer to the use of data mining methods to sample parts of a larger population data set that are (or may be) too small for reliable statistical inferences to be made about the validity of any patterns discovered. These methods can, however, be used in creating new hypotheses to test against the larger data populations.
Views: 4934 The Audiopedia
S-HELP - The Development of a Decision Support System (DSS)
 
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An overview of the S-HELP project.
Views: 121 S-HELP Project
International Journal of Database Management Systems ( IJDMS )
 
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International Journal of Database Management Systems ( IJDMS ) ISSN : 0975-5705 (Online); 0975-5985 (Print) http://airccse.org/journal/ijdms/index.html ******************************************************************* Scope & Topics The International Journal of Database Management Systems (IJDMS) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the database management systems & its applications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on understanding Modern developments in this filed, and establishing new collaborations in these areas. Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Database management systems. Topics of interest include, but are not limited to, the following . Constraint Modelling and Processing . Data and Information Integration & Modelling . Data and Information Networks . Data and Information Privacy and Security . Data and Information Quality . Data and Information Semantics . Data and Information Streams . Data Management in Grid and P2P Systems . Data Mining Algorithms . Data Mining Systems, Data Warehousing, OLAP . Data Structures and Data Management Algorithms . Database and Information System Architecture and Performance . DB Systems & Applications . Digital Libraries . Distributed, Parallel, P2P, and Grid-based Databases . Electronic Commerce and Web Technologies . Electronic Government & eParticipation . Expert Systems and Decision Support Systems . Expert Systems, Decision Support Systems & applications . Information Retrieval and Database Systems . Information Systems . Interoperability . Knowledge Acquisition, discovery & Management . Knowledge and information processing . Knowledge Modelling . Knowledge Processing . Metadata Management . Mobile Data and Information . Multi-databases and Database Federation . Multimedia, Object, Object Relational, and Deductive Databases . Pervasive Data and Information . Process Modelling . Process Support and Automation . Query Processing and Optimization . Semantic Web and Ontologies . Sensor Data Management . Statistical and Scientific Databases . Temporal, Spatial, and High Dimensional Databases . Trust, Privacy & Security in Digital Business . User Interfaces to Databases and Information Systems . Very Large Data Bases . Workflow Management and Databases . WWW and Databases . XML and Databases Paper Submission: Authors are invited to submit papers for this journal through e-mail [email protected] Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal. Important Dates: Submission Deadline : January 06, 2018 Acceptance Notification : February 06, 2018 Final Manuscript Due : February 08, 2018 Publication Date: Determined by the Editor-in-Chief For other details please visit http://airccse.org/journal/ijdms/index.html
Views: 2 Iju Journal
Privacy-Preserving Patient-Centric Clinical Decision Support System on Naїve Bayesian Classification
 
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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: 251 myproject bazaar
Data Mining Lecture - - Finding frequent item sets | Apriori Algorithm | Solved Example (Eng-Hindi)
 
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In this video Apriori algorithm is explained in easy way in data mining Thank you for watching share with your friends Follow on : Facebook : https://www.facebook.com/wellacademy/ Instagram : https://instagram.com/well_academy Twitter : https://twitter.com/well_academy data mining in hindi, Finding frequent item sets, data mining, data mining algorithms in hindi, data mining lecture, data mining tools, data mining tutorial,
Views: 133234 Well Academy
Data Mining
 
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Views: 4406 Azis Ikwanto
KDD ( knowledge data discovery )  in data mining in hindi
 
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Sample Notes : https://drive.google.com/file/d/19xmuQO1cprKqqbIVKcd7_-hILxF9yfx6/view?usp=sharing for notes fill the form : https://goo.gl/forms/C7EcSPmfOGleVOOA3 For full course:https://goo.gl/bYbuZ2 More videos coming soon so Subscribe karke rakho  :  https://goo.gl/85HQGm for full notes   please fill the form for notes :https://goo.gl/forms/MJD1mAOaTzyag64P2 For full hand made  notes of data warehouse and data mining  its only 200 rs payment options is PAYTM :7038604912 once we get payment notification we will mail you the notes on your email id contact us at :[email protected] For full course :https://goo.gl/Y1UcLd Topic wise: Introduction to Datawarehouse:https://goo.gl/7BnSFo Meta data in 5 mins :https://goo.gl/7aectS Datamart in datawarehouse :https://goo.gl/rzE7SJ Architecture of datawarehouse:https://goo.gl/DngTu7 how to draw star schema slowflake schema and fact constelation:https://goo.gl/94HsDT what is Olap operation :https://goo.gl/RYQEuN OLAP vs OLTP:https://goo.gl/hYL2kd decision tree with solved example:https://goo.gl/nNTFJ3 K mean clustering algorithm:https://goo.gl/9gGGu5 Introduction to data mining and architecture:https://goo.gl/8dUADv Naive bayes classifier:https://goo.gl/jVUNyc Apriori Algorithm:https://goo.gl/eY6Kbx Agglomerative clustering algorithmn:https://goo.gl/8ktMss KDD in data mining :https://goo.gl/K2vvuJ ETL process:https://goo.gl/bKnac9 FP TREE Algorithm:https://goo.gl/W24ZRF Decision tree:https://goo.gl/o3xHgo more videos coming soon so channel ko subscribe karke rakho
Views: 48294 Last moment tuitions
Data Mining : Data Visualization Techniques
 
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This video explains various visualization techniques in data mining. Video Lecture by Anisha Lalwani.
Views: 1367 topNotch Tutorials
Using the Python Programming Language to Build a Clinical Decision Support System
 
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A clinical decision support program using the decision tree method. I use the Python program and Pyside graphical user interface.
Views: 4276 Sam Shukla
Data Mining
 
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KDD Process & DM Architecture
Views: 10775 Gotlur Karuna
Data Mining - Clustering
 
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What is clustering Partitioning a data into subclasses. Grouping similar objects. Partitioning the data based on similarity. Eg:Library. Clustering Types Partitioning Method Hierarchical Method Agglomerative Method Divisive Method Density Based Method Model based Method Constraint based Method These are clustering Methods or types. Clustering Algorithms,Clustering Applications and Examples are also Explained.
Web Based Medical Decision Support Systems For hree Way Medical Decision Making With Game Theoretic
 
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Views: 57 spiroprojects
What is DATA WAREHOUSE? What does DATA WAREHOUSE mean? DATA WAREHOUSE meaning & explanation
 
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What is DATA WAREHOUSE? What does DATA WAREHOUSE mean? DATA WAREHOUSE meaning - DATA WAREHOUSE definition - DATA WAREHOUSE explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ In computing, a data warehouse (DW or DWH), also known as an enterprise data warehouse (EDW), is a system used for reporting and data analysis, and is considered a core component of business intelligence. DWs are central repositories of integrated data from one or more disparate sources. They store current and historical data in one single place and are used for creating analytical reports for knowledge workers throughout the enterprise. The data stored in the warehouse is uploaded from the operational systems (such as marketing or sales). The data may pass through an operational data store and may require data cleansing for additional operations to ensure data quality before it is used in the DW for reporting. The typical Extract, transform, load (ETL)-based data warehouse uses staging, data integration, and access layers to house its key functions. The staging layer or staging database stores raw data extracted from each of the disparate source data systems. The integration layer integrates the disparate data sets by transforming the data from the staging layer often storing this transformed data in an operational data store (ODS) database. The integrated data are then moved to yet another database, often called the data warehouse database, where the data is arranged into hierarchical groups, often called dimensions, and into facts and aggregate facts. The combination of facts and dimensions is sometimes called a star schema. The access layer helps users retrieve data. The main source of the data is cleansed, transformed, catalogued and made available for use by managers and other business professionals for data mining, online analytical processing, market research and decision support. However, the means to retrieve and analyze data, to extract, transform, and load data, and to manage the data dictionary are also considered essential components of a data warehousing system. Many references to data warehousing use this broader context. Thus, an expanded definition for data warehousing includes business intelligence tools, tools to extract, transform, and load data into the repository, and tools to manage and retrieve metadata. A data warehouse maintains a copy of information from the source transaction systems. This architectural complexity provides the opportunity to: Integrate data from multiple sources into a single database and data model. Mere congregation of data to single database so a single query engine can be used to present data is an ODS. Mitigate the problem of database isolation level lock contention in transaction processing systems caused by attempts to run large, long running, analysis queries in transaction processing databases. Maintain data history, even if the source transaction systems do not. Integrate data from multiple source systems, enabling a central view across the enterprise. This benefit is always valuable, but particularly so when the organization has grown by merger. Improve data quality, by providing consistent codes and descriptions, flagging or even fixing bad data. Present the organization's information consistently. Provide a single common data model for all data of interest regardless of the data's source. Restructure the data so that it makes sense to the business users. Restructure the data so that it delivers excellent query performance, even for complex analytic queries, without impacting the operational systems. Add value to operational business applications, notably customer relationship management (CRM) systems. Make decision–support queries easier to write. Optimized data warehouse architectures allow data scientists to organize and disambiguate repetitive data. The environment for data warehouses and marts includes the following: Source systems that provide data to the warehouse or mart; Data integration technology and processes that are needed to prepare the data for use; Different architectures for storing data in an organization's data warehouse or data marts; Different tools and applications for the variety of users; Metadata, data quality, and governance processes must be in place to ensure that the warehouse or mart meets its purposes. In regards to source systems listed above, Rainer states, "A common source for the data in data warehouses is the company's operational databases, which can be relational databases"....
Views: 760 The Audiopedia
Let’s Write a Decision Tree Classifier from Scratch - Machine Learning Recipes #8
 
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Hey everyone! Glad to be back! Decision Tree classifiers are intuitive, interpretable, and one of my favorite supervised learning algorithms. In this episode, I’ll walk you through writing a Decision Tree classifier from scratch, in pure Python. I’ll introduce concepts including Decision Tree Learning, Gini Impurity, and Information Gain. Then, we’ll code it all up. Understanding how to accomplish this was helpful to me when I studied Machine Learning for the first time, and I hope it will prove useful to you as well. You can find the code from this video here: https://goo.gl/UdZoNr https://goo.gl/ZpWYzt Books! Hands-On Machine Learning with Scikit-Learn and TensorFlow https://goo.gl/kM0anQ Follow Josh on Twitter: https://twitter.com/random_forests Check out more Machine Learning Recipes here: https://goo.gl/KewA03 Subscribe to the Google Developers channel: http://goo.gl/mQyv5L
Views: 136725 Google Developers
Weka Text Classification for First Time & Beginner Users
 
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59-minute beginner-friendly tutorial on text classification in WEKA; all text changes to numbers and categories after 1-2, so 3-5 relate to many other data analysis (not specifically text classification) using WEKA. 5 main sections: 0:00 Introduction (5 minutes) 5:06 TextToDirectoryLoader (3 minutes) 8:12 StringToWordVector (19 minutes) 27:37 AttributeSelect (10 minutes) 37:37 Cost Sensitivity and Class Imbalance (8 minutes) 45:45 Classifiers (14 minutes) 59:07 Conclusion (20 seconds) Some notable sub-sections: - Section 1 - 5:49 TextDirectoryLoader Command (1 minute) - Section 2 - 6:44 ARFF File Syntax (1 minute 30 seconds) 8:10 Vectorizing Documents (2 minutes) 10:15 WordsToKeep setting/Word Presence (1 minute 10 seconds) 11:26 OutputWordCount setting/Word Frequency (25 seconds) 11:51 DoNotOperateOnAPerClassBasis setting (40 seconds) 12:34 IDFTransform and TFTransform settings/TF-IDF score (1 minute 30 seconds) 14:09 NormalizeDocLength setting (1 minute 17 seconds) 15:46 Stemmer setting/Lemmatization (1 minute 10 seconds) 16:56 Stopwords setting/Custom Stopwords File (1 minute 54 seconds) 18:50 Tokenizer setting/NGram Tokenizer/Bigrams/Trigrams/Alphabetical Tokenizer (2 minutes 35 seconds) 21:25 MinTermFreq setting (20 seconds) 21:45 PeriodicPruning setting (40 seconds) 22:25 AttributeNamePrefix setting (16 seconds) 22:42 LowerCaseTokens setting (1 minute 2 seconds) 23:45 AttributeIndices setting (2 minutes 4 seconds) - Section 3 - 28:07 AttributeSelect for reducing dataset to improve classifier performance/InfoGainEval evaluator/Ranker search (7 minutes) - Section 4 - 38:32 CostSensitiveClassifer/Adding cost effectiveness to base classifier (2 minutes 20 seconds) 42:17 Resample filter/Example of undersampling majority class (1 minute 10 seconds) 43:27 SMOTE filter/Example of oversampling the minority class (1 minute) - Section 5 - 45:34 Training vs. Testing Datasets (1 minute 32 seconds) 47:07 Naive Bayes Classifier (1 minute 57 seconds) 49:04 Multinomial Naive Bayes Classifier (10 seconds) 49:33 K Nearest Neighbor Classifier (1 minute 34 seconds) 51:17 J48 (Decision Tree) Classifier (2 minutes 32 seconds) 53:50 Random Forest Classifier (1 minute 39 seconds) 55:55 SMO (Support Vector Machine) Classifier (1 minute 38 seconds) 57:35 Supervised vs Semi-Supervised vs Unsupervised Learning/Clustering (1 minute 20 seconds) Classifiers introduces you to six (but not all) of WEKA's popular classifiers for text mining; 1) Naive Bayes, 2) Multinomial Naive Bayes, 3) K Nearest Neighbor, 4) J48, 5) Random Forest and 6) SMO. Each StringToWordVector setting is shown, e.g. tokenizer, outputWordCounts, normalizeDocLength, TF-IDF, stopwords, stemmer, etc. These are ways of representing documents as document vectors. Automatically converting 2,000 text files (plain text documents) into an ARFF file with TextDirectoryLoader is shown. Additionally shown is AttributeSelect which is a way of improving classifier performance by reducing the dataset. Cost-Sensitive Classifier is shown which is a way of assigning weights to different types of guesses. Resample and SMOTE are shown as ways of undersampling the majority class and oversampling the majority class. Introductory tips are shared throughout, e.g. distinguishing supervised learning (which is most of data mining) from semi-supervised and unsupervised learning, making identically-formatted training and testing datasets, how to easily subset outliers with the Visualize tab and more... ---------- Update March 24, 2014: Some people asked where to download the movie review data. It is named Polarity_Dataset_v2.0 and shared on Bo Pang's Cornell Ph.D. student page http://www.cs.cornell.edu/People/pabo/movie-review-data/ (Bo Pang is now a Senior Research Scientist at Google)
Views: 128877 Brandon Weinberg
International Journal of Database Management Systems ( IJDMS )
 
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International Journal of Database Management Systems ( IJDMS ) ISSN : 0975-5705 (Online); 0975-5985 (Print) http://airccse.org/journal/ijdms/index.html ******************************************************************* Scope & Topics The International Journal of Database Management Systems (IJDMS) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the database management systems & its applications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on understanding Modern developments in this filed, and establishing new collaborations in these areas. Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Database management systems. Topics of interest include, but are not limited to, the following . Constraint Modelling and Processing . Data and Information Integration & Modelling . Data and Information Networks . Data and Information Privacy and Security . Data and Information Quality . Data and Information Semantics . Data and Information Streams . Data Management in Grid and P2P Systems . Data Mining Algorithms . Data Mining Systems, Data Warehousing, OLAP . Data Structures and Data Management Algorithms . Database and Information System Architecture and Performance . DB Systems & Applications . Digital Libraries . Distributed, Parallel, P2P, and Grid-based Databases . Electronic Commerce and Web Technologies . Electronic Government & eParticipation . Expert Systems and Decision Support Systems . Expert Systems, Decision Support Systems & applications . Information Retrieval and Database Systems . Information Systems . Interoperability . Knowledge Acquisition, discovery & Management . Knowledge and information processing . Knowledge Modelling . Knowledge Processing . Metadata Management . Mobile Data and Information . Multi-databases and Database Federation . Multimedia, Object, Object Relational, and Deductive Databases . Pervasive Data and Information . Process Modelling . Process Support and Automation . Query Processing and Optimization . Semantic Web and Ontologies . Sensor Data Management . Statistical and Scientific Databases . Temporal, Spatial, and High Dimensional Databases . Trust, Privacy & Security in Digital Business . User Interfaces to Databases and Information Systems . Very Large Data Bases . Workflow Management and Databases . WWW and Databases . XML and Databases Paper Submission: Authors are invited to submit papers for this journal through e-mail [email protected] Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal. For other details please visit http://airccse.org/journal/ijdms/index.html