Search results “Itemset trees for targeted association mining”
To use the given data set to generate association rules using Apriori algorithm.
More Data Mining with Weka (3.3: Association rules)
More Data Mining with Weka: online course from the University of Waikato Class 3 - Lesson 3: Association rules http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/nK6fTv https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 15994 WekaMOOC
Final Year Projects | Parallel Frequent Item Set Mining with Selective Item Replication
Final Year Projects | Parallel Frequent Item Set Mining with Selective Item Replication More Details: Visit http://clickmyproject.com/a-secure-erasure-codebased-cloud-storage-system-with-secure-data-forwarding-p-128.html Including Packages ======================= * 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: 361 Clickmyproject
Data mining is the process of revealing nontrivial,previously unknown and potentially useful information from large databases. Discovering useful patterns hidden in the database plays an essential role in several data mining tasks,such as frequent pattern mining, weighted pattern mining and high utility pattern mining. This Project aims at mining the different combination of itemsets with high utility like profits from the transactional database. Utility based data mining is a new research area interested in all types of utility factors in data mining processes and targeted at incorporating utility considerations in data mining tasks. The UMining algorithm is used to find all high utility itemsets within the given utility constraint threshold. This algorithm has a pruning strategy of its own. Fast Utility Mining is a novel algorithm which is faster and simpler than the original UMining algorithm for generating high utility itemsets. The experimental evaluation on artificial datasets show that this algorithm executes faster than UMining algorithm. Another algorithm, Fast Utility Frequent Mining, is a more precise and very recent algorithm. It takes both the utility and the support measure into consideration.
Views: 790 Deepika Starz
Weka - Association Rules (Apiori)
วิดีโอ Neural Nework - https://www.youtube.com/watch?v=UIEvah7E0yI
Views: 3061 chaiyoelf
A Weighted Frequent Itemset Mining Algorithm for Intelligent Decision in Smart Systems
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: 124 manju nath
Lift (data mining)
In data mining and association rule learning, lift is a measure of the performance of a targeting model (association rule) at predicting or classifying cases as having an enhanced response (with respect to the population as a whole), measured against a random choice targeting model. A targeting model is doing a good job if the response within the target is much better than the average for the population as a whole. Lift is simply the ratio of these values: target response divided by average response. For example, suppose a population has an average response rate of 5%, but a certain model (or rule) has identified a segment with a response rate of 20%. Then that segment would have a lift of 4.0 (20%/5%). This video is targeted to blind users. Attribution: Article text available under CC-BY-SA Creative Commons image source in video
Views: 8202 Audiopedia
An Overview of Association Rules
Introduction to Association Rules My web page: www.imperial.ac.uk/people/n.sadawi
Views: 55332 Noureddin Sadawi
Target-Based, Privacy Preserving, and Incremental Association Rule Mining
Greetings from ChennaiSunday Systems Pvt Ltd www.chennaisunday.com Our motto is to bridge the knowledge gap between the academics and the industry.We provide project support for all courses include Ph.D,M.Phil, M.E/M.Tech, B.E/B.Tech, MCA/BCA, MBA/BBA, M.SC/B.Sc and etc.We undertake project works of all major universities 1. BIG DATA – MONGODB WITH NOSQL, JAVA WITH ANGULARJS, NODEJS 2. ANDROID , ANDROID WITH JSON AND PHP , CLOUD IMPLEMENTATION 3. DOT NET MVC FOR RAZOR FRAMEWORK
Views: 52 Siva Kumar
Final Year Projects | Knowledge Fusion for Probabilistic Generative Classifiers with Data Mining
Including Packages ======================= * 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:+91 967-778-1155 Visit Our Channel: http://www.youtube.com/myprojectbazaar Mail Us: [email protected]
Views: 370 myproject bazaar
036 Association analysis in KNIME
Data Science Foundations: Data Mining http://bc.vc/jSMxfA3
Views: 1600 Tukang Leding
Complete Data Science Course | What is Data Science? | Data Science for Beginners | Edureka
** Data Science Master Program: https://www.edureka.co/masters-program/data-scientist-certification ** This Edureka video on "Data Science" provides an end to end, detailed and comprehensive knowledge on Data Science. This Data Science video will start with basics of Statistics and Probability and then move to Machine Learning and Finally end the journey with Deep Learning and AI. For Data-sets and Codes discussed in this video, drop a comment. This video will be covering the following topics: 1:23 Evolution of Data 2:14 What is Data Science? 3:02 Data Science Careers 3:36 Who is a Data Analyst 4:20 Who is a Data Scientist 5:14 Who is a Machine Learning Engineer 5:44 Salary Trends 6:37 Road Map 9:06 Data Analyst Skills 10:41 Data Scientist Skills 11:47 ML Engineer Skills 12:53 Data Science Peripherals 13:17 What is Data ? 15:23 Variables & Research 17:28 Population & Sampling 20:18 Measures of Center 20:29 Measures of Spread 21:28 Skewness 21:52 Confusion Matrix 22:56 Probability 25:12 What is Machine Learning? 25:45 Features of Machine Learning 26:22 How Machine Learning works? 27:11 Applications of Machine Learning 34:57 Machine Learning Market Trends 36:05 Machine Learning Life Cycle 39:01 Important Python Libraries 40:56 Types of Machine Learning 41:07 Supervised Learning 42:27 Unsupervised Learning 43:27 Reinforcement Learning 46:27 Supervised Learning Algorithms 48:01 Linear Regression 58:12 What is Logistic Regression? 1:01:22 What is Decision Tree? 1:11:10 What is Random Forest? 1:18:48 What is Naïve Bayes? 1:30:51 Unsupervised Learning Algorithms 1:31:55 What is Clustering? 1:34:02 Types of Clustering 1:35:00 What is K-Means Clustering? 1:47:31 Market Basket Analysis 1:48:35 Association Rule Mining 1:51:22 Apriori Algorithm 2:00:46 Reinforcement Learning Algorithms 2:03:22 Reward Maximization 2:06:35 Markov Decision Process 2:08:50 Q-Learning 2:18:19 Relationship Between AI and ML and DL 2:20:10 Limitations of Machine Learning 2:21:19 What is Deep Learning ? 2:22:04 Applications of Deep Learning 2:23:35 How Neuron Works? 2:24:17 Perceptron 2:25:12 Waits and Bias 2:25:36 Activation Functions 2:29:56 Perceptron Example 2:31:48 What is TensorFlow? 2:37:05 Perceptron Problems 2:38:15 Deep Neural Network 2:39:35 Training Network Weights 2:41:04 MNIST Data set 2:41:19 Creating a Neural Network 2:50:30 Data Science Course Masters Program Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS Machine Learning Podcast: https://castbox.fm/channel/id1832236 Instagram: https://www.instagram.com/edureka_learning Slideshare: https://www.slideshare.net/EdurekaIN/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka #edureka #DataScienceEdureka #whatisdatascience #Datasciencetutorial #Datasciencecourse #datascience - - - - - - - - - - - - - - About the Master's Program This program follows a set structure with 6 core courses and 8 electives spread across 26 weeks. It makes you an expert in key technologies related to Data Science. At the end of each core course, you will be working on a real-time project to gain hands on expertise. By the end of the program you will be ready for seasoned Data Science job roles. - - - - - - - - - - - - - - Topics Covered in the curriculum: Topics covered but not limited to will be : Machine Learning, K-Means Clustering, Decision Trees, Data Mining, Python Libraries, Statistics, Scala, Spark Streaming, RDDs, MLlib, Spark SQL, Random Forest, Naïve Bayes, Time Series, Text Mining, Web Scraping, PySpark, Python Scripting, Neural Networks, Keras, TFlearn, SoftMax, Autoencoder, Restricted Boltzmann Machine, LOD Expressions, Tableau Desktop, Tableau Public, Data Visualization, Integration with R, Probability, Bayesian Inference, Regression Modelling etc. - - - - - - - - - - - - - - For more information, Please write back to us at [email protected] or call us at: IND: 9606058406 / US: 18338555775 (toll free)
Views: 50758 edureka!
Part 3:  Calculating Lift, How We Make Smart Online Product Recommendations
In this Video Professor Drake explains the Lift calculation when doing market basket analysis. Lift tells you how much better than chance item x will appear in the cart if you already know that item Y is in the cart.
Views: 7078 Perry Drake
Efficient Data Mining Method to Predict the Risk of Heart Diseases Through Frequent Itemsets
Abstract Data mining techniques are used in the field of medicine for various purposes. Mining association rule is one of the interesting topics in data mining which is used to generate frequent itemsets. It was first proposed for market basket analysis. Researchers proposed variations in techniques to generate frequent itemsets. Generating large number of frequent itemsets is a time consuming process. In this paper, the authors devised a method to predict the risk level of the patients having heart disease through frequent itemsets. The dataset of various heart disease patients are used for this research work. Frequent itemsets are generated based on the chosen symptoms and minimum support value. The extracted frequent itemsets help the medical practitioner to make diagnostic decisions and determine the risk level of patients at an early stage. The proposed method can be applied to any medical dataset to predict the risk factors with risk level of the patients based on chosen factors. An experimental result shows that the developed method identifies the risk level of patients efficiently from frequent itemsets.
Views: 100 1 Crore Projects
Heuristic (/hjʉˈrɪstɨk/; Greek: "Εὑρίσκω", "find" or "discover") refers to experience-based techniques for problem solving, learning, and discovery that give a solution which is not guaranteed to be optimal. Where the exhaustive search is impractical, heuristic methods are used to speed up the process of finding a satisfactory solution via mental shortcuts to ease the cognitive load of making a decision. Examples of this method include using a rule of thumb, an educated guess, an intuitive judgment, stereotyping, or common sense. In more precise terms, heuristics are strategies using readily accessible, though loosely applicable, information to control problem solving in human beings and machines. This video is targeted to blind users. Attribution: Article text available under CC-BY-SA Creative Commons image source in video
Views: 1629 Audiopedia
On the Security of a Ticket-Based Anonymity System with Traceability Property
On the Security of a Ticket-Based Anonymity System with Traceability Property in Wireless Mesh Networks TO GET THIS PROJECT IN ONLINE OR THROUGH TRAINING SESSIONS CONTACT: Chennai Office: JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai – 83. Landmark: Next to Kotak Mahendra Bank / Bharath Scans. Landline: (044) - 43012642 / Mobile: (0)9952649690 Pondicherry Office: JP INFOTECH, #45, Kamaraj Salai, Thattanchavady, Puducherry – 9. Landmark: Opp. To Thattanchavady Industrial Estate & Next to VVP Nagar Arch. Landline: (0413) - 4300535 / Mobile: (0)8608600246 / (0)9952649690 Email: [email protected], Website: http://www.jpinfotech.org, Blog: http://www.jpinfotech.blogspot.com In 2011, Sun et al. [5] proposed a security architecture to ensure unconditional anonymity for honest users and traceability of misbehaving users for network authorities in wireless mesh networks (WMNs). It strives to resolve the conflicts between the anonymity and traceability objectives. In this paper, we attacked Sun et al. scheme's traceability. Our analysis showed that trusted authority (TA) cannot trace the misbehavior client (CL) even if it double-time deposits the same ticket.
Views: 98 jpinfotechprojects
Data mining weka thesis
Contact Best Phd Projects Visit us: http://www.phdprojects.org/ http://www.phdprojects.org/publications/
Filtering Unwanted Messages in Online Social Networking User walls
Filtering Unwanted Messages in Online Social Networking User walls 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: http://www.jpinfotech.org, Blog: http://www.jpinfotech.blogspot.com One fundamental issue in today’s Online Social Networks (OSNs) is to give users the ability to control the messages posted on their own private space to avoid that unwanted content is displayed. Up to now, OSNs provide little support to this requirement. To fill the gap, in this paper, we propose a system allowing OSN users to have a direct control on the messages posted on their walls. This is achieved through a flexible rule-based system, which allows users to customize the filtering criteria to be applied to their walls, and a Machine Learning-based soft classifier automatically labeling messages in support of content-based filtering.
What is PINCER MOVEMENT? What does PINCER MOVEMENT mean? PINCER MOVEMENT meaning & explanation
What is PINCER MOVEMENT? What does PINCER MOVEMENT mean? PINCER MOVEMENT meaning - PINCER MOVEMENT definition - PINCER MOVEMENT explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. The pincer movement, or double envelopment, is a military maneuver in which forces simultaneously attack both flanks (sides) of an enemy formation. The name comes from visualizing the action as the split attacking forces "pinching" the enemy. The pincer movement typically occurs when opposing forces advance towards the center of an army that responds by moving its outside forces to the enemy's flanks to surround it. At the same time, a second layer of pincers may attack on the more distant flanks to keep reinforcements from the target units. A full pincer movement leads to the attacking army facing the enemy in front, on both flanks, and in the rear. If attacking pincers link up in the enemy's rear, the enemy is encircled. Such battles often end in surrender or destruction of the enemy force, but the encircled force can try to break out. They can attack the encirclement from the inside to escape, or a friendly external force can attack from the outside to open an escape route. Sun Tzu, in The Art of War (traditionally dated to the 6th century BC), speculated on the maneuver but advised against trying it for fear that an army would likely run first before the move could be completed. He argued that it was best to allow the enemy a path to escape (or at least the appearance of one), as the target army would fight with more ferocity when completely surrounded, but it would lose formation and be more vulnerable to destruction if shown an avenue of escape. The maneuver may have first been used at the Battle of Marathon in 490 BC. The historian Herodotus describes how the Athenian general Miltiades deployed 10,000 Athenian and 900 Plataean hoplite forces in a U-formation, with the wings manned much more deeply than the centre. His enemy outnumbered him heavily, and Miltiades chose to match the breadth of the Persian battle line by thinning out the centre of his forces while reinforcing the wings. In the course of the battle, the weaker central formations retreated, allowing the wings to converge behind the Persian battle line and drive the more numerous, but lightly armed Persians to retreat in panic. The tactic was used by Alexander the Great at the Battle of the Hydaspes in 326 BC. Launching his attack at the Indian left flank, the Indian king Porus reacted by sending the cavalry on the right of his formation around in support. Alexander had positioned two cavalry units on the left of his formation, hidden from view, under the command of Coenus and Demitrius. The units were then able to follow Porus's cavalry around, trapping them in a classic pincer movement. That tactically-astute move from Alexander was key in ensuring what many regard as his last great victory. The most famous example of its use was at the Battle of Cannae in 216 BC, when Hannibal executed the maneuver against the Romans. Military historians view it as one of the greatest battlefield maneuvers in history and cite it as the first successful use of the pincer movement that was recorded in detail, by the Greek historian Polybius. It was also later used effectively by Khalid ibn al-Walid at the Battle of Walaja in 633, by Alp Arslan at the Battle of Manzikert in 1071 (under the name crescent tactic), at Battle of Mohács by Süleyman the Magnificent in 1526 and by Field Marshal Carl Gustav Rehnskiöld at the Battle of Fraustadt in 1706.
Views: 4454 The Audiopedia
Final Year Projects | Cross-Domain Sentiment Classification
Including Packages ======================= * 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:+91 967-778-1155 +91 958-553-3547 +91 967-774-8277 Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected] chat: http://support.elysiumtechnologies.com/support/livechat/chat.php
Views: 172 myproject bazaar
Epic Seven - How to get COMMANDER LORINA in 2-4 days?
Epic Seven - How to get COMMANDER LORINA in 2-4 days? Some tips to help you finish this quest faster. Happy with your waifu! If you have any question, pm me in Discord: slinzz#1330 Join us: Epic7 Global Discord: https://discord.gg/CTetAqC Donate to support my work here: http://bit.ly/2zMWNMj Credit: [Future Bass] - WRLD - Triumph [Monstercat Release] https://www.youtube.com/watch?v=5YxVMyeIGvA Top 20 Songs by NCS 2018 - Best of NCS - The Best of 2018 https://www.youtube.com/watch?v=LTSaV7PWSHg&t
Views: 153164 Grass Angel
Mod-01 Lec-39 Parsing Ambiguous Sentences; Probabilistic Parsing
Natural Language Processing by Prof. Pushpak Bhattacharyya, Department of Computer science & Engineering,IIT Bombay.For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 2541 nptelhrd
Mage WeakAuras BFA Patch 8.1.5 + Guide - Arcane, Fire and Frost
This is my complete 8.1.5 BFA Mage WeakAuras for World of Warcraft: Battle for Azeroth. These contain a complete setup for Arcane Mage, Fire Mage and Frost Mage by covering basic and advanced rotations, resources, utilities and cooldowns tracking. ---------------------------------------------------------------- ▶ MAGE WEAKAURAS ◀ https://www.luxthos.com/weakauras/mage ---------------------------------------------------------------- ▶ RESOURCES ◀ ◾ Clean Icons - Thin ➜ https://www.wowinterface.com/downloads/info24559-CleanIcons-ThinFanUpdate.html ◾ SharedMedia ➜ https://www.curseforge.com/wow/addons/sharedmedia ◾ WeakAuras Companion ➜ https://www.weakauras.wtf ---------------------------------------------------------------- ▶ SUPPORT ◀ Streaming on Twitch ➜ https://www.twitch.tv/luxthos Patreon ➜ https://www.patreon.com/luxthos PayPal ➜ https://www.paypal.me/Luxthos Twitter ➜ https://twitter.com/luxthos Discord ➜ http://www.luxthos.com/discord
Views: 71282 Luxthos
Section GG: Functional Abilities and Goals
This video from the May 2018 Long-Term Care Hospital (LTCH) Quality Reporting Program (QRP) Provider Training held May 8 and 9, 2018, focuses on helping providers gain a working knowledge of changes to Section GG: Funcational Abilities and Goals and how to complete associated items on the LTCH CARE Data Set v4.00.
Views: 298 CMSHHSgov
Catia V5|Assembly Design|Move|Manipulation Tool|Part 2
Contact me for personal One day CATIA training at 100$, Do you like my works? Do you think I can be a part of your organization in someway? Does your organization require training or project delivery? Feel free to contact me at [email protected] Hello friends, I am Mohammed Shakeel and welcome to howENGINEERSdoit! YouTube channel, Here you can find technical videos related to CAD, especially CATIA, 3ds Max, AutoCAD, Marvelous Designer and also other cool technology videos like Android phone, laptop, desktop tips and tricks, troubleshoot videos, Google product tips and tricks etc Catia V5 is a Mechanical/Aerospace/Architectural/MEP/Electrical Design/Analysis software. There is a lot that you can do with this software. In this tutorial series, I will explain well in detail the important commands used to model different Mechanical/Aerospace/Product/Ship/Building Elements/Parts. It is used in major industries like Boeing,Airbus,Dassault Aviation, Eurofighter,BMW, Porsche, McLaren Automotive,Chrysler, Honda,United States Navy,Alstom Power,ABB Group,Michelin,Nikon,Nokia,Suzlon,Procter & Gamble. CATIA (an acronym of computer aided three-dimensional interactive application) (in English, usually pronounced multi-platform computer-aided design (CAD)/computer-aided manufacturing (CAM)/computer-aided engineering (CAE) software suite developed by the French company Dassault Systèmes. written C++ programming language. 3D Product Lifecycle Management software suite, CATIA supports multiple stages of product development (CAx),including conceptualization,design (CAD),engineering(CAE) manufacturing (CAM).CATIA facilitates collaborative engineering across disciplines around its 3DEXPERIENCE platform, including surfacing & shape design,electrical fluid & electronics systems design, mechanical engineering and systems engineering CATIA facilitates the design electronic,electrical,fluid HVAC systems,production of documentation manufacturing CATIA enables creation of 3D parts,from 3D sketches, sheetmetal,composites,molded,forged or tooling parts up to the definition of mechanical assemblies. The software provides advanced technologies mechanical surfacing & BIW It provides tools complete product definition, including functional tolerances as well as kinematics definition. CATIA provides wide range applications tooling design,for both generic tooling and mold & die CATIA offers a solution to shape design,styling surfacing workflow and visualization to create, modify, and validate complex innovative shapes from industrial design Class-A surfacing with the ICEM surfacing technologies.CATIA supports multiple stages of product design whether started from scratch or 2D sketches.CATIA v5 able read produce STEP format files for reverse engineering surface reuse. CATIA Systems Engineering solution delivers unique extensible systems engineering development platform that fully integrates the cross-discipline modeling, simulation, verification and business process support developing complex ‘cyber-physical’ products. enables organizations evaluate requests for changes or develop products system variants utilizing unified performance based systems engineering approach solution addresses the Model Based Systems Engineering (MBSE) users developing today’s smart products systems comprises the following elements: Requirements Engineering, Systems Architecture Modeling, Systems Behavior Modeling & Simulation, Configuration Management & Lifecycle Traceability Automotive Embedded Systems Development (AUTOSAR Builder) Industrial Automation Systems Development (ControlBuild) CATIA uses the open Modelica language both CATIA Dynamic Behavior Modeling Dymola engineering disciplines. CATIA & Dymola extended through domain specific Modelica libraries simulate wide range of complex systems automotive vehicle dynamics through to aircraft flight dynamics CATIA offers solution facilitate design manufacturing routed tubing,piping,Heating,Ventilating & Air Conditioning(HVAC).Capabilities 2D diagrams for defining hydraulic,pneumatic and HVAC systems used for the 787 series aircraft.Dassault Systèmes 3D PLM FNSS Vought Aircraft Industries Anglo/Italian Helicopter company AgustaWestland CATIA V4 V5 Safran use CATIA,Eurofighter Typhoon,main helicopters U.S Military forces,Sikorsky Aircraft Corp,P3 Voith,Bell Helicopter,Bell Boeing V-22 Osprey, has used CATIA V4,V5 V6,Dassault Aviation using CATIA currently working CATIA V6,BMW,Porsche, McLaren Automotive,Chrysler,Honda,Audi,Jaguar Land Rover,Volkswagen,SEAT,Škoda,Bentley Motors Limited,Volvo,Fiat,Benteler International,PSA Peugeot Citroën,Renault,Toyota, Ford,Scania, Hyundai,Tesla Motors,Rolls Royce Motors,Valmet Automotive,Proton,Elba,Tata motors Mahindra & Mahindra Limited
Views: 42077 howENGINEERSdoit!
Trunk Branch Ensemble Convolutional Neural Networks for Video based Face Recognition
Trunk Branch Ensemble Convolutional Neural Networks for Video based Face Recognition- 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 131. Efficient kNN Classification With Different Numbers of Nearest Neighbors 132. Anomaly Detection for Road Traffic: A Visual Analytics Framework 133. Visualizing Rank Time Series of Wikipedia Top-Viewed Pages 134. Durable and Energy Efficient In-Memory Frequent Pattern Mining 135. A Feature Selection and Classification Algorithm Based on Randomized Extraction of Model Populations 136. Collaborative Filtering Service Recommendation Based on a Novel Similarity Computation Method 137. A New Methodology for Mining Frequent Itemsets on Temporal Data 138. RAPARE: A Generic Strategy for Cold-Start Rating Prediction Problem 139. Analyzing Sentiments in One Go: A Supervised Joint Topic Modeling Approach 140. EHAUPM: Efficient High Average-Utility Pattern Mining with Tighter Upper-Bounds 141. User Vitality Ranking and Prediction in Social Networking Services: a Dynamic Network Perspective 142. A Hybrid Intelligent System for Risk Assessment based on Unstructured Data 143. Analysis of users behaviour in structured e-commerce websites 144. Efficient Keyword-Aware RepresentativeTravel Route Recommendation 145. Dengue Disease Prediction Using Decision Tree and Support Vector Machine 146. Supervised and Unsupervised Aspect Category Detection for Sentiment Analysis With Co-Occurrence Data 147. Survey on classification and detection of plant leaf disease in agriculture environment 148. Modeling the Evolution of Users’ Preferences and Social Links in Social Networ king Ser vices 149. Finding Related Forum Posts through Content Similarity over Intention-based Segmentation 150. Large-scale Location Prediction for Web Pages 151. Multi-view Unsupervised Feature Selection with Adaptive Similarity and View Weight 152. Credit Card Fraud Detection: A Realistic Modeling and a Novel Learning Strategy 153. Earthquake Prediction based on Spatio-Temporal Data Mining: An LSTM Network Approach 154. Wind Turbine Accidents: A Data Mining Study 155. Discovery and Clinical Decision Support for Personalized Healthcare 156. Data Mining and Analytics in the Process Industry: The Role of Machine Learning 157. An Efficient Parallel Method for Mining Frequent Closed Sequential Patterns 158. Target-Based, Privacy Preserving, and Incremental Association Rule Mining 159. ACID: association correction for imbalanced data in GWAS 160. Complementary Aspect-based Opinion Mining 161. Event Detection and User Interest Discovering in Social Media Data Streams 162. Detecting Stress Based on Social Interactions in Social Networks 163. Mining Coherent Topics with Pre-learned Interest 164. A Novel Continuous Blood Pressure Estimation Approach Based on Data M ining Techniques 165. HappyMeter: An Automated System for Real-Time Twitter Sentiment Analysis 166. Distantly Supervised Lifelong Learning for Large-Scale Social Media Sentiment Analysis 167. A Workflow Management System for Scalable Data Mining on Clouds 168. Efficient High Utility Pattern Mining for Establishing Manufacturing Plans with Sliding Window Control 169. An Approach for Building Efficient and Accurate Social Recommender Systems using Individual Relationship Networks 170. A Data Mining Approach Combining K-Means Clustering with Bagging Neural Network for Short-term Wind Power Forecasting