How To Understand The Impact Of A Computer On Organization
|
|
|
- Letitia Garrison
- 5 years ago
- Views:
Transcription
1 International Journal of Research in Engineering & Technology (IJRET) Vol. 1, Issue 1, June 2013, 1-6 Impact Journals IMPACT OF COMPUTER ON ORGANIZATION A. D. BHOSALE 1 & MARATHE DAGADU MITHARAM 2 1 Department of Commerce, G. S. Science, Arts & Commerce College, Khamgaon, Maharashtra, India 2 Department of Commerce & Management, R. C. Patel A. C. S. College, Shirpur, Maharashtra, India ABSTRACT Computers are commonly used items in many areas. The computer is one of the most powerful forces in organization today. Computers impacted in organization in many ways. Organization received some benefits using computer (Greater Efficiency, Higher Quality products, Better service, Recreational and educational benefits, Better information retrieval, fast working, Accuracy etc) In this paper show some benefits as well as positive impact of computers on organization. KEYWORDS: Computer Organization, Web Mining INTRODUCTION In organization computer creates positive impacts over production sell, distributions exchange and conjunction etc. In history Mid-1970s it is felt that computers systems will no longer be mere tools for accomplishing in organizational function; they will be thoroughly immersed in tactical planning. By mid- 1980s John Diebold see computers as heart of the organizational structure and expects all levels of management to be involved in one or another information processing activity in commercial organizations. Computer Impact on the Organization Major Glenn F. Mention, The rapid growth of computer use in business, government, and the military services has led to much speculation concerning the impact computers will have on the using organizations. Early investigation on the organizational impact of the computer by academic researchers suggested that the conventional hierarchical pyramid would be replaced with new organizational patterns. It was felt that there would be a significant change in traditional organizational concepts including structure, middle management roles, centralization versus decentralization, and the interrelationships between functional elements. THE POSITIVE IMPACT Organization uses computers for keeping track of accounts, money or items etc. Online help programs within the software. Computer in Organization / Manufacturing Application Order Management Computer Aided Design (CAD) Manufacturing Resource Planning SAP MIS
2 2 A. D. Bhosale & Marathe Dagadu Mitharam Some Employee, Customer, Organization Point of View Employee Benefits Managers decide on what action is most effective for the organization. In Computers perform planning of future activities. Managers may not to spend as much time controlling operation when computer can respond with computed reports. Sales people can receive more timely information about products in stock. Decision Making In the organization decision making is the most important thing. Various kinds of algorithms implement decision making software. In decision making if any wrong decision may be created then some problem occur. Greater Efficiency Computers can significantly improve productivity, the amount of goods or services. In any kind of operation get greater efficiency. Quality Product Computers may also help improve the quality of product and services we receive. In Computer-Aided Design (CAD) is a term to refer integration of computers and graphics oriented software for the purpose of automating the design and drafting process. WEKA this is tool to check various patterns and discovery of any kind of information. Huge of Data Computer can store huge amount of data in very small space and stored information is also very easy to organize, manipulate and retrieve. Artificial Intelligent Computer can respond to requests given to them and provide information. This is accomplished by the power of the programmers installed in them. Evidence of this is given in industrial robotics. Teach employees using computer because improve their performance. In many organizations arrange seminars, workshop for new workers how to handle the computer. It means knows the importance of computer in the organizations. Solving the problems. Generating any kinds of reports in few seconds. Video Conferencing Tools also use for the any kind of executives manager/ managers. Stored the data in database or numbers of files. Market intelligence, sales reports and customer insights generated from such systems. Payment gateways, accounts, purchase order, an organizational websites. Transactional and operational point of view.
3 Impact of Computer on Organization 3 Better control an organization. To make research as social point of view and economical point of view. EXPERIMENTAL Visit Priyadarshani Sahakari Soot Girani Ltd TANDE Tal- Shirpur Dist- Dhule (Maharashtra) is leading commercial manufacturing organization. Organization is to provide the maximum work environment, maximum profit of framers in this district. It is large scale organization to visit this organization to conduct some interview for employee and manager also. In this organization SAP is working. There are four module uses in SAP technology. SAP technology generates various kinds of reports. E.g. SAP server any fire damage occure then whole and sole data is transferred into one chip. Creates some questionnaire and arrange some interviews in Feb and March In this questionnaire create questions like this, which is impact of computer in every department, MIS department uses the computer or not, in auditing computer uses or not, auditing perform by using computer, production check by using computer or not, various report generate using computer or not, employee daily attendance perform using computer or not, etc Check quality of product using computers. Quality assurance software uses in this organization. Employee thump print is use for to starting the machine, if suppose employee print the thump then check operation perform in computer. Unauthorized data cannot access other person because security provided by using computer. The WEKA package will be used for the analysis of collected and tabulated data. Based on analysis, interpretation will be made to reach the meaningful conclusions. S/W designing Case tool also use data analysis, relationship of data, flow of data etc. To perform operation in collected data in WEKA Figure 1
4 4 A. D. Bhosale & Marathe Dagadu Mitharam RESULTS Figure 2 Computer impacts on organization to perform test- Output Test Mode: 10-fold cross-validation === Classifier model (full training set) === ZeroR Predicts Class Value: Time Taken to Build Model: true 0seconds === Stratified cross-validation === === Summary === Correctly Classified Instances 8 80 % Incorrectly Classified Instances 2 20 % Kappa statistic 0 Mean absolute error Root mean squared error Relative absolute error 100% Root relative squared error 100% Total Number of Instances 10 === Detailed Accuracy by Class ===
5 Impact of Computer on Organization 5 TP Rate FP Rate Precision Recall F-Measure ROC Area Class true false Weighted Avg === Confusion Matrix === a b <-- classified as 8 0 a = true 2 0 b = false Visualize Output CONCLUSIONS True Figure 3 False This paper has attempted to for the purpose of Computer usages in organizations. The proposed methods were successfully tested on collected data. In the organization computer is useful and impact also create in the organization. The results which were obtained after the analysis were satisfactory and contained valuable information about the organization. REFERENCES 1. The Impact of Office Automation on the Organization, Margrethe H.Olson, 25 Issue 11, Nov Impact of computers in organizations, Thomas L. Whisler, 1970 Year 3. Priyadarshani Shoot Girni, Shirpur 4. Web Mining, Bing liu, Springer 5. WEKA, Manual Preprocessing of web usages mining- D.M. Marathe
6
Overview. Evaluation Connectionist and Statistical Language Processing. Test and Validation Set. Training and Test Set
Overview Evaluation Connectionist and Statistical Language Processing Frank Keller [email protected] Computerlinguistik Universität des Saarlandes training set, validation set, test set holdout, stratification
Evaluation & Validation: Credibility: Evaluating what has been learned
Evaluation & Validation: Credibility: Evaluating what has been learned How predictive is a learned model? How can we evaluate a model Test the model Statistical tests Considerations in evaluating a Model
Classification of Titanic Passenger Data and Chances of Surviving the Disaster Data Mining with Weka and Kaggle Competition Data
Proceedings of Student-Faculty Research Day, CSIS, Pace University, May 2 nd, 2014 Classification of Titanic Passenger Data and Chances of Surviving the Disaster Data Mining with Weka and Kaggle Competition
Experiments in Web Page Classification for Semantic Web
Experiments in Web Page Classification for Semantic Web Asad Satti, Nick Cercone, Vlado Kešelj Faculty of Computer Science, Dalhousie University E-mail: {rashid,nick,vlado}@cs.dal.ca Abstract We address
Empirical Study of Decision Tree and Artificial Neural Network Algorithm for Mining Educational Database
Empirical Study of Decision Tree and Artificial Neural Network Algorithm for Mining Educational Database A.O. Osofisan 1, O.O. Adeyemo 2 & S.T. Oluwasusi 3 Department of Computer Science, University of
A Decision Tree for Weather Prediction
BULETINUL UniversităŃii Petrol Gaze din Ploieşti Vol. LXI No. 1/2009 77-82 Seria Matematică - Informatică - Fizică A Decision Tree for Weather Prediction Elia Georgiana Petre Universitatea Petrol-Gaze
Analysis of WEKA Data Mining Algorithm REPTree, Simple Cart and RandomTree for Classification of Indian News
Analysis of WEKA Data Mining Algorithm REPTree, Simple Cart and RandomTree for Classification of Indian News Sushilkumar Kalmegh Associate Professor, Department of Computer Science, Sant Gadge Baba Amravati
Keywords Data mining, Classification Algorithm, Decision tree, J48, Random forest, Random tree, LMT, WEKA 3.7. Fig.1. Data mining techniques.
International Journal of Emerging Research in Management &Technology Research Article October 2015 Comparative Study of Various Decision Tree Classification Algorithm Using WEKA Purva Sewaiwar, Kamal Kant
DATA MINING TECHNIQUES AND APPLICATIONS
DATA MINING TECHNIQUES AND APPLICATIONS Mrs. Bharati M. Ramageri, Lecturer Modern Institute of Information Technology and Research, Department of Computer Application, Yamunanagar, Nigdi Pune, Maharashtra,
Analyzing Customer Churn in the Software as a Service (SaaS) Industry
Analyzing Customer Churn in the Software as a Service (SaaS) Industry Ben Frank, Radford University Jeff Pittges, Radford University Abstract Predicting customer churn is a classic data mining problem.
Introduction to Data Mining
Introduction to Data Mining Jay Urbain Credits: Nazli Goharian & David Grossman @ IIT Outline Introduction Data Pre-processing Data Mining Algorithms Naïve Bayes Decision Tree Neural Network Association
Data Mining Application in Direct Marketing: Identifying Hot Prospects for Banking Product
Data Mining Application in Direct Marketing: Identifying Hot Prospects for Banking Product Sagarika Prusty Web Data Mining (ECT 584),Spring 2013 DePaul University,Chicago [email protected] Keywords:
Performance Analysis of Naive Bayes and J48 Classification Algorithm for Data Classification
Performance Analysis of Naive Bayes and J48 Classification Algorithm for Data Classification Tina R. Patil, Mrs. S. S. Sherekar Sant Gadgebaba Amravati University, Amravati [email protected], [email protected]
Implementing Heuristic Miner for Different Types of Event Logs
Implementing Heuristic Miner for Different Types of Event Logs Angelina Prima Kurniati 1, GunturPrabawa Kusuma 2, GedeAgungAry Wisudiawan 3 1,3 School of Compuing, Telkom University, Indonesia. 2 School
COC131 Data Mining - Clustering
COC131 Data Mining - Clustering Martin D. Sykora [email protected] Tutorial 05, Friday 20th March 2009 1. Fire up Weka (Waikako Environment for Knowledge Analysis) software, launch the explorer window
Strategic Management System for Effective Health Care Planning (SMS-EHCP)
674 Strategic Management System for Effective Health Care Planning (SMS-EHCP) 1 O. I. Omotoso, 2 I. A. Adeyanju, 3 S. A. Ibraheem 4 K. S. Ibrahim 1,2,3,4 Department of Computer Science and Engineering,
Application of Business Intelligence in Transportation for a Transportation Service Provider
Application of Business Intelligence in Transportation for a Transportation Service Provider Mohamed Sheriff Business Analyst Satyam Computer Services Ltd Email: [email protected], [email protected]
Performance Measures in Data Mining
Performance Measures in Data Mining Common Performance Measures used in Data Mining and Machine Learning Approaches L. Richter J.M. Cejuela Department of Computer Science Technische Universität München
Data Mining - Evaluation of Classifiers
Data Mining - Evaluation of Classifiers Lecturer: JERZY STEFANOWSKI Institute of Computing Sciences Poznan University of Technology Poznan, Poland Lecture 4 SE Master Course 2008/2009 revised for 2010
Mining the Software Change Repository of a Legacy Telephony System
Mining the Software Change Repository of a Legacy Telephony System Jelber Sayyad Shirabad, Timothy C. Lethbridge, Stan Matwin School of Information Technology and Engineering University of Ottawa, Ottawa,
ASSOCIATION RULE MINING ON WEB LOGS FOR EXTRACTING INTERESTING PATTERNS THROUGH WEKA TOOL
International Journal Of Advanced Technology In Engineering And Science Www.Ijates.Com Volume No 03, Special Issue No. 01, February 2015 ISSN (Online): 2348 7550 ASSOCIATION RULE MINING ON WEB LOGS FOR
Arti Tyagi Sunita Choudhary
Volume 5, Issue 3, March 2015 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com Web Usage Mining
PROJECT COMMUNICATIONS MANAGEMENT - A WEB BASED CONSTRUCTION MANAGEMENT APPROACH
PROJECT COMMUNICATIONS MANAGEMENT - A WEB BASED CONSTRUCTION MANAGEMENT APPROACH Jagdish J Gavade. 1,Basavraj A.Konnur. 2, Amol S.Thorbole 3 1 Assistant Prof., Civil Engineering Dept, Sanjeevan Engineering
Chapter 6. The stacking ensemble approach
82 This chapter proposes the stacking ensemble approach for combining different data mining classifiers to get better performance. Other combination techniques like voting, bagging etc are also described
Distributed Framework for Data Mining As a Service on Private Cloud
RESEARCH ARTICLE OPEN ACCESS Distributed Framework for Data Mining As a Service on Private Cloud Shraddha Masih *, Sanjay Tanwani** *Research Scholar & Associate Professor, School of Computer Science &
SVM Ensemble Model for Investment Prediction
19 SVM Ensemble Model for Investment Prediction Chandra J, Assistant Professor, Department of Computer Science, Christ University, Bangalore Siji T. Mathew, Research Scholar, Christ University, Dept of
AN EFFICIENT APPROACH TO PERFORM PRE-PROCESSING
AN EFFIIENT APPROAH TO PERFORM PRE-PROESSING S. Prince Mary Research Scholar, Sathyabama University, hennai- 119 [email protected] E. Baburaj Department of omputer Science & Engineering, Sun Engineering
Journal of Engineering Science and Technology Review 7 (4) (2014) 89-96
Jestr Journal of Engineering Science and Technology Review 7 (4) (2014) 89-96 JOURNAL OF Engineering Science and Technology Review www.jestr.org Applying Fuzzy Logic and Data Mining Techniques in Wireless
ISSN: 2321-7782 (Online) Volume 3, Issue 7, July 2015 International Journal of Advance Research in Computer Science and Management Studies
ISSN: 2321-7782 (Online) Volume 3, Issue 7, July 2015 International Journal of Advance Research in Computer Science and Management Studies Research Article / Survey Paper / Case Study Available online
DATA MINING TECHNOLOGY. Keywords: data mining, data warehouse, knowledge discovery, OLAP, OLAM.
DATA MINING TECHNOLOGY Georgiana Marin 1 Abstract In terms of data processing, classical statistical models are restrictive; it requires hypotheses, the knowledge and experience of specialists, equations,
Data Mining Analysis (breast-cancer data)
Data Mining Analysis (breast-cancer data) Jung-Ying Wang Register number: D9115007, May, 2003 Abstract In this AI term project, we compare some world renowned machine learning tools. Including WEKA data
Artificial Neural Network, Decision Tree and Statistical Techniques Applied for Designing and Developing E-mail Classifier
International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-1, Issue-6, January 2013 Artificial Neural Network, Decision Tree and Statistical Techniques Applied for Designing
BOOSTING - A METHOD FOR IMPROVING THE ACCURACY OF PREDICTIVE MODEL
The Fifth International Conference on e-learning (elearning-2014), 22-23 September 2014, Belgrade, Serbia BOOSTING - A METHOD FOR IMPROVING THE ACCURACY OF PREDICTIVE MODEL SNJEŽANA MILINKOVIĆ University
FRAUD DETECTION IN ELECTRIC POWER DISTRIBUTION NETWORKS USING AN ANN-BASED KNOWLEDGE-DISCOVERY PROCESS
FRAUD DETECTION IN ELECTRIC POWER DISTRIBUTION NETWORKS USING AN ANN-BASED KNOWLEDGE-DISCOVERY PROCESS Breno C. Costa, Bruno. L. A. Alberto, André M. Portela, W. Maduro, Esdras O. Eler PDITec, Belo Horizonte,
COURSE RECOMMENDER SYSTEM IN E-LEARNING
International Journal of Computer Science and Communication Vol. 3, No. 1, January-June 2012, pp. 159-164 COURSE RECOMMENDER SYSTEM IN E-LEARNING Sunita B Aher 1, Lobo L.M.R.J. 2 1 M.E. (CSE)-II, Walchand
Azure Machine Learning, SQL Data Mining and R
Azure Machine Learning, SQL Data Mining and R Day-by-day Agenda Prerequisites No formal prerequisites. Basic knowledge of SQL Server Data Tools, Excel and any analytical experience helps. Best of all:
Data Quality Mining: Employing Classifiers for Assuring consistent Datasets
Data Quality Mining: Employing Classifiers for Assuring consistent Datasets Fabian Grüning Carl von Ossietzky Universität Oldenburg, Germany, [email protected] Abstract: Independent
International Journal of Computer Science Trends and Technology (IJCST) Volume 2 Issue 3, May-Jun 2014
RESEARCH ARTICLE OPEN ACCESS A Survey of Data Mining: Concepts with Applications and its Future Scope Dr. Zubair Khan 1, Ashish Kumar 2, Sunny Kumar 3 M.Tech Research Scholar 2. Department of Computer
Final Project Report
CPSC545 by Introduction to Data Mining Prof. Martin Schultz & Prof. Mark Gerstein Student Name: Yu Kor Hugo Lam Student ID : 904907866 Due Date : May 7, 2007 Introduction Final Project Report Pseudogenes
Pentaho Data Mining Last Modified on January 22, 2007
Pentaho Data Mining Copyright 2007 Pentaho Corporation. Redistribution permitted. All trademarks are the property of their respective owners. For the latest information, please visit our web site at www.pentaho.org
Data Mining Algorithms Part 1. Dejan Sarka
Data Mining Algorithms Part 1 Dejan Sarka Join the conversation on Twitter: @DevWeek #DW2015 Instructor Bio Dejan Sarka ([email protected]) 30 years of experience SQL Server MVP, MCT, 13 books 7+ courses
Machine Learning Techniques for Stock Prediction. Vatsal H. Shah
Machine Learning Techniques for Stock Prediction Vatsal H. Shah 1 1. Introduction 1.1 An informal Introduction to Stock Market Prediction Recently, a lot of interesting work has been done in the area of
Advanced analytics at your hands
2.3 Advanced analytics at your hands Neural Designer is the most powerful predictive analytics software. It uses innovative neural networks techniques to provide data scientists with results in a way previously
Choosing the Best Classification Performance Metric for Wrapper-based Software Metric Selection for Defect Prediction
Choosing the Best Classification Performance Metric for Wrapper-based Software Metric Selection for Defect Prediction Huanjing Wang Western Kentucky University [email protected] Taghi M. Khoshgoftaar
Identifying the Number of Visitors to improve Website Usability from Educational Institution Web Log Data
Identifying the Number of to improve Website Usability from Educational Institution Web Log Data Arvind K. Sharma Dept. of CSE Jaipur National University, Jaipur, Rajasthan,India P.C. Gupta Dept. of CSI
Urban planning and management information systems analysis and design based on GIS
Available online at www.sciencedirect.com Physics Procedia 33 (2012 ) 1440 1445 2012 International Conference on Medical Physics and Biomedical Engineering Urban planning and management information systems
UNDERGRADUATE DEGREE PROGRAMME IN COMPUTER SCIENCE ENGINEERING SCHOOL OF COMPUTER SCIENCE ENGINEERING, ALBACETE
UNDERGRADUATE DEGREE PROGRAMME IN COMPUTER SCIENCE ENGINEERING SCHOOL OF COMPUTER SCIENCE ENGINEERING, ALBACETE SCHOOL OF COMPUTER SCIENCE, CIUDAD REAL Core Subjects (CS) Compulsory Subjects (CPS) Optional
Comparative Analysis of Classification Algorithms on Different Datasets using WEKA
Volume 54 No13, September 2012 Comparative Analysis of Classification Algorithms on Different Datasets using WEKA Rohit Arora MTech CSE Deptt Hindu College of Engineering Sonepat, Haryana, India Suman
Studying Auto Insurance Data
Studying Auto Insurance Data Ashutosh Nandeshwar February 23, 2010 1 Introduction To study auto insurance data using traditional and non-traditional tools, I downloaded a well-studied data from http://www.statsci.org/data/general/motorins.
Business Intelligence and Decision Support Systems
Chapter 12 Business Intelligence and Decision Support Systems Information Technology For Management 7 th Edition Turban & Volonino Based on lecture slides by L. Beaubien, Providence College John Wiley
CAD and Creativity. Contents
CAD and Creativity K C Hui Department of Automation and Computer- Aided Engineering Contents Various aspects of CAD CAD training in the university and the industry Conveying fundamental concepts in CAD
CHAPTER 1. Introduction to CAD/CAM/CAE Systems
CHAPTER 1 1.1 OVERVIEW Introduction to CAD/CAM/CAE Systems Today s industries cannot survive worldwide competition unless they introduce new products with better quality (quality, Q), at lower cost (cost,
CLINICAL DECISION SUPPORT FOR HEART DISEASE USING PREDICTIVE MODELS
CLINICAL DECISION SUPPORT FOR HEART DISEASE USING PREDICTIVE MODELS Srpriva Sundaraman Northwestern University [email protected] Sunil Kakade Northwestern University [email protected]
Enhancing Quality of Data using Data Mining Method
JOURNAL OF COMPUTING, VOLUME 2, ISSUE 9, SEPTEMBER 2, ISSN 25-967 WWW.JOURNALOFCOMPUTING.ORG 9 Enhancing Quality of Data using Data Mining Method Fatemeh Ghorbanpour A., Mir M. Pedram, Kambiz Badie, Mohammad
Introduction to Management Information Systems
IntroductiontoManagementInformationSystems Summary 1. Explain why information systems are so essential in business today. Information systems are a foundation for conducting business today. In many industries,
A Conceptual Approach to Data Visualization for User Interface Design of Smart Grid Operation Tools
A Conceptual Approach to Data Visualization for User Interface Design of Smart Grid Operation Tools Dong-Joo Kang and Sunju Park Yonsei University [email protected], [email protected] Abstract
1. Classification problems
Neural and Evolutionary Computing. Lab 1: Classification problems Machine Learning test data repository Weka data mining platform Introduction Scilab 1. Classification problems The main aim of a classification
Active Learning SVM for Blogs recommendation
Active Learning SVM for Blogs recommendation Xin Guan Computer Science, George Mason University Ⅰ.Introduction In the DH Now website, they try to review a big amount of blogs and articles and find the
DESIGN AND STRUCTURE OF FUZZY LOGIC USING ADAPTIVE ONLINE LEARNING SYSTEMS
Abstract: Fuzzy logic has rapidly become one of the most successful of today s technologies for developing sophisticated control systems. The reason for which is very simple. Fuzzy logic addresses such
First Semester Computer Science Students Academic Performances Analysis by Using Data Mining Classification Algorithms
First Semester Computer Science Students Academic Performances Analysis by Using Data Mining Classification Algorithms Azwa Abdul Aziz, Nor Hafieza IsmailandFadhilah Ahmad Faculty Informatics & Computing
Performance Based Evaluation of New Software Testing Using Artificial Neural Network
Performance Based Evaluation of New Software Testing Using Artificial Neural Network Jogi John 1, Mangesh Wanjari 2 1 Priyadarshini College of Engineering, Nagpur, Maharashtra, India 2 Shri Ramdeobaba
Fault Localization in a Software Project using Back- Tracking Principles of Matrix Dependency
Fault Localization in a Software Project using Back- Tracking Principles of Matrix Dependency ABSTRACT Fault identification and testing has always been the most specific concern in the field of software
A Content based Spam Filtering Using Optical Back Propagation Technique
A Content based Spam Filtering Using Optical Back Propagation Technique Sarab M. Hameed 1, Noor Alhuda J. Mohammed 2 Department of Computer Science, College of Science, University of Baghdad - Iraq ABSTRACT
INTERNATIONAL JOURNAL FOR ENGINEERING APPLICATIONS AND TECHNOLOGY DATA MINING IN HEALTHCARE SECTOR. [email protected]
IJFEAT INTERNATIONAL JOURNAL FOR ENGINEERING APPLICATIONS AND TECHNOLOGY DATA MINING IN HEALTHCARE SECTOR Bharti S. Takey 1, Ankita N. Nandurkar 2,Ashwini A. Khobragade 3,Pooja G. Jaiswal 4,Swapnil R.
Building a Database to Predict Customer Needs
INFORMATION TECHNOLOGY TopicalNet, Inc (formerly Continuum Software, Inc.) Building a Database to Predict Customer Needs Since the early 1990s, organizations have used data warehouses and data-mining tools
Performance Measures for Machine Learning
Performance Measures for Machine Learning 1 Performance Measures Accuracy Weighted (Cost-Sensitive) Accuracy Lift Precision/Recall F Break Even Point ROC ROC Area 2 Accuracy Target: 0/1, -1/+1, True/False,
Maschinelles Lernen mit MATLAB
Maschinelles Lernen mit MATLAB Jérémy Huard Applikationsingenieur The MathWorks GmbH 2015 The MathWorks, Inc. 1 Machine Learning is Everywhere Image Recognition Speech Recognition Stock Prediction Medical
Divide-n-Discover Discretization based Data Exploration Framework for Healthcare Analytics
for Healthcare Analytics Si-Chi Chin,KiyanaZolfaghar,SenjutiBasuRoy,AnkurTeredesai,andPaulAmoroso Institute of Technology, The University of Washington -Tacoma,900CommerceStreet,Tacoma,WA980-00,U.S.A.
Practical Data Science with Azure Machine Learning, SQL Data Mining, and R
Practical Data Science with Azure Machine Learning, SQL Data Mining, and R Overview This 4-day class is the first of the two data science courses taught by Rafal Lukawiecki. Some of the topics will be
IDS for SAP. Application Based IDS Reporting in the ERP system SAP R/3
IDS for SAP Application Based IDS Reporting in the ERP system SAP R/3 1 Research Question How is the performance of this SAP IDS when running with reduction of false positives and anonymization? Hypothesis
Impact of Feature Selection Technique on Email Classification
Impact of Feature Selection Technique on Email Classification Aakanksha Sharaff, Naresh Kumar Nagwani, and Kunal Swami Abstract Being one of the most powerful and fastest way of communication, the popularity
Master of Science in Health Information Technology Degree Curriculum
Master of Science in Health Information Technology Degree Curriculum Core courses: 8 courses Total Credit from Core Courses = 24 Core Courses Course Name HRS Pre-Req Choose MIS 525 or CIS 564: 1 MIS 525
How To Understand How Weka Works
More Data Mining with Weka Class 1 Lesson 1 Introduction Ian H. Witten Department of Computer Science University of Waikato New Zealand weka.waikato.ac.nz More Data Mining with Weka a practical course
Edifice an Educational Framework using Educational Data Mining and Visual Analytics
I.J. Education and Management Engineering, 2016, 2, 24-30 Published Online March 2016 in MECS (http://www.mecs-press.net) DOI: 10.5815/ijeme.2016.02.03 Available online at http://www.mecs-press.net/ijeme
Enterprise-Wide Information Systems in the Modern Organization
Enterprise-Wide Information Systems in the Modern Organization Module SAP 1 1 ERP in Modern Business: Topics Historical Perspective of IS in Business Levels of IT in Organization Examine ERP s and SAP
Web Forensic Evidence of SQL Injection Analysis
International Journal of Science and Engineering Vol.5 No.1(2015):157-162 157 Web Forensic Evidence of SQL Injection Analysis 針 對 SQL Injection 攻 擊 鑑 識 之 分 析 Chinyang Henry Tseng 1 National Taipei University
Machine Learning. Chapter 18, 21. Some material adopted from notes by Chuck Dyer
Machine Learning Chapter 18, 21 Some material adopted from notes by Chuck Dyer What is learning? Learning denotes changes in a system that... enable a system to do the same task more efficiently the next
KEY KNOWLEDGE MANAGEMENT TECHNOLOGIES IN THE INTELLIGENCE ENTERPRISE
KEY KNOWLEDGE MANAGEMENT TECHNOLOGIES IN THE INTELLIGENCE ENTERPRISE RAMONA-MIHAELA MATEI Ph.D. student, Academy of Economic Studies, Bucharest, Romania [email protected] Abstract In this rapidly
Dong-Joo Kang* Dong-Kyun Kang** Balho H. Kim***
Visualization Issues of Mass Data for Efficient HMI Design on Control System in Electric Power Industry Visualization in Computerized Operation & Simulation Tools Dong-Joo Kang* Dong-Kyun Kang** Balho
Supplier Relationship Management Tools
Supplier Relationship Management Tools Contents The Need for Supplier Management Automation VSAAM Capabilities The Magic of VSAAM The VSAAM Value Proposition VSAAM System Integration and Deployment Process
DECISION TREE INDUCTION FOR FINANCIAL FRAUD DETECTION USING ENSEMBLE LEARNING TECHNIQUES
DECISION TREE INDUCTION FOR FINANCIAL FRAUD DETECTION USING ENSEMBLE LEARNING TECHNIQUES Vijayalakshmi Mahanra Rao 1, Yashwant Prasad Singh 2 Multimedia University, Cyberjaya, MALAYSIA 1 [email protected]
White Paper Case Study: How Collaboration Platforms Support the ITIL Best Practices Standard
White Paper Case Study: How Collaboration Platforms Support the ITIL Best Practices Standard Abstract: This white paper outlines the ITIL industry best practices methodology and discusses the methods in
ARTIFICIAL INTELLIGENCE METHODS IN EARLY MANUFACTURING TIME ESTIMATION
1 ARTIFICIAL INTELLIGENCE METHODS IN EARLY MANUFACTURING TIME ESTIMATION B. Mikó PhD, Z-Form Tool Manufacturing and Application Ltd H-1082. Budapest, Asztalos S. u 4. Tel: (1) 477 1016, e-mail: [email protected]
How To Prevent Network Attacks
Ali A. Ghorbani Wei Lu Mahbod Tavallaee Network Intrusion Detection and Prevention Concepts and Techniques )Spri inger Contents 1 Network Attacks 1 1.1 Attack Taxonomies 2 1.2 Probes 4 1.2.1 IPSweep and
EFFICIENCY OF DECISION TREES IN PREDICTING STUDENT S ACADEMIC PERFORMANCE
EFFICIENCY OF DECISION TREES IN PREDICTING STUDENT S ACADEMIC PERFORMANCE S. Anupama Kumar 1 and Dr. Vijayalakshmi M.N 2 1 Research Scholar, PRIST University, 1 Assistant Professor, Dept of M.C.A. 2 Associate
Sentiment Analysis on Big Data
SPAN White Paper!? Sentiment Analysis on Big Data Machine Learning Approach Several sources on the web provide deep insight about people s opinions on the products and services of various companies. Social
Predictive Model Representation and Comparison: Towards Data and Predictive Models Governance
Predictive Model Representation and Comparison Towards Data and Predictive Models Governance Mokhairi Makhtar, Daniel C. Neagu, Mick Ridley School of Computing, Informatics and Media, University of Bradford
Database Marketing, Business Intelligence and Knowledge Discovery
Database Marketing, Business Intelligence and Knowledge Discovery Note: Using material from Tan / Steinbach / Kumar (2005) Introduction to Data Mining,, Addison Wesley; and Cios / Pedrycz / Swiniarski
IDENTIFIC ATION OF SOFTWARE EROSION USING LOGISTIC REGRESSION
http:// IDENTIFIC ATION OF SOFTWARE EROSION USING LOGISTIC REGRESSION Harinder Kaur 1, Raveen Bajwa 2 1 PG Student., CSE., Baba Banda Singh Bahadur Engg. College, Fatehgarh Sahib, (India) 2 Asstt. Prof.,
Management Decision Making. Hadi Hosseini CS 330 David R. Cheriton School of Computer Science University of Waterloo July 14, 2011
Management Decision Making Hadi Hosseini CS 330 David R. Cheriton School of Computer Science University of Waterloo July 14, 2011 Management decision making Decision making Spreadsheet exercise Data visualization,
Business Information System Courses Description
Business Information System Courses Description 1903101 Fundamentals of Information Technology: (Prerequisite none) Information Technology components, computer hardware: memory, CPU, machine cycle. numbering
The multilayer sentiment analysis model based on Random forest Wei Liu1, Jie Zhang2
2nd International Conference on Advances in Mechanical Engineering and Industrial Informatics (AMEII 2016) The multilayer sentiment analysis model based on Random forest Wei Liu1, Jie Zhang2 1 School of
