1. MINING INDUSTRIES IN THE PHILIPPINES: * Luis Gonzales Jr. Mining * Goldenboys ‚Inc. * Steelfab Water Solutions Asia Inc. * Fil Edward Rey A. Manila * Mg-gelber Trading Co. * Sytenco Philippines Corporation * Maricalum Mining Corporation * Tangshan Relia Industrial Pump. Co.‚Itd Maricalum Mining Corporation (MMC) in south Negros. Having 2‚673 claims on mining in Barangays Cansauro‚ Hinablan‚ Tao-angan and Hinablan‚ of the towns of Cauayan and Sipalay‚ Negros Occidental
Premium Philippines Government Provinces of the Philippines
Role Mining - Revealing Business Roles for Security Administration using Data Mining Technology Martin Kuhlmann Dalia Shohat SYSTOR Security Solutions GmbH Hermann-Heinrich-Gossen-Strasse 3 D 50858 Cologne [martin.kuhlmann|dalia.shohat] @systorsecurity.com Gerhard Schimpf SMF TEAM IT-Security Consulting Am Waldweg 23 D 75173 Pforzheim Gerhard.Schimpf@smfteam.de ABSTRACT In this paper we describe the work devising a new technique for role-finding to implement Role-Based Security Administration
Premium Access control Data mining
Forensic Accounting in New Zealand: Exploring the gap between education and practice. Jennette Boys Auckland University of Technology‚ New Zealand Abstract ‘Accounting practice has always been concerned with fraud’ (Lehman & Okcabol‚ 2005) The global business environment is rapidly changing and this has resulted in evolutionary changes in the skills accountants need to meet the requirements of their clients so they can continue to add value to their businesses
Premium Accountant Fraud Accountancy
Accidents Due To Machinery Welcome to all Participants Trend of fatal Accidents in non -coal Mines Year Fatal Accidents 1998 61 2000 51 2002 52 2004 57 2005 48 2006 62 Accidents due to Machinery in Non – Coal Mines Year Fatal Accidents Due to Machinery 2001 71 33 2002 52 19 2003 52 21 2004 57 26 2005 48 23 2006 62 25 Causes of fatal Accidents due to Machinery‚non coal Mines in 2005 Transport machinery(winding)- nil Transport machinery(other
Premium Water Water pollution Coal
Effect of various data collection techniques on quality and reliability of data. By Maria Latif- 12371 FozanMosadeq -11426 Muhammad Ismail - Muhammad Zubair Agha -11591 ZainabMorawala - 11516 A research report Submitted in partial fulfillment of the requirements For the degree of Bachelor of Business Administration To Iqra University Research Center (IURC) At the Iqra University‚ Main campus‚ Karachi Karachi‚ Pakistan July‚ 2012 ACKNOWLEDGEMENTS We
Free Scientific method Quantitative research Research
Statistical Techniques for Handling Missing Data Dr. John M. Cavendish 4 Part a1 Data were collected from 430 undergraduate college students for the purpose of examining the relationship between student personality characteristics and their preference for personality styles in their lecturers. Table 1 below presents a summary of the data collected. Of the 430 subjects for whom data was attempted‚ with 5 subjects providing no data‚ Of the 425 subjects included in data analysis‚ 307 were female
Premium Statistics Regression analysis
activities within the business environment. Trends observed indicate a need for increasing secure management. Review of scholarly journals‚ industry publications and text books were performed to gather data. Findings support that implementation of fraud reporting mechanisms assist in detecting fraud. Abstract 3 Introduction 4 What is Password Hashing? 4 Defined 4 Hashing Methodologies 5 How Password Hashing is Used 6 Add SALT for Taste? 7 Who Should Care and Why Hashing is Important
Premium Fraud
Fraud Fraud is listed by the 2003 UK Threat Assessment issued by the National Criminal Intelligence Service as one of the seven most significant threats facing the world1. What is a Fraud? A fraud is when one party deceives or takes unfair advantage of another. A fraud includes any act‚ omission‚ or concealment‚ involving a breach of legal or equitable duty or trust‚ which results in disadvantage or injury to another. In fact‚ in a broad strokes definition‚ fraud is a deliberate misrepresentation which
Premium Fraud
Can corporate fraud ever be eliminated from the workplace? Abstract Corporate fraud has been a growing issue since Enron bankruptcy. The factors that contribute to the occurrence of corporate fraud are hard to control and methods used to prevent and detect fraud both by internal control and outside auditing have unavoidable weaknesses. Despite all the effort being made‚ it is highly unlikely that corporate fraud can be eliminated from the workplace. Introduction After Enron scandal got
Premium Fraud Auditing Audit
Multiple Linear Regression Data Mining for Business Intelligence Shmueli‚ Patel & Bruce © Galit Shmueli and Peter Bruce 2010 Topics Explanatory vs. predictive modeling with regression Example: prices of Toyota Corollas Fitting a predictive model Assessing predictive accuracy Selecting a subset of predictors (variable selection) Explanatory Modeling Goal: Explain relationship between predictors (explanatory variables) and target Familiar use of regression in data analysis Multiple linear
Premium Regression analysis