AI in optical character recognition Artificial intelligence (AI) is the intelligence of machines. AI textbooks define the field as "the study and design of intelligent agents" where an intelligent agent is a system that perceives its environment and takes actions that maximize its chances of success. There are different fields under optical character recognition. Intelligent character recognition Handwriting recognition Automatic number plate recognition In computer science‚ intelligent
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MULTIPLE INTELLIGENCES Psychologist of Harvard University‚ Howard Gardner‚ defined ‘Intelligence’ as; A biopsychological potential to process information that can be activated in a cultural setting to solve problems or create products that are of value in a culture. Howard Gardner proposed that the entire human species possesses not just one single type of intelligence‚ but rather many different intelligences. Gardner suggested that there are 8 categories of intelligence: Linguistic‚ Bodily-Kinaesthetic
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Revenue Recognition Revenue is the electricity that drives business. Revenue has been the starting point on every income statement generated‚ every sales meeting conducted‚ and is on every entrepreneur’s wish list. The basic concept for revenue recognition is that revenue should not be recognized until it is realized or realizable and earned. There are also four criteria must be met in order to recognize revenue: 1) persuasive evidence of an arrangement exists: Consider the substance of the transaction
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methods‚ measurement systems and procedures for presenting disclosures. Accounting policies differ from accounting principles in that the principles are the rules and the policies are a company’s way of adhering to the rules. Revenue Recognition: Revenue recognition determines the accounting period‚ in which revenues and expenses are recognized. General Mills recognize sales revenue when the shipment is accepted by their customer. Sales include shipping and handling charges billed to the customer
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MULTIPLE CHOICE 1. The balance sheet reports a. assets. b. revenue. c. expenses. d. net income. ANS: A DIF: Easy OBJ: LO 5-1 MSC: AACSB Communication 2. The fourth pair of columns on a 10-column work sheet prepared at the end of the period would be the a. Income Statement columns. b. Adjustments columns. c. Balance Sheet columns. d. Adjusted Trial Balance columns. ANS: A DIF: Easy OBJ: LO 5-2 MSC: AACSB Communication 3. The third pair of columns on a 10-column work sheet prepared at the
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ISBN: 972-8924-16-X © 2006 IADIS THE EFFECTS OF GENDER AND MULTIPLE INTELLIGENCES IN LEARNING WITH DIGITAL MUSEUM Peng-heng Tsai Department of Industrial Technology Education‚ National Kaohsiung Normal University. No.41‚ Longsing St.‚ Cianjhen District‚ Kaohsiung City 806‚ Taiwan (R.O.C.) Tien-Yu Huang Department of Computer Science‚ National Pingtung University of Education. No.4-18‚ Minsheng Rd.‚ Pingtung City‚ Pingtung County 900‚ Taiwan (R.O.C.) Hsungrow Chen Department of Mathematics
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Face to Face Communication versus Computer Mediated Communication In the 21st century we are now living in‚ the rapid advancement of technology has completely changed our lives‚ especially the tools we use to communicate. In the olden days where snail mail is one of the popular ways in communicating‚ people would rather choose interact face to face because it is the only way that could physically maintain their relationship as they can see the facial expressions and body gestures shown by the person
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4.11 Multiple Regression Analysis For hypotheses testing of this study‚ multiple regression analysis was conducted. Some assumptions of the relationship between dependent and independent variables need to be met for performing multiple regression analysis like‚ normality‚ linearity‚ homoscedasticity and multicollinearity (Hair et al.‚ 1998). As mentioned earlier‚ the required assumptions have already been met and multiple regression analysis was appropriate. Usually‚ multiple regression analyses
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Automatic Emotion Recognition from Speech using Reduced Feature Set & Different Classifiers U09CO202 U09CO207 U09CO206 U09CO240 Outline ● Project Preliminary : A quick recap ● Running the SVM classifier - Weka ● Improvising the baseline model ○ Principal Component Analysis ○ Feature Subset Selection ● Comparison of different models ● Building a local database ● Next Steps... Outline ● Project Preliminary : A quick recap ● Running the SVM classifier - Weka ● Improvising the baseline
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Brand Recognition of Television Ads CHAPTER 1 THE PROBLEM AND ITS BACKGROUND INTRODUCTION Standing out amid a massive chorus of competitors is a challenge for any company in today’s business climate. Want evidence? Look at any magazine‚ TV show or surf the Internet. The number of offers and sales pitches one receives on a daily basis is simply staggering and increasingly ineffective. It’s no wonder‚ then‚ why businesses are seeking new and more effective ways of increasing the influence of their
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