This document provides a review of how data mining is used in the banking sector. It discusses how data mining can be used for fraud detection, risk management, customer relationship management, and other applications. The document outlines the key steps in data mining including data selection, preparation, transformation, mining, evaluation, and representation. It then discusses specific examples of how data mining has been applied in banking for areas like customer segmentation, credit analysis, fraud detection, and more. Overall, the document reviews the significance and advantages of using data mining technologies in the banking and financial sectors.
Due to the arrival of new technologies, devices, and communication means, the amount of data produced by mankind is growing rapidly every year. This gives rise to the era of big data. The term big data comes with the new challenges to input, process and output the data. The paper focuses on limitation of traditional approach to manage the data and the components that are useful in handling big data. One of the approaches used in processing big data is Hadoop framework, the paper presents the major components of the framework and working process within the framework.
Classmate 1Organizations are widely associated with the use and.docxbartholomeocoombs
Ā
Classmate 1:
Organizations are widely associated with the use and application of data and information in which data management has become the main concern (Tuchkova & Kondrasheva, 2019). Poor data quality and management will create barriers for the development of organizations due to which they may not be able to maintain proper records, make precise decisions, enforce the strategic processing techniques, and enhance the improvement of business standards. All these will show impacts on the efficiency of organizations very badly (Neha K, 2012). Moreover, it was estimated that companies face a loss of 9.7 million dollars of capital per year. Therefore, poor quality management of data will shred down the business of organizations (Aberer, 2011).
Data mining is the process of unveiling the data patterns from a big and voluminous set of data and detecting anomalies to prevent them from corrupting useful information. Data mining is an analytical tool that has been used to prevail in the business era, and it helps organizations to expand the ambit of achieving success with the support of precise decision-making (Aberer, 2011). It can be logically related that the ability of decision-making will be enhanced with the critical analytics of data with an increased set of predictions that probably help make effective changes in organizations (Neha K, 2012). Hence data mining is also defined as the process of knowledge discovery in databases (Tuchkova & Kondrasheva, 2019)
Text mining is a part of data mining, which can also be called as text data mining. Similar to data mining, text mining involves the process of confidential and quality information from the textual context of data (Aberer, 2011). It is a procedure that involves statistical analytics that unveils the patterns of data (Tuchkova & Kondrasheva, 2019). Text mining is being used in various fields like call centers for transcribing, customer surveys, and online reviews. The main motive of text mining is to transform the textual content into actions (Neha K, 2012).
Classmate 2:
Business costs:
Business costs are identified as one of the priority areas for SMEs. The purpose of this study is to simplify the management cost of SMEs and effectively address the issue of the Evaluation Index system. (Guo Yan, 2015) The continuous change is characteristic of not only social space but also economic structure and business. (Ponisciakova, 2015) For the reasons and reasons for these changes in our agents, our financial circumstances are changing, but the process has been operating for many years, and the market policy has become real, but the importance and impact of the new phenomenon are growing.
The advantages of the factor-entropy method are shown in the evaluation index system. However, the emergence of global trends and transitions depends on the private sector and its impact on development. (Daniel M. Franks et al., 2014) It identifies environmental and Business costs as an additional means of trans.
Oral Pseudo-Defense PPT DropboxPlease submit here a narrated P.docxgerardkortney
Ā
Oral "Pseudo-Defense" PPT Dropbox
Please submit here a narrated PowerPoint for your final presentation of your thesis proposal. Remember that this is not you reading a paper -- this is more of a "sales pitch" than anything if you're in need of a metaphor.Ā
Requirements:
- Must be Narrated.Ā Must be narrated.Ā TEST IT BEFORE SENDING.Ā I should be able to open it up and hit Present/Play and just let it go. If you fail to meet this requirement you will automatically lose 30% of the grade (3/10 possible points).
- Introduction topics, including criteria such as your project motivation, the gap/needs that brought you to it, and what significant considerations and context surround the topic area
- RQs/Hypotheses/Objectives, operationalized and justified. There should be NO ambiguity here. (Remember, "What is the impact of Big Data on Security"Ā orĀ "How do we make X better?"Ā are not specific enough.)
- Literature Review, presented as the primary topics you separated your review into, the reason they are important and frame your study successfully, and what key sources/authors you identified (the important few). Discuss how this literature further informed your research agenda, methodology, and consideration of conclusions/limitations for your thesis
- Methodology, as precise as you can make it. Sample, collection, framework (if qualitative), analysis, intended outcome
- Conclusion, timeline and future projections of issues, routes to completion, etc.Ā
MOBILE SCAN PAYMENTS SECURITY ISSUES AND STRATEGIES
VINIL REDDY KASULA
ID#210243
HARRISBURG UNIVERSITY OF SCIENCE AND TECHNOLOGYResearch Methodology & Writing (GRAD 695)
Professor-Richard Wirth
MOBILE SCAN PAYMENT
3
Table of Contents
ABSTRACT 4
1. Introduction 5
1.1 Background 5
1.2 Research aim and objectives 7
1.3 Research questions 7
Research question 1 7
Research question 2 8
Research question 3 8
1.4 Problem statement 8
1.5 Significance of the study 10
1.6 Relationship to CPT 11
LITERATURE REVIEW 11
MOBILE PAYMENT SYSTEMS 12
Mobile payment platform 13
Independent mobile payment system 14
MOBILE PAYMENT SECURITY 15
THREATS IN MOBILE PAYMENT SYSTEMS 17
Research Background and Rationale 18
Research Aims and Objectives 19
Research Questions 19
Research Methodology 19
Ethical Considerations 22
Limitations of the Research 23
Research Timeline 23
CONCLUSION 24
References 25
ABSTRACT
In the present decade and the modern age, mobile payments as a medium for financial transactions have gained much popularity. Mobile technology has emerged as a clear and new channel in the space of banking and payment transactions. With the significant advancement in the field of technology have made this field as one of the burgeoning growth in the financial services. People are involved in the application of the widespread smartphone technology and the customers are very comfortable with their mobile devices as a form of communicating device and this has resulted in the increased interest in .
FLORIDA NATIONAL UNIVERSITYRN-BSN PROGRAMNURSING DEPARTMENTN.docxclydes2
Ā
FLORIDA NATIONAL UNIVERSITY
RN-BSN PROGRAM
NURSING DEPARTMENT
NUR4636 ā COMMUNITY HEALTH NURSING
COMMUNITY HEALTH ASSESSMENT/WINSHIELD SURVEY
PROF. EDDIE CRUZ, RN MSN
GUIDELINES:
Also, you must present a table as an appendix with the following topics and description;
Ā· Community description.
Ā· Community health status (can be obtain from the department of health).
Ā· The role of the community as a client.
Ā· Healthy people 2020, leading health indicators in your community.
Ā· The age, nature, and condition of the communityās available housing
Ā· Infrastructure needs ā roads, bridges, streetlights, etc.
Ā· The presence or absence of functioning businesses and industrial facilities
Ā· The location, condition, and use of public spaces
Ā· The amount of activity on the streets at various times of the day, week, or year
Ā· The noise level in various parts of the community
Ā· The amount and movement of traffic at various times of day
Ā· The location and condition of public buildings ā the city or town hall, courthouse, etc.
Ā· Transportation
Ā· Race and ethnicity
Ā· Open spaces
Ā· Service centers
Ā· Religion and politics
The assignment will be posted in both the discussion tab of the blackboard under the forum title āCommunity assessmentā and in the SafeAssign exercise in the assignment tab. The assignment is due on Sunday, May 24, 2020 @ 11:59 and then I will open for you to review your peers and post two replies about their assessment. The value of the replies is 20 points (10 points for each reply).
The due date to post the assignment in on Sunday, May 24, 2020 @ 11:59 PM and for the replies on Wednesday, May 27th, 2020. After the 24th only the replies will be accepted.
This assignment has a total value of 100 points, 80 for the survey and the replies 20 points. I will be monitoring plagiarism very closely.
If you have any question you can contact me via FNU email.
Discussion-3
byĀ Vijay ManoharĀ - Tuesday, May 19, 2020, 8:08 PM
What are the business costs and risks of poor data quality?
Every company, Analytics should be executed on Standard data and it should be made mandatory. Poor Data Quality will have an adverse effect on the performance, ideologies, and also master plan of the company. In the Performace factor, Poor Data Quality end up in price rise, workers at the end of the day won't feel happy with their job. As a direct proportion, this would end up in customers not being happy with our product. They order a product and it ended up in delivering to a different address. The main disadvantage is we end up losing in all ways and correcting poor data quality involves a lot of dollars and hours getting wasted.(Redman, T. C., 1998)(Celko, J., 1995)(Davenport, T.H., 1997)
What is Data Mining?
Data Mining is a technology used in order to filter the data and pull out the knowledge from the dataset. The mechanism involved in order to pull out meaningful information which is mixed along with raw data present in unlike datasets across, unlike dat.
Due to the arrival of new technologies, devices, and communication means, the amount of data produced by mankind is growing rapidly every year. This gives rise to the era of big data. The term big data comes with the new challenges to input, process and output the data. The paper focuses on limitation of traditional approach to manage the data and the components that are useful in handling big data. One of the approaches used in processing big data is Hadoop framework, the paper presents the major components of the framework and working process within the framework.
Classmate 1Organizations are widely associated with the use and.docxbartholomeocoombs
Ā
Classmate 1:
Organizations are widely associated with the use and application of data and information in which data management has become the main concern (Tuchkova & Kondrasheva, 2019). Poor data quality and management will create barriers for the development of organizations due to which they may not be able to maintain proper records, make precise decisions, enforce the strategic processing techniques, and enhance the improvement of business standards. All these will show impacts on the efficiency of organizations very badly (Neha K, 2012). Moreover, it was estimated that companies face a loss of 9.7 million dollars of capital per year. Therefore, poor quality management of data will shred down the business of organizations (Aberer, 2011).
Data mining is the process of unveiling the data patterns from a big and voluminous set of data and detecting anomalies to prevent them from corrupting useful information. Data mining is an analytical tool that has been used to prevail in the business era, and it helps organizations to expand the ambit of achieving success with the support of precise decision-making (Aberer, 2011). It can be logically related that the ability of decision-making will be enhanced with the critical analytics of data with an increased set of predictions that probably help make effective changes in organizations (Neha K, 2012). Hence data mining is also defined as the process of knowledge discovery in databases (Tuchkova & Kondrasheva, 2019)
Text mining is a part of data mining, which can also be called as text data mining. Similar to data mining, text mining involves the process of confidential and quality information from the textual context of data (Aberer, 2011). It is a procedure that involves statistical analytics that unveils the patterns of data (Tuchkova & Kondrasheva, 2019). Text mining is being used in various fields like call centers for transcribing, customer surveys, and online reviews. The main motive of text mining is to transform the textual content into actions (Neha K, 2012).
Classmate 2:
Business costs:
Business costs are identified as one of the priority areas for SMEs. The purpose of this study is to simplify the management cost of SMEs and effectively address the issue of the Evaluation Index system. (Guo Yan, 2015) The continuous change is characteristic of not only social space but also economic structure and business. (Ponisciakova, 2015) For the reasons and reasons for these changes in our agents, our financial circumstances are changing, but the process has been operating for many years, and the market policy has become real, but the importance and impact of the new phenomenon are growing.
The advantages of the factor-entropy method are shown in the evaluation index system. However, the emergence of global trends and transitions depends on the private sector and its impact on development. (Daniel M. Franks et al., 2014) It identifies environmental and Business costs as an additional means of trans.
Oral Pseudo-Defense PPT DropboxPlease submit here a narrated P.docxgerardkortney
Ā
Oral "Pseudo-Defense" PPT Dropbox
Please submit here a narrated PowerPoint for your final presentation of your thesis proposal. Remember that this is not you reading a paper -- this is more of a "sales pitch" than anything if you're in need of a metaphor.Ā
Requirements:
- Must be Narrated.Ā Must be narrated.Ā TEST IT BEFORE SENDING.Ā I should be able to open it up and hit Present/Play and just let it go. If you fail to meet this requirement you will automatically lose 30% of the grade (3/10 possible points).
- Introduction topics, including criteria such as your project motivation, the gap/needs that brought you to it, and what significant considerations and context surround the topic area
- RQs/Hypotheses/Objectives, operationalized and justified. There should be NO ambiguity here. (Remember, "What is the impact of Big Data on Security"Ā orĀ "How do we make X better?"Ā are not specific enough.)
- Literature Review, presented as the primary topics you separated your review into, the reason they are important and frame your study successfully, and what key sources/authors you identified (the important few). Discuss how this literature further informed your research agenda, methodology, and consideration of conclusions/limitations for your thesis
- Methodology, as precise as you can make it. Sample, collection, framework (if qualitative), analysis, intended outcome
- Conclusion, timeline and future projections of issues, routes to completion, etc.Ā
MOBILE SCAN PAYMENTS SECURITY ISSUES AND STRATEGIES
VINIL REDDY KASULA
ID#210243
HARRISBURG UNIVERSITY OF SCIENCE AND TECHNOLOGYResearch Methodology & Writing (GRAD 695)
Professor-Richard Wirth
MOBILE SCAN PAYMENT
3
Table of Contents
ABSTRACT 4
1. Introduction 5
1.1 Background 5
1.2 Research aim and objectives 7
1.3 Research questions 7
Research question 1 7
Research question 2 8
Research question 3 8
1.4 Problem statement 8
1.5 Significance of the study 10
1.6 Relationship to CPT 11
LITERATURE REVIEW 11
MOBILE PAYMENT SYSTEMS 12
Mobile payment platform 13
Independent mobile payment system 14
MOBILE PAYMENT SECURITY 15
THREATS IN MOBILE PAYMENT SYSTEMS 17
Research Background and Rationale 18
Research Aims and Objectives 19
Research Questions 19
Research Methodology 19
Ethical Considerations 22
Limitations of the Research 23
Research Timeline 23
CONCLUSION 24
References 25
ABSTRACT
In the present decade and the modern age, mobile payments as a medium for financial transactions have gained much popularity. Mobile technology has emerged as a clear and new channel in the space of banking and payment transactions. With the significant advancement in the field of technology have made this field as one of the burgeoning growth in the financial services. People are involved in the application of the widespread smartphone technology and the customers are very comfortable with their mobile devices as a form of communicating device and this has resulted in the increased interest in .
FLORIDA NATIONAL UNIVERSITYRN-BSN PROGRAMNURSING DEPARTMENTN.docxclydes2
Ā
FLORIDA NATIONAL UNIVERSITY
RN-BSN PROGRAM
NURSING DEPARTMENT
NUR4636 ā COMMUNITY HEALTH NURSING
COMMUNITY HEALTH ASSESSMENT/WINSHIELD SURVEY
PROF. EDDIE CRUZ, RN MSN
GUIDELINES:
Also, you must present a table as an appendix with the following topics and description;
Ā· Community description.
Ā· Community health status (can be obtain from the department of health).
Ā· The role of the community as a client.
Ā· Healthy people 2020, leading health indicators in your community.
Ā· The age, nature, and condition of the communityās available housing
Ā· Infrastructure needs ā roads, bridges, streetlights, etc.
Ā· The presence or absence of functioning businesses and industrial facilities
Ā· The location, condition, and use of public spaces
Ā· The amount of activity on the streets at various times of the day, week, or year
Ā· The noise level in various parts of the community
Ā· The amount and movement of traffic at various times of day
Ā· The location and condition of public buildings ā the city or town hall, courthouse, etc.
Ā· Transportation
Ā· Race and ethnicity
Ā· Open spaces
Ā· Service centers
Ā· Religion and politics
The assignment will be posted in both the discussion tab of the blackboard under the forum title āCommunity assessmentā and in the SafeAssign exercise in the assignment tab. The assignment is due on Sunday, May 24, 2020 @ 11:59 and then I will open for you to review your peers and post two replies about their assessment. The value of the replies is 20 points (10 points for each reply).
The due date to post the assignment in on Sunday, May 24, 2020 @ 11:59 PM and for the replies on Wednesday, May 27th, 2020. After the 24th only the replies will be accepted.
This assignment has a total value of 100 points, 80 for the survey and the replies 20 points. I will be monitoring plagiarism very closely.
If you have any question you can contact me via FNU email.
Discussion-3
byĀ Vijay ManoharĀ - Tuesday, May 19, 2020, 8:08 PM
What are the business costs and risks of poor data quality?
Every company, Analytics should be executed on Standard data and it should be made mandatory. Poor Data Quality will have an adverse effect on the performance, ideologies, and also master plan of the company. In the Performace factor, Poor Data Quality end up in price rise, workers at the end of the day won't feel happy with their job. As a direct proportion, this would end up in customers not being happy with our product. They order a product and it ended up in delivering to a different address. The main disadvantage is we end up losing in all ways and correcting poor data quality involves a lot of dollars and hours getting wasted.(Redman, T. C., 1998)(Celko, J., 1995)(Davenport, T.H., 1997)
What is Data Mining?
Data Mining is a technology used in order to filter the data and pull out the knowledge from the dataset. The mechanism involved in order to pull out meaningful information which is mixed along with raw data present in unlike datasets across, unlike dat.
Why Data Science is Getting Popular in 2023?kavyagaur3
Ā
Data science employs mathematics, statistics, advanced programming techniques, analytics and artificial intelligence (AI) to uncover insights that drive business value for their organisation. Then, this information can be used for strategic planning and decision-making.
Data has flooded in massive amounts as a result of digitization. Businesses are making their utmost efforts to take advantage of every opportunity to increase their businesses. This makes the best opportunity for individuals who want to pursue Data Science. The first step is to get the best data science training.
Supervised and unsupervised data mining approaches in loan default prediction IJECEIAES
Ā
Given the paramount importance of data mining in organizations and the possible contribution of a data-driven customer classification recommender systems for loan-extending financial institutions, the study applied supervised and supervised data mining approaches to derive the best classifier of loan default. A total of 900 instances with determined attributes and class labels were used for the training and cross-validation processes while prediction used 100 new instances without class labels. In the training phase, J48 with confidence factor of 50% attained the highest classification accuracy (76.85%), k-nearest neighbors (k-NN) 3 the highest (78.38%) in IBk variants, naĆÆve Bayes has a classification accuracy of 76.65%, and logistic has 77.31% classification accuracy. k-NN 3 and logistic have the highest classification accuracy, F-measures, and kappa statistics. Implementation of these algorithms to the test set yielded 48 non-defaulters and 52 defaulters for k-NN 3 while 44 non-defaulters and 56 defaulters under logistic. Implications were discussed in the paper.
Selection of Articles using Data Analytics for Behavioral Dissertation Resear...PhD Assistance
Ā
Outcomes in health-related issues including psychological, educational, Behavioral, environmental, and social are intended to sustain positive change by digital interferences. These changes may be delivered using any digital device like a phone or computer, and make them gainful for the provider. Complex and large-scale datasets that contain usage data can be yielded by testing a digital intervention. This data provides invaluable detail about how the users interact with these interventions and notify their knowledge of engagement, if they are analyzed properly. This paper recommends an innovative framework for the process of analyzing usage associated with a digital intervention .
PhD Assistance is an Academic The Best Dissertation Writing Service & Consulting Support Company established in 2001. specialiWeze in providing PhD Assignments, PhD Dissertation Writing Help , Statistical Analyses, and Programming Services to students in the USA, UK, Canada, UAE, Australia, New Zealand, Singapore and many more.
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DATA MINING WITH CLUSTERING ON BIG DATA FOR SHOPPING MALLāS DATASETAM Publications
Ā
Big Data is the extremely large sets of data that their sizes are beyond the ability of capturing, managing, processing and storage by most software tools and people which is ever increasing day-by-day. In most enterprise scenarios the data is too big or it moves too fast that extremely exceeds current processing capacity. The term big data is also used by vendors, may refer to the technology which includes tools and processes that an organization requires to handle the large amounts of data and storage facilities. This advancement in technology leads to make relationship marketing a reality for todayās competitive world. But at the same time this huge amount of data cannot be analyzed in a traditional manner, by using manual data analysis. For this, technologies such as data warehousing and data mining have made customer relationship management as a new area where business firms can gain a competitive advantage for identifying their customer behaviors and needs. This paper mainly focuses on data mining technique that performs the extraction of hidden predictive information from large databases and organizations can identify valuable customers and predicts future user behaviors. This enables different organizations to make proactive, knowledge-driven decisions. Data mining tools answer business questions that in the past were too time-consuming, this makes customer relationship management possible. For this in this paper, we are trying explain the use of data mining technique to accomplish the goals of todayās customer relationship management and Decision making for different companies that deals with big data.
A potential objective of every financial organization is to retain existing customers and attain new
prospective customers for long-term. The economic behaviour of customer and the nature of the
organization are controlled by a prescribed form called Know Your Customer (KYC) in manual banking.
Depositor customers in some sectors (business of Jewellery/Gold, Arms, Money exchanger etc) are with
high risk; whereas in some sectors (Transport Operators, Auto-delear, religious) are with medium risk;
and in remaining sectors (Retail, Corporate, Service, Farmer etc) belongs to low risk. Presently, credit risk
for counterparty can be broadly categorized under quantitative and qualitative factors. Although there are
many existing systems on customer retention as well as customer attrition systems in bank, these rigorous
methods suffers clear and defined approach to disburse loan in business sector. In the paper, we have used
records of business customers of a retail commercial bank in the city including rural and urban area of
(Tangail city) Bangladesh to analyse the major transactional determinants of customers and predicting of a
model for prospective sectors in retail bank. To achieve this, data mining approach is adopted for
analysing the challenging issues, where pruned decision tree classification technique has been used to
develop the model and finally tested its performance with Weka result. Moreover, this paper attempts to
build up a model to predict prospective business sectors in retail banking.
EXTENSION OF TECHNOLOGY ACCEPTANCE MODEL (TAM): A STUDY ON INDIAN INTERNET BA...IAEME Publication
Ā
Internet banking plays significant role in the development of banking business in our country. An application of electronic service brings predominant changes in the way of doing banking transactions. In simpler terms, internet banking refers to banking through bankās website with the help of internet connection. Internet banking provides lot of benefits to the customers as well as the banks. Internet banking provides different kinds of services to the customers in the form checking balances, account statement, pay utility bills etc
Big data analytics and its impact on internet usersStruggler Ever
Ā
Big Data Analytic tools are promising techniques for a future prediction in many aspects of our life. The need for such predictive techniques has been exponentially increasing. even though, there are many challenges and risks are still of concern of researchers and decision makers, the outcome from the use of these techniques will considerable revolutionize our world to a new era of technology.
Predicting customer's intentions to use internet banking: the role of technol...Samar Rahi
Ā
Information and communication technology (ICT) developments and trends in recent years have
had great impacts on banking sector worldwide. Therefore, the disruptive innovative technology
has accelerated changes in the way of banking business. The purpose of this paper is to explore the
factors that influence on Pakistani customerās intentions to adopt internet banking. The sample
used in this empirical study includes 265 responses of internet banking users collected through
structured questionnaire. For statistical analysis, structural equation model (SEM) approach was
used. The present study suggests that internet banking use increases as long as customer perceives
it as useful tool. Findings confirmed that perceived usefulness, perceived ease of use and attitude
were the key constructs for promoting internet banking usage in Pakistan. Furthermore, the importance
performance matrix analysis has shown that attitude was the most important factor. Thus,
banks can focus on cultivation of positive attitudinal beliefs about internet banking among prospect
customers.
Running head DATABASE AND DATA WAREHOUSING DESIGNDATABASE AND.docxtodd271
Ā
Running head: DATABASE AND DATA WAREHOUSING DESIGN
DATABASE AND DATA WAREHOUSING DESIGN 10
Database and Data Warehousing Design
Necosa Hollie
Dr. Ford
Information Systems Capstone CIS499
May 5, 2019
Introduction
Somar and Co. Data Collection Company collects and analyzes data by using operational systems and web analytics. The data used in the analysis is collected from diverse operating systems such as ERP software. Various applications such as payrolls, human resources, and insurance claims are used in, modern-day enterprises and data from them keep on increasing day by day (Schoenherr, & SpeierāPero, 2015). The ever-increasing data has been overwhelming organizationsā ability to analyze it due to its complex nature. This challenge has forced Somar and Co. Data Collection Company to seek a solution to it to deliver quality results to their clients. As the chief information officer (CIO) at the company, will be in charge of designing the solution that will incorporate data warehousing. This will make it possible to be consolidating large amounts of data quickly and be creating quality analytical reports within the shortest time possible.
Need for Data Warehousing
Data warehouses are central storage systems in companies where vital information from other applications such as ERP system is deposited. The data is periodically extracted from these applications. Data is sent to the data warehouse in different formats as different applications have distinct ways of keeping information. Then the data warehouse by having a uniform operational system will process and analyze discrete data into a more straightforward form. Somar and Co. Data Collection Company manages data from various clients with the information having been collected from multiple departments such as marketing, sales, and finance. To develop an active data warehouse, data consistency from different applications plays a crucial part (Waller, & Fawcett, 2013). This enables establishing of a constant process for all types of data. The information is analyzed for analytical reports, market research and decision report. The processed data also gives insight about the direction of the company to the management. The data is considered by the management during decision making and strategic planning.
Due to the importance of the data reposted in the data warehouse to the management, it should be analyzed in such a way that it is easy to comprehend and interpret (Schoenherr, & SpeierāPero, 2015). As the processed data originates from different departments of the organization, this makes it be a reliable source of information to the management. If every department were to analyze its data, this would result in different information in different formats hence tricky for the administration to interpret it accurately. The data warehouse helps to resolve this problem by offering a centralized syste.
The Critical Factors that influencing Web-Based DSS Successin Online Shopping...IJRES Journal
Ā
This research aimed to examine the main factors that influence the success of Web-Based Decision Support Systems in online shopping context. It investigated a set of factors which are; online shopping system quality, data quality, knowledge management, consumer decision making satisfaction, and perceived net benefit. A questionnaire was distributed to a sample of (140) respondents to collect primary data, & based on a convenience sample the response rate was about 82%. Furthermore, the findings were analyzed using the Statistical Package for Social Software (SPSS), indicated that online shopping system quality, data quality, and knowledge management have a positive & significant influence on the consumer decision making satisfaction, and there is mutual effect between consumer decision making satisfaction, and perceived net benefit. In other words, all research variables have significant effect on success of web-based DSS in online shopping context.Based on the research findings & conclusions, a number ofrecommendations & future research suggestions are proposed.
Framework to Analyze Customerās Feedback in Smartphone Industry Using Opinion...IJECEIAES
Ā
In the present age, cellular phones are the largest selling products in the world. Big Data Analytics is a method used for examining large and varied data, which we know as big data. Big data analytics is very useful for understanding the world of cellphone business. It is important to understand the requirements, demands, and opinions of the customer. Opinion Mining is getting more important than ever before, for performing analysis and forecasting customer behavior and preferences. This study proposes a framework about the key features of cellphones based on which, customers buy them and rate them accordingly. This research work also provides balanced and well researched reasons as to why few companies enjoy dominance in the market, while others do not make as much of an impact.
Running head MANAGEMENT INFORMATION SYSTEM1MANAGEMENT INFORM.docxcowinhelen
Ā
Running head: MANAGEMENT INFORMATION SYSTEM 1
MANAGEMENT INFORMATION SYSTEM 6
Management Information System
Vijay chilakala
Wilmington university
5/20/18
Introduction
A management information system can be described as a computerized system that accepts data and organizes data in a systematic way so that the data can be used for other purposes like analysis, decision making and also problem solving. Most of the business organizations use these kinds of system to store and organize their data for decision making purposes. Management information systems are applied in various departments like banking institutions, military use, hospitals, meteorological institutions, National Space Aeronautics (NASA) and many more. An example of Management information system (MIS) is Automated Teller Machines (ATM) used for cash transactions (Li, Xie, & Zhang, 2015)Risks associated with Management Information System
Management of information systems in relation to risk is a wide area to g by. There are a few important security concepts that can help in the management of these risks. Therefore security for data and information is important in these sectors. Information security entails a number of factors; integrity refers to when the management of data and information is dealt with by transparent and authorized ways. Confidentiality refers to limiting the data and information so that the authorized parties get to view it while writing off the unauthorized parties (Batini & Scannapieco, 2016). Availability of data and information is also information and therefore data and information should be available for the right people at a given time. There are a number of risks or vulnerabilities in the information systems department.
Impersonation is an example of a risk. You find that one user takes the identity of another person to accomplish certain hidden agendas. Cybercrimes like hacking or cracking to gain access into the system. Theft is also a risk which usually results into loss of computer hardware (Li, Xie, & Zhang, 2015)Management of Information systems related to use
Information system has a number of uses. They play an important role in managing data and information. A management information system can be used in processing transactions like the Automated Teller Machines. This function is mostly used by financing institutions like Banks to store, organize and process data. Management information systems can be used in libraries to organize and keep record of books as they are issued to the parties that need them. They are also used by organizations to compile data used for analysis; the data can later be used for decision making policies.Management of Information systems related to data storage
Data storage provides a platform where information that is used by any information system can be kept and backed up for future usage. It also means that the data stored can be accessed at any given whenever the authorized parties feel like using them. ...
A COMPREHENSIVE STUDY ON POTENTIAL RESEARCH OPPORTUNITIES OF BIG DATA ANALYTI...ijcseit
Ā
Companies, organizations and policy makers shake out with flood flowing volume of transactional data,
accumulating trillions of bytes of information about their customers, suppliers and operations. The advanced networked sensors are being implanted in devices such as mobile phones, smart energy meters,automobiles and industrial machines that sense, generate and transfer data to multiple storage devices. In fact, as they go about their business and interact with individuals, they are producing an incredible amount of fatigue digital data. Social media sites, smart phones, and other customer devices have allowed billions
of individuals around the world to contribute to the amount of data available. In addition, the extremely
increasing size of multimedia data has also take part a key role in the rapid growth of data. The technology
of high-definition video creates more than 2,000 times as many bytes as necessary to store as normal text
data. Moreover, in a digitized world, consumers are leaving enormous amount of data about their day-today
communicating, browsing, buying, sharing, searching and so on. As a result, it evolved as a big data and in turn has motivated the advances in big data analytics paradigms, endorsed as a basic motivation factor for the present researchers.
A COMPREHENSIVE STUDY ON POTENTIAL RESEARCH OPPORTUNITIES OF BIG DATA ANALYTI...ijcseit
Ā
Companies, organizations and policy makers shake out with flood flowing volume of transactional data, accumulating trillions of bytes of information about their customers, suppliers and operations. The advanced networked sensors are being implanted in devices such as mobile phones, smart energy meters, automobiles and industrial machines that sense, generate and transfer data to multiple storage devices. In fact, as they go about their business and interact with individuals, they are producing an incredible amount of fatigue digital data. Social media sites, smart phones, and other customer devices have allowed billions of individuals around the world to contribute to the amount of data available. In addition, the extremely increasing size of multimedia data has also take part a key role in the rapid growth of data. The technology of high-definition video creates more than 2,000 times as many bytes as necessary to store as normal text data. Moreover, in a digitized world, consumers are leaving enormous amount of data about their day-today communicating, browsing, buying, sharing, searching and so on. As a result, it evolved as a big data and in turn has motivated the advances in big data analytics paradigms, endorsed as a basic motivation factor for the present researchers.
The authors in the present paper conduct a comprehensive study to explore the impact of big data analytics in key domains namely, Health Care (HC), Retail Industry (RI), Public Governance (PG), Pubic Security & Safety (PSS) and Personal Location Tracking (PLT). Initially, the study looks at the insights of data sources along with their characteristics in each domain. Later, it presents the highly productive and competitive big data applications with innovative big data technologies. Subsequently, the study showcases the impact of big data on each domain to capture value addition in its services. Finally, the study put forwards many more research opportunities as all these domains differ in their complexity and development in the usage of big data analytics
Why Data Science is Getting Popular in 2023?kavyagaur3
Ā
Data science employs mathematics, statistics, advanced programming techniques, analytics and artificial intelligence (AI) to uncover insights that drive business value for their organisation. Then, this information can be used for strategic planning and decision-making.
Data has flooded in massive amounts as a result of digitization. Businesses are making their utmost efforts to take advantage of every opportunity to increase their businesses. This makes the best opportunity for individuals who want to pursue Data Science. The first step is to get the best data science training.
Supervised and unsupervised data mining approaches in loan default prediction IJECEIAES
Ā
Given the paramount importance of data mining in organizations and the possible contribution of a data-driven customer classification recommender systems for loan-extending financial institutions, the study applied supervised and supervised data mining approaches to derive the best classifier of loan default. A total of 900 instances with determined attributes and class labels were used for the training and cross-validation processes while prediction used 100 new instances without class labels. In the training phase, J48 with confidence factor of 50% attained the highest classification accuracy (76.85%), k-nearest neighbors (k-NN) 3 the highest (78.38%) in IBk variants, naĆÆve Bayes has a classification accuracy of 76.65%, and logistic has 77.31% classification accuracy. k-NN 3 and logistic have the highest classification accuracy, F-measures, and kappa statistics. Implementation of these algorithms to the test set yielded 48 non-defaulters and 52 defaulters for k-NN 3 while 44 non-defaulters and 56 defaulters under logistic. Implications were discussed in the paper.
Selection of Articles using Data Analytics for Behavioral Dissertation Resear...PhD Assistance
Ā
Outcomes in health-related issues including psychological, educational, Behavioral, environmental, and social are intended to sustain positive change by digital interferences. These changes may be delivered using any digital device like a phone or computer, and make them gainful for the provider. Complex and large-scale datasets that contain usage data can be yielded by testing a digital intervention. This data provides invaluable detail about how the users interact with these interventions and notify their knowledge of engagement, if they are analyzed properly. This paper recommends an innovative framework for the process of analyzing usage associated with a digital intervention .
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DATA MINING WITH CLUSTERING ON BIG DATA FOR SHOPPING MALLāS DATASETAM Publications
Ā
Big Data is the extremely large sets of data that their sizes are beyond the ability of capturing, managing, processing and storage by most software tools and people which is ever increasing day-by-day. In most enterprise scenarios the data is too big or it moves too fast that extremely exceeds current processing capacity. The term big data is also used by vendors, may refer to the technology which includes tools and processes that an organization requires to handle the large amounts of data and storage facilities. This advancement in technology leads to make relationship marketing a reality for todayās competitive world. But at the same time this huge amount of data cannot be analyzed in a traditional manner, by using manual data analysis. For this, technologies such as data warehousing and data mining have made customer relationship management as a new area where business firms can gain a competitive advantage for identifying their customer behaviors and needs. This paper mainly focuses on data mining technique that performs the extraction of hidden predictive information from large databases and organizations can identify valuable customers and predicts future user behaviors. This enables different organizations to make proactive, knowledge-driven decisions. Data mining tools answer business questions that in the past were too time-consuming, this makes customer relationship management possible. For this in this paper, we are trying explain the use of data mining technique to accomplish the goals of todayās customer relationship management and Decision making for different companies that deals with big data.
A potential objective of every financial organization is to retain existing customers and attain new
prospective customers for long-term. The economic behaviour of customer and the nature of the
organization are controlled by a prescribed form called Know Your Customer (KYC) in manual banking.
Depositor customers in some sectors (business of Jewellery/Gold, Arms, Money exchanger etc) are with
high risk; whereas in some sectors (Transport Operators, Auto-delear, religious) are with medium risk;
and in remaining sectors (Retail, Corporate, Service, Farmer etc) belongs to low risk. Presently, credit risk
for counterparty can be broadly categorized under quantitative and qualitative factors. Although there are
many existing systems on customer retention as well as customer attrition systems in bank, these rigorous
methods suffers clear and defined approach to disburse loan in business sector. In the paper, we have used
records of business customers of a retail commercial bank in the city including rural and urban area of
(Tangail city) Bangladesh to analyse the major transactional determinants of customers and predicting of a
model for prospective sectors in retail bank. To achieve this, data mining approach is adopted for
analysing the challenging issues, where pruned decision tree classification technique has been used to
develop the model and finally tested its performance with Weka result. Moreover, this paper attempts to
build up a model to predict prospective business sectors in retail banking.
EXTENSION OF TECHNOLOGY ACCEPTANCE MODEL (TAM): A STUDY ON INDIAN INTERNET BA...IAEME Publication
Ā
Internet banking plays significant role in the development of banking business in our country. An application of electronic service brings predominant changes in the way of doing banking transactions. In simpler terms, internet banking refers to banking through bankās website with the help of internet connection. Internet banking provides lot of benefits to the customers as well as the banks. Internet banking provides different kinds of services to the customers in the form checking balances, account statement, pay utility bills etc
Big data analytics and its impact on internet usersStruggler Ever
Ā
Big Data Analytic tools are promising techniques for a future prediction in many aspects of our life. The need for such predictive techniques has been exponentially increasing. even though, there are many challenges and risks are still of concern of researchers and decision makers, the outcome from the use of these techniques will considerable revolutionize our world to a new era of technology.
Predicting customer's intentions to use internet banking: the role of technol...Samar Rahi
Ā
Information and communication technology (ICT) developments and trends in recent years have
had great impacts on banking sector worldwide. Therefore, the disruptive innovative technology
has accelerated changes in the way of banking business. The purpose of this paper is to explore the
factors that influence on Pakistani customerās intentions to adopt internet banking. The sample
used in this empirical study includes 265 responses of internet banking users collected through
structured questionnaire. For statistical analysis, structural equation model (SEM) approach was
used. The present study suggests that internet banking use increases as long as customer perceives
it as useful tool. Findings confirmed that perceived usefulness, perceived ease of use and attitude
were the key constructs for promoting internet banking usage in Pakistan. Furthermore, the importance
performance matrix analysis has shown that attitude was the most important factor. Thus,
banks can focus on cultivation of positive attitudinal beliefs about internet banking among prospect
customers.
Running head DATABASE AND DATA WAREHOUSING DESIGNDATABASE AND.docxtodd271
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Running head: DATABASE AND DATA WAREHOUSING DESIGN
DATABASE AND DATA WAREHOUSING DESIGN 10
Database and Data Warehousing Design
Necosa Hollie
Dr. Ford
Information Systems Capstone CIS499
May 5, 2019
Introduction
Somar and Co. Data Collection Company collects and analyzes data by using operational systems and web analytics. The data used in the analysis is collected from diverse operating systems such as ERP software. Various applications such as payrolls, human resources, and insurance claims are used in, modern-day enterprises and data from them keep on increasing day by day (Schoenherr, & SpeierāPero, 2015). The ever-increasing data has been overwhelming organizationsā ability to analyze it due to its complex nature. This challenge has forced Somar and Co. Data Collection Company to seek a solution to it to deliver quality results to their clients. As the chief information officer (CIO) at the company, will be in charge of designing the solution that will incorporate data warehousing. This will make it possible to be consolidating large amounts of data quickly and be creating quality analytical reports within the shortest time possible.
Need for Data Warehousing
Data warehouses are central storage systems in companies where vital information from other applications such as ERP system is deposited. The data is periodically extracted from these applications. Data is sent to the data warehouse in different formats as different applications have distinct ways of keeping information. Then the data warehouse by having a uniform operational system will process and analyze discrete data into a more straightforward form. Somar and Co. Data Collection Company manages data from various clients with the information having been collected from multiple departments such as marketing, sales, and finance. To develop an active data warehouse, data consistency from different applications plays a crucial part (Waller, & Fawcett, 2013). This enables establishing of a constant process for all types of data. The information is analyzed for analytical reports, market research and decision report. The processed data also gives insight about the direction of the company to the management. The data is considered by the management during decision making and strategic planning.
Due to the importance of the data reposted in the data warehouse to the management, it should be analyzed in such a way that it is easy to comprehend and interpret (Schoenherr, & SpeierāPero, 2015). As the processed data originates from different departments of the organization, this makes it be a reliable source of information to the management. If every department were to analyze its data, this would result in different information in different formats hence tricky for the administration to interpret it accurately. The data warehouse helps to resolve this problem by offering a centralized syste.
The Critical Factors that influencing Web-Based DSS Successin Online Shopping...IJRES Journal
Ā
This research aimed to examine the main factors that influence the success of Web-Based Decision Support Systems in online shopping context. It investigated a set of factors which are; online shopping system quality, data quality, knowledge management, consumer decision making satisfaction, and perceived net benefit. A questionnaire was distributed to a sample of (140) respondents to collect primary data, & based on a convenience sample the response rate was about 82%. Furthermore, the findings were analyzed using the Statistical Package for Social Software (SPSS), indicated that online shopping system quality, data quality, and knowledge management have a positive & significant influence on the consumer decision making satisfaction, and there is mutual effect between consumer decision making satisfaction, and perceived net benefit. In other words, all research variables have significant effect on success of web-based DSS in online shopping context.Based on the research findings & conclusions, a number ofrecommendations & future research suggestions are proposed.
Framework to Analyze Customerās Feedback in Smartphone Industry Using Opinion...IJECEIAES
Ā
In the present age, cellular phones are the largest selling products in the world. Big Data Analytics is a method used for examining large and varied data, which we know as big data. Big data analytics is very useful for understanding the world of cellphone business. It is important to understand the requirements, demands, and opinions of the customer. Opinion Mining is getting more important than ever before, for performing analysis and forecasting customer behavior and preferences. This study proposes a framework about the key features of cellphones based on which, customers buy them and rate them accordingly. This research work also provides balanced and well researched reasons as to why few companies enjoy dominance in the market, while others do not make as much of an impact.
Running head MANAGEMENT INFORMATION SYSTEM1MANAGEMENT INFORM.docxcowinhelen
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Running head: MANAGEMENT INFORMATION SYSTEM 1
MANAGEMENT INFORMATION SYSTEM 6
Management Information System
Vijay chilakala
Wilmington university
5/20/18
Introduction
A management information system can be described as a computerized system that accepts data and organizes data in a systematic way so that the data can be used for other purposes like analysis, decision making and also problem solving. Most of the business organizations use these kinds of system to store and organize their data for decision making purposes. Management information systems are applied in various departments like banking institutions, military use, hospitals, meteorological institutions, National Space Aeronautics (NASA) and many more. An example of Management information system (MIS) is Automated Teller Machines (ATM) used for cash transactions (Li, Xie, & Zhang, 2015)Risks associated with Management Information System
Management of information systems in relation to risk is a wide area to g by. There are a few important security concepts that can help in the management of these risks. Therefore security for data and information is important in these sectors. Information security entails a number of factors; integrity refers to when the management of data and information is dealt with by transparent and authorized ways. Confidentiality refers to limiting the data and information so that the authorized parties get to view it while writing off the unauthorized parties (Batini & Scannapieco, 2016). Availability of data and information is also information and therefore data and information should be available for the right people at a given time. There are a number of risks or vulnerabilities in the information systems department.
Impersonation is an example of a risk. You find that one user takes the identity of another person to accomplish certain hidden agendas. Cybercrimes like hacking or cracking to gain access into the system. Theft is also a risk which usually results into loss of computer hardware (Li, Xie, & Zhang, 2015)Management of Information systems related to use
Information system has a number of uses. They play an important role in managing data and information. A management information system can be used in processing transactions like the Automated Teller Machines. This function is mostly used by financing institutions like Banks to store, organize and process data. Management information systems can be used in libraries to organize and keep record of books as they are issued to the parties that need them. They are also used by organizations to compile data used for analysis; the data can later be used for decision making policies.Management of Information systems related to data storage
Data storage provides a platform where information that is used by any information system can be kept and backed up for future usage. It also means that the data stored can be accessed at any given whenever the authorized parties feel like using them. ...
A COMPREHENSIVE STUDY ON POTENTIAL RESEARCH OPPORTUNITIES OF BIG DATA ANALYTI...ijcseit
Ā
Companies, organizations and policy makers shake out with flood flowing volume of transactional data,
accumulating trillions of bytes of information about their customers, suppliers and operations. The advanced networked sensors are being implanted in devices such as mobile phones, smart energy meters,automobiles and industrial machines that sense, generate and transfer data to multiple storage devices. In fact, as they go about their business and interact with individuals, they are producing an incredible amount of fatigue digital data. Social media sites, smart phones, and other customer devices have allowed billions
of individuals around the world to contribute to the amount of data available. In addition, the extremely
increasing size of multimedia data has also take part a key role in the rapid growth of data. The technology
of high-definition video creates more than 2,000 times as many bytes as necessary to store as normal text
data. Moreover, in a digitized world, consumers are leaving enormous amount of data about their day-today
communicating, browsing, buying, sharing, searching and so on. As a result, it evolved as a big data and in turn has motivated the advances in big data analytics paradigms, endorsed as a basic motivation factor for the present researchers.
A COMPREHENSIVE STUDY ON POTENTIAL RESEARCH OPPORTUNITIES OF BIG DATA ANALYTI...ijcseit
Ā
Companies, organizations and policy makers shake out with flood flowing volume of transactional data, accumulating trillions of bytes of information about their customers, suppliers and operations. The advanced networked sensors are being implanted in devices such as mobile phones, smart energy meters, automobiles and industrial machines that sense, generate and transfer data to multiple storage devices. In fact, as they go about their business and interact with individuals, they are producing an incredible amount of fatigue digital data. Social media sites, smart phones, and other customer devices have allowed billions of individuals around the world to contribute to the amount of data available. In addition, the extremely increasing size of multimedia data has also take part a key role in the rapid growth of data. The technology of high-definition video creates more than 2,000 times as many bytes as necessary to store as normal text data. Moreover, in a digitized world, consumers are leaving enormous amount of data about their day-today communicating, browsing, buying, sharing, searching and so on. As a result, it evolved as a big data and in turn has motivated the advances in big data analytics paradigms, endorsed as a basic motivation factor for the present researchers.
The authors in the present paper conduct a comprehensive study to explore the impact of big data analytics in key domains namely, Health Care (HC), Retail Industry (RI), Public Governance (PG), Pubic Security & Safety (PSS) and Personal Location Tracking (PLT). Initially, the study looks at the insights of data sources along with their characteristics in each domain. Later, it presents the highly productive and competitive big data applications with innovative big data technologies. Subsequently, the study showcases the impact of big data on each domain to capture value addition in its services. Finally, the study put forwards many more research opportunities as all these domains differ in their complexity and development in the usage of big data analytics
Similar to A Review On Data Mining In Banking Sector (20)
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Ā
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
Francesca Gottschalk - How can education support child empowerment.pptxEduSkills OECD
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Francesca Gottschalk from the OECDās Centre for Educational Research and Innovation presents at the Ask an Expert Webinar: How can education support child empowerment?
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
Ā
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
A Strategic Approach: GenAI in EducationPeter Windle
Ā
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Operation āBlue Starā is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
Palestine last event orientationfvgnh .pptxRaedMohamed3
Ā
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
The Roman Empire A Historical Colossus.pdfkaushalkr1407
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The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesarās dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empireās birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empireās society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
Acetabularia Information For Class 9 .docxvaibhavrinwa19
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Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
Honest Reviews of Tim Han LMA Course Program.pptxtimhan337
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Personal development courses are widely available today, with each one promising life-changing outcomes. Tim Hanās Life Mastery Achievers (LMA) Course has drawn a lot of interest. In addition to offering my frank assessment of Success Insiderās LMA Course, this piece examines the courseās effects via a variety of Tim Han LMA course reviews and Success Insider comments.
2. Vikas Jayasree and R.V. Siva Balan / American Journal of Applied Sciences 10 (10): 1160-1165, 2013
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knowledge (Witten et al., 2011; Liao et al., 2012). With
the mounting growth of data in every application, data
mining meets the valuable and efficient requirements for
effective, scalable and flexible data analysis.
Data Mining is the process of identifying and
discovering the interesting patterns from massive amount
of data (Mabroukeh and Ezeife, 2010). Data Mining can
be conducted on any kind of data as long as the data are
meaningful for a target application. Data Mining can be
considered as a natural evaluation of information
technology and a confluence of several related disciplines
and application domains. (Blake and Mangiameli, 2011)
The knowledge Discovery process, depicts as Fig. 1,
consists of following steps which describes how
unprocessed data converts into meaningful information
(Bhambri, 2011; Chen et al., 2009):
ā¢ Data Selection. In this step, identifies the location
of the data and relevance of the data for the
business objectives. Because of electronic data are
so pervasive, the quality of data plays a critical role
in all business and governmental applications
(Batini et al., 2009; Yap et al., 2011)
ā¢ Data Preparation. Once the data and its location are
identified, data cleaning and integration is done in this
step. In Data Cleaning, noise data and irrelevant data
are removed from the collected data. In Data
Integration, different data sources are combined in
a common source. Data quality is a major
challenge in data mining (Blake and Mangiameli,
2011; Petry and Zhao, 2009) Data analysis underlies
many computing applications as a part of their on-line
operations or in the design phase. Data analysis
procedures can be classified as either exploratory or
confirmatory, based on the availability of existing and
appropriate models for the data source, but the main
point of interest in both types of procedures (whether
for hypothesis formation or decision-making) is the
grouping, clustering or classification of measurements
based on either (i) goodness-of-fit to a postulated
model, or (ii) natural groupings (clustering) revealed
through analysis (Shinde, 2012)
ā¢ Fung et al. (2010) Data Transformation. In this
step, the selected data is transformed into the
form appropriate for the input of data mining
process (Han et al., 2011; Moin and Ahmed,
2012; Prakash et al., 2012)
ā¢ Data Mining. This is the vital step on which
effective algorithms (Bhambri, 2011; Hsu et al.,
2012) and techniques applied to process the data
into potentially useful patterns to achieve business
objectives (Hammawa, 2011; Batini et al., 2009)
ā¢ Evaluation. Patterns (Tremblay et al., 2010)
representing knowledge are identified based on given
measures (Kontonasios et al., 2012; Wikum et al.,
2009)
ā¢ Representation. The step on which discovered
knowledge visually represents. Visualization
techniques (Herawan and Deris, 2011) are more
effective in understanding the output for end users
Data Mining encompasses many different
techniques and algorithms. These differ in the kinds of
data that can be analyzed and the kinds of knowledge
representation used to convey the discovered
knowledge (Mabroukeh and Ezeife, 2010).
1.1. Application of Data Mining in Banking Sector
There are various areas in which data mining can
be used in financial sectors (Ramageri and Desai,
2013; Moradi et al., 2013; Moin and Ahmed, 2012;
Hammawa, 2011) like customer segmentation and
profitability, credit analysis, predicting payment default,
marketing, fraudulent transactions, ranking investments,
optimizing stock portfolios, cash management and
forecasting operations, high risk loan applicants, most
profitable Credit Card Customers and Cross Selling. Certain
examples where banking industry has been utilizing the data
mining technology effectively as follows.
1.2. Fraud Detection
Fraud detection (Delamaire et al., 2009;
Ravisankar et al., 2011; Raj and Portia, 2011; Wang et al.,
2009; Petry and Zhao, 2009; Hu and Liao, 2011) is the
recognition of symptoms of fraud where no prior
suspicion or tendency to fraud exists. According to The
American Heritage dictionary, second college edition,
fraud is defined as āa deception deliberately practiced in
order to secure unfair of unlawful gain.
Fraud detection refers to detection of criminal
activities occurring in commercial organizations such as
banks, credit card issuing organizations, insurance
agencies, mobile companies, stock market. The
malicious users might be the actual customers of the
organization or might be posing as a customer (also
known as identity theft) (Changdola et al., 2009).
Financial Organizations especially banking sectors
follows mainly two approaches (Ramageri and Desai,
2013; Moin and Ahmed, 2012) towards determining the
fraud patterns, online transaction check and Offline
transaction Check. For this purpose, the institutions
purchase and maintain data warehouses of sanctions
and Politically Exposed Persons data files from
Compliance and Anti Money Laundering solution and
data providers like The Office of Foreign Assets
Control (OFAC) of the US.
3. Vikas Jayasree and R.V. Siva Balan / American Journal of Applied Sciences 10 (10): 1160-1165, 2013
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Fig. 1. Data mining as a step in the process of knowledge discovery
Department Treasury, World Check and update upon the
changes in external data files. Data mining plays important
role in the fraud detection from the transaction data towards
external data ware houses. The regulators define certain
rules for finding the fraud transaction patterns and data
mining process can be applied with different techniques to
show the desired fraudulent patterns as output.
Regulatory authorities like Financial Action Task Force
(FATAF), Financial Market Supervisory Authority
(FINMA), Financial Services Authority (FSA), Hong Kong
Monitory Authority (KKMA), Reserve Bank of India (RBI)
set standard and requests financial organizations to produce
different reports on regular basis. System can be accessed
different data sources and preparing reports based on
combinations of data mining techniques (Handl and
Knowles, 2012; Poovammal and Ponnavaiko, 2009;
Wang and Dong, 2009; Issam, 2012; Tremblay et al.,
2010; Akbar et al., 2010; Herawan and Deris, 2011;
Petry and Zhao, 2009) like classifying, clustering,
segmentation, association rules, sequencing, regression,
pattern analysis and decision trees.
1.3. Marketing
Most widely used area of data mining in banking
technology (Ramageri and Desai, 2013; Wang and Dong,
2009; Hammawa, 2011; Sergio et al., 2011; Bhambri,
2011) is commercial and consumer product marketing.
Sales and Marketing department of Financial
organizations can use data mining algorithm, to analyze
the existing customers and find the products which they
are interested (Petry and Zhao, 2009) and how can they
market a another product in association with the
existing one. They can use DM techniques to analyze
the past trends, find the current demands and predict
the customer behavior of various products and services
in order to attain more business opportunities, there by
establishing or maintaining their position highest in the
market (Bhattacharya et al., 2011). Part of maintaining
a highest position in the competitive market, financial
institution are focusing on promoting unique products with
high quality service (Abdullah and Titus, 2010) and its
trend analysis can be done by data mining techniques.
Data mining techniques helps strategic planning
department to cluster their customers in different buckets
like highly potential, good, low and the periodic evaluation
on them and thereby providing better service to appropriate
clusters (Li et al., 2010). Data mining techniques can be
used to identify the customerās reaction on adjustment in
interest rates on deposit and borrowing products and its
installment changes (Sergio et al., 2011).
Data Mining can improve telemarketing and
electronic marketing (Hu and Liao, 2011) by identifying
potential customers who are adhere to modern
technologies like web, smart phone. In the areas of e-
Banking (Aburrous et al., 2009) and other web services
used for banking can use another algorithm called
sequence pattern-mining effectively. A sequential
pattern-mining algorithm mines the sequence database
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looking for repeating patterns (Mimno, 2012) (known as
frequent sequences) that can be used later by end users or
management to find associations between the different
items or events in their data (Abdullah and Titus, 2010).
1.4. Risk Management
Data Mining is used to identify the risk factors in each
department of banking business (Moradi et al., 2013).
Credit Approval authorities in the financial organization
used data mining techniques to determine the risk factors
in lending decisions (Chen et al., 2009; Chen and Huang,
2011) by analyzing the data based on nationality,
repayment capacity and so on. Retail marketing
department uses data mining methodologies to find the
reliability (Yap et al., 2011) and the behavior of credit card
applicant (Delamaire et al., 2009) while selling the credit
cards. They uses data mining techniques on existing
customers to sell credit cards or increase customers credits
or top up on credit card loans (Bhattacharya et al., 2011).
In commercial lending, data mining plays a vital role. In
commercial lending, risk assessment is usually an attempt
to quantify the risk of default or loss to the lender when
making a particular lending decision or approving a credit
facility (Chen and Huang, 2011).
Here credit risk can be quantified by the changes in the
value of a credit products or of a whole credit customer
portfolios, which is based on changes in the high risk
tendency, default probability, instrumentās rating and
recovery rate (Yap et al., 2011; Ravisankar et al., 2011;
Liu et al., 2012) of the instrument in case of default. The
major part of implementation and care of credit risk
management system (Raj and Portia, 2011) will be a typical
data mining problem: the modeling of the credit
instrumentās value through the default probabilities,
recovery rates and rating migrations (Fung et al., 2010).
Data Mining can be used to derive credit behavior
(Delamaire et al., 2009) of individual borrowers with
parameters card loans, mortgage value, repayment and
using characteristics such as history of credit, employment
period and length of residency. A score is thus produced
that allow a lender to evaluate the customer and decide
whether the person is a good candidate for a loan, or if
there is a tendency to become high risk of default (Raj and
Portia, 2011). Customers who have been with bank for a
longer periods of time, remained better with bank and
have good credit history and have higher salaries/wages,
are more likely to receive a loan than a new customer who
has no credit history with the bank, or who earns low
salaries/wages (Ravisankar et al., 2011). Bank can reduce
the risk factors to maintain a better position by knowing
the chances of a customer to become default (Dorr and
Anne, 2009; Tsai, 2012).
1.5. Customer Relationship Management
Data Mining can be useful in all three phases of
customer relationship cycle: Customer Acquisition, Increase
Value of the customer and Customer Retention
(Prakash et al., 2012; Ping and Liang, 2010). Financial
organizations especially banking sector recruits
Relationship Managers or team of executives to pay proper
attention to their customers. Due to the tight competition
exists in the market (Sergio et al., 2011; Wang et al., 2009;
Chen et al., 2009), customers will always with banks which
provide better facility and more secured transaction option.
Data Mining techniques (Prakash et al., 2012; Wikum et al.,
2009) can be used to determine the list of customers as
per the set of definitions (Sergio et al., 2011; Wang et al.,
2009; Corne et al., 2012) and interest and the institution
can offer better facilities to them (Abdullah and Titus,
2010) customers are varying from their approach in
banking, like certain customers interested only electronic
banking while others want banking through the counter.
Classifying such customers can easily done using data
mining techniques and provide better facilities.
Data mining can be used to find out customers holding
one product (Wu and Chou, 2011) having interest in similar
to other one, there by promoting the product which benefits
the organization. Not only can data mining help the banking
industry to gain new customers, it can also helps to maintain
the existing customers with better service (Tremblay et al.,
2010; Kontonasios et al., 2012; Liu et al., 2012).
Within the context of Customer Relationship
Management (CRM), data mining can be seen as a
business driven process aimed at the discovery and
consistent use of profitable knowledge from
organizational data (Wu and Chou, 2011). It can be used
to fasten the decision making and guide to forecast the
effects of decisions (Prakash et al., 2012). Data Mining
can be used to increase the response rate of marketing
campaign. This can be done by segmenting the customers
into groups with their needs and characteristics, it can
predict how likely an existing customer is to take the
business to a competitor (Mimno, 2012). Each of the
CRM elements can be supported by different data mining
models (Vaillancourt, 2010; Abdullah and Titus, 2010;
Herawan and Deris, 2011; Akbar et al., 2010; Shinde,
2012; Delamaire et al., 2009) which generally include
classification, association, sequence discovery, clustering,
regression, forecasting and visualization.
2. CONCLUSION
Data Mining is a tool and techniques used to extract
meaningful information from the collected data, enables
financial institutions to make better decision-making
process. Data Collections are in the form of maintaining
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proper ware housing based on different databases and
other related sources like files into a acceptable data
format which becomes the input for data mining process.
Based on the standard or rules set by the organization
and regulatory authorities, data mining tool extract the
knowledge based on the rule set and throws the output in
visual tools, thereby making end user life easy to make
decisions properly. Banks and Financial organizations
started allocating funds and time for implementing data
mining tools in the area of decision making by realizing
the necessity of data mining in their system.
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