scholarly journals Customer Segmentation Analysis and Visualization

2021 ◽  
pp. 280-284
Author(s):  
K. Keerthi ◽  
G. Lakshmi Thirupathamma ◽  
N. Vijayalakshmi ◽  
D. Aparna ◽  
U. Vineela
Keyword(s):  
Customer Segmentation ◽  
Machine Learning Techniques ◽  
Business World ◽  
Competitive Edge ◽  
Financial Gain ◽  
New Concepts ◽  
Segmentation Analysis ◽  
Information Set ◽  
And Behavior ◽  
Algorithmic Rule

Today’s world is all concerning Innovation and new concepts, where everybody desires to contend to measure higher than others. In the business world, it is crucial to know the client's desires and behavior patterns concerning buying merchandise. With the giant number of merchandise the businesses square measure confused to work out the potential customers to sell their merchandise to earn the large profits. To solve this real-time downside we tend to use machine learning techniques and algorithms. We can conclude the hidden patterns of knowledge. So that we can observe choices for earning a lot of profits. For this, we tend to take client information and divides the purchasers into totally different teams conjointly known as segmentation. segmentation permits businesses to create higher use of their selling budgets, gain a competitive edge over rival corporations, and, significantly, demonstrate much better information about your customer's desires and needs. In this project, we tend to square measure implementing k-means agglomeration algorithmic rule to analyze the results of clusters obtained from the algorithmic rule. A code is developed in python and it’s trained on an information set having 201 data samples that are taken from the native shopping center. All the offered data within the dataset is placed along to own a concept concerning client age, gender, annual financial gain, and outlay score(Expenditure) of mall customers dataset. Finally, this understanding information is analyzed to the simplest of our knowledge under the abled guidance of our mentor.

Leveraging Machine Learning in Financial Fraud Forensics in the Age of Cybersecurity

2022 ◽  
pp. 220-249
Author(s):  
Md Ariful Haque ◽  
Sachin Shetty
Keyword(s):  
Machine Learning ◽  
Financial Institutions ◽  
Machine Learning Techniques ◽  
Cyber Attack ◽  
Financial Industry ◽  
Security Breaches ◽  
Financial Gain ◽  
Learning Techniques ◽  
Machine Learning Model ◽  
Machine Learning Models

Financial sectors are lucrative cyber-attack targets because of their immediate financial gain. As a result, financial institutions face challenges in developing systems that can automatically identify security breaches and separate fraudulent transactions from legitimate transactions. Today, organizations widely use machine learning techniques to identify any fraudulent behavior in customers' transactions. However, machine learning techniques are often challenging because of financial institutions' confidentiality policy, leading to not sharing the customer transaction data. This chapter discusses some crucial challenges of handling cybersecurity and fraud in the financial industry and building machine learning-based models to address those challenges. The authors utilize an open-source e-commerce transaction dataset to illustrate the forensic processes by creating a machine learning model to classify fraudulent transactions. Overall, the chapter focuses on how the machine learning models can help detect and prevent fraudulent activities in the financial sector in the age of cybersecurity.

The Readiness of IDSC to Adopt Knowledge Management

2003 ◽  
pp. 239-262
Author(s):  
Ahmed A.K. Hussein ◽  
Khaled Wahba
Keyword(s):  
Knowledge Management ◽  
Human Resources ◽  
Organizational Memory ◽  
High Turnover ◽  
Business World ◽  
Competitive Edge ◽  
Common Mistake ◽  
Corporate Memory ◽  
Egyptian Government ◽  
The Brain

Over the last two years Knowledge Management has become the latest hot topic in the business world. Companies are realizing that their competitive edge is mostly the brain power or intellectual capital of their employees and management. Many organizations are drowning in information, but starving for knowledge. In order to stay ahead of the pack, organizations must leverage their knowledge internally and externally to survive. Knowledge management is believed to be the current savior of organizations. Creative and innovative people form the core of any organization. In turn, those people form the corporate memory. The Information Decision Support Center for the Cabinet of Ministers for the Egyptian Government (IDSC) faces a problem of employees’ high turnover rate (17%), which threatens to cause IDSC to lose its memory. One common mistake many organizations make when they implement KM initiatives is to place too much emphasis on the technological aspect of KM and ignore the human resources aspects. IDSC developed a knowledge management system called the Organizational Memory (http://www.home.idsc.gov.eg/), but ignored the human factor of KM. The purpose of this chapter is to test the readiness of employees and managers working at IDSC to adopt knowledge management. Human issues were clearly shown to outweigh any technology constraints, and views of managers and employees differed to some extent. It is recommended that these human and managerial concerns be addressed if KM is to be successful in organizations.

Taking Cisco into a new era

2015 ◽  
Vol 31 (11) ◽  
pp. 1-3
Keyword(s):  
Information Technology ◽  
Design Methodology ◽  
Reading Time ◽  
Diverse Range ◽  
Business World ◽  
Content Type ◽  
New Era ◽  
Competitive Edge ◽  
Pertinent Information ◽  
Practical Implications

Purpose – This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies. Design/methodology/approach – This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context. Findings – One of the most predictable things about today’s business world is its unpredictability. Hence, even the largest operators have to frequently evolve to retain their competitive edge. But that’s all in a day’s work for the folks at Cisco Systems. You don’t get to be world leader in any industry for nothing. So to achieve that position in such a competitive and rapidly changing sector as technology is perhaps even more remarkable. Since its inception in late 1984, the company has been driven by a desire to, in its own words, “connect the unconnected”. Few would argue that Cisco constantly achieves this objective. This innovative firm’s diverse range of customers have successfully navigated various shifts within communications and information technology thanks to its design, manufacture and supply of different groundbreaking solutions. Practical implications – The paper provides strategic insights and practical thinking that have influenced some of the world’s leading organizations. Originality/value – The briefing saves busy executives and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.

scholarly journals Predictive Model for Detection of Depression Based on Uncertainty Analysis Methods

Proceedings ◽  
2018 ◽  
Vol 2 (19) ◽  
pp. 551
Author(s):  
Alicia Martínez ◽  
Richard Benítez ◽  
Hugo Estrada ◽  
Yasmín Hernández
Keyword(s):  
Older Adults ◽  
Predictive Model ◽  
Smart Cities ◽  
Machine Learning Techniques ◽  
World Population ◽  
Novel Technologies ◽  
Learning Techniques ◽  
Problematic Situations ◽  
And Behavior ◽  
Important Disease

: Currently, advances in technology have permitted increases in the life expectancy of older adults. As a result, a large segment of the world population is 60’s years old, and over. Depression is an important disease in older adults is depression, which seriously affects the moods and behavior of elderly. Novel technologies for smart cities allow us to monitor people and prevent problematic situations related to this mental illness. In this paper, we propose a predictive model to automatically detect depression in older adults. The model is based on machine-learning techniques to analyze the data obtained by a sensor that monitores the daily activities of older adults. Also, the model was evaluated obtaining promising results.

scholarly journals CUSTOMER SEGMENTATION IN PRIVATE BANKING SECTOR USING MACHINE LEARNING TECHNIQUES

2013 ◽  
Vol 14 (5) ◽  
pp. 923-939 ◽  
Author(s):  
Ion Smeureanu ◽  
Gheorghe Ruxanda ◽  
Laura Maria Badea
Keyword(s):  
Machine Learning ◽  
Character Recognition ◽  
Foreign Exchange Market ◽  
Banking Sector ◽  
Customer Segmentation ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Techniques ◽  
Fraud Prediction ◽  
Private Banking

Machine learning techniques have proven good performance in classification matters of all kinds: medical diagnosis, character recognition, credit default and fraud prediction, and also foreign exchange market prognosis. Customer segmentation in private banking sector is an important step for profitable business development, enabling financial institutions to address their products and services to homogeneous classes of customers. This paper approaches two of the most popular machine learning techniques, Neural Networks and Support Vector Machines, and describes how each of these perform in a segmentation process.

scholarly journals The Limits of Hegemony?

2015 ◽  
Vol 15 (1) ◽  
pp. 5-30
Author(s):  
Ľuboš Blaha
Keyword(s):  
Behavior Patterns ◽  
Public Official ◽  
Business World ◽  
The Public ◽  
Official Discourse ◽  
Marginalized Groups ◽  
The Social ◽  
Regulative Principle ◽  
The Media ◽  
And Behavior

Abstract In this study I will try to put forward the views of the social theorists and critics who consider “postmodern culture” (Jameson) as deeply manipulative. The fundamental patterns of the system of the ideology preach to the spread of the values of consumerism, individualism and hedonism (Fromm). As the study shows, the media play a key role in spreading these values (Chomsky). The media became the main “ideological apparatus” (Althusser) and the business world, the world of culture and politics is controlled by these media. Economic system thus gains support of the population and can reproduce itself. According to some interpretations there is no escape from the environment of the systemic manipulation (Jameson, Foucault, Marcuse), but there are also opinions according to which systemic indoctrination can intervene only in the public - official discourse, but not culture and behavior patterns of marginalized groups (Scott, Bloch, Williams). I will try to interpret and analyze systematically these two intuitive views. In this context, I will develop the thesis that the value of truth, not as an epistemologically or metaphysically regulative principle, but as a socio-emancipating force which can have in the environment of the absolute manipulation a decisive impact in the formulation of alternative to the current (post)modern global-capitalist society. The study is based on the author's book Matrix of Capitalism: Is the Revolution Coming? (Veda, Bratislava 2011).

scholarly journals СУЧАСНИЙ СТАН ТА ПЕРСПЕКТИВИ РОЗВИТКУ ГОТЕЛЬНОГО БІЗНЕСУ УКРАЇНИ: ЧИННИКИ ТА ПРОБЛЕМИ

2020 ◽  
Vol 143 (1) ◽  
pp. 41-51
Author(s):  
Юлія А. Шевчук
Keyword(s):  
Hotel Industry ◽  
Environmental Safety ◽  
Public Governance ◽  
Crimean Peninsula ◽  
Mass Tourism ◽  
Competitive Edge ◽  
Current State ◽  
New Concepts ◽  
Hospitality Sector ◽  
Systemic Problems

The article discusses the current state of hotel business in Ukraine and provides grounds and prospects for its further development along with revealing the main challenges faced by the hotel sector in Ukraine in recent years. Since the beginning of 2014, the Ukrainian hotel services market has experienced sharp decline affected by volatile socioeconomic situation, external armed aggression in the East part of the country, the annexation of the Crimean Peninsula, as well as a range of other critical systemic problems. The study suggests promising vectors in the national hotel industry development, provides its dynamics statistics and presents a forecast as to the number of hotels and similar accommodation in Ukraine. The findings have identified major trends in the dynamics of the domestic tourist flows that greatly affect the hotel business development. It is emphasized that currently the hotel industry is facing a serious crisis. The study also reveals the key barriers to successful development of hotel business in Ukraine together with identifying the critical factors driving the Ukrainian hospitality sector, such as public governance, social, economic, financial, environmental, safety factors, etc. Practical implications of the research cover a set of priority measures to enhance the Ukrainian hotel industry performance which involve in particular the creation of a strong investment climate; ensuring comfortable and safe tourist environment to visit the Ukraine; building effective policies to promote mass tourism development and its implementation at the governmental level; re-thinking of the tourist tax mechanisms; designing projects to improve the condition of historic monuments and to construct new mass tourism facilities; gaining a competitive edge in the hospitality sector by implementing new management models, modern research and technology advances in hotels; ensuring hotel industry transparency with a focus towards customers, etc. A special emphasis is put to the critical need of further research to boost the search for new mechanisms to reform the hospitality sector, to develop new concepts and management methods, since the permanent turbulence of both internal and external environment trigger new problems and challenges to the Ukrainian hotel business realia.

scholarly journals Application of Data Mining Technique in Stock Market : An Analysis

2014 ◽  
pp. 294-295
Author(s):  
SACHIN KAMBEY ◽  
R. S. THAKUR ◽  
SHAILESH JALORI
Keyword(s):  
Data Mining ◽  
Stock Market ◽  
Association Rule ◽  
Movement Direction ◽  
Data Mining Technique ◽  
Business World ◽  
Mining Technique ◽  
Hidden Patterns ◽  
Technology Tool ◽  
And Behavior

Stock market prediction with data mining technique is one of the most important issues to be investigated and it is one of the fascinating issues of stock market research over the past decade. Many attempts have been made to predict stock market data using statistical and traditional methods, but these methods are no longer adequate for analyzing this huge amount of data. Data mining is one of most important powerful information technology tool in today’s competitive business world, it is able to uncover hidden patterns and predict future trends and behavior in stock market. This paper also highlights the application of association rule in stock market and their future movement direction.

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