scholarly journals Socio–demographic Impacts on the Personal Savings Portfolio Choice - a Decision Tree Approach

2022 ◽  
Vol 9 (1) ◽  
pp. 0-0
Keyword(s):  
Decision Tree ◽  
Portfolio Choice ◽  
Financial System ◽  
Pension Plans ◽  
Standard Of Living ◽  
Low Level ◽  
Current State ◽  
Survey Results ◽  
Personal Savings ◽  
Decision Tree Method

Insufficiently developed financial system, poor standard of living and inappropriate education of citizens on the saving products, lead to low level of investment in the financial market of developing countries. In this paper special attention is paid to examining the socio-demographic profile of Montenegrin citizens that invest their funds in some of the offered form of savings, as well as examining main factors that restrict their investment. For this purpose, data collected through the survey of Montenegrin citizens were processed using Decision Tree method. Survey results have shown that there is a low level of savings, as well as that citizens prefer deposits and life insurance products rather than pension plans and debt securities. Also, the results indicate that the main causes of the current state of savings in Montenegro are low standard of living, citizens´ poor awareness and the financial system which causes the insufficiently attractive supply of savings.

Rancang Bangun Aplikasi Sistem Pakar untuk Menangani Masalah Kecantikan pada Wajah Menggunakan Metode Decision Tree

2014 ◽  
Vol 6 (1) ◽  
pp. 9-14
Author(s):  
Stefanie Sirapanji ◽  
Seng Hansun
Keyword(s):  
Decision Tree ◽  
Uneven Distribution ◽  
Dry Skin ◽  
Average Accuracy ◽  
Accuracy Level ◽  
Index Terms ◽  
Decision Tree Method ◽  
Tree Method

Beauty is a precious asset for everyone. Everyone wants to have a healthy face. Unfortunately, there are always those problems that pops out on its own. For example, acnes, freckles, wrinkles, dull, oily and dry skin. Therefore, nowadays, there are a lot of beauty clinics available to help those who wants to solve their beauty troubles. But, not everyone can enjoy the facilities of those beauty clinics, for example those in the suburbs. The uneven distribution of doctors and the expensive cost of treatments are some of the reasons. In this research, the system that could help the patients to find the solution of their beauty problems is built. The decision tree method is used to take decision based on the shown schematic. Based on the system’s experiment, the average accuracy level hits 100%. Index Terms–Acnes, Decision Tree, Dry Skin, Dull, Facial Problems, Freckles, Wrinkles, Oily Skin, Eexpert System.

Marketing Model Investigation of Cable Television Service Based on BP Neural Network and Decision Tree

2013 ◽  
Vol 774-776 ◽  
pp. 1757-1761
Author(s):  
Bing Xiang Liu ◽  
Xu Dong Wu ◽  
Ying Xi Li ◽  
Xie Wei Wang
Keyword(s):  
Neural Network ◽  
Decision Tree ◽  
Bp Neural Network ◽  
Cable Television ◽  
Model Building ◽  
Television System ◽  
Marketing Model ◽  
Television Service ◽  
Decision Tree Method ◽  
Tree Method

This paper takes more than four hundred records of some cable television system for example, makes data mining according to users data record, uses BP neural network and decision tree method respectively to have model building and finds the best model fits for users to order press service. The results of the experiment validate the methods feasibility and validity.

Data Mining for ERW Welded Tube Scheduling Rules Based on Decision Tree

2011 ◽  
Vol 403-408 ◽  
pp. 1804-1807
Author(s):  
Ning Zhao ◽  
Shao Hua Dong ◽  
Qing Tian
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Arc Welding ◽  
Data Cleaning ◽  
Data Extraction ◽  
Electric Arc ◽  
Welded Tube ◽  
Electric Arc Welding ◽  
Decision Tree Method ◽  
Tree Method

In order to optimize electric- arc welding （ERW） welded tube scheduling , the paper introduces data cleaning, data extraction and transformation in detail and defines the datasets of sample attribute, which is based on analysis of production process of ERW welded tube. Furthermore, Decision-Tree method is adopted to achieve data mining and summarize scheduling rules which are validated by an example.

A comparative study to recognize fake ratings in recommendation system using classification techniques

2021 ◽  
pp. 1-8
Author(s):  
P. Shanmuga Sundari ◽  
M. Subaji
Keyword(s):  
Decision Tree ◽  
False Positive ◽  
Recommendation System ◽  
Target Item ◽  
User Profile ◽  
Method Performance ◽  
Classification Techniques ◽  
Attack Model ◽  
Unique Method ◽  
Decision Tree Method

The recommendation system is affected with attacks when the users are given liberty to rate the items based on their impression about the product or service. Some malicious user or other competitors’ try to inject fake rating to degrade the item’s graces that are mostly adored by several users. Attacks in the rating matrix are not executed just by a single profile. A group of users profile is injected into rating matrix to decrease the performance. It is highly complex to extract the fake ratings from the mixture of genuine profile as it resides the same pattern. Identifying the attacked profile and the target item of the fake rating is a challenging task in the big data environment. This paper proposes a unique method to identify the attacks in collaborating filtering method. The process of extracting fake rating is carried out in two phases. During the initial phase, doubtful user profile is identified from the rating matrix. In the following phase, the target item is analysed using push attack count to reduce the false positive rates from the doubtful user profile. The proposed model is evaluated with detection rate and false positive rates by considering the filler size and attacks size. The experiment was conducted with 6%, 8% and 10% filler sizes and with different attack sizes that ranges from 0%–100%. Various classification techniques such as decision tree, logistic regression, SVM and random forest methods are used to classify the fake ratings. From the results, it is witnessed that SVM model works better with random and bandwagon attack models at an average of 4% higher accuracy. Similarly the decision tree method performance better at an average of 3% on average attack model.

Assessment committees: good practices from ARL libraries

2016 ◽  
Vol 17 (3) ◽  
pp. 224-240 ◽  
Author(s):  
Michelle H. Brannen ◽  
Sojourna J. Cunningham ◽  
Regina Mays
Keyword(s):  
Academic Libraries ◽  
Academic Library ◽  
Content Type ◽  
Good Practices ◽  
Research Libraries ◽  
Association Of Research Libraries ◽  
Library Assessment ◽  
Current State ◽  
Survey Results ◽  
Assessment Activities

Purpose Assessment activities in academic libraries continue to grow as libraries explore assessment endeavors. Ranging from basic stats gathering and reporting to surveys, focus groups, and usability studies and beyond. Many practitioners are finding it necessary to create new processes and programs, with little guidance. The purpose of this paper is to paint a broad picture of assessment activities in Association of Research Libraries (ARL) university libraries with the goal of creating a resource for libraries developing or improving their assessment programs. Design/methodology/approach A survey was developed that asked questions about assessment personnel, activities, mission, and website. A total of 113 surveys were sent to academic library members of ARL. Survey results were analyzed to compile a list of recommended good practices for assessment and working with assessment committees in academic libraries. Findings The investigators had a response rate of 43 percent. The open-ended nature of the survey questions allowed for the respondents to specifically narrow down the problems and opportunities inherent in library assessment committees. Originality/value This study takes the temperature of the current state of assessment programs in ARL libraries, demonstrating the growth of assessment programs. It begins to document the practices of these libraries, particularly in regards to the sometimes informal and hard to track use of committees and other in-house collaborations, as a first step toward developing best practices for the field. The results illuminate productive areas for further study, including investigating how to measure a culture of assessment and maximizing impact of assessment information presented on assessment websites.

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