scholarly journals Analysis of Demography, Psychograph and Behavioral Aspects of Telecom Customers Using Predictive Analytics to Increase Voice Package Sales

2021 ◽  
Vol 6 (1) ◽  
pp. 1-19
Author(s):  
Billy Goenandar ◽  
Maya Ariyanti
Keyword(s):  
Predictive Analytics ◽  
Customer Segmentation ◽  
Training Data ◽  
Data Sets ◽  
Model Accuracy ◽  
Digital Services ◽  
Testing Data ◽  
User Data ◽  
Input Variables ◽  
Customer Profiles

In 2018, Telkomsel's core business shifted its main services from Telephone and SMS services to Data and Digital services, since a declining trend of revenue starting 2014. However, telephone service still contributed 28.4% to the revenue and was the second largest, while SMS gave 4.1%. This research predicts voice package buyers using predictive analytics to identify customer profiles and significant variables to form appropriate target customer segmentation. Logistic regression was used to predict customers who would buy voice packages using 15 input variables. Next, analytics was done by dividing the data into 70% training data sets and 30% testing data obtained from customer voice package user data. The model accuracy gained 97.2%, and the top seven significant variables were formed. Then five clusters of customer segmentation were formed based on top significant variables using the K-Means clustering technique. Based on the results of the prediction model and clustering, behavioral targeting was conducted to provide targeted gimmick products based on five segmentations formed, and then it was divided into two main target customers by considering the similarity of behaviors based on revenue voice, minutes of voice usage, voice transactions, day of voice usage and data payload, thus it was more targeted.

scholarly journals PREDICTING TELECOMMUNICATION TOWER COSTS USING FUZZY SUBTRACTIVE CLUSTERING

2014 ◽  
Vol 21 (1) ◽  
pp. 67-74 ◽  
Author(s):  
Mohamed Marzouk ◽  
Mohamed Alaraby
Keyword(s):  
Model Performance ◽  
Training Data ◽  
Percentage Error ◽  
Subtractive Clustering ◽  
Data Sets ◽  
Test Model ◽  
First Order ◽  
Testing Data ◽  
Telecommunication Towers ◽  
Tower Height

This paper presents a fuzzy subtractive modelling technique to predict the weight of telecommunication towers which is used to estimate their respective costs. This is implemented through the utilization of data from previously installed telecommunication towers considering four input parameters: a) tower height; b) allowed tilt or deflection; c) antenna subjected area loading; and d) wind load. Telecommunication towers are classified according to designated code (TIA-222-F and TIA-222-G standards) and structures type (Self-Supporting Tower (SST) and Roof Top (RT)). As such, four fuzzy subtractive models are developed to represent the four classes. To build the fuzzy models, 90% of data are utilized and fed to Matlab software as training data. The remaining 10% of the data are utilized to test model performance. Sugeno-Type first order is used to optimize model performance in predicting tower weights. Errors are estimated using Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) for both training and testing data sets. Sensitivity analysis is carried to validate the model and observe the effect of clusters’ radius on models performance.

scholarly journals The Data-Driven Fuzzy System with Fuzzy Subtractive Clustering for Time Series Modeling

2020 ◽  
Vol 9 (3) ◽  
pp. 3357-3362
Keyword(s):  
Fuzzy System ◽  
Fuzzy Rule ◽  
Training Data ◽  
Subtractive Clustering ◽  
A Value ◽  
Testing Data ◽  
System Input ◽  
Input Variables ◽  
Rule Bases ◽  
Takagi Sugeno

The paper aims to identify input variables of fuzzy systems, generate fuzzy rule bases by using the fuzzy subtractive clustering, and apply fuzzy system of Takagi Sugeno to predict rice stocks in Indonesia. The monthly rice procurement dataset in the period January 2000 to March 2017 are divided into training data (January 2000 to March 2016 and testing data (April 2016 to March 2017). The results of identification of the fuzzy system input variables are lags as system input including . The Input-output clustering fuzzy subtractive and selecting optimal groups by using the cluster thigness measures indicator produced 4 fuzzy rules.The fuzzy system performance in the training data has a value of R2 of 0.8582, while the testing data produces an R2 of 0.7513.

scholarly journals Skin Cancer Detection using CNN Algorithm

2020 ◽  
Vol 9 (6) ◽  
pp. 45-49
Keyword(s):  
Neural Network ◽  
Skin Cancer ◽  
Convolutional Neural Network ◽  
Well Being ◽  
Training Data ◽  
Data Sets ◽  
Data Set ◽  
Sequential Model ◽  
Pigmented Lesions ◽  
Testing Data

The project “Disease Prediction Model” focuses on predicting the type of skin cancer. It deals with constructing a Convolutional Neural Network(CNN) sequential model in order to find the type of a skin cancer which takes a huge troll on mankind well-being. Since development of programmed methods increases the accuracy at high scale for identifying the type of skin cancer, we use Convolutional Neural Network, CNN algorithm in order to build our model . For this we make use of a sequential model. The data set that we have considered for this project is collected from NCBI, which is well known as HAM10000 dataset, it consists of massive amounts of information regarding several dermatoscopic images of most trivial pigmented lesions of skin which are collected from different sufferers. Once the dataset is collected, cleaned, it is split into training and testing data sets. We used CNN to build our model and using the training data we trained the model , later using the testing data we tested the model. Once the model is implemented over the testing data, plots are made in order to analyze the relation between the echos and loss function. It is also used to analyse accuracy and echos for both training and testing data.

scholarly journals AI Testing: Ensuring a Good Data Split Between Data Sets (Training and Test) using K-means Clustering and Decision Tree Analysis

2021 ◽  
Vol 12 (1) ◽  
pp. 1-11
Author(s):  
Kishore Sugali ◽  
Chris Sprunger ◽  
Venkata N Inukollu
Keyword(s):  
Decision Tree ◽  
Software Testing ◽  
Training Data ◽  
Data Sets ◽  
Full Data ◽  
Data Set ◽  
Full Dataset ◽  
Development Methodology ◽  
Testing Data ◽  
Long Time

Artificial Intelligence and Machine Learning have been around for a long time. In recent years, there has been a surge in popularity for applications integrating AI and ML technology. As with traditional development, software testing is a critical component of a successful AI/ML application. The development methodology used in AI/ML contrasts significantly from traditional development. In light of these distinctions, various software testing challenges arise. The emphasis of this paper is on the challenge of effectively splitting the data into training and testing data sets. By applying a k-Means clustering strategy to the data set followed by a decision tree, we can significantly increase the likelihood of the training data set to represent the domain of the full dataset and thus avoid training a model that is likely to fail because it has only learned a subset of the full data domain.

scholarly journals Connected Component and Morphology Based Extraction of Arterial Centerlines of the Heart (CocomoBeach)

2008 ◽  
Author(s):  
Pieter Kitslaar ◽  
Michel Frenay ◽  
Elco Oost ◽  
Jouke Dijkstra ◽  
Berend Stoel ◽  
...  
Keyword(s):  
Coronary Artery ◽  
Training Data ◽  
Data Sets ◽  
Fast Marching ◽  
Geometrical Properties ◽  
Testing Data ◽  
Heart Region ◽  
Extraction Scheme ◽  
The Right ◽  
Coronary Artery Tree

This document describes a novel scheme for the automated extraction of the central lumen lines of coronary arteries from computed tomography angiography (CTA) data. The scheme first obtains a seg- mentation of the whole coronary tree and subsequently extracts the centerlines from this segmentation. The first steps of the segmentation algorithm consist of the detection of the aorta and the entire heart region. Next, candidate coronary artery components are detected in the heart region after the masking of the cardiac blood pools. Based on their location and geometrical properties the structures representing the right and left arterties are selected from the candidate list. Starting from the aorta, connections between these structures are made resulting in a final segmentation of the whole coronary artery tree, A fast-marching level set method combined with a backtracking algorithm is employed to obtain the initial centerlines within this segmentation. For all vessels a curved multiplanar reformatted image (CMPR) is constructed and used to detect the lumen contours. The final centerline was then defined by determining the center of gravity of the detected lumen in the transversal CMPR slices. Within the scope of the MICCAI Challenge “Coronary Artery Tracking 2008”, the coronary tree segmentation and centerline extraction scheme was used to automatically detect a set of centerlines in 24 datasets. For 8 data sets reference centerlines were available. This training data was used during the development and tuning of the algorithm. Sixteen other data sets were provided as testing data. Evaluation of the proposed methodology was performed through submission of the resulting centerlines to the MICCAI Challenge website

scholarly journals Narrative Review of Predictive Analytics of Patient-Reported Outcomes in Adult Spinal Deformity Surgery

2020 ◽  
pp. 219256822096306
Author(s):  
Kurt Lehner ◽  
Jeff Ehresman ◽  
Zach Pennington ◽  
A. Karim Ahmed ◽  
Daniel Lubelski ◽  
...  
Keyword(s):  
Spinal Deformity ◽  
Patient Reported Outcomes ◽  
Predictive Analytics ◽  
Adult Spinal Deformity ◽  
Narrative Review ◽  
Receiver Operating Curve ◽  
Data Sets ◽  
Significant Heterogeneity ◽  
Model Accuracy ◽  
Patient Reported

Study Design: Narrative review Objective: Decision making in surgery for adult spinal deformity (ASD) is complex due to the multifactorial etiology, numerous surgical options, and influence of multiple medical and psychosocial factors on patient outcomes. Predictive analytics provide computational tools to analyze large data sets and generate hypotheses regarding new data. In this review, we examine the use of predictive analytics to predict patient-reported outcomes (PROs) in ASD surgery. Methods: A search of PubMed, Web of Science, and Embase databases was performed to identify all potentially relevant studies up to February 1, 2020. Studies were included based on the use of predictive analytics to predict PROs in ASD. Results: Of 57 studies identified and reviewed, 7 studies were included. Multiple algorithms including supervised and unsupervised methods were used. Significant heterogeneity was observed with choice of PROs modeled including ODI, SRS22, and SF36, assessment of model accuracy, and with the model accuracy and area under the receiver operating curve values (ranging from 30% to 86% and 0.57 to 0.96, respectively). Models were built with data sets of patients ranging from 89 to 570 patients with a range of 22 to 267 variables. Conclusions: Predictive analytics makes accurate predictions regarding PROs regarding pain, disability, and work and social function; PROs regarding satisfaction, self-image, and psychologic aspects of ASD were predicted with the lowest accuracy. Our review demonstrates a relative paucity of studies on ASD with limited databases. Future studies should include larger and more diverse databases and provide external validation of preexisting models.

A Novel Model-Based Approach in Classification Using Extension Distance

2014 ◽  
Vol 644-650 ◽  
pp. 2009-2012 ◽  
Author(s):  
Hai Tao Zhang ◽  
Bin Jun Wang
Keyword(s):  
Experimental Results ◽  
Training Data ◽  
Data Sets ◽  
Benchmark Data ◽  
Model Based ◽  
Original Dataset ◽  
Testing Data ◽  
Data Points ◽  
Low Efficiency ◽  
Novel Model

In order to solve the low efficiency problem of KNN or K-Means like algorithms in classification, a novel extension distance of interval is proposed to measure the similarity between testing data and the class domain. The method constructs representatives for data points in shorter time than traditional methods which replace original dataset to serve as the basis of classification. Virtually, the construction of the model containing representatives makes classification faster. Experimental results from two benchmark data sets, verify the effectiveness and applicability of the proposed work. The model based method using extension distance can effectively build data models to represent whole training data, and thus a high cost of classifying new instances problem is solved.

scholarly journals complexFuzzy: A novel clustering method for selecting training instances of cross-project defect prediction

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Muhammed Maruf Ozturk
Keyword(s):  
Area Under The Curve ◽  
Prediction Performance ◽  
Training Data ◽  
Defect Prediction ◽  
Data Sets ◽  
Clustering Method ◽  
Testing Data ◽  
Proper Training ◽  
Comparison Algorithms ◽  
Cross Project

Over the last decade, researchers have investigated to what extent cross-project defect prediction (CPDP) shows advantages over traditional defect prediction settings. These works do not take training and testing data of defect prediction from the same project. Instead, dissimilar projects are employed. Selecting proper training data plays an important role in terms of the success of CPDP. In this study, a novel clustering method named complexFuzzy is presented for selecting training data of CPDP. The method is developed by determining membership values with the help of some metrics which can be considered as indicators of complexity. First, CPDP combinations are created on 29 different data sets. Subsequently, complexFuzzy is evaluated by considering cluster centers of data sets and comparing some performance measures including area under the curve (AUC) and F-measure. The method is superior to other five comparison algorithms in terms of the distance of cluster centers and prediction performance.

scholarly journals South German Credit Data Classification Using Random Forest Algorithm to Predict Bank Credit Receipts

2020 ◽  
Vol 3 (2) ◽  
Author(s):  
Yoga Religia ◽  
Gatot Tri Pranoto ◽  
Egar Dika Santosa
Keyword(s):  
Data Mining ◽  
Random Forest ◽  
Large Data ◽  
Classification Algorithm ◽  
Large Data Sets ◽  
Training Data ◽  
Data Sets ◽  
Testing Data ◽  
Credit Data

Normally, most of the bank's wealth is obtained from providing credit loans so that a marketing bank must be able to reduce the risk of non-performing credit loans. The risk of providing loans can be minimized by studying patterns from existing lending data. One technique that can be used to solve this problem is to use data mining techniques. Data mining makes it possible to find hidden information from large data sets by way of classification. The Random Forest (RF) algorithm is a classification algorithm that can be used to deal with data imbalancing problems. The purpose of this study is to discuss the use of the RF algorithm for classification of South German Credit data. This research is needed because currently there is no previous research that applies the RF algorithm to classify South German Credit data specifically. Based on the tests that have been done, the optimal performance of the classification algorithm RF on South German Credit data is the comparison of training data of 85% and testing data of 15% with an accuracy of 78.33%.

Human Activity Recognition using Fourier Transform Inspired Deep Learning Combination Model

2019 ◽  
Vol 9 (1) ◽  
pp. 16-31
Author(s):  
Kyungkoo Jun
Keyword(s):  
Fourier Transform ◽  
Deep Learning ◽  
Short Term Memory ◽  
Window Size ◽  
Sensor Data ◽  
Data Sets ◽  
Data Set ◽  
Proposed Model ◽  
Testing Data ◽  
Labeling Scheme

Background & Objective: This paper proposes a Fourier transform inspired method to classify human activities from time series sensor data. Methods: Our method begins by decomposing 1D input signal into 2D patterns, which is motivated by the Fourier conversion. The decomposition is helped by Long Short-Term Memory (LSTM) which captures the temporal dependency from the signal and then produces encoded sequences. The sequences, once arranged into the 2D array, can represent the fingerprints of the signals. The benefit of such transformation is that we can exploit the recent advances of the deep learning models for the image classification such as Convolutional Neural Network (CNN). Results: The proposed model, as a result, is the combination of LSTM and CNN. We evaluate the model over two data sets. For the first data set, which is more standardized than the other, our model outperforms previous works or at least equal. In the case of the second data set, we devise the schemes to generate training and testing data by changing the parameters of the window size, the sliding size, and the labeling scheme. Conclusion: The evaluation results show that the accuracy is over 95% for some cases. We also analyze the effect of the parameters on the performance.

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