Telecommunication Analytics Based on Customer Segmentation Using Unsupervised Algorithms

2021 ◽  
Author(s):  
Henwy Wibowo ◽  
Kristina Pestaria Sinaga
Keyword(s):  
Customer Segmentation ◽  
Unsupervised Algorithms

Deep Learning Based High-Resolution Remote Sensing Image classification

2017 ◽  
Vol 7 (10) ◽  
pp. 22
Author(s):  
Sumit Kaur
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Deep Learning ◽  
Image Classification ◽  
Language Processing ◽  
Object Perception ◽  
Remote Sensing Image ◽  
Research Area ◽  
Remote Sensing Image Classification ◽  
Unsupervised Algorithms

Abstract- Deep learning is an emerging research area in machine learning and pattern recognition field which has been presented with the goal of drawing Machine Learning nearer to one of its unique objectives, Artificial Intelligence. It tries to mimic the human brain, which is capable of processing and learning from the complex input data and solving different kinds of complicated tasks well. Deep learning (DL) basically based on a set of supervised and unsupervised algorithms that attempt to model higher level abstractions in data and make it self-learning for hierarchical representation for classification. In the recent years, it has attracted much attention due to its state-of-the-art performance in diverse areas like object perception, speech recognition, computer vision, collaborative filtering and natural language processing. This paper will present a survey on different deep learning techniques for remote sensing image classification. 

scholarly journals Mitigating DoS attacks in IoT using Supervised and Unsupervised Algorithms – A Survey

2021 ◽  
Vol 1055 (1) ◽  
pp. 012072
Author(s):  
S.B. Gopal ◽  
C. Poongodi ◽  
D. Nanthiya ◽  
R. Snega Priya ◽  
G. Saran ◽  
...  
Keyword(s):  
Dos Attacks ◽  
Unsupervised Algorithms

scholarly journals Indirectly Supervised Anomaly Detection of Clinically Meaningful Health Events from Smart Home Data

2021 ◽  
Vol 12 (2) ◽  
pp. 1-18
Author(s):  
Jessamyn Dahmen ◽  
Diane J. Cook
Keyword(s):  
Anomaly Detection ◽  
Time Series Data ◽  
Bayesian Optimization ◽  
Detection Methods ◽  
Series Data ◽  
Detection Techniques ◽  
Health Events ◽  
Warm Start ◽  
Health Related ◽  
Unsupervised Algorithms

Anomaly detection techniques can extract a wealth of information about unusual events. Unfortunately, these methods yield an abundance of findings that are not of interest, obscuring relevant anomalies. In this work, we improve upon traditional anomaly detection methods by introducing Isudra, an Indirectly Supervised Detector of Relevant Anomalies from time series data. Isudra employs Bayesian optimization to select time scales, features, base detector algorithms, and algorithm hyperparameters that increase true positive and decrease false positive detection. This optimization is driven by a small amount of example anomalies, driving an indirectly supervised approach to anomaly detection. Additionally, we enhance the approach by introducing a warm-start method that reduces optimization time between similar problems. We validate the feasibility of Isudra to detect clinically relevant behavior anomalies from over 2M sensor readings collected in five smart homes, reflecting 26 health events. Results indicate that indirectly supervised anomaly detection outperforms both supervised and unsupervised algorithms at detecting instances of health-related anomalies such as falls, nocturia, depression, and weakness.

Revenue management and price optimization techniques in the hotel sector

2018 ◽  
Vol 24 (6) ◽  
pp. 720-752 ◽  
Author(s):  
Aldric Vives ◽  
Marta Jacob ◽  
Marga Payeras
Keyword(s):  
Revenue Management ◽  
Airline Industry ◽  
New Technologies ◽  
Customer Segmentation ◽  
Optimization Techniques ◽  
Hotel Management ◽  
Price Optimization ◽  
Hotel Sector ◽  
Measurement Effects

Pricing and revenue management (RM) techniques have become a popular field of research in hotel management literature. The sector’s background framework and evolution and the widespread use of new technologies have allowed a customer-oriented approach to be taken to pricing and the development of RM tools, while also contributing to better processes in hotel management performance at individual hotel level. Thus, price optimization (PO) methods that seek to maximize hotel revenue are based on inventory scarcity, customer segmentation and pricing. In the hotel sector, as in the airline industry, different pricing policies have a greater impact than competition measurement effects. This is mainly as differentiation strategies and specific policies at hotels can reduce the pressure of a competitive environment. The main contributions of the article are the presentation, description and classification of the principal RM and PO techniques in hotel sector literature.

Fishing for whales: a segmentation model for social casinos

2018 ◽  
Vol 10 (4) ◽  
pp. 400-421 ◽  
Author(s):  
Jonathan Barsky
Keyword(s):  
Design Methodology ◽  
Social Dynamics ◽  
Customer Segmentation ◽  
Price Sensitivity ◽  
Content Type ◽  
Casino Industry ◽  
Social Casino Games ◽  
Spending Patterns ◽  
The Social ◽  
Insight Into

Purpose The purpose of this paper is to introduce a new customer segmentation model for the social casino industry. The key contribution of this model is the introduction of original psychographic/taste data, including a player emotions scale. Design/methodology/approach The data for this research are based on player feedback from 22 countries, with evaluations of the top 100 social casino titles (apps). The new segmentation model splits the industry into distinct customer groups based on spending patterns, behavioral dimensions and attitudinal dimensions. Findings The results provide insight into game mechanics, social dynamics, player emotions, spend, price sensitivity, loyalty and other elements that impact monetization. Critical behaviors and preferences of social casino players that will help companies better understand and connect with their target customers are described. Originality/value This is the first study to develop a rigorous segmentation model of social casino games based on behavioral and psychographic data.

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