A Data-Driven Customer Quality of Experience System for a Cellular Network

Jung, Hyunglok; Mo, Jeonghoon; Park, Jungju

doi:10.1155/2017/4670231

Cited by 3 publications

(3 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Packet length allocation is: Where the likelihood of a non-segmented message packet is that a packet has a continuous length and a possibility its packet belongs to segments and a distance lower than that of a packet. The probability is [4]   ( )  …”

Section: Iii30 Methodologymentioning

confidence: 99%

“…In this job, the techniques were not addressed only the outcomes were shown graphically. [4][5] The objective of carrying out this research was to evaluate the voice quality in Long Tern Evolution (LTE) through the use of various codec mode set. This study has shown that all mode sets worked in the same manner under distinct circumstances of track loss.…”

Section: Iireview Of Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A Virtual Switching Model for Improvement of Call Quality in Integrated Communication System

Amanze¹,

Okolie²,

S.O.³

et al. 2020

IJSCE

View full text Add to dashboard Cite

Poor voice quality in VoIP models during communication has been a common occurrence which VoIP users experience, this can be frustrating when users cannot communicate efficiently. Most people find it difficult to think straight when they make calls and there is an echo. In addition to this frustration, the caller’s money, time, effort, energy is all wasted without compensation of any kind. Users are also frustrated by not receiving, transmitting or misunderstanding voice messages correctly. Given the need for voice quality in calls, it is of no importance when there is no proper communication. This study aims to reduce the threat of bad calls and improve the quality of voice calls. Nonetheless, we need to raise the filter duration to a high value in some real telecom’s environments with long echo delays. But, because of high computational complexity, it is not efficient in efficiency. In this study, we suggest a solution that uses a computational formula to compensate long echo, delay, packet loss, jitter and noise. The model designed was developed using MATLAB 2019b. This approach demonstrated productivity in terms of both voice quality and system speed.

show abstract

Section: Iii30 Methodologymentioning

confidence: 99%

Section: Iireview Of Related Workmentioning

confidence: 99%

A Virtual Switching Model for Improvement of Call Quality in Integrated Communication System

Amanze¹,

Okolie²,

S.O.³

et al. 2020

IJSCE

View full text Add to dashboard Cite

show abstract

“…From the results of the clustering analysis, the features/variables/attributes that significantly explain the label can be identified. Rejection of features/variables/attributes can be done based on explanatory power considering the multicollinearity between the features/variables/attributes and explanation rate of each feature/variable/attribute (Jung et al, 2017). LDA method finds the projection hyperplane that minimizes the interclass variance and maximizes the distance between the projected means of the classes (Xanthopoulos et al, 2013).…”

Section: Analyzing and Synthesizing Datamentioning

confidence: 99%

A feature selection for video quality of experience modeling: A systematic literature review

Skaka-Čekić

Husić

2023

WIREs Data Min & Knowl

View full text Add to dashboard Cite

Quality of Experience (QoE) multidimensional concept is the key for successful delivery of multimedia services. Higher user requirements for new experiences such as augmented reality, virtual reality, and future 6G services set higher requirements for QoE. A more complex QoE space requires the use of data mining methods in order to process the data for better QoE prediction. The increased dimensionality of the QoE space becomes a limiting factor for achieving the desired QoE prediction accuracy. Existing studies considering the QoE multidimensional concept with approaches that overcome the challenge of increased QoE space dimensionality are of great importance for future research. Accordingly, this article aims to review the applications of Feature Selection (FS) methods in video QoE modeling. It provides a comprehensive overview of the existing studies with the categorization and review of applied FS methods with reference to the data collection and data modeling steps. The analysis included 71 studies which provides overview of the FS methods applications in video QoE modeling depending on the input Influence Factor (IF) dimension sizes, type of IFs, QoE prediction methods used and QoE evaluation type. Our review revealed the advantages of using FS methods in video QoE modeling, frequency of application of FS methods with potential of applying more FS methods in a series or a parallel, gives an overview of the achieved dimensionality reduction degree for different methods, and provides insights in opportunities for researchers for applying FS methods on complex multidimensional QoE space. This article is categorized under: Technologies > Data Preprocessing Algorithmic Development > Multimedia

show abstract

Açıklanabilir Yapay Zekaya Dayalı Müşteri Kaybı Analizi ve Elde Tutma Önerisi

Kök

2024

Mühendislik Bilimleri Ve Araştırmaları Dergisi

View full text Add to dashboard Cite

Mobil telekomünikasyon pazarında aboneler yüksek hizmet kalitesi, rekabetçi fiyatlandırma ve gelişmiş servis beklentisindedirler. Müşteri bu beklentilerini telekom servis sağlayıcısından karşılayamaması durumunda onu değiştirme yoluna gitmektedir. Hizmet sağlayıcı operatörlerin ise abone kaybı olarak nitelendirilen bu durumla başa çıkmak için abonelerin iletişim kalıpları, davranışları ve abonelik planlarına ait verileri analiz ederek stratejik öngörü sağlayan yorumlanabilir müşteri kaybı tahmin modellerine ihtiyacı vardır. Bu çalışmada biz K-En Yakın Komşu, Karar Ağacı, Rastgele Orman, Destek Vektör Makinesi ve Naïve Bayes algoritmalarına dayalı müşteri kaybı tahmin modelleri geliştiriyoruz. Aynı zamanda en başarılı algoritma sonuçlarının açıklanabilirliği ve yorumlanabilirliği için ELI5, LIME, SHAP ve karşıolgusal açıklanabilir yapay zeka yöntemleri kullanıyoruz. Bu sayede geliştirilen modeller incelenen abonelerin sadece operatörü değiştirip değiştirmediği değil aynı zamanda abone davranışına sebep olan özellikleri de çıktı olarak vermektedir. Geliştirilen açıklanabilir modeller aracılığıyla servis sağlayıcılara müşteri davranışlarının nasıl ve neden gerçekleştiğine dair kapsamlı analizler sunuyoruz.

show abstract

A Data-Driven Customer Quality of Experience System for a Cellular Network

Cited by 3 publications

References 19 publications

A Virtual Switching Model for Improvement of Call Quality in Integrated Communication System

A Virtual Switching Model for Improvement of Call Quality in Integrated Communication System

A feature selection for video quality of experience modeling: A systematic literature review

Açıklanabilir Yapay Zekaya Dayalı Müşteri Kaybı Analizi ve Elde Tutma Önerisi

Contact Info

Product

Resources

About