scholarly journals Analysis of spammers’ behavior on a live streaming chat

2021 ◽  
Vol 10 (1) ◽  
pp. 139
Author(s):  
Sawita Yousukkee ◽  
Nawaporn Wisitpongphan
Keyword(s):  
Growth Rate ◽  
Decision Tree ◽  
Streaming Media ◽  
Live Streaming ◽  
Primary Target ◽  
Live Chat ◽  
Characteristic Features ◽  
Advertising Company ◽  
Number Of Customers ◽  
Suitable Technique

<span id="docs-internal-guid-f908fd2e-7fff-1849-4fda-c2cf9baed97e"><span>Live streaming is becoming a popular channel for advertising and marketing. An advertising company can use this feature to broadcast and reach a large number of customers. YouTube is one of the streaming media with an extreme growth rate and a large number of viewers. Thus, it has become a primary target of spammers and attackers. Understanding the behavior of users on live chat may reduce the moderator’s time in identifying and preventing spammers from disturbing other users. In this paper, we analyzed YouTube live streaming comments in order to understand spammers’ behavior. Seven user’s behavior features and message characteristic features were comprehensively analyzed. According to our findings, features that performed best in terms of run time and classification efficiency is the relevant score together with the time spent in live chat and the number of messages per user. The accuracy is as high as 66.22 percent. In addition, the most suitable technique for real-time classification is a decision tree.</span></span>

Other indicators of severity

2012 ◽  
Author(s):  
Odo Diekmann ◽  
Hans Heesterbeek ◽  
Tom Britton
Keyword(s):  
Growth Rate ◽  
Structured Populations ◽  
Infectious Period ◽  
Final Size ◽  
Transmission Parameters ◽  
Contact Rates ◽  
Major Outbreak ◽  
Characteristic Features ◽  
Deterministic Setting ◽  
The Basic Reproduction Number

This chapter is devoted to the initial real-time growth rate r, the probability of a major outbreak, the final size, and the endemic level, in structured populations, with special attention for computational simplifications in the case of separable mixing. Chapter 7 studied the basic reproduction number R₀ for epidemic models in populations manifesting various forms of heterogeneity. It was illustrated that R₀ depends on the transmission parameters, contact rates, the infectious period and on the community structure. The importance of R₀ lies in the fact that an epidemic can, and will in the deterministic setting, take off only if R₀ > 1, a characteristic referred to as supercritical. In a community having births or immigration of susceptibles, this also means that the disease can become endemic. If the parameters and community are such that R₀ < 1 (or R₀ = 1), we are in the subcritical (critical) regime and an epidemic outbreak cannot occur. The chapter examines important supplementary characteristic features and shows how they depend on the different parameters of the model.

Research of Distributed P2P Network Streaming Media Technology

2014 ◽  
Vol 556-562 ◽  
pp. 5259-5262
Author(s):  
Jin Hai Zhang
Keyword(s):  
Streaming Media ◽  
Live Streaming ◽  
Media Technology ◽  
Computer Performance ◽  
Network Bandwidth ◽  
P2p Live Streaming ◽  
Media Distribution ◽  
Streaming Media Technology ◽  
High Bandwidth ◽  
Media Applications

With the popularity of the Internet, personal computer performance and network bandwidth continues to increase and the rapidly improving network streaming media applications have become more abundant. Due to the high bandwidth for streaming media have characteristics, traditional methods of C/S can easily cause server performance bottlenecks, introduce the idea of P2P live streaming media service, through normal computer exercise part of the server's functionality, decentralization of services, can effectively reduce the load on the server, as well as local high network bandwidth consumption. Therefore, P2P live streaming media distribution technology is potentially broad application prospects.

scholarly journals PENERAPAN METODE HTB DAN DIFFSERV GUNA PENINGKATAN QOS PADA LAYANAN VIDEO STREAMING

2019 ◽  
Vol 9 (3) ◽  
pp. 35-40
Author(s):  
Mitra Unik ◽  
Soni Soni ◽  
Randra Aguslan Pratama
Keyword(s):  
Quality Of Service ◽  
Video Streaming ◽  
Packet Loss ◽  
Digital Video ◽  
Streaming Media ◽  
Fundamental Problem ◽  
Streaming Video ◽  
Live Streaming ◽  
Delay Jitter

Abstract One of the popular internet services in use today is video streaming, either live (live streaming) or pre-recorder. Streaming video is a type of streaming media where data from video files is continuously transmitted over the internet to remote users. This fundamental problem appears to be influenced by the biggest factor which is the limited infrastructure of network resources which causes poor video quality. The process of digital video communication is known to consume quite a large resource, because in general the bandwidth requirements for sending Video and Audio signals. To maintain the quality of the video being played, there are several instruments needed, one of which is a data connection that is required to have Quality of Service (QoS). The parameters used in the measurement of QoS are delay, jitter, packet loss, throughput. This study uses the PPDIO method as a workflow with a Network Lifecycle approach. In this research, there are many factors that influence the quality of video, namely network factors and hardware factors. The test results obtained are not absolute, so it is possible that there will be differences in subsequent testing. Encoding also affects the quality of the video. Bandwidth equalization according to priority when the traffic conditions of all packets are full. Based on a comparative analysis of QoS parameter calculations using HTB and Diffserv methods, a comparison of throughput, jitter and delay does not differ greatly between clients. Keywords: Video Streaming, Diffserv, HTB, QoS Abstrak Salah satu layanan dari internet yang populer digunakan saat ini adalah video streaming, baik secara langsung (live streaming) atau pre-recorder. Streaming video merupakan jenis streaming media dimana data dari file video secara terus menerus dikirimkan melalui jaringan internet ke pengguna jarak jauh. Permasalahan mendasar ini muncul dipengaruhi oleh faktor terbesar yaitu terbatasnya infrastruktur sumber daya jaringan yang menyebabkan kualitas video yang buruk. Proses  komunikasi  digital  video,  diketahui  menghabiskan  resource  yang  cukup  besar, dikarenakan Secara umum kebutuhan bandwidth untuk mengirimkan sinyal Video dan Audio. Guna menjaga kualitas dari video yang dimainkan, terdapat beberapa instrument yang dibutuhkan, salah satunya adalah koneksi data yang wajib memiliki Quality of Service (QoS). Adapun Parameter yang digunakan dalam pengukuran QoS adalah delay, jitter, packet loss, Throughput. Penelitian ini menggunakan metode PPDIO sebagai alur kerja dengan pendekatan Network Lifecycle. Pada penelitian ini didapat Banyak faktor yang mempengaruhi kualitas dari video yaitu faktor jaringan dan faktor dari Hardware. Hasil pengujian didapat tidaklah mutlak sehingga tidak menutup kemungkinan akan ada perbedaan pada pengujian selanjutnya. Encoding juga mempengaruhi kualitas dari video. pemerataan Bandwidth sesuai prioritasnya saat kondisi traffic seluruh paket penuh. Berdasarkan analisa perbandingan perhitungan parameter QoS menggunakan metode HTB dan Diffserv, didapatkan  perbandingan troughput, jitter dan delay yang tidak berbeda jauh antara klien. Kata kunci: Video streaming, Diffserv, HTB, QoS  

scholarly journals Edukasi Pemasaran Digital di Era Pandemi Covid-19 pada Rumah Produksi Baso Aci Tata Snack

2021 ◽  
Vol 6 (4) ◽  
pp. 675-681
Author(s):  
Ana Komari ◽  
Lolyka Dewi Indrasari ◽  
Afiff Yudha Tripariyanto ◽  
Heribertus Budi Santosa ◽  
Sri Rahayuningsih
Keyword(s):  
Digital Marketing ◽  
Live Streaming ◽  
Live Chat

Rumah produksi Bakso Aci Tata Snack memiliki produk bakso aci dengan pemasaran menggunakan digital marketing. Media promosi yang digunakan adalah Shopee dan Instagram. Kegiatan pemasaran yang saat ini sedang digunakan lebih dominan adalah kemampuan dalam menggunakan perangkat elektronik berupa smartphone, perangkat personal komputer atau laptop, dan pengetahuan tentang internet untuk meningkatkan penjualan. Pengabdian masyarakat ini ditujukan kepada tenaga kerja dan pemilik Rumah Produksi Bakso Aci Tata Snack. Kegiatan ini diikuti seluruh karyawan yang berjumlah 16 orang dan dilaksanakan menggunakan metode ceramah, diskusi, tanya jawab dan usulan perbaikan digital marketing. Kegiatan ini mengungkapkan persamaan penggunaan Shopee dan Instagram yang meliputi fitur foto produk yang menarik, gratis ongkos kirim, follower yang ditingkatkan, tampilan toko menarik, tema konsisten dan daya tarik caption serta posting. Sedangkan yang membedakan untuk akun Shopee menggunakan star seller dan live chat. Adapun akun Instagram menggunakan Instagram for Business dan live streaming.

scholarly journals Prediksi Customer Churn dengan Algoritma Decision Tree C4.5 Berdasarkan Segmentasi Pelanggan untuk Mempertahankan Pelanggan pada Perusahaan Retail

2018 ◽  
Vol 1 (1) ◽  
pp. 16-24
Author(s):  
Ni Wayan Wardani ◽  
Gede Rasben Dantes ◽  
Gede Indrawan
Keyword(s):  
Decision Tree ◽  
Prediction Models ◽  
Confusion Matrix ◽  
Customer Segmentation ◽  
Customer Churn ◽  
Rfm Model ◽  
Performance Results ◽  
Retail Company ◽  
Number Of Customers ◽  
A Company

Customer is a very important asset for retail companies. This is the reason why retail companies should plan and use a fairly clear strategy in treating customers. With the large number of customers, the problem that must be faced is how to identify the characteristics of all customers and able to retain existing customers in order not to stop buying and moving to a competitor retail company. By applying the concept of CRM, a company can identify customers by segmenting customers while also being able to implement customer retention programs by predicting potential churn on each customer class. The data used comes from UD.Mawar Sari. Customer segmentation process uses RFM model to get customer class. UD. Mawar Sari customer class is dormant, everyday, golden and superstar. The construction of prediction models using the Decision Tree C4.5. The application of the prediction model obtains performance results, that is: Dormant: Recall 97.51%, Precision 75.18%, Accuracy 76.18%. Everyday: Recall 100%, Precision 99.04%, Accuracy 99.04%.  Golden: Recall 100%, Precision 98.84%, Accuracy 98.84%. Superstar: Recall 96.15%, Precision 99.43%, Accuracy 95.63%. Results of the evaluation with confusion matrix it can be concluded that the dormant customer class is a potentially churn customer class.

scholarly journals Prediction of Churn in Telecom Service: Exploring Call Behaviors and using Machine Learning

2019 ◽  
Vol 9 (2) ◽  
pp. 3831-3834
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Customer Service ◽  
Service Providers ◽  
Mobile Network ◽  
High Likelihood ◽  
Telecom Service ◽  
Number Of Customers ◽  
Mobile Network Operators

Churn has a significant impact on mobile network operators and telecommunications service providers. Many studies on churn have been reported, but no one can say that they can create universal human tools for predicting churn or that we can see all the reasons for it. The purpose of this study is to derive the call behavior factors of churning customers and to find ways to reduce the churn of target customers who exhibit these call behaviors. For this, this study uses decision tree and machine learning for the prediction of churn in telecom service. Based on the analysis results, first, the fact that the total number of customers who have more than 316.7 in churn shows that the higher the number of calls, the higher the chance of churn. Second, among customers with total day minutes above 316.7, those with customer service calls above 8.5 show a high likelihood of churn among complaining customers. The overall accuracy is 91.4%. Among the customers who predicted not to be churned, the accuracy that would not be churned was 92.87%, and the accuracy that was churned was 78.4% among the customers predicted to be churned

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