incoming-call identification

2011 ◽  
Keyword(s):  
Incoming Call

COMMUNICATION OF COMMON AND MARGINAL CHARACTERISTICS OF CONDITIONS IN A TWO-CHANNEL SYSTEM WITH SIMPLE STREAMLINES OF APPLICATIONS

2017 ◽  
pp. 7-19 ◽  
Author(s):  
Georgiy Aleksandrovich Popov
Keyword(s):  
Operating Systems ◽  
Queue Length ◽  
Recurrence Relations ◽  
Queuing Systems ◽  
Channel System ◽  
Current Period ◽  
Multichannel Systems ◽  
Incoming Call ◽  
Marginal Values ◽  
Time Required

The article deals with a two-channel queuing system with a Poisson incoming call flow, in which the application processing time on each of the devices is different. Such models are used, in particular, when describing the operation of the system for selecting service requests in a number of operating systems. A complex system characteristic was introduced at the time of service endings on at least one of the devices, including the queue length, the remaining service time on the occupied device, and the time since the beginning of the current period of employment. This characteristic determines the state of the system at any time. Recurrence relations are obtained that connect this characteristic with its marginal values when there is no queue in the system. The method of introducing additional events was chosen as one of the main methods for analyzing the model. The relationships presented in this article can be used for analysis of the average characteristics of this system, as well as in the process of its simulation. Summarizing the results of work on multichannel systems with an arbitrary number of servicing devices will significantly reduce the time required for simulating complex systems described by sets of multichannel queuing systems.

Wie Incoming Call Center funktionieren

1998 ◽  
pp. 129-142
Author(s):  
Brad Cleveland ◽  
Julia Mayben ◽  
Günter Greff
Keyword(s):  
Call Center ◽  
Incoming Call

Ambient Intelligence

2009 ◽  
pp. 32-39 ◽  
Author(s):  
Fortunato Sorrentino
Keyword(s):  
Wireless Communications ◽  
Ambient Intelligence ◽  
Knowledge Society ◽  
Contemporary Society ◽  
Practical Orientation ◽  
The Future ◽  
Incoming Call ◽  
Surrounding Space

“Ambient intelligence” (AmI) refers to both a theoretical and a practical orientation of technology, involving the most innovative areas of the ICT sector. Recognized as a powerful trend, Ambient Intelligence has an increasing impact in several domains of our contemporary society, the so-called “knowledge society”. Let us look at the two words “ambient” and “intelligence”. Today we often use the attribute intelligent or smart referring to artifacts that show “a behavior”, have “a memory”, appear to take nontrivial “initiatives”. Take, for instance, a smartphone, which is able, when there is an incoming call, to put up on the screen the image of our correspondent. The “intelligence” in the words “Ambient Intelligence” precisely refers to those special embedded capabilities of certain things around us, capabilities that we are not aware of until they come into action. The word ambient, means “existing in the surrounding space” and signals that there is a particular diffused property of such a space. It has an essential charateristic, which is neither explicit nor obtrusive, but widely exploited by our Knowledge Society: the capability to transmit information without the need of wires (wireless communications). Like its underlying technologies, Ambient Intelligence is an expanding, evolving concept, projected far into the future.

scholarly journals National trends and state-level variation in the duration of incoming quitline calls to 1-800-QUIT-NOW during 2012–2015

2020 ◽  
Vol 15 (2) ◽  
pp. 59-66
Author(s):  
Nathan Mann ◽  
Ann Malarcher ◽  
Lei Zhang ◽  
Asma Shaikh ◽  
Jesse Thompson ◽  
...  
Keyword(s):  
Service Delivery ◽  
Service Provider ◽  
State Level ◽  
Level Variation ◽  
Call Duration ◽  
Incoming Call ◽  
Using Data ◽  
National Trends ◽  
Delivery Models ◽  
Call Volume

AbstractIntroductionThe duration of incoming quitline calls may serve as a crude proxy for the potential amount of reactive counseling provided.AimsTo explore whether call duration may be useful for monitoring quitline capacity and service delivery.MethodsUsing data on the duration of incoming quitline calls to 1-800-QUIT-NOW from 2012 through 2015, we examined national trends and state-level variation in average call duration. We estimated a regression model of average call duration as a function of total incoming calls, nationally and by state, controlling for confounders.ResultsFrom 2012 through 2015, average call duration was 11.4 min, nationally, and was 10 min or longer in 33 states. Average call duration was significantly correlated with quitline service provider. Higher incoming call volume was significantly associated with lower average call duration in 32 states and higher average call duration in five states (P-value <0.05). The relationship between call volume and call duration was not correlated with quitline service provider.ConclusionsVariation in average call duration across states likely reflects different service delivery models. Average call duration was associated with call volume in many states. Significant changes in call duration may highlight potential quitline capacity issues that warrant further investigation.

Driven by a Social and Interactional Routine

2012 ◽  
Vol 2 (3) ◽  
pp. 39-58
Author(s):  
Mirka Rauniomaa ◽  
Pentti Haddington
Keyword(s):  
Qualitative Study ◽  
Mobile Phone ◽  
Conversation Analysis ◽  
Real Life ◽  
Text Message ◽  
Video Recordings ◽  
Incoming Call ◽  
Audio Video ◽  
Immediate Response ◽  
Three Phases

The article reports findings from a qualitative study that draws on the methods of conversation analysis and on audio-video recordings of ordinary, real-life, non-experimental driving situations. The article shows what happens in a car after a mobile phone summons, i.e., the initial ring or beep of a car occupant’s phone. It identifies three phases (i.e., orienting to, locating and handling a phone) that follow the summons and lead to an attempt at verbally responding to the summons. It is shown that the ringing of a phone (indicating an incoming call) or the beeping of a phone (indicating an incoming text message), as a socially and interactionally significant action, is treated as requiring a more or less immediate response. It is argued that this routinization of responding to a summons explains drivers’, and possible passengers,’ use of a mobile phone while traveling in a car.

Detection of Mobile Phone Fraud Using Possibilistic Fuzzy C-Means Clustering and Hidden Markov Model

2016 ◽  
Vol 7 (2) ◽  
pp. 23-44 ◽  
Author(s):  
Sharmila Subudhi ◽  
Suvasini Panigrahi ◽  
Tanmay Kumar Behera
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Mobile Phone ◽  
Hidden Markov ◽  
Clustering Methods ◽  
Fuzzy C Means ◽  
Novel Approach ◽  
Incoming Call ◽  
Fuzzy C Means Clustering ◽  
Reality Mining

This paper presents a novel approach for fraud detection in mobile phone networks by using a combination of Possibilistic Fuzzy C-Means clustering and Hidden Markov Model (HMM). The clustering technique is first applied on two calling features extracted from the past call records of a subscriber generating a behavioral profile for the user. The HMM parameters are computed from the profile, which are used to generate some profile sequences for training. The trained HMM model is then applied for detecting fraudulent activities on incoming call sequences. A calling instance is detected as forged when the new sequence is not accepted by the trained model with sufficiently high probability. The efficacy of the proposed system is demonstrated by extensive experiments carried out with Reality Mining dataset. Furthermore, the comparative analysis performed with other clustering methods and another approach recently proposed in the literature justifies the effectiveness of the proposed algorithm.

Modeling and Analysis of Zone-Based Registration in Mobile Communication Network by Considering Busy-Line Effect and Implicit Registration

2016 ◽  
Vol 33 (04) ◽  
pp. 1650024 ◽  
Author(s):  
Jong Hun Park ◽  
Jihee Jung ◽  
Janghyun Baek
Keyword(s):  
Mathematical Modeling ◽  
Communication Networks ◽  
Mobile Communication ◽  
Modeling And Analysis ◽  
Network Database ◽  
Incoming Call ◽  
Mobile Communication Networks ◽  
And Performance ◽  
Current Zone ◽  
Paging Cost

In this study, we consider zone-based registration (ZBR) in mobile communication networks. In ZBR, when a mobile moves to a new zone, it registers its zone to the network database to keep the mobile’s current zone, and to connect an incoming call to the mobile when it is generated. A mobile can store one zone, or more than one zones. Among various types of ZBR, we focus on two-zone-based registration (TZR), which is known to have good performance. In TZR, a mobile can store two zones that it has recently registered, and does not register when it crosses either zone that it has already kept. In general, in TZR, a mobile registers its zone less often than in single-zone-based registration (SZR). However, TZR increases the paging cost, because the network may not know the exact zone where the mobile is. Mathematical modeling and performance analysis are performed to obtain the exact performance of SZR and TZR, by considering the busy-line effect and implicit registration effect of outgoing calls from a mobile. From numerical results for various circumstances, it is shown that TZR is superior to SZR in most cases.

Forecasting incoming call volumes in call centers with recurrent Neural Networks

2016 ◽  
Vol 69 (11) ◽  
pp. 4811-4814 ◽  
Author(s):  
Mona Ebadi Jalal ◽  
Monireh Hosseini ◽  
Stefan Karlsson
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Call Centers ◽  
Incoming Call
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