A Large-Scale Customer-Facility Network Model for Customer Service Centre Location Applications

2019 ◽  
pp. 68-77
Author(s):  
David Chalupa ◽  
Peter Nielsen ◽  
Zbigniew Banaszak ◽  
Grzegorz Bocewicz
Keyword(s):  
Customer Service ◽  
Network Model ◽  
Large Scale ◽  
Service Centre

scholarly journals Image Classification Using Transfer Learning and Deep Learning

2021 ◽  
Vol 10 (9) ◽  
pp. 25394-25398
Author(s):  
Chitra Desai
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Image Classification ◽  
Transfer Learning ◽  
Network Model ◽  
Neural Network Model ◽  
Large Scale ◽  
Deep Neural Network ◽  
Visual Recognition ◽  
Classification Of Images

Deep learning models have demonstrated improved efficacy in image classification since the ImageNet Large Scale Visual Recognition Challenge started since 2010. Classification of images has further augmented in the field of computer vision with the dawn of transfer learning. To train a model on huge dataset demands huge computational resources and add a lot of cost to learning. Transfer learning allows to reduce on cost of learning and also help avoid reinventing the wheel. There are several pretrained models like VGG16, VGG19, ResNet50, Inceptionv3, EfficientNet etc which are widely used.   This paper demonstrates image classification using pretrained deep neural network model VGG16 which is trained on images from ImageNet dataset. After obtaining the convolutional base model, a new deep neural network model is built on top of it for image classification based on fully connected network. This classifier will use features extracted from the convolutional base model.

scholarly journals A Wireless Sensor Network Model for E-Metering and Fault Identification in Smart Water Distribution Systems

2019 ◽  
Vol 8 (10S) ◽  
pp. 227-232
Keyword(s):  
Wireless Sensor Network ◽  
Real Time ◽  
Sensor Network ◽  
Network Model ◽  
Distribution System ◽  
Water Distribution ◽  
Large Scale ◽  
Water Distribution System ◽  
Wireless Sensor ◽  
Smart Water

Water distribution system is a network that supplies water to all the consumers through different means. Proper means of providing water to houses without compromising in quantity and quality is always a challenge. As it is a huge network keeping track of the utilization is difficult for the utility. Hence through this project we come up with a solution to solve this issue. Current technologies like Low Power Wide Area Networks, LoRa and sensor deployment techniques have been in research and were also tested in few rural areas but issues due to hardware deployment and large scale real time implementation was a challenge hence through this system we aim to create and simulate a real time scenario to test a sensor network model that could be implemented in large scale further. This project aims in building a wireless sensor network model for a smart water distribution system. In this system there is bidirectional communication between the consumer and the utility. Each house has a meter through which the amount of water consumed is sent to the utility board. The data has two fields containing the house ID and the data (water consumed); it is being sent to the data collection unit (DCU) which in-turn sends it to the central server so that the consumption is monitored in real time. All this is simulated using NETSIM and MATLAB.

scholarly journals How advocacy affects Twitter migraine conversations: A pilot cross-sectional survey of Northeast American “migraine” landscape on Twitter from May to June 2020

2020 ◽  
Vol 3 ◽  
pp. 251581632097208
Author(s):  
Pengfei Zhang ◽  
Santosh Bhaskarabhatla
Keyword(s):  
United States ◽  
Social Media ◽  
Network Analysis ◽  
Network Model ◽  
Large Scale ◽  
The United States ◽  
Closeness Centrality ◽  
Control Group ◽  
Annual Conference ◽  
Sources Of Information

Background: Twitter is a leading microblogging platform, with over 126 million daily active users as of 2019, which allows for large-scale analysis of tweets related to migraine. June 2020 encompassed the National Migraine and Headache Awareness Month in the United States and the American Headache Society’s virtual annual conference, which offer opportunities for us to study online migraine advocacy. Objective: We aim to study the content of individual tweets about migraine, as well as study patterns of other topics that were discussed in those tweets. In addition, we aim to study the sources of information that people reference within their tweets. Thirdly, we want to study how online awareness and advocacy movements shape these conversations about migraine. Methods: We designed a Twitter robot that records all unique public tweets containing the word “migraine” from May 8th, 2020 to June 23rd, 2020, within a 400 km radius of New Brunswick, New Jersey, United States. We built two network analysis models, one for the months of May 2020 and June 2020. The model for the month of May served as a control group for the model for the month of June, the Migraine Awareness Month. Our network model was developed with the following rule: if two hashtag topics co-exist in a single tweet, they are considered nodes connected by an edge in our network model. We then determine the top 30 most important hashtags in the month of May and June through applications of degree, between-ness, and closeness centrality. We also generated highly connected subgraphs (HCS) to categorize clusters of conversations within each of our models. Finally, we tally the websites referenced by these tweets during each month and categorized these websites according to the HCS subgroups. Results: Migraine advocacy related tweets are more popular in June when compared to May as judged by degree and closeness centrality measurements. They remained unchanged when judged by between-ness centralities. The HCS algorithm categorizes the hashtags into a large single dominant conversation in both months. In each of the months, advocacy related hashtags are apart of each of the dominant conversation. There are more hashtag topics as well as more unique websites referenced in the dominant conversation in June than in May. In addition, there are many smaller subgroups of migraine-related hashtags, and in each of these subgroups, there are a maximum of two websites referenced. Conclusion: We find a network analysis approach to be fruitful in the area of migraine social media research. Migraine advocacy tweets on Twitter not only rise in popularity during migraine awareness month but also may potentially bring in more diverse sources of online references into the Twitter migraine conversation. The smaller subgroups we identified suggest that there are marginalized conversations referencing a limited number of websites, creating a possibility of an “echo chamber” phenomenon. These subgroups provide an opportunity for targeted migraine advocacy. Our study therefore highlights the success as well as potential opportunities for social media advocacy on Twitter.

scholarly journals Designing Incentive Mechanisms for Mobile Crowdsensing with Intermediaries

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Yatong Chen ◽  
Huangxun Chen ◽  
Shuo Yang ◽  
Xiaofeng Gao ◽  
Yunhe Guo ◽  
...  
Keyword(s):  
Network Model ◽  
Large Scale ◽  
Incentive Compatibility ◽  
Rapid Development ◽  
Sensor Technology ◽  
Incentive Mechanisms ◽  
Mobile Crowdsensing ◽  
Network Applications ◽  
Sensing Platform

In the past decade, with the rapid development of wireless communication and sensor technology, ubiquitous smartphones equipped with increasingly rich sensors have more powerful computing and sensing abilities. Thus, mobile crowdsensing has received extensive attentions from both industry and academia. Recently, plenty of mobile crowdsensing applications come forth, such as indoor positioning, environment monitoring, and transportation. However, most existing mobile crowdsensing systems lack vast user bases and thus urgently need appropriate incentive mechanisms to attract mobile users to guarantee the service quality. In this paper, we propose to incorporate sensing platform and social network applications, which already have large user bases to build a three-layer network model. Thus, we can publicize the sensing platform promptly in large scale and provide long-term guarantee of data sources. Based on a three-layer network model, we design incentive mechanisms for both intermediaries and the crowdsensing platform and provide a solution to cope with the problem of user overlapping among intermediaries. We theoretically prove the properties of our proposed incentive mechanisms, including incentive compatibility, individual rationality, and efficiency. Furthermore, we evaluate our incentive mechanisms by extensive simulations. Evaluation results validate the effectiveness and efficiency of our proposed mechanisms.

A biofilter network model — importance of the pore structure and other large-scale heterogeneities

2001 ◽  
Vol 56 (2) ◽  
pp. 475-483 ◽  
Author(s):  
Benjamin C.E. Schwarz ◽  
Joseph S. Devinny ◽  
Theodore T. Tsotsis
Keyword(s):  
Pore Structure ◽  
Network Model ◽  
Large Scale

THE WORKPLACE, THE SOCIETY AND THE WIDER WORLD: THE OFFSHORING AND OUTSOURCING INDUSTRY

2009 ◽  
Vol 29 ◽  
pp. 145-167 ◽  
Author(s):  
Liz Hamp-Lyons ◽  
Jane Lockwood
Keyword(s):  
Customer Service ◽  
Language Proficiency ◽  
Call Center ◽  
Large Scale ◽  
Call Centers ◽  
Emerging Market ◽  
Language Assessment ◽  
Poor Quality ◽  
The Philippines ◽  
Business Success

Workplace language assessment poses special issues for language testers, but also, when it becomes very large scale, it poses issues for language policy. This article looks at these issues, focusing on the offshore and outsourcing (O&O) industry as it is transitioning from native-speaking (NS) countries into nonnative-speaking (NNS) destinations such as India and the Philippines. This is obviously most impacted in call centers, where the ability of customer service representatives (CSRs) to communicate with ease with their native-English speaking customers is central to business success and can be key to a nation's economy. Having reviewed the (limited) research in this area, we take the Philippines as our example to explore how government, academe, and the business sector are dealing with the language proficiency and personnel-training issues caused by the exponential growth in this industry. Appropriate language assessments that are practical, while also being valid and reliable, are critical if the Philippines is to retain its position in this emerging market. Currently, call centers in Philippines complain of very poor recruitment rates due to poor language ability, and of poor quality communication outcomes measures: But how do they assess these key areas? We describe and evaluate the current situation in call center language assessment in the Philippines and discuss possible ways forward, for the Philippines and for the O&O industry more broadly.

scholarly journals Trafficking Networks and the Mexican Drug War

2015 ◽  
Vol 105 (6) ◽  
pp. 1738-1779 ◽  
Author(s):  
Melissa Dell
Keyword(s):  
Empirical Evidence ◽  
Network Model ◽  
Large Scale ◽  
Regression Discontinuity ◽  
Spillover Effects ◽  
Drug Trade ◽  
Drug War ◽  
Mexican Drug War ◽  
Alternative Drug ◽  
Drug Traffic

Drug trade-related violence has escalated dramatically in Mexico since 2007, and recent years have also witnessed large-scale efforts to combat trafficking, spearheaded by Mexico's conservative PAN party. This study examines the direct and spillover effects of Mexican policy toward the drug trade. Regression discontinuity estimates show that drug-related violence increases substantially after close elections of PAN mayors. Empirical evidence suggests that the violence reflects rival traffickers' attempts to usurp territories after crackdowns have weakened incumbent criminals. Moreover, the study uses a network model of trafficking routes to show that PAN victories divert drug traffic, increasing violence along alternative drug routes. (JEL D72, D85, K42, O17, Z13)

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