scholarly journals Transfer learning-based method for automated e-waste recycling in smart cities

2021 ◽  
pp. 169337
Author(s):  
Nermeen Baker ◽  
Paul Szabo-Müller ◽  
Uwe Handmann
Keyword(s):  
Transfer Learning ◽  
Smart Cities ◽  
Waste Recycling

WASTE RECYCLING SYSTEM AS THE BASIS OF «SMART CITY» FUNCTIONING

2020 ◽  
Vol 13 (4) ◽  
pp. 102-111
Author(s):  
D.L. Kuimov ◽  
◽  
N.V. Bondarchuk ◽  
Keyword(s):  
Russian Federation ◽  
Smart City ◽  
Regional Economics ◽  
Smart Cities ◽  
Waste Recycling ◽  
Main Element ◽  
Effective Work ◽  
Foreign Countries ◽  
The Russian Federation ◽  
Recycling Systems

This article presents the concepts of «smart cities» origin and peculiarities of their functioning. The article analyzes the experience of foreign countries in the field of creating «smart cities», useful for the development of complex concepts of building «smart cities» in the Russian Federation. As the main element of «smart cities» functioning and development, the system of waste recycling and features of its effective work were considered. The article is intended for economists specializing in regional economics, the creation of «smart cities» and waste recycling systems.

scholarly journals Enabling accurate indoor localization for different platforms for smart cities using a transfer learning algorithm

2020 ◽  
Author(s):  
Halgurd S. Maghdid ◽  
Kayhan Zrar Ghafoor ◽  
Abdulbasit Al‐Talabani ◽  
Ali Safaa Sadiq ◽  
Pranav Kumar Singh ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Indoor Localization ◽  
Learning Algorithm ◽  
Smart Cities

scholarly journals Social Media Meets Big Urban Data: A Case Study of Urban Waterlogging Analysis

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Ningyu Zhang ◽  
Huajun Chen ◽  
Jiaoyan Chen ◽  
Xi Chen
Keyword(s):  
Social Media ◽  
Transfer Learning ◽  
Traffic Management ◽  
Smart Cities ◽  
Learning Method ◽  
Small Cities ◽  
China And India ◽  
The Moment ◽  
Traffic Operation

With the design and development of smart cities, opportunities as well as challenges arise at the moment. For this purpose, lots of data need to be obtained. Nevertheless, circumstances vary in different cities due to the variant infrastructures and populations, which leads to the data sparsity. In this paper, we propose a transfer learning method for urban waterlogging disaster analysis, which provides the basis for traffic management agencies to generate proactive traffic operation strategies in order to alleviate congestion. Existing work on urban waterlogging mostly relies on past and current conditions, as well as sensors and cameras, while there may not be a sufficient number of sensors to cover the relevant areas of a city. To this end, it would be helpful if we could transfer waterlogging. We examine whether it is possible to use the copious amounts of information from social media and satellite data to improve urban waterlogging analysis. Moreover, we analyze the correlation between severity, road networks, terrain, and precipitation. Moreover, we use a multiview discriminant transfer learning method to transfer knowledge to small cities. Experimental results involving cities in China and India show that our proposed framework is effective.

scholarly journals Waste Management in Green and Smart Cities: A Case Study of Russia

2019 ◽  
Vol 12 (1) ◽  
pp. 94 ◽  
Author(s):  
Zhanna Mingaleva ◽  
Natalia Vukovic ◽  
Irina Volkova ◽  
Tatiana Salimova
Keyword(s):  
Waste Management ◽  
Smart Cities ◽  
Research Process ◽  
Waste Recycling ◽  
Green Technologies ◽  
Modern Cities ◽  
Recycling Economy ◽  
Urban Territories ◽  
Smart Technologies

This article aims to investigate the role of waste management in the development of modern green and smart cities and to determine the existence of several key points in programs transforming cities into green cities with smart technologies. The relevance of the research is determined by the need to develop a theoretical and methodological basis for the green and smart city concepts. The research process involved the following methods: Scientific analysis, comparison, and synthesis. The research results of the case study of Russia determined that for urban territories with great distances between urban districts, waste sorting stations should be located as parts of so-called waste recycling complexes at intermunicipal landfills. This will allow a more fully implementation of the concept of recycling economy not only in Russian cities, but also in other cities with sparse populations across the world. Further, the authors conclude that the effectiveness of green technologies in modern cities, especially in waste management, depends on the level of participation of citizens. People are active participants in the life processes of cities and have a direct impact on the urban environment. Consequently, the introduction of green technologies can only be achieved in harmony with the well-established behavioral attitudes of city residents together with the implementation of green and smart urban technologies.

scholarly journals Detection of myocardial infarction based on novel deep transfer learning methods for urban healthcare in smart cities

2020 ◽  
Author(s):  
Ahmed Alghamdi ◽  
Mohamed Hammad ◽  
Hassan Ugail ◽  
Asmaa Abdel-Raheem ◽  
Khan Muhammad ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Transfer Learning ◽  
Smart Cities ◽  
Learning Methods

scholarly journals Monthly Electric Load Forecasting Using Transfer Learning for Smart Cities

2020 ◽  
Vol 12 (16) ◽  
pp. 6364 ◽  
Author(s):  
Seung-Min Jung ◽  
Sungwoo Park ◽  
Seung-Won Jung ◽  
Eenjun Hwang
Keyword(s):  
Transfer Learning ◽  
Smart Cities ◽  
Pearson Correlation ◽  
Load Forecasting ◽  
Machine Learning Techniques ◽  
Weather Data ◽  
Similar Data ◽  
Electric Load ◽  
Learning Techniques ◽  
Electric Load Forecasting

Monthly electric load forecasting is essential to efficiently operate urban power grids. Although diverse forecasting models based on artificial intelligence techniques have been proposed with good performance, they require sufficient datasets for training. In the case of monthly forecasting, because just one data point is generated per month, it is not easy to collect sufficient data to construct models. This lack of data can be alleviated using transfer learning techniques. In this paper, we propose a novel monthly electric load forecasting scheme for a city or district based on transfer learning using similar data from other cities or districts. To do this, we collected the monthly electric load data from 25 districts in Seoul for five categories and various external data, such as calendar, population, and weather data. Then, based on the available data of the target city or district, we selected similar data from the collected datasets by calculating the Pearson correlation coefficient and constructed a forecasting model using the selected data. Lastly, we fine-tuned the model using the target data. To demonstrate the effectiveness of our model, we conducted an extensive comparison with other popular machine-learning techniques through various experiments. We report some of the results.

Adapting Gaussian YOLOv3 with transfer learning for overhead view human detection in smart cities and societies

2021 ◽  
Vol 70 ◽  
pp. 102908
Author(s):  
Imran Ahmed ◽  
Gwanggil Jeon ◽  
Abdellah Chehri ◽  
Mohammad Mehedi Hassan
Keyword(s):  
Transfer Learning ◽  
Smart Cities ◽  
Human Detection ◽  
Overhead View
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