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AI and Deep Learning for Urban Computing [chapter]

Senzhang Wang, Jiannong Cao
<span title="">2021</span> <i title="Springer Singapore"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/b4phtjt3hvdyfb45mr5maz4mlq" style="color: black;">The Urban Book Series</a> </i> &nbsp;
Then we briefly introduce the AI techniques that are widely used in urban computing, including supervised learning, semi-supervised learning, unsupervised learning, matrix factorization, graphic models  ...  With the recent advances of deep-learning techniques, models such as CNN and RNN have shown significant performance gains in many applications.  ...  Both deep-learning models and traditional machine-learning models are used to address various issues in urban transportation such as traffic flow prediction (Zhang et al. 2019a, b; Du et al. 2019 ) and  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-981-15-8983-6_43">doi:10.1007/978-981-15-8983-6_43</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/uq7j3hvsvzfl5lq33omx64un3i">fatcat:uq7j3hvsvzfl5lq33omx64un3i</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210718083639/https://link.springer.com/content/pdf/10.1007%2F978-981-15-8983-6_43.pdf?error=cookies_not_supported&amp;code=976ab774-5d92-4ffd-8e12-baf8cb91600a" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/67/81/6781eaddd8eefaee283c7e3cc3e4bcafa8dc5cff.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-981-15-8983-6_43"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> springer.com </button> </a>

Prioritization of Mobile IoT Data Transmission Based on Data Importance Extracted from Machine Learning Model

Yuichi Inagaki, Ryoichi Shinkuma, Takehiro Sato, Eiji Oki
<span title="">2019</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
The mobile IoT devices for real-time spatial information prediction generate an extremely high volume of data, making it impossible to collect all of it through mobile networks.  ...  Predicting real-time spatial information from data collected by the mobile Internet of Things (IoT) devices is one solution to the social problems related to road traffic.  ...  The data collected by mobile IoT devices are uploaded to edge servers, which process the uploaded data and apply machine learning techniques to predict real-time spatial information such as road-traffic  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2019.2928216">doi:10.1109/access.2019.2928216</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/5agst4isonb53gon5dry7ugzja">fatcat:5agst4isonb53gon5dry7ugzja</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210429151100/https://ieeexplore.ieee.org/ielx7/6287639/8600701/08759870.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/b2/e7/b2e781d1695a1538a0045fc562da391f9a9da8f7.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2019.2928216"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> ieee.com </button> </a>

An Experimental Investigation of Mobile Network Traffic Prediction Accuracy

Ali Yadavar Nikravesh, Samuel A. Ajila, Chung-Horng Lung, Wayne Ding
<span title="">2016</span> <i title="Services Society"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/pqkp46wnrffkbnf4wtilugp4bm" style="color: black;">Services Transactions on Big Data</a> </i> &nbsp;
In addition, this paper investigates the accuracy of machine learning techniques -Multi-Layer Perceptron (MLP), Multi-Layer Perceptron with Weight Decay (MLPWD), and Support Vector Machines (SVM) -to predict  ...  The analysis results are further used to enhance the accuracy of predicting the mobile traffic.  ...  A comparison between three machine learning algorithms (i.e., Support Vector Machine, Multi-Layer Perceptron, and Multi-Layer Perceptron with Weight Decay) to predict the future traffic of a mobile network  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.29268/stbd.2016.3.1.1">doi:10.29268/stbd.2016.3.1.1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/zwemll7ionbavjhd6bcvxt6r3i">fatcat:zwemll7ionbavjhd6bcvxt6r3i</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190728110206/http://hipore.com:80/stbd/2016/STBD-Vol3-No1-2016-pp1-16-Nikravesh.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/9a/61/9a61230b53bfd4df750ee4137311f55003f61c3a.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.29268/stbd.2016.3.1.1"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

A Research of Traffic Prediction using Deep Learning Techniques

<span title="2019-08-31">2019</span> <i title="Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/cj3bm7tgcffurfop7xzswxuks4" style="color: black;">VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE</a> </i> &nbsp;
The traffic flow prediction is an appealing study field. Many techniques of data mining are employed to forecast traffic.  ...  Deep learning techniques can be used with technological progress to prevent information from real time. Deep algorithms are discussed to forecast real-world traffic data.  ...  CONCLUSION We discuss the rich mobility data and deep learning about urban traffic predictions. Deep learning promotes traffic predictions through powerful fair representation learning.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.35940/ijitee.i1151.0789s219">doi:10.35940/ijitee.i1151.0789s219</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/fegznlfgdnczplovnsk6zupigu">fatcat:fegznlfgdnczplovnsk6zupigu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220304085522/https://www.ijitee.org/wp-content/uploads/papers/v8i9S2/I11510789S219.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/9c/29/9c294c05013bc49588a1e94fc5060fa818ea3661.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.35940/ijitee.i1151.0789s219"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Long-Term Mobile Traffic Forecasting Using Deep Spatio-Temporal Neural Networks [article]

Chaoyun Zhang, Paul Patras
<span title="2017-12-21">2017</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We then introduce a Double STN technique (D-STN), which uniquely combines the STN predictions with historical statistics, thereby making faithful long-term mobile traffic projections.  ...  of mobile traffic due to user mobility.  ...  Finally, we implement the proposed (D-)STN prediction techniques on a GPU cluster and conduct experiments on publicly available real-world mobile traffic data sets collected over 60 days and released through  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1712.08083v1">arXiv:1712.08083v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/mueo5gutxzgclczzcmodurspj4">fatcat:mueo5gutxzgclczzcmodurspj4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200930031045/https://arxiv.org/pdf/1712.08083v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/94/2e/942e9318c44afda4399fc1cd0f0f2a00f5c65a87.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1712.08083v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Role of Machine Learning in WSN and VANETs

Maryam Gillani, Hafiz Adnan Niaz, Muhammad Tayyab
<span title="2021-06-14">2021</span> <i title="International Journal of Electrical and Computer Engineering Research"> International Journal of Electrical and Computer Engineering Research </i> &nbsp;
For such dynamicity, Machine learning (ML) approaches are considered favourable.  ...  ML can be described as the process or method of self-learning without human intervention that can assist through various tools to deal with heterogeneous data to attain maximum benefits from the network  ...  Machine learning is effective in enhancing the flow in predicting traffic performance and achieve a real-time response.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.53375/ijecer.2021.24">doi:10.53375/ijecer.2021.24</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/72mtbdb3uvaepj6efg7lfz6x6q">fatcat:72mtbdb3uvaepj6efg7lfz6x6q</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210716150009/https://ijecer.org/ijecer/article/download/24/6" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/d9/cb/d9cbcee068c05217a67ee4210746ea0d5078725b.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.53375/ijecer.2021.24"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>

Data Mining and Machine Learning to Promote Smart Cities: A Systematic Review from 2000 to 2018

Jovani Souza, Antonio Francisco, Cassiano Piekarski, Guilherme Prado
<span title="2019-02-19">2019</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/oglosmy3gbhuzobyjit4qalakq" style="color: black;">Sustainability</a> </i> &nbsp;
Predictive analytics was the most common technique and the studies focused primarily on the areas of smart mobility and smart environment.  ...  As such, the aim of this research is to present a systematic review regarding data mining (DM) and machine learning (ML) approaches adopted in the promotion of smart cities.  ...  Group 2: "Data mining," "machine learning," "predictive analysis," "descriptive analytics," and "deep learning."  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/su11041077">doi:10.3390/su11041077</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/4kelzsmpvnd5reu3uioxtgavty">fatcat:4kelzsmpvnd5reu3uioxtgavty</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190502222848/https://res.mdpi.com/sustainability/sustainability-11-01077/article_deploy/sustainability-11-01077.pdf?filename=&amp;attachment=1" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/04/b5/04b55233b72a6ff2f69f2eb1d8ad32590508e733.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/su11041077"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a>

Long-Term Mobile Traffic Forecasting Using Deep Spatio-Temporal Neural Networks

Chaoyun Zhang, Paul Patras
<span title="">2018</span> <i title="ACM Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/c4l4x7lntbdcrigp6kd7u2itqq" style="color: black;">Proceedings of the Eighteenth ACM International Symposium on Mobile Ad Hoc Networking and Computing - Mobihoc &#39;18</a> </i> &nbsp;
We then introduce a Double STN technique (D-STN), which uniquely combines the STN predictions with historical statistics, thereby making faithful long-term mobile traffic projections.  ...  of mobile traffic due to user mobility.  ...  Finally, we implement the proposed (D-)STN prediction techniques on a GPU cluster and conduct experiments on publicly available real-world mobile traffic data sets collected over 60 days and released through  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/3209582.3209606">doi:10.1145/3209582.3209606</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/mobihoc/ZhangP18.html">dblp:conf/mobihoc/ZhangP18</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/e77ogifuenffjiwsampkwwcnzu">fatcat:e77ogifuenffjiwsampkwwcnzu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190427170756/https://www.research.ed.ac.uk/portal/files/56312541/traffic_forecasting.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/ee/1d/ee1d072d83ab84496b18e5b286a61035ded9bf53.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/3209582.3209606"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> acm.org </button> </a>

Gap, techniques and evaluation: traffic flow prediction using machine learning and deep learning

Noor Afiza Mat Razali, Nuraini Shamsaimon, Khairul Khalil Ishak, Suzaimah Ramli, Mohd Fahmi Mohamad Amran, Sazali Sukardi
<span title="">2021</span> <i title="Springer Science and Business Media LLC"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/pkhnkszyprhb3orbf6g7tqmgiu" style="color: black;">Journal of Big Data</a> </i> &nbsp;
Based on our findings, the common and frequent machine learning techniques that have been applied for traffic flow prediction are Convolutional Neural Network and Long-Short Term Memory.  ...  (AI) methods such as machine learning (ML).  ...  Techniques in SLR for traffic flow prediction using machine learning The following section discusses the techniques for traffic flow prediction using machine learning based on SLR.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1186/s40537-021-00542-7">doi:10.1186/s40537-021-00542-7</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/qjkwrgq5yjdsdifjxsv3brvsbu">fatcat:qjkwrgq5yjdsdifjxsv3brvsbu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20211206070541/https://journalofbigdata.springeropen.com/track/pdf/10.1186/s40537-021-00542-7.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/dd/d8/ddd8d2b13f8cd0be1490e718c46364795e861db0.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1186/s40537-021-00542-7"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> springer.com </button> </a>

Machine Learning for 5G Mobile Networks: a Pragmatic Essay on where, how and why [chapter]

Paolo Dini, Michele Rossi
<span title="2019-12-01">2019</span> <i title="Zenodo"> Zenodo </i> &nbsp;
of context information (traffic, mobility, etc.).  ...  In this chapter, we discuss selected applications of Machine Learning (ML) algorithms to 5G systems.  ...  research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 675891 (SCAVENGE) and has been supported, in part, by MIUR (Italian Ministry of Education, University and Research) through  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5281/zenodo.3675362">doi:10.5281/zenodo.3675362</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/kkzk6ho4urcv3igzs3233yzzei">fatcat:kkzk6ho4urcv3igzs3233yzzei</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200220044359/https://zenodo.org/record/3675362/files/Machine%20Learning%20for%205G%20Mobile%20Networks.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/b4/0d/b40d1528902e3e8b5823610d37a9cf8376a0c8d2.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5281/zenodo.3675362"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> zenodo.org </button> </a>

SURVEY ON ARTIFICIAL INTELLIGENCE TECHNIQUES IN 5G NETWORKS

A. Abdellah, A. Koucheryavy
<span title="">2020</span> <i title="Bonch-Bruevich State University of Telecommunications"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/znc4l4awubbpjeqr3roro5ait4" style="color: black;">Telecom IT</a> </i> &nbsp;
The scientific tasks for the fifth generation communication networks, in which the use of artificial intelligence seems appropriate, including machine and deep learning, are identified.  ...  Artificial intelligence techniques include interdisciplinary techniques including machine learning (supervised learning, unsupervised learning, and reinforced learning), deep learning, improvement theory  ...  Also, ML algorithm is successfully used in traffic estimation and prediction based on our live traffic estimated data.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.31854/2307-1303-2020-8-1-1-10">doi:10.31854/2307-1303-2020-8-1-1-10</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ndxxpu2i35holeidd7v24rlhgy">fatcat:ndxxpu2i35holeidd7v24rlhgy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220224114633/http://www.sut.ru/doci/nauka/1AEA/ITT/2020_1/1-10.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/2c/ea/2cea4be592906ba0571f04454684271e914ae06b.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.31854/2307-1303-2020-8-1-1-10"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Modeling and Prediction of Freight Delivery for Blocked and Unblocked Street Using Machine Learning Techniques

Prachi Pandya, Rajesh Gujar, Vinay Vakharia
<span title="">2020</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/gwls5vrgbbbk5kjhtvpcuku2eu" style="color: black;">Transportation Research Procedia</a> </i> &nbsp;
To predict the vehicle capacity and delay estimation, machine learning models, Support vector machine and Artificial neural network was utilized.  ...  To predict the vehicle capacity and delay estimation, machine learning models, Support vector machine and Artificial neural network was utilized.  ...  Machine learning techniques such as SVM and ANN were used to predict the capacity and delay for blocked and unblocked conditions.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.trpro.2020.08.059">doi:10.1016/j.trpro.2020.08.059</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/axe2lecejffiviniqihtvjan5a">fatcat:axe2lecejffiviniqihtvjan5a</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210527114245/https://pdf.sciencedirectassets.com/308315/1-s2.0-S2352146520X0007X/1-s2.0-S2352146520304750/main.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEJv%2F%2F%2F%2F%2F%2F%2F%2F%2F%2FwEaCXVzLWVhc3QtMSJGMEQCIHmPm5E9Szt4zDtk%2B60qruX3cKgo%2FNaLaf9ARtBzUoE0AiBvB63xZ0AGjhzA6%2Ff90ksMTH2k%2FM2FD2Ao0N9DVk7xfSr6AwhEEAMaDDA1OTAwMzU0Njg2NSIMxCU8yrpc5vJYPz0VKtcDcmMl3vBh0lrrPN2IJCNSjZ4h8XyNst%2F2pf9Zn%2BjF1ea%2BaQhzskM13KmR6W%2FCMngnY23AJnP3sxTf4ASbKaCQjE2bQxlfuN8vCWRbVB4kXBFTw9CQ93tnqQfm8zWWjYEpukpXxsJhZ3dG%2FASvd%2BnP3OVJ4zabKyyvecBuwFioUVhfsuxWPe5x%2FBx3i%2F8kuM7Cc5mdTYh76X7%2Bxe72NbGeNguwChDwSwadvL%2BlcDT9DLmBdts3QaViGWFVYUZL4g62TLtlwYJ%2BCtmkZ1ulv%2Bi4M9Lckpng4ZTBh6%2BA%2BhjgrGrmZE4nbEAL7Zpzj8QzKbTJH9wAKfk0sSP6aPzSnNW2MBjQ1WlntIa91Cxkccu%2FCB23tfSuT9OQEcJJFmpnPj1dz4gXbmq3s5bJ7VrS%2FUt%2Be88AWKaiAbgEEUGnu%2BauFZA%2BND7qGq8E6x7sg2qPMSAXkHeyKjwgkoi3vV7Cp55NbcYZKKYnzIWlCtiD9TrzkuKcBWuZ8E1qDhxWWng2qFcev0IkFrEvAeIrkpP3%2BcPCbl7KqXBXMq8myclOFYy6Y%2BDs3Aa%2B%2BviOC9DORBGoK6fKPx37jvDmuxtrHu1JZIpqphQv%2FoLagxzKnNAhId0LcBi37ofiDJiwMPzmvYUGOqYBMwEwbjQZ41rr6tQyg5ffgdKFOmfaXPgG%2BXWatVuSWF%2F1rK4o6S9bloX0rs0BR1aipieaLfvFB6qfhuHbwen543fmLhs4tg5XRbr0hiFzWkFz6yzM%2B4dFe9jpXFo4mbufJmQhXTNYL8rwUe3MG%2B8F%2FIAyPFJK8RtN%2BDrnCpJldosRUvEIW0VAL0%2Bv90FAOrK4qVvoD7f0mGqG4zfWIi3U2YKptjSlAQ%3D%3D&amp;X-Amz-Algorithm=AWS4-HMAC-SHA256&amp;X-Amz-Date=20210527T114240Z&amp;X-Amz-SignedHeaders=host&amp;X-Amz-Expires=300&amp;X-Amz-Credential=ASIAQ3PHCVTYQR534C6X%2F20210527%2Fus-east-1%2Fs3%2Faws4_request&amp;X-Amz-Signature=d9ad7ea545d8c9da372aad47e07f9d99f23ef0e0ac862b96b6e29ce0de0aab9f&amp;hash=85da3033b66911a49d0c3ca946591443d3b509802f73f30985bcb41ed55cc12e&amp;host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61&amp;pii=S2352146520304750&amp;tid=spdf-978c82eb-2ccd-4469-bc78-02541c2e5331&amp;sid=a1b4013883f981409f5abe04ce30b47903d1gxrqa&amp;type=client" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/7c/40/7c40f664e506abe7290818bd1f6dc1b95cd9bf5a.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.trpro.2020.08.059"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> elsevier.com </button> </a>

Users' Evaluation of Traffic Congestion in LTE Networks using Deep Learning Techniques

Bamidele Moses Kuboye, Tosin Opeyemi Aratunde, Gbadamosi Ayomide A.
<span title="2021-12-22">2021</span> <i title="Foundation of Computer Science"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/b637noqf3vhmhjevdfk3h5pdsu" style="color: black;">International Journal of Computer Applications</a> </i> &nbsp;
The work evaluates existence of traffic congestion in LTE networks using Convolutional Neural Networks (CNN) and Long Short-Term Memories (LSTMs) as Deep learning techniques.  ...  Deep learning is a division of machine learning built on a set of algorithms that attempt to model high-level abstractions in data by using prototypical architectures with complex structures.  ...  As a result, the issue was leverage using the deep LSTM learning technique to make localized prediction of the traffic load at the eNB.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5120/ijca2021921842">doi:10.5120/ijca2021921842</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/dysduq5qfzajljhmtbnjngwplu">fatcat:dysduq5qfzajljhmtbnjngwplu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220130083313/https://www.ijcaonline.org/archives/volume183/number44/kuboye-2021-ijca-921842.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/21/3b/213bc3e011f3d7c431734599f05c818a03f581cd.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5120/ijca2021921842"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Data Categorization and Noise Analysis in Mobile Communication Using Machine Learning Algorithms

Raghavendra Phani Kumar, Malleswara Rao, Dsvgk Kaladhar
<span title="">2012</span> <i title="Scientific Research Publishing, Inc,"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/afgx2slacfh5bnqpirnhoxrrg4" style="color: black;">Wireless Sensor Network</a> </i> &nbsp;
Machine learning and pattern recognition contains well-defined algorithms with the help of complex data, provides the accuracy of the traffic levels, heavy traffic hours within a cluster.  ...  In this paper the base stations and also the noise levels in the busy hour can be predicted. J48 pruned tree contains 23 nodes with busy traffic hour provided in east Godavari.  ...  We cover the application of machine learning from the formulation of the problem to the delivery of a system for field testing which includes soft handoff traffic and busy traffic hour, soft handoff rate  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.4236/wsn.2012.44015">doi:10.4236/wsn.2012.44015</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/aukwayfc2bcg5l6o2iwew2a654">fatcat:aukwayfc2bcg5l6o2iwew2a654</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20141017153716/http://www.scirp.org/journal/PaperDownload.aspx?paperID=18621" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/c8/1a/c81ac9067ad54b80c84f5dbb62d0395d23bec7f5.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.4236/wsn.2012.44015"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>

Prediction of Traffic Generated by IoT Devices Using Statistical Learning Time Series Algorithms

Shilpa P. Khedkar, R. Aroul Canessane, Moslem Lari Najafi, VIMAL SHANMUGANATHAN
<span title="2021-08-02">2021</span> <i title="Hindawi Limited"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/6o4hgxplrbehxg4t53ub7zmfha" style="color: black;">Wireless Communications and Mobile Computing</a> </i> &nbsp;
traffic prediction techniques.  ...  Based on enormous growth in devices and their connectivity, IoT contributes to the bulk of Internet traffic. Prediction of network traffic is very important function of any network.  ...  learning techniques to time series forecasting through supervised learning.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2021/5366222">doi:10.1155/2021/5366222</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/s3eu62dntveqvptdob6udz7spe">fatcat:s3eu62dntveqvptdob6udz7spe</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210803233809/https://downloads.hindawi.com/journals/wcmc/2021/5366222.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/a8/2c/a82c5c133b78eeedc4713f04ff75b2af69d07727.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2021/5366222"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> hindawi.com </button> </a>
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