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IOTA-Based Mobile Crowd Sensing: Detection of Fake Sensing Using Logit-Boosted Machine Learning Algorithms

Mazhar Hameed, Fengbao Yang, Muhammad Imran Ghafoor, Fawwad Hassan Jaskani, Umar Islam, Muhammad Fayaz, Gulzar Mehmood, Narasimhan Venkateswaran
2022 Wireless Communications and Mobile Computing  
IOTA-based mobile crowd sensing technology is being developed in this study using machine learning to detect and prevent mobile users from engaging in fake sensing activities.  ...  In the Internet of Things (IoT) era, the mobile crowd sensing system (MCS) has become increasingly important.  ...  Marie et al. established another hybrid way to merge the deep-belief network (DBN) with SVM, using the ensemble approach.  ... 
doi:10.1155/2022/6274114 fatcat:esgwcd2an5arvjeiuc6si32eva

Sensing and Forecasting Crowd Distribution in Smart Cities: Potentials and Approaches

Alket Cecaj, Marco Lippi, Marco Mamei, Franco Zambonelli
2021 IoT  
The objective of this survey is to overview: (i) the many potential application areas of crowd sensing and prediction; (ii) the technologies that can be exploited to sense crowd along with their potentials  ...  The possibility of sensing and predicting the movements of crowds in modern cities is of fundamental importance for improving urban planning, urban mobility, urban safety, and tourism activities.  ...  By using temporal receptive fields, CNNs applied to signals learn filters that extract features from raw data.  ... 
doi:10.3390/iot2010003 fatcat:3iidezw7xrezthunye5ljb7yri

Ensemble-Based Hybrid Context-Aware Misbehavior Detection Model for Vehicular Ad Hoc Network

Ghaleb, Maarof, Zainal, Alrimy, Alsaeedi, Boulila
2019 Remote Sensing  
For accurate representation, the output of the statistical classifiers, vehicles' scores, context reference parameters, and the derived features were used as input to an ensemble learning-based algorithm  ...  These features were used to construct a dynamic context reference using the Hampel filter.  ...  from neighboring vehicles with the information predicted using the mobility model.  ... 
doi:10.3390/rs11232852 fatcat:5m33cbcewvbx5k2coh6unrapk4

Table of Contents

2021 2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)  
Using an Ensemble Feature Selection Method 1570764765 Addressing Spectrum Scarcity in Indonesia Dense Urban Market by Using 700 MHz for 4G LTE-Advanced Network Deployment 1570762234 Speech Master: Natural  ...  The Rise and Fall of Bitcoin: Predicting Market Direction Using Machine 1570764851 A Novel Method for Automated Identification and Prediction of Invasive 17.4 Species Using Deep Learning Learning Models  ... 
doi:10.1109/iemcon53756.2021.9623133 fatcat:vf7xvqlhnfdnziz5pfsgroq36e

Artificial Intelligence and Metaheuristic-Based Location-Based Advertising

Vinita Rohilla, Sudeshna Chakraborty, Mandeep Kaur, Punit Gupta
2022 Scientific Programming  
Initially, the potential location information is evaluated utilizing the geographic information system (GIS). Thereafter, significant features are computed using the location data.  ...  Location-based advertising (LBA) are utilized for abandoning the user location and to offer assistance by using the obtained information.  ...  In [12] , ensembling of J48, NB, and ANN was achieved for predicting the locations of mobile crowd. Hybrid features were utilized to achieve the performance further.  ... 
doi:10.1155/2022/7518823 fatcat:eh2dhdy3wfbkrgv7ao2xzrfftm

Ensemble learning for large-scale crowd flow prediction

Vladislav Karbovskii, Michael Lees, Alva Presbitero, Alexey Kurilkin, Daniil Voloshin, Ivan Derevitskii, Andrey Karsakov, Peter M.A. Sloot
2021 Engineering applications of artificial intelligence  
In other words, each feature in the object-feature matrix of the ensemble model corresponds to the predictions made using the individual models.  ...  at learning rate 0.001, and one hidden layer with 10 neurons, (b) for XGBoost, we used 100 estimators and a learning rate of 0.001. 4 The input for the machine learning model is an object-feature matrix  ...  To check the normality of distribution, we used the Shapiro-Wilk test as it gives favourable results with a score of at least 0.95 for each time series. Appendix B.  ... 
doi:10.1016/j.engappai.2021.104469 fatcat:xph4m3hqpjdxto3ziza2wq3cvm

Moving Vehicle Detection and Classification Using Gaussian Mixture Model and Ensemble Deep Learning Technique

Preetha Jagannathan, Sujatha Rajkumar, Jaroslav Frnda, Parameshachari Bidare Divakarachari, Prabu Subramani, Laurie Cuthbert
2021 Wireless Communications and Mobile Computing  
Finally, the extracted features are given as the input to an ensemble deep learning technique for vehicle classification.  ...  In the simulation phase, the proposed ensemble deep learning technique obtained 99.13% and 99.28% of classification accuracy on the MIOvision Traffic Camera Dataset and the Beijing Institute of Technology  ...  thanks to the support from the Operational Program Integrated Infrastructure for the Project: Identification and possibilities of implementation of new technological measures in transport to achieve safe mobility  ... 
doi:10.1155/2021/5590894 fatcat:i6ot2uodzfanlklhiqjhqjikgm

ICCUBEA 2019 Table of Contents

2019 2019 5th International Conference On Computing, Communication, Control And Automation (ICCUBEA)  
REAL TIME FLOOD PREDICTION 38 Types of Data with algorithms for Assessing Mental Health Conditions 39 Bio inspired Ensemble Feature Selection Model with Machine Learning and Data Mining Algorithms  ...  for Autistic People: A survey 22 Steering Angle Prediction in Autonomous Vehicles Using Deep Learning 23 Sentiment Analysis of Telugu data and comparing advanced ensemble techniques using different  ... 
doi:10.1109/iccubea47591.2019.9128707 fatcat:3s3o5v2x3nepxeftk4jd64hjnq

A New Hybrid Deep Learning Algorithm for Prediction of Wide Traffic Congestion in Smart Cities

G. Kothai, E. Poovammal, Gaurav Dhiman, Kadiyala Ramana, Ashutosh Sharma, Mohammed A. AlZain, Gurjot Singh Gaba, Mehedi Masud, VIMAL SHANMUGANATHAN
2021 Wireless Communications and Mobile Computing  
To overcome the congestion problem, this paper proposes a new hybrid boosted long short-term memory ensemble (BLSTME) and convolutional neural network (CNN) model that ensemble the powerful features of  ...  CNN with BLSTME to negotiate the dynamic behavior of the vehicle and to predict the congestion in traffic effectively on roads.  ...  In the AdaBoost approach, hybrid ensemble learning algorithms are established by integrating the LSTM networks with AdaBoost learning algorithms for strengthening the weak classifiers.  ... 
doi:10.1155/2021/5583874 fatcat:vr46nugo4rg3lmwh465ux336ly

Deep Learning for Spatio-Temporal Data Mining: A Survey [article]

Senzhang Wang, Jiannong Cao, Philip S. Yu
2019 arXiv   pre-print
predictive learning, representation learning, anomaly detection and classification.  ...  transportation, climate science, human mobility, location based social network, crime analysis, and neuroscience.  ...  The matrix is input into a CNN with multiple convolutional layers to learn the latent features for next visited semantic location prediction.  ... 
arXiv:1906.04928v2 fatcat:4zrdtgkvirfuniq3rb2gl7ohpy

Utilizing an Adaptive Neuro-Fuzzy Inference System (ANFIS) for Overcrowding Level Risk Assessment in Railway Stations

Hamad Alawad, Min An, Sakdirat Kaewunruen
2020 Applied Sciences  
The prediction result shows very high accuracy in predicting the risk level performance, and proves the AI model capabilities to learn, to make predictions, and to capture risk level values in real time  ...  This study is the world's first to establish the hybrid artificial intelligence (AI) model, which has the potency to manage risk uncertainties and learns through artificial neural networks (ANNs) by integrated  ...  The ANFIS is a hybrid method that has several features of interest. For instance, the ANN can identify patterns in advance using the adaptation to the environment with its learning ability.  ... 
doi:10.3390/app10155156 fatcat:zllviurbqzgeppjcgixogztc5u

Urban flows prediction from spatial-temporal data using machine learning: A survey [article]

Peng Xie, Tianrui Li, Jia Liu, Shengdong Du, Xin Yang, Junbo Zhang
2019 arXiv   pre-print
Urban spatial-temporal flows prediction is of great importance to traffic management, land use, public safety, etc.  ...  learning-based, reinforcement learning-based and transfer learning-based methods.  ...  [8] used a K-labelsets ensemble method based on mutual information and joint entropy to deal with inblanced data. Gong et al.  ... 
arXiv:1908.10218v1 fatcat:yzt6qe4oxnczzmklyirxkpkw7q

A Research of Traffic Prediction using Deep Learning Techniques

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.  ...  Now –a day'smany intelligent transport systems use modern technologies to predict traffic flow, to minimize accidents on road, to predict speed of a vehicle and etc.  ...  CONCLUSION We discuss the rich mobility data and deep learning about urban traffic predictions. Deep learning promotes traffic predictions through powerful fair representation learning.  ... 
doi:10.35940/ijitee.i1151.0789s219 fatcat:fegznlfgdnczplovnsk6zupigu

Guest Editorial Introduction to the Special Issue on Deep Learning Models for Safe and Secure Intelligent Transportation Systems

Alireza Jolfaei, Neeraj Kumar, Min Chen, Krishna Kant
2021 IEEE transactions on intelligent transportation systems (Print)  
He was a Research Professor with the Center for Secure Information Systems, George Mason University.  ...  He is currently a Professor with the Department of Computer and Information Science, Temple University, Philadelphia, PA, USA, where he directs the IUCRC Center on Intelligent Storage.  ...  In the article entitled "A deep learning-based mobile crowd sensing scheme by predicting vehicle mobility," Zhu et al. address the problem of vehicle recruitment by proposing a deep learning-based scheme  ... 
doi:10.1109/tits.2021.3090721 fatcat:c2o2vno6bjbnxdn6y4zm7ztmvq

Machine Learning Classifier Approach with Gaussian Process, Ensemble boosted Trees, SVM, and Linear Regression for 5G Signal Coverage Mapping

Akansha Gupta, Kamal Ghanshala, R. C. Joshi
2021 International Journal of Interactive Multimedia and Artificial Intelligence  
Four machine learning classifiers have been investigated to identify the classifier with the least RMSE: Gaussian Process, Ensemble Boosted Tree, SVM, and Linear Regression.  ...  loss, used for network planning to achieve maximum coverage.  ...  ) was observed with the Ensemble boosted tree.  ... 
doi:10.9781/ijimai.2021.03.004 fatcat:moo6ygq3wfh2pm35s3srkpyg24
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