scholarly journals Traffic Congestion Analysis Based on a Web-GIS and Data Mining of Traffic Events from Twitter

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 2964
Author(s):  
Juan Salazar-Carrillo ◽  
Miguel Torres-Ruiz ◽  
Clodoveu A. Davis ◽  
Rolando Quintero ◽  
Marco Moreno-Ibarra ◽  
...  
Keyword(s):  
Traffic Congestion ◽  
Smart Cities ◽  
Communication Technologies ◽  
Spatiotemporal Analysis ◽  
Good Alternative ◽  
Web Gis ◽  
Training Dataset ◽  
Support Vector ◽  
Machine Method ◽  
Spatiotemporal Information

Smart cities are characterized by the use of massive information and digital communication technologies as well as sensor networks where the Internet and smart data are the core. This paper proposes a methodology to geocode traffic-related events that are collected from Twitter and how to use geocoded information to gather a training dataset, apply a Support Vector Machine method, and build a prediction model. This model produces spatiotemporal information regarding traffic congestions with a spatiotemporal analysis. Furthermore, a spatial distribution represented by heat maps is proposed to describe the traffic behavior of specific and sensed areas of Mexico City in a Web-GIS application. This work demonstrates that social media are a good alternative that can be leveraged to gather collaboratively Volunteered Geographic Information for sensing the dynamic of a city in which citizens act as sensors.

scholarly journals Adaptive Smart Traffic Accidents Management System

2021 ◽  
Vol 15 (14) ◽  
pp. 72
Author(s):  
Faisal Yousef Alzyoud ◽  
Abdallah Altahan Alnuaimi ◽  
Faiz Al Shrouf
Keyword(s):  
Traffic Congestion ◽  
Traffic Accidents ◽  
Smart Cities ◽  
New Technology ◽  
Software Tool ◽  
Communication Technologies ◽  
Smart Devices ◽  
Battery Life ◽  
Customer Services ◽  
Smart Traffic

<p>The proliferation of smart devices, IoT applications and wireless communication technologies contribute in countries development, society’s security, cost reduction, and customer services satisfactions; since they are used in different aspects of our life. Traffic congestion and accidents are increased recently and reached critical limits, so these contribute in initiating sever problems for researchers, governments and industry over the last few decades. Traffic accidents have many defects relating to increase number of death, infrastructure distribution, and health injuries; therefore, there is a crucial need to develop and modify an approach that utilizes the new technology to limit and prevent the traffic accidents.  Wireless sensors networks are developed to support smart solutions in smart cities like smart traffic, smart grid and others. In this research we developed a comprehensive approach to achieve the following three important goals in smart accident elimination. The first goal is to minimize the number of exchange information packets between sensors to save the battery life through developing and adapting clustering schema to minimize the number of exchanges information packets. The second goal is to calculate and determine the optimum route from accident location to the nearest rescue location by developing a dynamic routing schema   that is calculated by the control station depending on a cost heuristics function. The third goal is to predicate the accident causes and minimize the probability of accidents occur using a warning message schema and drawing some obstacles on some routing paths. Cupcarbon simulator and MATLAB software tool are developed to simulate different scenarios in order to proof the research goals.</p>

Technologies Disability and Inclusion

2018 ◽  
Author(s):  
Tomas Brusell
Keyword(s):  
Social Media ◽  
Augmented Reality ◽  
Information And Communication Technologies ◽  
Future Development ◽  
Smart Cities ◽  
Communication Technologies ◽  
Modern Technology ◽  
The Social ◽  
The Disabled ◽  
Information And Communication

When modern technology permeates every corner of life, there are ignited more and more hopes among the disabled to be compensated for the loss of mobility and participation in normal life, and with Information and Communication Technologies (ICT), Exoskeleton Technologies and truly hands free technologies (HMI), it's possible for the disabled to be included in the social and pedagogic spheres, especially via computers and smartphones with social media apps and digital instruments for Augmented Reality (AR) .In this paper a nouvel HMI technology is presented with relevance for the inclusion of disabled in every day life with specific focus on the future development of &quot;smart cities&quot; and &quot;smart homes&quot;.

ABC-Gly: identifying protein lysine glycation sites with artificial bee colony algorithm

2019 ◽  
Vol 17 ◽  
Author(s):  
Yanqiu Yao ◽  
Xiaosa Zhao ◽  
Qiao Ning ◽  
Junping Zhou
Keyword(s):  
Support Vector Machine ◽  
Amino Acid ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Training Dataset ◽  
Support Vector ◽  
Supplementary File ◽  
Feature Subset ◽  
Lipid Molecule ◽  
Bee Colony

Background: Glycation is a nonenzymatic post-translational modification process by attaching a sugar molecule to a protein or lipid molecule. It may impair the function and change the characteristic of the proteins which may lead to some metabolic diseases. In order to understand the underlying molecular mechanisms of glycation, computational prediction methods have been developed because of their convenience and high speed. However, a more effective computational tool is still a challenging task in computational biology. Methods: In this study, we showed an accurate identification tool named ABC-Gly for predicting lysine glycation sites. At first, we utilized three informative features, including position-specific amino acid propensity, secondary structure and the composition of k-spaced amino acid pairs to encode the peptides. Moreover, to sufficiently exploit discriminative features thus can improve the prediction and generalization ability of the model, we developed a two-step feature selection, which combined the Fisher score and an improved binary artificial bee colony algorithm based on support vector machine. Finally, based on the optimal feature subset, we constructed the effective model by using Support Vector Machine on the training dataset. Results: The performance of the proposed predictor ABC-Gly was measured with the sensitivity of 76.43%, the specificity of 91.10%, the balanced accuracy of 83.76%, the area under the receiver-operating characteristic curve (AUC) of 0.9313, a Matthew’s Correlation Coefficient (MCC) of 0.6861 by 10-fold cross-validation on training dataset, and a balanced accuracy of 59.05% on independent dataset. Compared to the state-of-the-art predictors on the training dataset, the proposed predictor achieved significant improvement in the AUC of 0.156 and MCC of 0.336. Conclusion: The detailed analysis results indicated that our predictor may serve as a powerful complementary tool to other existing methods for predicting protein lysine glycation. The source code and datasets of the ABC-Gly were provided in the Supplementary File 1.

scholarly journals A new hybrid record linkage process to make epidemiological databases interoperable: application to the GEMO and GENEPSO studies involving BRCA1 and BRCA2 mutation carriers

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yue Jiao ◽  
Fabienne Lesueur ◽  
Chloé-Agathe Azencott ◽  
Maïté Laurent ◽  
Noura Mebirouk ◽  
...  
Keyword(s):  
Record Linkage ◽  
Gold Standard ◽  
Brca2 Mutation ◽  
Epidemiological Studies ◽  
Supervised Machine Learning ◽  
Training Dataset ◽  
Support Vector ◽  
Genetic Modifiers ◽  
Brca1 And Brca2 ◽  
Mutation Carriers

Abstract Background Linking independent sources of data describing the same individuals enable innovative epidemiological and health studies but require a robust record linkage approach. We describe a hybrid record linkage process to link databases from two independent ongoing French national studies, GEMO (Genetic Modifiers of BRCA1 and BRCA2), which focuses on the identification of genetic factors modifying cancer risk of BRCA1 and BRCA2 mutation carriers, and GENEPSO (prospective cohort of BRCAx mutation carriers), which focuses on environmental and lifestyle risk factors. Methods To identify as many as possible of the individuals participating in the two studies but not registered by a shared identifier, we combined probabilistic record linkage (PRL) and supervised machine learning (ML). This approach (named “PRL + ML”) combined together the candidate matches identified by both approaches. We built the ML model using the gold standard on a first version of the two databases as a training dataset. This gold standard was obtained from PRL-derived matches verified by an exhaustive manual review. Results The Random Forest (RF) algorithm showed a highest recall (0.985) among six widely used ML algorithms: RF, Bagged trees, AdaBoost, Support Vector Machine, Neural Network. Therefore, RF was selected to build the ML model since our goal was to identify the maximum number of true matches. Our combined linkage PRL + ML showed a higher recall (range 0.988–0.992) than either PRL (range 0.916–0.991) or ML (0.981) alone. It identified 1995 individuals participating in both GEMO (6375 participants) and GENEPSO (4925 participants). Conclusions Our hybrid linkage process represents an efficient tool for linking GEMO and GENEPSO. It may be generalizable to other epidemiological studies involving other databases and registries.

scholarly journals Enabling Technologies for Urban Smart Mobility: Recent Trends, Opportunities and Challenges

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2143
Author(s):  
Sara Paiva ◽  
Mohd Abdul Ahad ◽  
Gautami Tripathi ◽  
Noushaba Feroz ◽  
Gabriella Casalino
Keyword(s):  
Traffic Congestion ◽  
Autonomous Vehicles ◽  
City Planning ◽  
Smart Cities ◽  
The Sustainable Development ◽  
Current Trends ◽  
Enabling Technologies ◽  
Entry Points ◽  
Development Goals ◽  
Smart Mobility

The increasing population across the globe makes it essential to link smart and sustainable city planning with the logistics of transporting people and goods, which will significantly contribute to how societies will face mobility in the coming years. The concept of smart mobility emerged with the popularity of smart cities and is aligned with the sustainable development goals defined by the United Nations. A reduction in traffic congestion and new route optimizations with reduced ecological footprint are some of the essential factors of smart mobility; however, other aspects must also be taken into account, such as the promotion of active mobility and inclusive mobility, encouraging the use of other types of environmentally friendly fuels and engagement with citizens. The Internet of Things (IoT), Artificial Intelligence (AI), Blockchain and Big Data technology will serve as the main entry points and fundamental pillars to promote the rise of new innovative solutions that will change the current paradigm for cities and their citizens. Mobility-as-a-service, traffic flow optimization, the optimization of logistics and autonomous vehicles are some of the services and applications that will encompass several changes in the coming years with the transition of existing cities into smart cities. This paper provides an extensive review of the current trends and solutions presented in the scope of smart mobility and enabling technologies that support it. An overview of how smart mobility fits into smart cities is provided by characterizing its main attributes and the key benefits of using smart mobility in a smart city ecosystem. Further, this paper highlights other various opportunities and challenges related to smart mobility. Lastly, the major services and applications that are expected to arise in the coming years within smart mobility are explored with the prospective future trends and scope.

scholarly journals Blockchain-Based Solutions for UAV-Assisted Connected Vehicle Networks in Smart Cities: A Review, Open Issues, and Future Perspectives

Telecom ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 108-140
Author(s):  
Paulo Álvares ◽  
Lion Silva ◽  
Naercio Magaia
Keyword(s):  
Traffic Congestion ◽  
Resource Constraints ◽  
Smart Cities ◽  
The Internet ◽  
Connected Vehicle ◽  
Sensitive Data ◽  
Internet Of Vehicles ◽  
Smart Vehicles ◽  
Security Properties ◽  
Vehicle Networks

It had been predicted that by 2020, nearly 26 billion devices would be connected to the Internet, with a big percentage being vehicles. The Internet of Vehicles (IoVa) is a concept that refers to the connection and cooperation of smart vehicles and devices in a network through the generation, transmission, and processing of data that aims at improving traffic congestion, travel time, and comfort, all the while reducing pollution and accidents. However, this transmission of sensitive data (e.g., location) needs to occur with defined security properties to safeguard vehicles and their drivers since attackers could use this data. Blockchain is a fairly recent technology that guarantees trust between nodes through cryptography mechanisms and consensus protocols in distributed, untrustful environments, like IoV networks. Much research has been done in implementing the former in the latter to impressive results, as Blockchain can cover and offer solutions to many IoV problems. However, these implementations have to deal with the challenge of IoV node’s resource constraints since they do not suffice for the computational and energy requirements of traditional Blockchain systems, which is one of the biggest limitations of Blockchain implementations in IoV. Finally, these two technologies can be used to build the foundations for smart cities, enabling new application models and better results for end-users.

scholarly journals Fugl-Meyer hand motor imagination recognition for brain–computer interfaces using only fNIRS

2021 ◽  
Author(s):  
Chenguang Li ◽  
Hongjun Yang ◽  
Long Cheng
Keyword(s):  
Classification Accuracy ◽  
Near Infrared ◽  
Support Vector ◽  
Classification Methods ◽  
Experimental Paradigm ◽  
Functional Near Infrared Spectroscopy ◽  
Machine Method ◽  
Physiological Signal ◽  
Computer Interfaces ◽  
Mode Decomposition

AbstractAs a relatively new physiological signal of brain, functional near-infrared spectroscopy (fNIRS) is being used more and more in brain–computer interface field, especially in the task of motor imagery. However, the classification accuracy based on this signal is relatively low. To improve the accuracy of classification, this paper proposes a new experimental paradigm and only uses fNIRS signals to complete the classification task of six subjects. Notably, the experiment is carried out in a non-laboratory environment, and movements of motion imagination are properly designed. And when the subjects are imagining the motions, they are also subvocalizing the movements to prevent distraction. Therefore, according to the motor area theory of the cerebral cortex, the positions of the fNIRS probes have been slightly adjusted compared with other methods. Next, the signals are classified by nine classification methods, and the different features and classification methods are compared. The results show that under this new experimental paradigm, the classification accuracy of 89.12% and 88.47% can be achieved using the support vector machine method and the random forest method, respectively, which shows that the paradigm is effective. Finally, by selecting five channels with the largest variance after empirical mode decomposition of the original signal, similar classification results can be achieved.

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