Selecting the Shortest Itinerary in a Cloud-Based Distributed Mobility Network

Fiošina, Jeļena; Fiošins, Maksims

doi:10.1007/978-3-319-00551-5_13

Cited by 5 publications

(2 citation statements)

References 5 publications

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“…The models were authorized to genuine information and joined into a traffic territory function; whenever power of the methods was assessed. A disseminated advancement method for the traffic moving way was the idea of in [47].…”

Section: Traffic Routing Problemmentioning

confidence: 99%

Analysis of the Big Data Methods, Challenges and Applications in Intelligent Transportation Systems

2020

IJATCSE

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Big Data is data that grows beyond the capability of traditional data mining methods. A big voluminous, heterogeneous, and speediness of data generated from numerous sensors attached near roadside infrastructure and devices associated with the vehicles. We need to design Big data models for highly scalable, techniques for complex data types, robust to noise, insensitive to the input order, ability to handling high-dimensional data, interpretability, and usability. There are many challenges in enormous amount of data to stockpile, processing, and operate. Apache Spark, Hadoop, and Flink are the platforms of Big Data Analytics. Massive information processing in transportation systems facing difficulties in terms of data privacy, security, data storage, and data quality. Various Big Transportation data analytics are performed, such as video and audio analytics, image analytics, and historical analytics. The globe is monitored and managed with Web of Devices (WoD) efficiently and effectively.

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Section: Traffic Routing Problemmentioning

confidence: 99%

Analysis of the Big Data Methods, Challenges and Applications in Intelligent Transportation Systems

2020

IJATCSE

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“…Finally, the emission probabilities model the variance of each state's associated values (mostly Gaussian Probability Density Functions or PDF) obtained from continuous variable observations. Estimating the model parameters is done by utilizing the Baum-Welch Expectation-Maximization (EM) algorithm, which is based on a forward-backward algorithm used in classifying Hidden Markov Chains [11,12]. The probabilities are calculated at any point of a sequence by inspecting previous observations, to find out how well the model describes the data, and following observations, to conclude how well the model predicts the rest of the sequence.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Self-Rehabilitation Movements by Hidden Markov Model

Rybarczyk

Deters

2018

2018 Innovations in Intelligent Systems and Applications (INISTA)

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This study aims to propose a statistical model to automatically assess the correctness of rehabilitation movements performed by patients. Ten Hidden Markov Models are developed and trained, in order to discriminate in real time the main faults in the execution of therapeutic exercises for reeducation after hip replacement surgery. An experiment on real patients shows that the algorithm is as accurate as the physiotherapists to discriminate and identify the error in the movement. The results are discussed in terms of the required setup for a successful implementation of this method in a tele-rehabilitation platform.

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Density-Based Clustering in Cloud-Oriented Collaborative Multi-Agent Systems

Fiošina

Fiošins

2013

Lecture Notes in Computer Science

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Selecting the Shortest Itinerary in a Cloud-Based Distributed Mobility Network

Cited by 5 publications

References 5 publications

Analysis of the Big Data Methods, Challenges and Applications in Intelligent Transportation Systems

Analysis of the Big Data Methods, Challenges and Applications in Intelligent Transportation Systems

Evaluation of Self-Rehabilitation Movements by Hidden Markov Model

Density-Based Clustering in Cloud-Oriented Collaborative Multi-Agent Systems

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