Multivariate deep learning approach for electric vehicle speed forecasting

NaitMalek, Y.; Najib, Mehdi; Bakhouya, Mohamed; Essaaidi, Mohamed

doi:10.26599/bdma.2020.9020027

Cited by 91 publications

(51 citation statements)

References 19 publications

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“…However, player evaluation is often a multi-dimensional decision-making problem while multi-dimensional decision-making problems often involves multiple-type data fusion, weight assignment as well as the optimization issue [21][22][23][24][25][26][27][28] . Therefore, we will further study the multisource data integration problem with weighting.…”

Section: Discussionmentioning

confidence: 99%

“…In addition, Mahalanobis Distance requires additional time cost to compute the covariance matrix of different dimensions; therefore, its time complexity is not very low. While time cost is critical for real world applications especially for the big data scenario [29][30][31][32][33][34][35] . Therefore, we would continuously refine our algorithm to further reduce its time costs so as to meet the quick response requirements from users.…”

Section: Discussionmentioning

confidence: 99%

“…Calculate DM(pi) by ( 7) 19 End For 20 For i = 1 to n 21 Rank p i by D M (p i ) in descending order 22 Put pi in Ranked_list (p1, …, pn) 23 End For 24 Return Ranked_list (p1, …, pn) point 0 is an ideal reference point for evaluating all the players, we hope that the Mahalanobis Distance DM(pi) "the larger, the better". With the above analyses, we can evaluate and rank the training scores of all n players p1, …, pn based on the Mahalanobis Distance DM(pi) ("the larger, the better").…”

Section: Outputmentioning

confidence: 99%

See 2 more Smart Citations

Correlation-Aware Sport Training Evaluation for Players With Trust Based on Mahalanobis Distance

Fan

Tian²,

et al. 2022

IEEE Access

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With the widely-adopted idea of health and longevity, sports have been becoming one of the most popular entertainment ways of the public. For the majority of sports, players need to know about their concrete physical conditions in a real time manner so as to pursue a good sport score or ranking in a competition or a race. Generally, we can achieve the above goal through analyzing and evaluating the daily training scores of each player. However, there are often multiple physical trainings for players and various correlations are existent among them, which significantly decrease the fairness and trust of player training score evaluation and ranking since traditional multi-dimensional data integration solutions are often based on a strong hypothesis, i.e., the involved multiple dimensions are independent with each other. In view of this shortcoming, we introduce the Mahalanobis Distance into the multi-dimensional player training score evaluation and further propose a correlation-aware player training score evaluation method with trust (abbreviated as CPEMD) based on Mahalanobis Distance. As Mahalanobis Distance can eliminate the hidden linear correlations among the involved multiple dimensions, we can guarantee the fairness and trust of Mahalanobis Distance-based player training score evaluation and ranking results. At last, we use a case study to show the feasibility of CPEMD in this paper.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Outputmentioning

confidence: 99%

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Correlation-Aware Sport Training Evaluation for Players With Trust Based on Mahalanobis Distance

Fan

Tian²,

et al. 2022

IEEE Access

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“…In addition, transparent player transfer with privacy protection can be regarded as a secure multi-party computation issue, i.e., secure similarity calculation with privacy-preservation. Therefore, we summarize the related work in the field from the following two perspectives: similarity-based matching [21][22][23][24][25][26] and collaboration with privacy-preservation.…”

Section: Related Workmentioning

confidence: 99%

Trusted Player Transfer Evaluation for Sport Markets Based on Blockchain and Locality-Sensitive Hashing

Liu

et al. 2021

IEEE Access

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With the wide popularity of various sport items and the continuous prosperity of sport business, player transfer events are becoming more and more frequent, which gives birth to a big player transfer market with attractive attentions of spectators. However, for the purposes of business competition or concerns of privacy disclosure, the multiple parties involved in an identical player transfer event are often reluctant to release the concrete transfer fees of a player as the transfer fee is always sensitive enough for the player himself or herself as well as the involved multiple sport clubs. The hidden player transfer fees block the fair and transparent sport business trades and cheat prospective spectators to some extent, which decreases the spectator trust towards player market value and finally, lowers the spectators' satisfaction towards sport business. In this situation, it is becoming a necessity to develop a trusted player transfer evaluation method supported by all the parties involved in player transfer events. Inspired by this challenging task, a trusted player transfer evaluation method with privacy protection is put forward in this paper, which is mainly based on Locality-Sensitive Hashing (LSH) and Blockchain technologies and named TPTELSH+B. At last, a series of simulated experiments are deployed to validate the feasibility of our proposed TPTELSH+B algorithm. Experimental results report that TPTELSH+B has a good evaluation performance with competitive approaches.

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“…Malek et al proposed an electric vehicle speed prediction method for univariate and multivariate contexts based on long shortterm memory. e results show that the multivariate context model is better than the univariate context model in shortterm and long-term prediction [18]. Khazbak et al proposed an enhanced scheme that allows a user to specify their location context privacy preference for user privacy in ridehailing services, and the scheme can better protect a user's privacy at the cost of limited matching accuracy [19].…”

Section: Related Literature Researchmentioning

confidence: 99%

Research of Contextual Semantic Reasoning Model Based on Domain Ontology

Zhan

et al. 2021

Scientific Programming

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With the development of mobile network technology and the popularization of mobile terminals, traditional information recommendation systems are gradually changing in the direction of real-time and mobile information recommendation. Information recommendation brings the problem of user contextual sensitivity within the mobile environment. For this problem, first, this paper constructs a domain ontology, which is applicable to the contextual semantic reasoning model. Second, based on the “5W + 1H” method, this paper constructs a context pedigree of the mobile environment using a model framework of a domain ontology. The contextual factors of the mobile environment are divided into six categories: the What-object context, the Where-place context, the When-time context, the Who-subject context, the Why-reason context, and the How-effect context. Then, considering the degree of influence of each contextual factor from the mobile context pedigree to the user is different, this paper uses contextual conditional entropy to calculate the contextual weight of each contextual attribute in the recommendation process. Based on this, a contextual semantic reasoning model based on a domain ontology is constructed. Finally, based on the open dataset provided by GroupLens, this paper verifies the validity and efficiency of the model through a simulation experiment.

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Multivariate deep learning approach for electric vehicle speed forecasting

Cited by 91 publications

References 19 publications

Correlation-Aware Sport Training Evaluation for Players With Trust Based on Mahalanobis Distance

Correlation-Aware Sport Training Evaluation for Players With Trust Based on Mahalanobis Distance

Trusted Player Transfer Evaluation for Sport Markets Based on Blockchain and Locality-Sensitive Hashing

Research of Contextual Semantic Reasoning Model Based on Domain Ontology

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