Spatio-Temporal Prediction of Baltimore Crime Events Using CLSTM Neural Networks

Esquivel, Nicolas; Nicolis, Orietta; Peralta, Billy; Mateu, Jorge

doi:10.1109/access.2020.3036715

Cited by 26 publications

(9 citation statements)

References 25 publications

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“…Another example of spatial-temporal analysis of data is in the field of crime investigation. Esquivel et al [30] employed an artificial neural network to build models in order to predict future crimes based on past patterns [31] through the development of a genetic-fuzzy system which encompasses spatial-temporal patterns for predicting future crimes.…”

Section: Examples Of Spatial-temporal Data Analysis Applications Outs...mentioning

confidence: 99%

Spatial-Temporal Event Analysis as a Prospective Approach for Signalling Emerging Food Fraud-Related Anomalies in Supply Chains

Jiménez-Carvelo

Erasmus

et al. 2022

Foods

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One of the pillars on which food traceability systems are based is the unique identification and recording of products and batches along the supply chain. Patterns of these identification codes in time and place may provide useful information on emerging food frauds. The scanning of codes on food packaging by users results in interesting spatial-temporal datasets. The analysis of these data using artificial intelligence could advance current food fraud detection approaches. Spatial-temporal patterns of the scanned codes could reveal emerging anomalies in supply chains as a result of food fraud in the chain. These patterns have not been studied yet, but in other areas, such as biology, medicine, credit card fraud, etc., parallel approaches have been developed, and are discussed in this paper. This paper projects these approaches for transfer and implementation in food supply chains in view of future applications for early warning of emerging food frauds.

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Section: Examples Of Spatial-temporal Data Analysis Applications Outs...mentioning

confidence: 99%

Spatial-Temporal Event Analysis as a Prospective Approach for Signalling Emerging Food Fraud-Related Anomalies in Supply Chains

Jiménez-Carvelo

Erasmus

et al. 2022

Foods

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“…Various events can attract marathon enthusiasts from all over the country to participate in the competition. Different from professional sports events, in marathon events, amateurs not only watch the race, but consider the multiple roles of runners, fitness practitioners, and consumers [6].…”

Section: Introductionmentioning

confidence: 99%

Attribution Interpretation of Urban Marathon Events Based on Random Matrix Model

2022

Mathematical Problems in Engineering

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The arrival of the national fitness boom has enabled the marathon as a large-scale sports event to attract the attention of a sufficient number of sports enthusiasts in a relatively short period of time. These people attracted by the event will have a series of expenses and expenditures also precisely reflect the prominent role of sports events on social and economic effects. In this paper, the AISAS consumption behavior model is used as the theoretical research model, and the survey data of urban marathon participants is used as the basis for analysis. The methods of generating attention, interest surveys, information search methods, actual consumption items and amounts during the action, and postmatch satisfaction and information sharing willingness and methods have been analyzed and counted in detail, and spss21.0 software is used to analyze the statistics. The results are analyzed. In addition, this paper studies the effect of the subspace weighting algorithm based on the singular value of random matrix on the urban marathon. Using the results of the eigenvalues of the sample covariance matrix in random matrix theory, the energy of each subspace is estimated, and then the subspace weighting matrix is constructed with the estimated energy. Through the calculation of the weights of the indicators, the weights of the three-level indicators of the event quality are ranked in the order of participation experience, cooperative media level, enthusiasm of local residents to watch the competition, registration status of the competition, number of sponsors, quality of sponsors, enthusiasm of local residents to participate in the competition, media value, and number of cooperative media algorithm, in which the marathon economic effect is defined as the estimated angle where the deviation between the estimated value and the true value exceeds 20%, that is, the root mean square error of the estimated angle is greater than 10%. In the first-level indicators, the quality of the event to evaluate the impact of the event itself has been added. The quality of the event is the influence of the urban marathon itself, which originates from the quality of the marathon itself. At the same time, the quality of the urban marathon is also the foundation of its economic, social, and environmental impact on the host site, so the quality of the urban marathon is an important impact indicator.

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“…In the following ten years, due to its shallow structure, easy over fitting, and slow parameter training speed, the once hot neural network gradually faded out of people's attention, until 2006, when G. E. Hinton and his student R. R. Salakhutdinov's research on neural networks set off an upsurge of deep learning in academia and industry, which opened the curtain of deep learning and marked the birth of deep learning [31]. So far, in the development of neural networks for nearly 80 years, with the rapid increase of computer processing speed and storage capacity, artificial neural network technology has been applied to all aspects of industrial life [32][33][34][35].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Fuzzy Neural Network PID Algorithm for BLDCM Speed Control System

Yin

Wang

et al. 2021

Mathematics

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Because of its simple structure, high efficiency, low noise, and high reliability, the brushless direct current motor (BLDCM) has an irreplaceable role compared with other types of motors in many aspects. The traditional proportional integral derivative (PID) control algorithm has been widely used in practical engineering because of its simple structure and convenient adjustment, but it has many shortcomings in control accuracy and other aspects. Therefore, in this paper, a fuzzy single neuron neural network (FSNNN) PID algorithm based on an automatic speed regulator (ASR) is designed and applied to a BLDCM control system. This paper introduces a BLDCM mathematical model and its control system and designs an FSNNN PID algorithm that takes speed deviation e at different sampling times as inputs of a neural network to adjust the PID parameters, and then it uses a fuzzy system to adjust gain K of the neural network. In addition, the frequency domain stability of a double closed loop PID control system is analyzed, and the control effect of traditional PID, fuzzy PID, and FSNNN PID algorithms are compared by setting different reference speeds, as well as the change rules of three-phase current, back electromotive force (EMF), electromagnetic torque, and rotor angle position. Finally, results show that a motor controlled by the FSNNN PID algorithm has certain superiority compared with traditional PID and fuzzy PID algorithms and also has better control effects.

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Spatio-Temporal Prediction of Baltimore Crime Events Using CLSTM Neural Networks

Cited by 26 publications

References 25 publications

Spatial-Temporal Event Analysis as a Prospective Approach for Signalling Emerging Food Fraud-Related Anomalies in Supply Chains

Spatial-Temporal Event Analysis as a Prospective Approach for Signalling Emerging Food Fraud-Related Anomalies in Supply Chains

Attribution Interpretation of Urban Marathon Events Based on Random Matrix Model

Adaptive Fuzzy Neural Network PID Algorithm for BLDCM Speed Control System

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