Addressing Vehicle Sharing through Behavioral Analysis: A Solution to User Clustering Using Recency-Frequency-Monetary and Vehicle Relocation Based on Neighborhood Splits

Brandizzi, Nicolo’; Russo, Samuele; Galati, Gaspare; Napoli, Christian

doi:10.3390/info13110511

Cited by 3 publications

(3 citation statements)

References 37 publications

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“…This model is also applicable to requirement mining based on behavior analysis. Requirement mining based on behavior analysis is applied to the prediction of shared vehicle demand [ 24 ], intelligent prediction of medical demand [ 25 ], etc.…”

Section: Related Workmentioning

confidence: 99%

Research on User Behavior Based on Higher-Order Dependency Network

Qian

Dou

Gong

et al. 2023

Entropy

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In the era of the popularization of the Internet of Things (IOT), analyzing people’s daily life behavior through the data collected by devices is an important method to mine potential daily requirements. The network method is an important means to analyze the relationship between people’s daily behaviors, while the mainstream first-order network (FON) method ignores the high-order dependencies between daily behaviors. A higher-order dependency network (HON) can more accurately mine the requirements by considering higher-order dependencies. Firstly, our work adopts indoor daily behavior sequences obtained by video behavior detection, extracts higher-order dependency rules from behavior sequences, and rewires an HON. Secondly, an HON is used for the RandomWalk algorithm. On this basis, research on vital node identification and community detection is carried out. Finally, results on behavioral datasets show that, compared with FONs, HONs can significantly improve the accuracy of random walk, improve the identification of vital nodes, and we find that a node can belong to multiple communities. Our work improves the performance of user behavior analysis and thus benefits the mining of user requirements, which can be used to personalized recommendations and product improvements, and eventually achieve higher commercial profits.

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Section: Related Workmentioning

confidence: 99%

Research on User Behavior Based on Higher-Order Dependency Network

Qian

Dou

Gong

et al. 2023

Entropy

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“…Still, in the same year, there was research related to the implementation of RFM in vehicle-sharing companies. In this study, it is claimed that clustering using RFM makes it easier for companies to decide on allocating their resources [44]. The allocation of these resources means that the company understands more about locations where many people need vehicles, which is seen from the use of vehicle parking lots which are always busy.…”

Section: Recency Frequency Monetary In Various Fieldsmentioning

confidence: 99%

Implementation of Recency, Frequency, and Monetary Patterns in Adaptive Blockchain-Based Transactions

Soesanto,

Ardiyanto,

Adji

2023

Atlantis Highlights in Engineering

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The development of cryptocurrency cannot be separated from the development of blockchain technology. However, problems arise related to the scalability of the blockchain itself. The long duration of the consensus process means that the scalability of the blockchain cannot increase. Various methods have been developed to overcome this consensus problem. One of the highlights is developing an adaptive consensus. But for adaptive consensus, clustering of blockchain transaction data is required. The Recency Frequency Monetary approach can be used for blockchain transaction clustering but with various adjustments. This research aims to implement Recency Frequency Monetary on blockchain transactions. The results of this study indicate that clustering can be carried out using adjusted Recency Frequency Monetary weighting for blockchain transaction data.

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“…These intangible restraints serve as impediments to daily activities, impeding productivity and negatively impacting both professional output and social interactions (Babaev et al, 2018 ; De Magistris et al, 2022 ). Individuals afflicted with these conditions frequently experience a sense of entrapment, as they actively avoid situations and stimuli that have the potential to exacerbate their symptoms (Dat et al, 2021 ; Brandizzi et al, 2022 ). However, a positive aspect can be identified (Marshall and Bewley, 2015 ).…”

Section: Introductionmentioning

confidence: 99%

Analyzing EEG patterns in young adults exposed to different acrophobia levels: a VR study

Russo,

Tibermacine,

Tibermacine

et al. 2024

Front. Hum. Neurosci.

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IntroductionThe primary objective of this research is to examine acrophobia, a widely prevalent and highly severe phobia characterized by an overwhelming dread of heights, which has a substantial impact on a significant proportion of individuals worldwide. The objective of our study was to develop a real-time and precise instrument for evaluating levels of acrophobia by utilizing electroencephalogram (EEG) signals.MethodsEEG data was gathered from a sample of 18 individuals diagnosed with acrophobia. Subsequently, a range of classifiers, namely Support Vector Classifier (SVC), K-nearest Neighbors (KNN), Random Forest (RF), Decision Tree (DT), Adaboost, Linear Discriminant Analysis (LDA), Convolutional Neural Network (CNN), and Artificial Neural Network (ANN), were employed in the analysis. These methodologies encompass both machine learning (ML) and deep learning (DL) techniques.ResultsThe Convolutional Neural Network (CNN) and Artificial Neural Network (ANN) models demonstrated notable efficacy. The Convolutional Neural Network (CNN) model demonstrated a training accuracy of 96% and a testing accuracy of 99%, whereas the Artificial Neural Network (ANN) model attained a training accuracy of 96% and a testing accuracy of 97%. The findings of this study highlight the effectiveness of the proposed methodology in accurately categorizing real-time degrees of acrophobia using EEG data. Further investigation using correlation matrices for each level of acrophobia showed substantial EEG frequency band connections. Beta and Gamma mean values correlated strongly, suggesting cognitive arousal and acrophobic involvement could synchronize activity. Beta and Gamma activity correlated strongly with acrophobia, especially at higher levels.DiscussionThe results underscore the promise of this innovative approach as a dependable and sophisticated method for evaluating acrophobia. This methodology has the potential to make a substantial contribution toward the comprehension and assessment of acrophobia, hence facilitating the development of more individualized and efficacious therapeutic interventions.

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Addressing Vehicle Sharing through Behavioral Analysis: A Solution to User Clustering Using Recency-Frequency-Monetary and Vehicle Relocation Based on Neighborhood Splits

Cited by 3 publications

References 37 publications

Research on User Behavior Based on Higher-Order Dependency Network

Research on User Behavior Based on Higher-Order Dependency Network

Implementation of Recency, Frequency, and Monetary Patterns in Adaptive Blockchain-Based Transactions

Analyzing EEG patterns in young adults exposed to different acrophobia levels: a VR study

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