A Novel RFID Data Mining System: Integration of Effective Sequential Pattern Mining and Fuzzy Rules Generation Techniques

Kochar, Barjesh; Chhillar, Rajender Singh

doi:10.1007/s10776-011-0151-3

Cited by 7 publications

(2 citation statements)

References 25 publications

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“…The model in the study is built using a Kaggle dataset [17]. The dataset (training set/data) has several properties, each with its own link.…”

Section: Discussionmentioning

confidence: 99%

Crime Prediction Model using Three Classification Techniques: Random Forest, Logistic Regression, and LightGBM

Alsubayhin,

Ramzan,

Alzahrani

2024

IJACSA

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Predicting the likelihood of a crime occurring is difficult, but machine learning can be used to develop models that can do so. Random forest, logistic regression, and LightGBM are three well-known classification methods that can be applied to crime prediction. Random forest is an ensemble learning algorithm that predicts by combining multiple decision trees. It is an effective method for classification tasks, and it is frequently employed for crime prediction because it handles imbalanced datasets well. Logistic regression is a linear model that can be used to predict the probability of a binary outcome, such as the occurrence of a crime. It is a relatively straightforward technique that can be effective for crime prediction if the features are carefully chosen. LightGBM is a gradient-boosting decision tree algorithm with a reputation for speed and precision. It is a relatively new algorithm, but because it can achieve high accuracy on even small datasets, it has rapidly gained popularity for crime prediction. The experimental results show that the LightGBM performs best for binary classification, followed by Random Forest and Logistic Regression.

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“…The model in the study is built using a Kaggle dataset [17]. The dataset (training set/data) has several properties, each with its own link.…”

Section: Discussionmentioning

confidence: 99%

Crime Prediction Model using Three Classification Techniques: Random Forest, Logistic Regression, and LightGBM

Alsubayhin,

Ramzan,

Alzahrani

2024

IJACSA

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“…As RFID technology has been widely implemented for production and logistics control, the utilization of logistics data is of dominating importance in supply chain research. For example, Kochar and Chhillar 34 proposed a framework based on fuzzy rules to extract the characteristics of the moving objects tagged by RFID devices. Zhong et al 35 proposed an approach based on RFID-collected logistics data to extract trajectory knowledge.…”

Section: Frequent Trajectory Patternsmentioning

confidence: 99%

A location prediction method for work-in-process based on frequent trajectory patterns

Cai

Guo

Lü

2017

Proceedings of the Institution of Mechanical Engineers, Part B:

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In the data-rich manufacturing environment, the production process of work-in-process is described and presented by trajectories with manufacturing significance. However, advanced approaches for work-in-process trajectory data analytics and prediction are comparatively inadequate. However, the location prediction of moving objects has drawn great attention in the manufacturing field. Yet most approaches for predicting future locations of objects are originally applied in geography domain. When applied to manufacturing shop floor, the prediction results lack manufacturing significance. This article focuses on predicting the next locations of work-in-process in the workshop. First, a data model is introduced to map the geographic trajectories into the logical space, in order to convert the manufacturing information into logical features. Based on the data model, a prediction method is proposed to predict the next locations using frequent trajectory patterns. A series of experiments are performed to examine the prediction method. The experiment results illustrate the impacts of the user-defined factors and prove that the proposed method is effective and efficient.

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Mining SOTs and dispatching rules from RFID-enabled real-time shopfloor production data

et al. 2012

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A Novel RFID Data Mining System: Integration of Effective Sequential Pattern Mining and Fuzzy Rules Generation Techniques

Cited by 7 publications

References 25 publications

Crime Prediction Model using Three Classification Techniques: Random Forest, Logistic Regression, and LightGBM

Crime Prediction Model using Three Classification Techniques: Random Forest, Logistic Regression, and LightGBM

A location prediction method for work-in-process based on frequent trajectory patterns

Mining SOTs and dispatching rules from RFID-enabled real-time shopfloor production data

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