A Survey of Location Prediction Using Trajectory Mining

Sabarish, B. A.; Karthi, R.; Kumar, T. Gireesh

doi:10.1007/978-81-322-2126-5_14

Cited by 9 publications

(4 citation statements)

References 17 publications

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“…A sigmoid layer called the 'forget gate layer' makes this decision. The output of this gate is shown in Equation (1), where u t is an input vector at time t (current input), h t−1 is memory or history value from the previous time step (previous history), w ðu:Þ : w ðh:Þ are the weight matrices related to the u : h values, respectively. b ð:Þ is a bias vector that defines the transformation of the particular gate.…”

Section: Recurrent Neural Networkmentioning

confidence: 99%

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Predicting user's movement path in indoor environments using the stacked deep learning method and the fuzzy soft‐max classifier

Bourjandi

Tabari

Golsorkhtabaramiri

2022

IET Signal Processing

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Accurate prediction of a user's movement path has various advantages for many applications, such as optimising a nurse's trajectory in a hospital and assisting elderly or disabled people and making them feel secure and protected in the places where they live. Recently, researchers have suggested techniques based on machine learning and deep learning in this field. However, these approaches have drawbacks such as their low accuracy in classifying the extracted features into associated movement paths, high sensitivity to noisy data, and ignoring time dependencies within raw data. In this work, a threephase stacked method named CNN-LSTM-FSC is proposed, which uses the Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Fuzzy Soft-max Classifier (FSC) to overcome the mentioned constraints. In the first phase, the CNN structure extracts time dependencies within raw data using the stacked convolutional and pooling layer. In the second phase, the long-term time dependency of the user's movement path is learnt using the LSTM layers, and the user path is determined using a new innovated fuzzy soft-max classifier. Finally, in the post-processing phase, by performing a majority voting technique on the k-adjacent sample predictions of the classifier, the authors have tried to reduce the effects of noise in identifying the user's movement path. Experiments were conducted on the MovementAAL_RSS dataset. The proposed method has successfully reached 93.86%, 93.71%, and 93.26% accuracy rate on the Move-mentAAL_RSS datasets, with 0%, 5%, and 10% Gaussian noise, respectively, and demonstrates superior results in comparison to the previous literature research. K E Y W O R D Sconvolutional neural network, fuzzy soft-max classifier, long short-term memory, user's movement path | INTRODUCTIONDifferent user movement prediction methods attempt to derive patterns within input data that predict future events [1][2][3]. These patterns can be used effectively to optimise resources and provide intelligent services. Today, motion prediction methods are widely used in a variety of fields, including wireless cellular networks, Location-based Services (LBS) (which include navigation services, social networking services, monitoring and tracking objects in warehouses) [4,5], Ambient Assisted Living (AAL) [6,13] and various applications (including utilities-based applications, health care, safety systems, and alert applications). Two key components in movement prediction systems include hardware environment with its indoor positioning technique and its prediction or classification technique. Hardware environments with indoor positioning can be made in different ways, such as Wireless Sensor Networks (WSN) [5] or Ultra-Wideband (UWB) [7]. A WSN-based system consists of small sensors armed with environmental power sources, sensing devices, radio, and processor units. User's movement is predicted by utilising Received Signal Strength (RSS) information [8] between some WSN and a user of wearable sensors. Then data-drivenThis is an ope...

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Section: Recurrent Neural Networkmentioning

confidence: 99%

“…Different user movement prediction methods attempt to derive patterns within input data that predict future events [1][2][3]. These patterns can be used effectively to optimise resources and provide intelligent services.…”

Section: Introductionmentioning

confidence: 99%

Predicting user's movement path in indoor environments using the stacked deep learning method and the fuzzy soft‐max classifier

Bourjandi

Tabari

Golsorkhtabaramiri

2022

IET Signal Processing

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“…Trajectories specify the path of the moving objects, and trajectory mining plays a vital role in predicting the geographic locations, behaviors and other properties of the objects. 38…”

Section: Literature Reviewmentioning

confidence: 99%

“…Trajectories specify the path of the moving objects, and trajectory mining plays a vital role in predicting the geographic locations, behaviors and other properties of the objects. 38 For example, Morzy 39,40 proved that the pattern knowledge extracted from historic trajectory data can be successfully used for real-time location prediction. This was done by utilizing a pattern matching algorithm to match the past trajectory of the moving object with movement rules.…”

Section: Predicting Next Locationsmentioning

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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