LSTM-Based Deep Learning Model for Predicting Individual Mobility Traces of Short-Term Foreign Tourists

The increasing availability of trajectory recordings has led to the mining of a massive amount of historical track data, allowing for a better understanding of travel behaviors by revealing meaningful motion patterns. In the context of human mobility analysis, the problem of motion prediction assumes a central role and is beneficial for a wide range of applications, including for touristic purposes, such as personalized services or targeted recommendations, and sustainability studies related to crowd management and resource redistribution. This paper tackles a particular case of the trajectory prediction problem, focusing on large-scale mobility traces of short-term foreign tourists. These sparse trajectories, short and non-repetitive, lack spatial and temporal regularity, making prediction analysis based on individual historical motion data unreliable. To face this issue, we hereby propose a deep learning-based approach, taking into account the collective mobility of tourists over the territory. The underlying semantics of motion patterns are captured by means of a long short-term memory (LSTM) neural network model trained on pre-processed location sequences, aiming to predict the next visited place in the trajectory. We tested the methodology on a real-world big dataset, demonstrating its higher feasibility with respect to traditional approaches.

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Polygenic Scores for Cognitive Abilities and their Association with Different Aspects of General Intelligence – a Deep Phenotyping Approach

10.1101/2020.06.03.131318 ◽

2020 ◽

Author(s):

Erhan Genç ◽

Caroline Schlüter ◽

Christoph Fraenz ◽

Larissa Arning ◽

Huu Phuc Nguyen ◽

...

Keyword(s):

Cognitive Abilities ◽

Large Scale ◽

Short Term Memory ◽

Life Outcomes ◽

Short Term ◽

Polygenic Scores ◽

Wide Range ◽

Dna Variants ◽

Trait Prediction ◽

Deep Phenotyping

AbstractIntelligence is a highly polygenic trait and GWAS have identified thousands of DNA variants contributing with small effects. Polygenic scores (PGS) can aggregate those effects for trait prediction in independent samples. As large-scale light-phenotyping GWAS operationalized intelligence as performance in rather superficial tests, the question arises which intelligence facets are actually captured. We used deep-phenotyping to investigate the molecular determinantes of individual differences in cognitive ability. We therefore studied the association between PGS of educational attainment (EA-PGS) and intelligence (IQ-PGS) with a wide range of intelligence facets in a sample of 320 healthy adults. EA-PGS and IQ-PGS had the highest incremental R2s for general (3.25%; 1.78%), verbal (2.55%; 2.39%) and numerical intelligence (2.79%; 1.54%) and the weakest for non-verbal intelligence (0.50%; 0.19%) and short-term memory (0.34%; 0.22%). These results indicate that PGS derived from light-phenotyping GWAS do not reflect different facets of intelligence equally well, and thus should not be interpreted as genetic indicators of intelligence per se. The findings refine our understanding of how PGS are related to other traits or life outcomes.

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Identifying Foreign Tourists’ Nationality from Mobility Traces via LSTM Neural Network and Location Embeddings

Applied Sciences ◽

10.3390/app9142861 ◽

2019 ◽

Vol 9 (14) ◽

pp. 2861 ◽

Cited By ~ 2

Author(s):

Alessandro Crivellari ◽

Euro Beinat

Keyword(s):

Neural Network ◽

Motion Tracking ◽

Large Scale ◽

Short Term Memory ◽

Human Mobility ◽

User Profiling ◽

Personalized Recommendation ◽

Mobility Patterns ◽

Trajectory Data ◽

Short Term

The interest in human mobility analysis has increased with the rapid growth of positioning technology and motion tracking, leading to a variety of studies based on trajectory recordings. Mapping the routes that people commonly perform was revealed to be very useful for location-based service applications, where individual mobility behaviors can potentially disclose meaningful information about each customer and be fruitfully used for personalized recommendation systems. This paper tackles a novel trajectory labeling problem related to the context of user profiling in “smart” tourism, inferring the nationality of individual users on the basis of their motion trajectories. In particular, we use large-scale motion traces of short-term foreign visitors as a way of detecting the nationality of individuals. This task is not trivial, relying on the hypothesis that foreign tourists of different nationalities may not only visit different locations, but also move in a different way between the same locations. The problem is defined as a multinomial classification with a few tens of classes (nationalities) and sparse location-based trajectory data. We hereby propose a machine learning-based methodology, consisting of a long short-term memory (LSTM) neural network trained on vector representations of locations, in order to capture the underlying semantics of user mobility patterns. Experiments conducted on a real-world big dataset demonstrate that our method achieves considerably higher performances than baseline and traditional approaches.

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Sentimental analysis using recurrent neural network

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i2.27.12635 ◽

2018 ◽

Vol 7 (2.27) ◽

pp. 88 ◽

Cited By ~ 1

Author(s):

Merin Thomas ◽

Latha C.A

Keyword(s):

Deep Learning ◽

Short Term Memory ◽

Learning Approaches ◽

Short Term ◽

Indian Language ◽

Natural Languages ◽

South Indian ◽

Learning Technique ◽

Long Short Term Memory ◽

Traditional Approaches

Sentiment analysis has been an important topic of discussion from two decades since Lee published his first paper on the sentimental analysis in 2002. Apart from the sentimental analysis in English, it has spread its wing to other natural languages whose significance is very important in a multi linguistic country like India. The traditional approaches in machine learning have paved better accuracy for the Analysis. Deep Learning approaches have gained its momentum in recent years in sentimental analysis. Deep learning mimics the human learning so expectations are to meet higher levels of accuracy. In this paper we have implemented sentimental analysis of tweets in South Indian language Malayalam. The model used is Recurrent Neural Networks Long Short-Term Memory, a deep learning technique to predict the sentiments analysis. Achieved accuracy was found increasing with quality and depth of the datasets.

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Trace2trace—A Feasibility Study on Neural Machine Translation Applied to Human Motion Trajectories

Sensors ◽

10.3390/s20123503 ◽

2020 ◽

Vol 20 (12) ◽

pp. 3503

Author(s):

Alessandro Crivellari ◽

Euro Beinat

Keyword(s):

Machine Translation ◽

Computational Linguistics ◽

Information Disclosure ◽

Large Scale ◽

Short Term Memory ◽

Human Mobility ◽

Human Motion ◽

Neural Machine Translation ◽

Crowd Management ◽

Motion Behavior

Neural machine translation is a prominent field in the computational linguistics domain. By leveraging the recent developments of deep learning, it gave birth to powerful algorithms for translating text from one language to another. This study aims to assess the feasibility of transferring the neural machine translation approach into a completely different context, namely human mobility and trajectory analysis. Building a conceptual parallelism between sentences (sequences of words) and motion traces (sequences of locations), we aspire to translate individual trajectories generated by a certain category of users into the corresponding mobility traces potentially generated by a different category of users. The experiment is inserted in the background of tourist mobility analysis, with the goal of translating the motion behavior of tourists belonging to a specific nationality into the motion behavior of tourists belonging to a different nationality. The model adopted is based on the seq2seq approach and consists of an encoder–decoder architecture based on long short-term memory (LSTM) neural networks and neural embeddings. The encoder turns an input location sequence into a corresponding hidden vector; the decoder reverses the process, turning the vector into an output location sequence. The proposed framework, tested on a real-world large-scale dataset, explores an effective attempt of motion transformation between different entities, arising as a potentially powerful source of mobility information disclosure, especially in the context of crowd management and smart city services.

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Comparison of Long Short-Term Memory Networks and Random Forest for Sentinel-1 Time Series Based Large Scale Crop Classification

Remote Sensing ◽

10.3390/rs13245000 ◽

2021 ◽

Vol 13 (24) ◽

pp. 5000

Author(s):

Felix Reuß ◽

Isabella Greimeister-Pfeil ◽

Mariette Vreugdenhil ◽

Wolfgang Wagner

Keyword(s):

Time Series ◽

Deep Learning ◽

Large Scale ◽

Short Term Memory ◽

Meteorological Data ◽

Learning Models ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory ◽

Crop Classification

To ensure future food security, improved agricultural management approaches are required. For many of those applications, precise knowledge of the distribution of crop types is essential. Various machine and deep learning models have been used for automated crop classification using microwave remote sensing time series. However, the application of these approaches on a large spatial and temporal scale is barely investigated. In this study, the performance of two frequently used algorithms, Long Short-Term Memory (LSTM) networks and Random Forest (RF), for crop classification based on Sentinel-1 time series and meteorological data on a large spatial and temporal scale is assessed. For data from Austria, the Netherlands, and France and the years 2015–2019, scenarios with different spatial and temporal scales were defined. To quantify the complexity of these scenarios, the Fisher Discriminant measurement F1 (FDR1) was used. The results demonstrate that both classifiers achieve similar results for simple classification tasks with low FDR1 values. With increasing FDR1 values, however, LSTM networks outperform RF. This suggests that the ability of LSTM networks to learn long-term dependencies and identify the relation between radar time series and meteorological data becomes increasingly important for more complex applications. Thus, the study underlines the importance of deep learning models, including LSTM networks, for large-scale applications.

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School Attendance Prediction among Students with Autism: A Deep Learning-based Framework

10.21203/rs.3.rs-691760/v1 ◽

2021 ◽

Author(s):

Mohammed Jarbou ◽

Daehan Won ◽

Jennifer Gillis ◽

Raymond Romanczyk

Keyword(s):

Deep Learning ◽

Short Term Memory ◽

School Attendance ◽

Neurodevelopmental Disorder ◽

The United States ◽

Autism Spectrum ◽

Short Term ◽

Children With Asd ◽

Wide Range

Abstract Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that affects the areas of social communication and behavior. The term “spectrum” refers to the wide range of symptoms observed across individuals with ASD. Many children with ASD experience difficulty with daily functioning at school andhome. ASD prevalenceincreases in the United States, with the most recent prevalence of 1.9%. Given the wide range of social and learning, difficulties experienced by children with ASD, it is paramount that they are able to attend school to receive the appropriate range of interventions. School absenteeism (SA) is a significant concern given its association with many negativeconsequences such as school drop-out.Early prediction of SA would help school districtto implement effective interventions to ameliorate this issue. Due to its heterogeneity, students with ASD show within-group differences concerning their SA. This research introduces a deep learning-based framework for predicting short-and long-term SA of students with ASD. The Long Short-Term Memory (LSTM) algorithm is used to predict short-term SA. Similarly, Multilayer Perceptron(MLP) and Random Forest (RF) algorithms are used to predict long-term SA. The proposed framework achieves a high accuracy of 89% and 90% to predict short-term and long-term SA, respectively.

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Implementation of Deep Learning Predictor (LSTM) Algorithm for Human Mobility Prediction

International Journal of Interactive Mobile Technologies (iJIM) ◽

10.3991/ijim.v14i18.16867 ◽

2020 ◽

Vol 14 (18) ◽

pp. 132

Author(s):

Ida Nurhaida ◽

Handrie Noprisson ◽

Vina Ayumi ◽

Hong Wei ◽

Erwin Dwika Putra ◽

...

Keyword(s):

Large Scale ◽

Short Term Memory ◽

Human Mobility ◽

Mobility Prediction ◽

Sequential Data ◽

Short Term ◽

Large Scale Dataset ◽

History Of ◽

Long Short Term Memory ◽

Trajectory Identification

The studies of human mobility prediction in mobile computing area gained due to the availability of large-scale dataset contained history of location trajectory. Previous work has been proposed many solutions for increasing of human mobility prediction result accuration, however, only few researchers have addressed the issue of human mobility for implementation of LSTM networks. This study attempted to use classical methodologies by combining LSTM and DBSCAN because those algorithms can tackle problem in human mobility, including large-scale sequential data modeling and number of clusters of arbitrary trajectory identification. The method of research consists of DBSCAN for clustering, long short-term memory (LSTM) algorithm for modelling and prediction, and Root Mean Square Error (RMSE) for evaluation. As the result, the prediction error or RMSE value reached score 3.551 by setting LSTM with parameter of epoch and batch_size is 100 and 20 respectively.

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Multilabel Text Classification in News Articles Using Long-Term Memory with Word2Vec

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) ◽

10.29207/resti.v4i2.1655 ◽

2020 ◽

Vol 4 (2) ◽

pp. 276-285

Author(s):

Winda Kurnia Sari ◽

Dian Palupi Rini ◽

Reza Firsandaya Malik ◽

Iman Saladin B. Azhar

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Text Classification ◽

Large Scale ◽

Short Term Memory ◽

Short Term ◽

Learning Methods ◽

Processing Variable ◽

Term Memory ◽

Long Short Term Memory

Multilabel text classification is a task of categorizing text into one or more categories. Like other machine learning, multilabel classification performance is limited to the small labeled data and leads to the difficulty of capturing semantic relationships. It requires a multilabel text classification technique that can group four labels from news articles. Deep Learning is a proposed method for solving problems in multilabel text classification techniques. Some of the deep learning methods used for text classification include Convolutional Neural Networks, Autoencoders, Deep Belief Networks, and Recurrent Neural Networks (RNN). RNN is one of the most popular architectures used in natural language processing (NLP) because the recurrent structure is appropriate for processing variable-length text. One of the deep learning methods proposed in this study is RNN with the application of the Long Short-Term Memory (LSTM) architecture. The models are trained based on trial and error experiments using LSTM and 300-dimensional words embedding features with Word2Vec. By tuning the parameters and comparing the eight proposed Long Short-Term Memory (LSTM) models with a large-scale dataset, to show that LSTM with features Word2Vec can achieve good performance in text classification. The results show that text classification using LSTM with Word2Vec obtain the highest accuracy is in the fifth model with 95.38, the average of precision, recall, and F1-score is 95. Also, LSTM with the Word2Vec feature gets graphic results that are close to good-fit on seventh and eighth models.

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Predicting medication adherence using ensemble learning and deep learning models with large scale healthcare data

Scientific Reports ◽

10.1038/s41598-021-98387-w ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Yingqi Gu ◽

Akshay Zalkikar ◽

Mingming Liu ◽

Lara Kelly ◽

Amy Hall ◽

...

Keyword(s):

Deep Learning ◽

Medication Adherence ◽

Ensemble Learning ◽

Large Scale ◽

Short Term Memory ◽

Population Level ◽

Learning Models ◽

Healthcare Data ◽

Wide Range ◽

The Status

AbstractClinical studies from WHO have demonstrated that only 50–70% of patients adhere properly to prescribed drug therapy. Such adherence failure can impact therapeutic efficacy for the patients in question and compromises data quality around the population-level efficacy of the drug for the indications targeted. In this study, we applied various ensemble learning and deep learning models to predict medication adherence among patients. Our contribution to this endeavour involves targeting the problem of adherence prediction for a particularly challenging class of patients who self-administer injectable medication at home. Our prediction pipeline, based on event history, comprises a connected sharps bin which aims to help patients better manage their condition and improve outcomes. In other words, the efficiency of interventions can be significantly improved by prioritizing the patients who are most likely to be non-adherent. The collected data comprising a rich event feature set may be exploited for the purposes of predicting the status of the next adherence state for individual patients. This paper reports on how this concept can be realized through an investigation using a wide range of ensemble learning and deep learning models on a real-world dataset collected from such a system. The dataset investigated comprises 342,174 historic injection disposal records collected over the course of more than 5 years. A comprehensive comparison of different models is given in this paper. Moreover, we demonstrate that the selected best performer, long short-term memory (LSTM), generalizes well by deploying it in a true future testing dataset. The proposed end-to-end pipeline is capable of predicting patient failure in adhering to their therapeutic regimen with 77.35 % accuracy (Specificity: 78.28 %, Sensitivity: 76.42%, Precision: 77.87%,F1 score: 0.7714, ROC AUC: 0.8390).

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CITY-SCALE HUMAN MOBILITY PREDICTION MODEL BY INTEGRATING GNSS TRAJECTORIES AND SNS DATA USING LONG SHORT-TERM MEMORY

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-v-4-2020-87-2020 ◽

2020 ◽

Vol V-4-2020 ◽

pp. 87-94

Author(s):

S. Miyazawa ◽

X. Song ◽

R. Jiang ◽

Z. Fan ◽

R. Shibasaki ◽

...

Keyword(s):

Deep Learning ◽

Prediction Model ◽

Short Term Memory ◽

Human Mobility ◽

Background Information ◽

Mobility Prediction ◽

Short Term ◽

Term Memory ◽

Mobility Data ◽

Long Short Term Memory

Abstract. Human mobility analysis on large-scale mobility data has contributed to multiple applications such as urban and transportation planning, disaster preparation and response, tourism, and public health. However, when some unusual events happen, every individual behaves differently depending on their personal routine and background information. To improve the accuracy of the crowd behavior prediction model, understanding supplemental spatiotemporal topics, such as when, where and what people observe and are interested in, is important. In this research, we develop a model integrating social network service (SNS) data into the human mobility prediction model as background information of the mobility. We employ multi-modal deep learning models using Long short-term memory (LSTM) architecture to incorporate SNS data to a human mobility prediction model based on Global Navigation Satellite System (GNSS) data. We process anonymized interpolated GNSS trajectories from mobile phones into mobility sequence with discretized grid IDs, and apply several topic modeling methods on geo-tagged data to extract spatiotemporal topic features in each spatiotemporal unit similar to the mobility data. Thereafter, we integrate the two datasets in the multi-modal deep learning prediction models to predict city-scale mobility. The experiment proves that the models with SNS topics performed better than baseline models.

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