A Simple Analysis of Multimodal Data Fusion

Cheng, Jiangchang; Dai, Yinglong; Yuan, Yao; Zhu, Hongli

doi:10.1109/trustcom50675.2020.00199

Cited by 5 publications

(2 citation statements)

References 17 publications

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“…The methods of multimodal data fusion can be broadly categorized into three groups based on the level of fusion, namely pixel-level fusion, feature-level fusion, and decisionlevel fusion [22]. Some methods combine elements from these categories.…”

Section: A Multimodal Data Fusionmentioning

confidence: 99%

Multimodal Deep Learning Crime Prediction Using Tweets

Tam,

Tanriöver

2023

IEEE Access

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Crime prevention relies on crime prediction as a crucial method to determine the most effective patrol strategy for law enforcement agencies. Various approaches and solutions have been utilized to predict criminal activity. Nonetheless the environment and nature of information for crime prediction is constantly changing. Although, potentially a useful source for gathering sentiments, social media content has been ignored by the prediction models. The utilization of social media for sharing information and ideas has experienced a significant surge. Twitter, in particular, is regarded as a valuable platform for gathering public sentiments, emotions, perspectives, and feedback. In this regard, techniques for analyzing the sentiment of tweets on Twitter have been developed to ascertain whether the textual content conveys a positive or negative viewpoint on crime incident. Therefore, our interest lies in investigating the potential and advantages of fusing the information of sentiment and crime modalities. In this paper, ConvBiLSTM is applied to train the model, features of both tweet and crime modalities were extracted independently at vector level and fused into a single representation that captures the information from all modalities. This study involved collecting and conducting experiments using two datasets. The first dataset consisted of crime incident data obtained from the Chicago police department, specifically covering the period between September 1 and September 30, 2019. The second dataset comprised tweets containing crime-related terminology specific to Chicago. The crime prediction using multimodal data fusion on ConvBiLSTM outperform against other models with 97.75% of accuracy.

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Section: A Multimodal Data Fusionmentioning

confidence: 99%

Multimodal Deep Learning Crime Prediction Using Tweets

Tam,

Tanriöver

2023

IEEE Access

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“…Data fusion algorithms contribute toward combining the strengths of different sensors, improving the spatiotemporal resolution of fused products in spatial, spectral, or temporal dimensions (Cheng et al, 2020; Lima et al, 2021; Moreno‐Martinez et al, 2018; Zurita‐Milla et al, 2009). The AVHRR NDVI product was downscaled using a geographically weighted linear mixture model at early times when the spatial resolution of the satellites was typically low.…”

Section: Introductionmentioning

confidence: 99%

Development of long‐term spatiotemporal continuous NDVI products for alpine grassland from 1982 to 2020 in the Qinghai–Tibet Plateau, China

Yang,

Huang,

et al. 2024

Grassland Research

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BackgroundThe time‐series data of the Normalized Difference Vegetation Index (NDVI) is a crucial indicator for global and regional vegetation monitoring. However, the current assessment of global and regional long‐term vegetation changes is subject to large uncertainties due to the lack of spatiotemporally continuous time‐series data sets.MethodsIn this study, a long time‐series monthly NDVI data set with a spatial resolution of 250 m from 1982 to 2020 was developed by combining Moderate Resolution Imaging Spectroradiometer (MODIS) and AVHRR (Advanced Very High‐Resolution Radiometer) time‐series NDVI products using the Random Forest (RF) downscaling model.ResultsCompared to the MODIS NDVI product, the fused product shows RMSE and mean absolute error ranging from 0 to 0.075 and from 0 to 0.05, respectively, with R2 values mostly above 0.7.ConclusionsThe long time‐series NDVI products generated in this study are reliable in terms of accuracy and have great potential for long‐term dynamic monitoring of terrestrial ecosystems on the Qinghai–Tibet Plateau.

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Research on Mobile Network User Complaint Warning Method Based on Multimodal Data

Di,

Xiao,

et al. 2023

2023 International Conference on Information and Communication Technologies for Disaster Management (ICT-DM)

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A Simple Analysis of Multimodal Data Fusion

Cited by 5 publications

References 17 publications

Multimodal Deep Learning Crime Prediction Using Tweets

Multimodal Deep Learning Crime Prediction Using Tweets

Development of long‐term spatiotemporal continuous NDVI products for alpine grassland from 1982 to 2020 in the Qinghai–Tibet Plateau, China

Research on Mobile Network User Complaint Warning Method Based on Multimodal Data

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