Multimodal deep learning based on multiple correspondence analysis for disaster management

Pouyanfar, Samira; Tao, Yudong; Tian, Haiman; Chen, Shu‐Ching; Shyu, Mei Ling

doi:10.1007/s11280-018-0636-4

Cited by 40 publications

(16 citation statements)

References 54 publications

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“…1. Mulitple Correspondence Analysis (MCA) is a statistical method that is widely used in the social sciences and which is applied in recent machine learning contributions [61,78]. It can analyze data without a priori assumptions concerning the data, such as data falling into discrete clusters or variables being independent [1,25].…”

Section: Machine Learning Analysismentioning

confidence: 99%

Decrypting distributed ledger design—taxonomy, classification and blockchain community evaluation

2021

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More than 1000 distributed ledger technology (DLT) systems raising $600 billion in investment in 2016 feature the unprecedented and disruptive potential of blockchain technology. A systematic and data-driven analysis, comparison and rigorous evaluation of the different design choices of distributed ledgers and their implications is a challenge. The rapidly evolving nature of the blockchain landscape hinders reaching a common understanding of the techno-socio-economic design space of distributed ledgers and the cryptoeconomies they support. To fill this gap, this paper makes the following contributions: (i) A conceptual architecture of DLT systems with which (ii) a taxonomy is designed and (iii) a rigorous classification of DLT systems is made using real-world data and wisdom of the crowd. (iv) A DLT design guideline is the end result of applying machine learning methodologies on the classification data. Compared to related work and as defined in earlier taxonomy theory, the proposed taxonomy is highly comprehensive, robust, explanatory and extensible. The findings of this paper can provide new insights and better understanding of the key design choices evolving the modeling complexity of DLT systems, while identifying opportunities for new research contributions and business innovation.

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Section: Machine Learning Analysismentioning

confidence: 99%

Decrypting distributed ledger design—taxonomy, classification and blockchain community evaluation

2021

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“…Although researchers in the past have designed and experimented with unimodal disaster assessment systems [2,3], realizing that multimodal systems may outperform unimodal frameworks [16], the focus has now shifted to leveraging information in different media forms for disaster management [20]. In addition to using several different media forms and feature extraction techniques, several researchers have also employed various methods to combine the information obtained from these modalities, to make a final decision [19]. Yang et al [28] developed a multimodal system-MADIS which leverages both text and image modalities, using hand-crafted features such as TF-IDF vectors, and low-level color features.…”

Section: Literature Reviewmentioning

confidence: 99%

MEMIS: Multimodal Emergency Management Information System

Agarwal

Leekha

Sawhney

et al. 2020

Lecture Notes in Computer Science

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The recent upsurge in the usage of social media and the multimedia data generated therein has attracted many researchers for analyzing and decoding the information to automate decision-making in several fields. This work focuses on one such application: disaster management in times of crises and calamities. The existing research on disaster damage analysis has primarily taken only unimodal information in the form of text or image into account. These unimodal systems, although useful, fail to model the relationship between the various modalities. Different modalities often present supporting facts about the task, and therefore, learning them together can enhance performance. We present MEMIS, a system that can be used in emergencies like disasters to identify and analyze the damage indicated by user-generated multimodal social media posts, thereby helping the disaster management groups in making informed decisions. Our leave-one-disaster-out experiments on a multimodal dataset suggest that not only does fusing information in different media forms improves performance, but that our system can also generalize well to new disaster categories. Further qualitative analysis reveals that the system is responsive and computationally efficient.

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“…It is mostly used to reduce dimensionality and extract functionality in most pattern recognition applications [16,17]. A modular multiple correspondence analysis (MCA), in an FPGA environment, is a scalable and robust architecture for the realtime face recognition framework [18]. Modular PCA has been demonstrated to enhance facial recognition performance when facial pictures have different expression and illumination [19].…”

Section: Introductionmentioning

confidence: 99%

Enhancing the feature-based 3D deformable face recognition using hybrid PCA-NN

Darujati¹,

Nugroho²,

Kurniawan³

et al. 2021

IJEECS

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<p>Facial recognition is one of the most important advancements in image processing. An important job is to build an automated framework with the same human capacity’s for recognizing face. The face is a complex 3D graphical model, and constructing a computational model is a challenging task. This paper aims at a facial detection technique focused on the coding and decoding of the facial feature object theory approach to data. One of the most natural and common principal component analysis (PCA) method. This approach transforms the face features into a minimal set of basic attributes, peculiarities, which are the critical components of the original learning image collection (or the training package). The proposed technique is a combination of the PCA system and the identification of components using the neural network (NN) feed-forward propagation method. This experiment proves that recognition of deformed 3D face is doable. By taking into account almost all forms of feature extraction and engineering, the NN yields a recognition score of 95%.</p>

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Multimodal deep learning based on multiple correspondence analysis for disaster management

Cited by 40 publications

References 54 publications

Decrypting distributed ledger design—taxonomy, classification and blockchain community evaluation

Decrypting distributed ledger design—taxonomy, classification and blockchain community evaluation

MEMIS: Multimodal Emergency Management Information System

Enhancing the feature-based 3D deformable face recognition using hybrid PCA-NN

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