A Deep Learning Driven Method to Analyze Large Scale Dataset of Korean Apartment Unit Floor Plan - Focused on the Korean Apartment from 1970 to 2020 -

2021 ◽  
Vol 30 (3) ◽  
pp. 65-76
Author(s):  
Hoyoung Maeng ◽  
Kyung Hoon Hyun
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Floor Plan ◽  
Large Scale Dataset

scholarly journals Maritime vessel re-identification: novel VR-VCA dataset and a multi-branch architecture MVR-net

2021 ◽  
Vol 32 (3) ◽  
Author(s):  
Amir Ghahremani ◽  
Tunc Alkanat ◽  
Egor Bondarev ◽  
Peter H. N. de With
Keyword(s):  
Deep Learning ◽  
Best Practices ◽  
Large Scale ◽  
Flow Analysis ◽  
Challenging Problem ◽  
Maritime Traffic ◽  
Large Scale Dataset ◽  
Identity Matching ◽  
Learning Architectures ◽  
Disjoint Camera Views

AbstractMaritime vessel re-identification (re-ID) is a computer vision task of vessel identity matching across disjoint camera views. Prominent applications of vessel re-ID exist in the fields of surveillance and maritime traffic flow analysis. However, the field suffers from the absence of a large-scale dataset that enables training of deep learning models. In this study, we present a new dataset that includes 4614 images of 729 vessels along with 5-bin orientation and 8-class vessel-type annotations to promote further research. A second contribution of this study is the baseline re-ID analysis of our new dataset. Performances of 10 recent deep learning architectures are quantitatively compared to reveal the best practices. Lastly, we propose a novel multi-branch deep learning architecture, Maritime Vessel Re-ID network (MVR-net), to address the challenging problem of vessel re-ID. Evaluation of our approach on the new dataset yields 74.5% mAP and 77.9% Rank-1 score, providing a performance increase of 5.7% mAP and 5.0% Rank-1 over the best-performing baseline. MVR-net also outperforms the PRN (a pioneering vehicle re-ID network), by 2.9% and 4.3% higher mAP and Rank-1, respectively.

scholarly journals Active fire detection in Landsat-8 imagery: A large-scale dataset and a deep-learning study

2021 ◽  
Vol 178 ◽  
pp. 171-186
Author(s):  
Gabriel Henrique de Almeida Pereira ◽  
Andre Minoro Fusioka ◽  
Bogdan Tomoyuki Nassu ◽  
Rodrigo Minetto
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Fire Detection ◽  
Landsat 8 ◽  
Learning Study ◽  
Active Fire ◽  
Large Scale Dataset

scholarly journals A Deep Learning Sentiment Analyser for Social Media Comments in Low-Resource Languages

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1133
Author(s):  
Zenun Kastrati ◽  
Lule Ahmedi ◽  
Arianit Kurti ◽  
Fatbardh Kadriu ◽  
Doruntina Murtezaj ◽  
...  
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Large Scale ◽  
Research Study ◽  
Low Resource ◽  
Analysis Task ◽  
Large Scale Dataset ◽  
Social Media Platforms ◽  
Experimental Findings

During the pandemic, when people needed to physically distance, social media platforms have been one of the outlets where people expressed their opinions, thoughts, sentiments, and emotions regarding the pandemic situation. The core object of this research study is the sentiment analysis of peoples’ opinions expressed on Facebook regarding the current pandemic situation in low-resource languages. To do this, we have created a large-scale dataset comprising of 10,742 manually classified comments in the Albanian language. Furthermore, in this paper we report our efforts on the design and development of a sentiment analyser that relies on deep learning. As a result, we report the experimental findings obtained from our proposed sentiment analyser using various classifier models with static and contextualized word embeddings, that is, fastText and BERT, trained and validated on our collected and curated dataset. Specifically, the findings reveal that combining the BiLSTM with an attention mechanism achieved the highest performance on our sentiment analysis task, with an F1 score of 72.09%.

scholarly journals Deep Learning for Segmentation Using an Open Large-Scale Dataset in 2D Echocardiography

2019 ◽  
Vol 38 (9) ◽  
pp. 2198-2210 ◽  
Author(s):  
Sarah Leclerc ◽  
Erik Smistad ◽  
Joao Pedrosa ◽  
Andreas Ostvik ◽  
Frederic Cervenansky ◽  
...  
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
2D Echocardiography ◽  
Large Scale Dataset

scholarly journals A New Deep Learning-Based Zero-Inflated Duration Model for Financial Data Irregularly Spaced in Time

2021 ◽  
Vol 9 ◽  
Author(s):  
Yong Shi ◽  
Wei Dai ◽  
Wen Long
Keyword(s):  
Deep Learning ◽  
Hybrid Model ◽  
Large Scale ◽  
Stock Exchange ◽  
Small Individual ◽  
Arrival Times ◽  
Large Scale Dataset ◽  
Duration Distribution ◽  
Zero Values ◽  
Individual Traders

In stock trading markets, trade duration (i. e., inter-arrival times of trades) usually exhibits high uncertainty and excessive zero values. To forecast conditional distribution of trade duration, this study proposes a hybrid model called “DL-ZIACD” for short, which addresses the problem of excessive zero values by a zero-inflated distribution. Meanwhile, dynamics of the distribution time-varying parameters are captured by a specially designed deep learning (DL) architecture in which the behavioral patterns of large traders and small individual traders are represented separately by different blocks. The proposed hybrid model takes advantage of the strong fitting ability of deep learning methods while allowing for providing a probabilistic output. This paper empirically applied the established model to a large-scale dataset, containing 9,900,000 transactions of the Chinese Shenzhen Stock Exchange 100 Index (SZSE 100) constituents. To the best of our knowledge, no previous studies have applied conditional duration models to a dataset of such a large scale. For both the central location forecasting and the extreme quantile forecasting, our proposed model exhibited significant superiority over the benchmark models, which indicates that our DL-ZIACD model can provide accurate forecasts in conditional duration distribution.

scholarly journals AI-Powered Oracle Bone Inscriptions Recognition and Fragments Rejoining

2020 ◽  
Author(s):  
Chongsheng Zhang ◽  
Ruixing Zong ◽  
Shuang Cao ◽  
Yi Men ◽  
Bofeng Mo
Keyword(s):  
Deep Learning ◽  
Real World ◽  
Template Matching ◽  
Large Scale ◽  
Matching Algorithm ◽  
Domain Experts ◽  
Detection Algorithms ◽  
Scene Text Detection ◽  
Large Scale Dataset ◽  
Scene Text

Oracle Bone Inscriptions (OBI) research is very meaningful for both history and literature. In this paper, we introduce our contributions in AI-Powered Oracle Bone (OB) fragments rejoining and OBI recognition. (1) We build a real-world dataset OB-Rejoin, and propose an effective OB rejoining algorithm which yields a top-10 accuracy of 98.39%. (2) We design a practical annotation software to facilitate OBI annotation, and build OracleBone-8000, a large-scale dataset with character-level annotations. We adopt deep learning based scene text detection algorithms for OBI localization, which yield an F-score of 89.7%. We propose a novel deep template matching algorithm for OBI recognition which achieves an overall accuracy of 80.9%. Since we have been cooperating closely with OBI domain experts, our effort above helps advance their research. The resources of this work are available at https://github.com/chongshengzhang/OracleBone.

Multi Disease-Prediction Framework Using Hybrid Deep Learning: An Optimal Prediction Model (Preprint)

2020 ◽  
Author(s):  
Anusha Ampavathi ◽  
Vijaya Saradhi T
Keyword(s):  
Feature Extraction ◽  
Big Data ◽  
Deep Learning ◽  
Weight Function ◽  
Optimization Algorithm ◽  
Large Scale ◽  
Heuristic Algorithms ◽  
Disease Prediction ◽  
Health Care Decisions ◽  
Proposed Model

UNSTRUCTURED Big data and its approaches are generally helpful for healthcare and biomedical sectors for predicting the disease. For trivial symptoms, the difficulty is to meet the doctors at any time in the hospital. Thus, big data provides essential data regarding the diseases on the basis of the patient’s symptoms. For several medical organizations, disease prediction is important for making the best feasible health care decisions. Conversely, the conventional medical care model offers input as structured that requires more accurate and consistent prediction. This paper is planned to develop the multi-disease prediction using the improvised deep learning concept. Here, the different datasets pertain to “Diabetes, Hepatitis, lung cancer, liver tumor, heart disease, Parkinson’s disease, and Alzheimer’s disease”, from the benchmark UCI repository is gathered for conducting the experiment. The proposed model involves three phases (a) Data normalization (b) Weighted normalized feature extraction, and (c) prediction. Initially, the dataset is normalized in order to make the attribute's range at a certain level. Further, weighted feature extraction is performed, in which a weight function is multiplied with each attribute value for making large scale deviation. Here, the weight function is optimized using the combination of two meta-heuristic algorithms termed as Jaya Algorithm-based Multi-Verse Optimization algorithm (JA-MVO). The optimally extracted features are subjected to the hybrid deep learning algorithms like “Deep Belief Network (DBN) and Recurrent Neural Network (RNN)”. As a modification to hybrid deep learning architecture, the weight of both DBN and RNN is optimized using the same hybrid optimization algorithm. Further, the comparative evaluation of the proposed prediction over the existing models certifies its effectiveness through various performance measures.

scholarly journals Deep Learning-Based Large-Scale Automatic Satellite Crosswalk Classification

2017 ◽  
Vol 14 (9) ◽  
pp. 1513-1517 ◽  
Author(s):  
Rodrigo F. Berriel ◽  
Andre Teixeira Lopes ◽  
Alberto F. de Souza ◽  
Thiago Oliveira-Santos
Keyword(s):  
Deep Learning ◽  
Large Scale
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