scholarly journals Deep Learning-Based Crowd Scene Analysis Survey

2020 ◽  
Vol 6 (9) ◽  
pp. 95
Author(s):  
Sherif Elbishlawi ◽  
Mohamed H. Abdelpakey ◽  
Agwad Eltantawy ◽  
Mohamed S. Shehata ◽  
Mostafa M. Mohamed
Keyword(s):  
Deep Learning ◽  
Automatic System ◽  
Scene Analysis ◽  
Crowd Counting ◽  
Public Events ◽  
Analysis Methods ◽  
The Difference ◽  
Evaluation Metric ◽  
Crowded Scenes

Recently, our world witnessed major events that attracted a lot of attention towards the importance of automatic crowd scene analysis. For example, the COVID-19 breakout and public events require an automatic system to manage, count, secure, and track a crowd that shares the same area. However, analyzing crowd scenes is very challenging due to heavy occlusion, complex behaviors, and posture changes. This paper surveys deep learning-based methods for analyzing crowded scenes. The reviewed methods are categorized as (1) crowd counting and (2) crowd actions recognition. Moreover, crowd scene datasets are surveyed. In additional to the above surveys, this paper proposes an evaluation metric for crowd scene analysis methods. This metric estimates the difference between calculated crowed count and actual count in crowd scene videos.

scholarly journals Deep learning-based framework for the distinction of membranous nephropathy: a new approach through hyperspectral imagery

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Tianqi Tu ◽  
Xueling Wei ◽  
Yue Yang ◽  
Nianrong Zhang ◽  
Wei Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Renal Biopsy ◽  
Membranous Nephropathy ◽  
Learning Algorithm ◽  
Hyperspectral Imagery ◽  
Chinese Patients ◽  
Support Vector ◽  
Deep Learning Algorithm ◽  
The Difference ◽  
Complex Deposition

Abstract Background Common subtypes seen in Chinese patients with membranous nephropathy (MN) include idiopathic membranous nephropathy (IMN) and hepatitis B virus-related membranous nephropathy (HBV-MN). However, the morphologic differences are not visible under the light microscope in certain renal biopsy tissues. Methods We propose here a deep learning-based framework for processing hyperspectral images of renal biopsy tissue to define the difference between IMN and HBV-MN based on the component of their immune complex deposition. Results The proposed framework can achieve an overall accuracy of 95.04% in classification, which also leads to better performance than support vector machine (SVM)-based algorithms. Conclusion IMN and HBV-MN can be correctly separated via the deep learning framework using hyperspectral imagery. Our results suggest the potential of the deep learning algorithm as a new method to aid in the diagnosis of MN.

The Structure and Reliability Analysis of Metro Building Automatic System

2011 ◽  
Vol 130-134 ◽  
pp. 2589-2593
Author(s):  
Jia Zhi Li ◽  
Jia Wang ◽  
Zhi Tan ◽  
Lei Zhang
Keyword(s):  
Reliability Analysis ◽  
Automatic System ◽  
Control Level ◽  
Network Configuration ◽  
Analysis Methods ◽  
Hardware Configuration ◽  
Software And Hardware

This paper analyzes the structure of Building Automatic System and introduces software and hardware configuration and network configuration of the system. Then the authors list two kinds of reliability analysis methods and describe their features. Based on the characteristics and the control level of station BAS, finally, we achieve reliability analysis, which has scientificity, rationality and practicability, and can be helpful for guiding the maintenance, design, and further perfect of BAS system.

scholarly journals KLASIFIKASI BATIK RIAU DENGAN MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORKS (CNN)

2020 ◽  
Vol 9 (1) ◽  
pp. 7-10
Author(s):  
Hendry Fonda
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
18Th Century ◽  
The Public ◽  
Artificial Neural ◽  
The Difference ◽  
Fully Connected

ABSTRACT Riau batik is known since the 18th century and is used by royal kings. Riau Batik is made by using a stamp that is mixed with coloring and then printed on fabric. The fabric used is usually silk. As its development, comparing Javanese  batik with riau batik Riau is very slowly accepted by the public. Convolutional Neural Networks (CNN) is a combination of artificial neural networks and deeplearning methods. CNN consists of one or more convolutional layers, often with a subsampling layer followed by one or more fully connected layers as a standard neural network. In the process, CNN will conduct training and testing of Riau batik so that a collection of batik models that have been classified based on the characteristics that exist in Riau batik can be determined so that images are Riau batik and non-Riau batik. Classification using CNN produces Riau batik and not Riau batik with an accuracy of 65%. Accuracy of 65% is due to basically many of the same motifs between batik and other batik with the difference lies in the color of the absorption in the batik riau. Kata kunci: Batik; Batik Riau; CNN; Image; Deep Learning   ABSTRAK   Batik Riau dikenal sejak abad ke 18 dan digunakan oleh bangsawan raja. Batik Riau dibuat dengan menggunakan cap yang dicampur dengan pewarna kemudian dicetak di kain. Kain yang digunakan biasanya sutra. Seiring perkembangannya, dibandingkan batik Jawa maka batik Riau sangat lambat diterima oleh masyarakat. Convolutional Neural Networks (CNN) merupakan kombinasi dari jaringan syaraf tiruan dan metode deeplearning. CNN terdiri dari satu atau lebih lapisan konvolutional, seringnya dengan suatu lapisan subsampling yang diikuti oleh satu atau lebih lapisan yang terhubung penuh sebagai standar jaringan syaraf. Dalam prosesnya CNN akan melakukan training dan testing terhadap batik Riau sehingga didapat kumpulan model batik yang telah terklasi    fikasi berdasarkan ciri khas yang ada pada batik Riau sehingga dapat ditentukan gambar (image) yang merupakan batik Riau dan yang bukan merupakan batik Riau. Klasifikasi menggunakan CNN menghasilkan batik riau dan bukan batik riau dengan akurasi 65%. Akurasi 65% disebabkan pada dasarnya banyak motif yang sama antara batik riau dengan batik lainnya dengan perbedaan terletak pada warna cerap pada batik riau. Kata kunci: Batik; Batik Riau; CNN; Image; Deep Learning

scholarly journals A High-robustness and Low Resource-consumption Crowd Counting Model

2021 ◽  
Vol 15 ◽  
pp. 46-54
Author(s):  
Han Jia ◽  
Xuecheng Zou
Keyword(s):  
Network Architecture ◽  
Resource Consumption ◽  
Portable Devices ◽  
Estimation Errors ◽  
Crowd Counting ◽  
Density Map ◽  
Almost All ◽  
Fully Connected ◽  
Crowded Scenes ◽  
Counting Model

A major problem of counting high-density crowded scenes is the lack of flexibility and robustness exhibited by existing methods, and almost all recent state-of-the-art methods only show good performance in estimation errors and density map quality for select datasets. The biggest challenge faced by these methods is the analysis of similar features between the crowd and background, as well as overlaps between individuals. Hence, we propose a light and easy-to-train network for congestion cognition based on dilated convolution, which can exponentially enlarge the receptive field, preserve original resolution, and generate a high-quality density map. With the dilated convolutional layers, the counting accuracy can be enhanced as the feature map keeps its original resolution. By removing fully-connected layers, the network architecture becomes more concise, thereby reducing resource consumption significantly. The flexibility and robustness improvements of the proposed network compared to previous methods were validated using the variance of data size and different overlap levels of existing open source datasets. Experimental results showed that the proposed network is suitable for transfer learning on different datasets and enhances crowd counting in highly congested scenes. Therefore, the network is expected to have broader applications, for example in Internet of Things and portable devices.

Log-Logistic Analysis of Herbicide Dose-Response Relationships

1995 ◽  
Vol 9 (2) ◽  
pp. 218-227 ◽  
Author(s):  
Steven S. Seefeldt ◽  
Jens Erik Jensen ◽  
E. Patrick Fuerst
Keyword(s):  
Dose Response ◽  
Logistic Model ◽  
Response Analysis ◽  
Analysis Method ◽  
Weed Science ◽  
Response Curves ◽  
Herbicide Resistant ◽  
Logistic Analysis ◽  
Analysis Methods ◽  
The Difference

Dose-response studies are an important tool in weed science. The use of such studies has become especially prevalent following the widespread development of herbicide resistant weeds. In the past, analyses of dose-response studies have utilized various types of transformations and equations which can be validated with several statistical techniques. Most dose-response analysis methods 1) do not accurately describe data at the extremes of doses and 2) do not provide a proper statistical test for the difference(s) between two or more dose-response curves. Consequently, results of dose-response studies are analyzed and reported in a great variety of ways, and comparison of results among various researchers is not possible. The objective of this paper is to review the principles involved in dose-response research and explain the log-logistic analysis of herbicide dose-response relationships. In this paper the log-logistic model is illustrated using a nonlinear computer analysis of experimental data. The log-logistic model is an appropriate method for analyzing most dose-response studies. This model has been used widely and successfully in weed science for many years in Europe. The log-logistic model possesses several clear advantages over other analysis methods and the authors suggest that it should be widely adopted as a standard herbicide dose-response analysis method.

Crowd Counting Using Deep Learning in Edge Devices

2021 ◽  
Author(s):  
Zuo Huang ◽  
Richard Sinnott ◽  
Qiuhong Ke
Keyword(s):  
Deep Learning ◽  
Crowd Counting

Abstract 15414: Diagnostic Performance of Deep Learning on 12-lead Electrocardiography to Distinguish Takotsubo Syndrome and Acute Anterior Myocardial Infarction

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Masato Shimizu ◽  
shummo cho ◽  
Yoshiki Misu ◽  
Mari Ohmori ◽  
Ryo Tateishi ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Deep Learning ◽  
Diagnostic Performance ◽  
P Wave ◽  
Takotsubo Syndrome ◽  
Anterior Myocardial Infarction ◽  
Color Mapping ◽  
Lead Avr ◽  
The Difference ◽  
And Gender

Introduction: Takotsubo syndrome (TTS) and acute anterior myocardial infarction (ant-AMI) show very similar 12-lead electrocardiography (ECG) featured at onset, and it is often difficult to distinguish them without cardiac catheterization. The difference of ECG between them was studied, but the diagnostic performance of machine learning (deep learning) for them had not been investigated. Hypothesis: Deep learning on 12-leads ECG has high diagnostic performance to diagnose TTS and ant-AMI at onset. Methods: Consecutive 50 patients of TTS were one-to-one matched to ant-AMI randomly by their age and gender, and total 100 patients were enrolled. No sinus rhythm patients were excluded. All ECGs were divided into each 12-lead, and 5 heart beats from one lead were extracted. For each lead, 250 ECG waves of TTS/AMI were sampled as 24bit bitmap image, and prediction model construction by convolutional neural network (CNN: transfer learning, using VGG16 architecture) underwent to distinguish the two diseases in each lead. Next, gradient weighted class activation color mapping (GradCam) was performed to detect the degree and position of convolutional importance in the leads. Results: Lead aVR (mean accuracy 0.748), I (0.733), and V1 (0.678) were the top 3 leads with high accuracy. In aVR lead, GradCam showed strong convolution of negative T wave in TTS, and sharp R wave in ant-AMI. In I lead, it spotlighted several parts of ECG wave in ant-AMI. However in TTS, whole shape of the wave, P wave onset, and negative T were invertedly convoluted in TTS. Conclusions: Deep learning was a powerful tool to distinguish TTS and ant-AMI at onset, and GradCam method gave us new insight of the difference on ECG between the two diseases.

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