scholarly journals Water tank and swimming pool detection based on remote sensing and deep learning: Relationship with socioeconomic level and applications in dengue control

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0258681
Author(s):  
Higor Souza Cunha ◽  
Brenda Santana Sclauser ◽  
Pedro Fonseca Wildemberg ◽  
Eduardo Augusto Militão Fernandes ◽  
Jefersson Alex dos Santos ◽  
...  
Keyword(s):  
Public Health ◽  
Deep Learning ◽  
Digital Images ◽  
Focal Length ◽  
Distribution Model ◽  
Socioeconomic Level ◽  
Water Tank ◽  
Learning Approaches ◽  
Swimming Pools ◽  
Water Tanks

Studies have shown that areas with lower socioeconomic standings are often more vulnerable to dengue and similar deadly diseases that can be spread through mosquitoes. This study aims to detect water tanks installed on rooftops and swimming pools in digital images to identify and classify areas based on the socioeconomic index, in order to assist public health programs in the control of diseases linked to the Aedes aegypti mosquito. This study covers four regions of Campinas, São Paulo, characterized by different socioeconomic contexts. With mosaics of images obtained by a 12.1 MP Canon PowerShot S100 (5.2 mm focal length) carried by unmanned aerial vehicles, we developed deep learning algorithms in the scope of computer vision for the detection of water tanks and swimming pools. An object detection model, which was initially created for areas of Belo Horizonte, Minas Gerais, was enhanced using the transfer learning technique, and allowed us to detect objects in Campinas with fewer samples and more efficiency. With the detection of objects in digital images, the proportions of objects per square kilometer for each region studied were estimated by adopting a Chi-square distribution model. Thus, we found that regions with low socioeconomic status had more exposed water tanks, while regions with high socioeconomic levels had more exposed pools. Using deep learning approaches, we created a useful tool for Ae. aegypti control programs to utilize and direct disease prevention efforts. Therefore, we concluded that it is possible to detect objects directly related to the socioeconomic level of a given region from digital images, which encourages the practicality of this approach for studies aimed towards public health.

scholarly journals An Experimental Comparison between Deep Learning and Classical Machine Learning Approaches for Writer Identification in Medieval Documents

2020 ◽  
Vol 6 (9) ◽  
pp. 89
Author(s):  
Nicole Dalia Cilia ◽  
Claudio De Stefano ◽  
Francesco Fontanella ◽  
Claudio Marrocco ◽  
Mario Molinara ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
High Performance ◽  
Ad Hoc ◽  
Digital Images ◽  
Experimental Comparison ◽  
Learning Approaches ◽  
Test Bed ◽  
Ancient Manuscripts ◽  
Ancient Documents

In the framework of palaeography, the availability of both effective image analysis algorithms, and high-quality digital images has favored the development of new applications for the study of ancient manuscripts and has provided new tools for decision-making support systems. The quality of the results provided by such applications, however, is strongly influenced by the selection of effective features, which should be able to capture the distinctive aspects to which the paleography expert is interested in. This process is very difficult to generalize due to the enormous variability in the type of ancient documents, produced in different historical periods with different languages and styles. The effect is that it is very difficult to define standard techniques that are general enough to be effectively used in any case, and this is the reason why ad-hoc systems, generally designed according to paleographers’ suggestions, have been designed for the analysis of ancient manuscripts. In recent years, there has been a growing scientific interest in the use of techniques based on deep learning (DL) for the automatic processing of ancient documents. This interest is not only due to their capability of designing high-performance pattern recognition systems, but also to their ability of automatically extracting features from raw data, without using any a priori knowledge. Moving from these considerations, the aim of this study is to verify if DL-based approaches may actually represent a general methodology for automatically designing machine learning systems for palaeography applications. To this purpose, we compared the performance of a DL-based approach with that of a “classical” machine learning one, in a particularly unfavorable case for DL, namely that of highly standardized schools. The rationale of this choice is to compare the obtainable results even when context information is present and discriminating: this information is ignored by DL approaches, while it is used by machine learning methods, making the comparison more significant. The experimental results refer to the use of a large sets of digital images extracted from an entire 12th-century Bibles, the “Avila Bible”. This manuscript, produced by several scribes who worked in different periods and in different places, represents a severe test bed to evaluate the efficiency of scribe identification systems.

scholarly journals Water Tanks and Swimming Pools Detection in Satellite Images: Exploiting Shallow and Deep-Based Strategies

2020 ◽  
Author(s):  
Eduardo Fernandes ◽  
Pedro Wildemberg ◽  
Jefersson Dos Santos
Keyword(s):  
Satellite Images ◽  
Detection Task ◽  
Support Vector ◽  
Water Tank ◽  
Learning Method ◽  
Swimming Pools ◽  
Average Precision ◽  
Belo Horizonte ◽  
Water Tanks ◽  
The City

This paper aims to study and to evaluate two distinct approaches for detecting water tanks and swimming pools in satellite images, which can be useful to monitor waterrelated diseases. The first approach, shallow, consists of using a Support Vector Machine in order to classify into positive and negative a discretized color histogram of a given segment of the original image. The second method employs the Faster R-CNN framework for detecting those objects. We built up swimming pools and water tanks datasets over the city of Belo Horizonte to support our experimental analysis. Our results show that the deep learning method greatly outperforms the shallow strategy, achieving an average precision at 0.5 IoU of over 93% on the swimming pool detection task, and over 73% on the water tank one. All the code and datasets are publicly available.

Deep Learning Approaches for Whiteboard Image Quality Enhancement

2019 ◽  
Vol 2019 (1) ◽  
pp. 360-368
Author(s):  
Mekides Assefa Abebe ◽  
Jon Yngve Hardeberg
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Image Data ◽  
Quality Enhancement ◽  
Network Architectures ◽  
Learning Approaches ◽  
Data Set ◽  
Image Quality Enhancement ◽  
Processing Techniques ◽  
White Balancing

Different whiteboard image degradations highly reduce the legibility of pen-stroke content as well as the overall quality of the images. Consequently, different researchers addressed the problem through different image enhancement techniques. Most of the state-of-the-art approaches applied common image processing techniques such as background foreground segmentation, text extraction, contrast and color enhancements and white balancing. However, such types of conventional enhancement methods are incapable of recovering severely degraded pen-stroke contents and produce artifacts in the presence of complex pen-stroke illustrations. In order to surmount such problems, the authors have proposed a deep learning based solution. They have contributed a new whiteboard image data set and adopted two deep convolutional neural network architectures for whiteboard image quality enhancement applications. Their different evaluations of the trained models demonstrated their superior performances over the conventional methods.

Study and Evaluation of Stored Drinking Water Characteristics Using Three Types of Roof Water Tanks in Kirkuk Governorate, Iraq

2019 ◽  
Vol 7 (1) ◽  
pp. 11-16
Author(s):  
Abdulkhaleq K Mahmood ◽  
Ali A Kamal ◽  
Ako R Hama
Keyword(s):  
Drinking Water ◽  
Storage Time ◽  
Total Dissolved Solids ◽  
Water Tank ◽  
Dissolved Solids ◽  
Water Characteristics ◽  
Three Samples ◽  
Water Metal ◽  
Water Tanks ◽  
Better Than

The scarcity of safe drinking water is one of the problems faced by the majority of cities in the world. Kirkuk city is one of these cities, which suffer from a shortage of drinking water. People have adopted the use of different rooftop tanks to overcome this problem. This research focuses on studying the effect of storage time on the five main characteristics of drinking water, which include, acid index (pH), electrical conductivity (EC), total suspended solids (TSS), total dissolved solids (TDS), and turbidity (Tr). Three types of tanks were used predominantly (galvanized metal, plastic, and aluminum tanks). By analyzing the results, the characteristics of three samples of municipal source water obtained. Three samples were taken from each tank at different periods (4, 8, and 12 days). The results showed that the storage time affected the characteristics of drinking water. These characteristics differed from one tank to another. Metal tanks showed an increase in total dissolved solids, due to the evaporation process, even as plastic and aluminum tanks showed an increase in pH. The properties of all storage water tanks changed with times, but overall, the results were within the Iraqi limitation for drinking water. It was not easy to only depend on the results of this study to believe that any one type of water tank was better than the other, as the values of most of the variables studied had varied from one type to other. However, many studies have indicated a number of health risks, and most significantly with regard to plastic tanks, which are said to contain dangerous organic compounds that can be transferred to water. Metal tanks can cause zinc leakage, caused by a number of environmental factors at high levels. Aluminum tanks also can have an effect on the water in tanks.

Assessment of the Risk Factors of MDD Recurrence Based on Deep Learning Approaches

2019 ◽  
Author(s):  
Qian Wu ◽  
Weiling Zhao ◽  
Xiaobo Yang ◽  
Hua Tan ◽  
Lei You ◽  
...  
Keyword(s):  
Risk Factors ◽  
Deep Learning ◽  
Learning Approaches

The Problem of Fraudulent Content on the Web: Deep Learning Approaches

2020 ◽  
Author(s):  
Priyanka Meel ◽  
Farhin Bano ◽  
Dr. Dinesh K. Vishwakarma
Keyword(s):  
Deep Learning ◽  
Learning Approaches ◽  
The Web

Project Review on Water Level Sensing Using PLC

2016 ◽  
Vol 2 (2) ◽  
Author(s):  
Osama Mahfooz ◽  
Mujtaba Memon ◽  
Asim Iftikhar
Keyword(s):  
Automatic Control ◽  
Water Level ◽  
Digital Computer ◽  
Human Error ◽  
Water Tank ◽  
Industrial Plants ◽  
Commercial Use ◽  
The Cost ◽  
Water Tanks

<span>A PLC is a digital computer used to automate electromechanical processes. This research is<span> based on automation of a water tank by using Siemens PLC. Automatic control of water tanks<span> can work continuously and can provide accurate quantity of water in less time. In such process<span> there is no need of labor so there is no human error. Without human error, the quality of product<span> is better and the cost of production would definitely decrease with no error in quantity required.<span> Water level sensing can be implemented in industrial plants, commercial use and even at home<br /><br class="Apple-interchange-newline" /></span></span></span></span></span></span>

scholarly journals Deep convolutional neural networks for cardiovascular vulnerable plaque detection

2019 ◽  
Vol 277 ◽  
pp. 02024 ◽  
Author(s):  
Lincan Li ◽  
Tong Jia ◽  
Tianqi Meng ◽  
Yizhe Liu
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Vulnerable Plaque ◽  
Recall Rate ◽  
Superior Performance ◽  
Learning Approaches ◽  
Deep Convolutional Neural Networks ◽  
Vulnerable Plaques ◽  
Plaque Detection

In this paper, an accurate two-stage deep learning method is proposed to detect vulnerable plaques in ultrasonic images of cardiovascular. Firstly, a Fully Convonutional Neural Network (FCN) named U-Net is used to segment the original Intravascular Optical Coherence Tomography (IVOCT) cardiovascular images. We experiment on different threshold values to find the best threshold for removing noise and background in the original images. Secondly, a modified Faster RCNN is adopted to do precise detection. The modified Faster R-CNN utilize six-scale anchors (122,162,322,642,1282,2562) instead of the conventional one scale or three scale approaches. First, we present three problems in cardiovascular vulnerable plaque diagnosis, then we demonstrate how our method solve these problems. The proposed method in this paper apply deep convolutional neural networks to the whole diagnostic procedure. Test results show the Recall rate, Precision rate, IoU (Intersection-over-Union) rate and Total score are 0.94, 0.885, 0.913 and 0.913 respectively, higher than the 1st team of CCCV2017 Cardiovascular OCT Vulnerable Plaque Detection Challenge. AP of the designed Faster RCNN is 83.4%, higher than conventional approaches which use one-scale or three-scale anchors. These results demonstrate the superior performance of our proposed method and the power of deep learning approaches in diagnose cardiovascular vulnerable plaques.

scholarly journals Deep learning systems detect dysplasia with human-like accuracy using histopathology and probe-based confocal laser endomicroscopy

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shan Guleria ◽  
Tilak U. Shah ◽  
J. Vincent Pulido ◽  
Matthew Fasullo ◽  
Lubaina Ehsan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Diagnostic Accuracy ◽  
High Sensitivity ◽  
Confocal Laser Endomicroscopy ◽  
Confocal Laser ◽  
Learning Approaches ◽  
Learning Models ◽  
Whole Slide Image ◽  
Slide Image ◽  
Level Model

AbstractProbe-based confocal laser endomicroscopy (pCLE) allows for real-time diagnosis of dysplasia and cancer in Barrett’s esophagus (BE) but is limited by low sensitivity. Even the gold standard of histopathology is hindered by poor agreement between pathologists. We deployed deep-learning-based image and video analysis in order to improve diagnostic accuracy of pCLE videos and biopsy images. Blinded experts categorized biopsies and pCLE videos as squamous, non-dysplastic BE, or dysplasia/cancer, and deep learning models were trained to classify the data into these three categories. Biopsy classification was conducted using two distinct approaches—a patch-level model and a whole-slide-image-level model. Gradient-weighted class activation maps (Grad-CAMs) were extracted from pCLE and biopsy models in order to determine tissue structures deemed relevant by the models. 1970 pCLE videos, 897,931 biopsy patches, and 387 whole-slide images were used to train, test, and validate the models. In pCLE analysis, models achieved a high sensitivity for dysplasia (71%) and an overall accuracy of 90% for all classes. For biopsies at the patch level, the model achieved a sensitivity of 72% for dysplasia and an overall accuracy of 90%. The whole-slide-image-level model achieved a sensitivity of 90% for dysplasia and 94% overall accuracy. Grad-CAMs for all models showed activation in medically relevant tissue regions. Our deep learning models achieved high diagnostic accuracy for both pCLE-based and histopathologic diagnosis of esophageal dysplasia and its precursors, similar to human accuracy in prior studies. These machine learning approaches may improve accuracy and efficiency of current screening protocols.

Two deep learning approaches to forecasting disaggregated freight flows: convolutional and encoder–decoder recurrent

2021 ◽  
Author(s):  
Isidro Lloret ◽  
José A. Troyano ◽  
Fernando Enríquez ◽  
Juan-José González-de-la-Rosa
Keyword(s):  
Deep Learning ◽  
Learning Approaches
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