scholarly journals Deep Learning and Internet of Things for Beach Monitoring: An Experimental Study of Beach Attendance Prediction at Castelldefels Beach

2021 ◽  
Vol 11 (22) ◽  
pp. 10735
Author(s):  
Mari Carmen Domingo
Keyword(s):  
Experimental Study ◽  
Deep Learning ◽  
Internet Of Things ◽  
Cyber Security ◽  
Learning Algorithm ◽  
Absolute Error ◽  
K Nearest Neighbors ◽  
Smart Transportation ◽  
Deep Learning Algorithm ◽  
Smart Healthcare

Smart seaside cities can fully exploit the capabilities brought by Internet of Things (IoT) and artificial intelligence to improve the efficiency of city services in traditional smart city applications: smart home, smart healthcare, smart transportation, smart surveillance, smart environment, cyber security, etc. However, smart coastal cities are characterized by their specific application domain, namely, beach monitoring. Beach attendance prediction is a beach monitoring application of particular importance for coastal managers to successfully plan beach services in terms of security, rescue, health and environmental assistance. In this paper, an experimental study that uses IoT data and deep learning to predict the number of beach visitors at Castelldefels beach (Barcelona, Spain) was developed. Images of Castelldefels beach were captured by a video monitoring system. An image recognition software was used to estimate beach attendance. A deep learning algorithm (deep neural network) to predict beach attendance was developed. The experimental results prove the feasibility of Deep Neural Networks (DNNs) for beach attendance prediction. For each beach, a classification of occupancy was estimated, depending on the number of beach visitors. The proposed model outperforms other machine learning models (decision tree, k-nearest neighbors, and random forest) and can successfully classify seven beach occupancy levels with the Mean Absolute Error (MAE), accuracy, precision, recall and F1-score of 0.03, 92.7%, 92.9%, 92.7%, and 92.7%, respectively.

scholarly journals DEEP LEARNING-BASED OBJECT RECOGNITION ROBOT CONTROL VIA WEB AND MOBILE USING AN INTERNET OF THINGS (IoT) CONNECTION

Kursor ◽  
2020 ◽  
Vol 10 (4) ◽  
Author(s):  
Basuki Rahmat ◽  
Budi Nugroho
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Robot Control ◽  
Learning Algorithm ◽  
Control Program ◽  
Robotic Control ◽  
Intelligent Surveillance ◽  
Deep Learning Algorithm ◽  
Electronic Board ◽  
Mobile Robot Control

The paper presents the intelligent surveillance robotic control techniques via web and mobile via an Internet of Things (IoT) connection. The robot is equipped with a Kinect Xbox 360 camera and a Deep Learning algorithm for recognizing objects in front of it. The Deep Learning algorithm used is OpenCV's Deep Neural Network (DNN). The intelligent surveillance robot in this study was named BNU 4.0. The brain controlling this robot is the NodeMCU V3 microcontroller. Electronic board based on the ESP8266 chip. With this chip, NodeMCU V3 can connect to the cloud Internet of Things (IoT). Cloud IoT used in this research is cloudmqtt (https://www.cloudmqtt.com). With the Arduino program embedded in the NodeMCU V3 microcontroller, it can then run the robot control program via web and mobile. The mobile robot control program uses the Android MQTT IoT Application Panel.

scholarly journals Real-Time Detection and Location of Potted Flowers Based on a ZED Camera and a YOLO V4-Tiny Deep Learning Algorithm

Horticulturae ◽  
2021 ◽  
Vol 8 (1) ◽  
pp. 21
Author(s):  
Jizhang Wang ◽  
Zhiheng Gao ◽  
Yun Zhang ◽  
Jing Zhou ◽  
Jianzhi Wu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Point Cloud ◽  
Learning Algorithm ◽  
Absolute Error ◽  
Frame Rate ◽  
3D Point Cloud ◽  
The Real ◽  
Deep Learning Algorithm ◽  
The Mean

In order to realize the real-time and accurate detection of potted flowers on benches, in this paper we propose a method based on the ZED 2 stereo camera and the YOLO V4-Tiny deep learning algorithm for potted flower detection and location. First, an automatic detection model of flowers was established based on the YOLO V4-Tiny convolutional neural network (CNN) model, and the center points on the pixel plane of the flowers were obtained according to the prediction box. Then, the real-time 3D point cloud information obtained by the ZED 2 camera was used to calculate the actual position of the flowers. The test results showed that the mean average precision (MAP) and recall rate of the training model was 89.72% and 80%, respectively, and the real-time average detection frame rate of the model deployed under Jetson TX2 was 16 FPS. The results of the occlusion experiment showed that when the canopy overlap ratio between the two flowers is more than 10%, the recognition accuracy will be affected. The mean absolute error of the flower center location based on 3D point cloud information of the ZED 2 camera was 18.1 mm, and the maximum locating error of the flower center was 25.8 mm under different light radiation conditions. The method in this paper establishes the relationship between the detection target of flowers and the actual spatial location, which has reference significance for the machinery and automatic management of potted flowers on benches.

scholarly journals A Robot Obstacle Avoidance Method based on Random Forest HTM Cortical Learning Algorithm

Webology ◽  
2020 ◽  
Vol 17 (2) ◽  
pp. 788-803
Author(s):  
Ahmed Mahdi Abdulkadium
Keyword(s):  
Deep Learning ◽  
Random Forest ◽  
Obstacle Avoidance ◽  
Time Complexity ◽  
Learning Algorithm ◽  
Operation Time ◽  
Absolute Error ◽  
Classification Model ◽  
Forecasting Accuracy ◽  
Deep Learning Algorithm

Robotics mainly concern with the movement of robot with improvement obstacle avoidance, this issue is handed. It contains of a Microcontroller to process the data, and Ultrasonic sensors to detect the obstacles on its path. Artificial intelligence is used to predict the presence of obstacle in the path. In this research random forest algorithm is used and it is improved by RFHTMC algorithm. Deep learning mainly compromises of reducing the mean absolute error of forecasting. Problem with random forest is time complexity, as it involves formation of many classification trees. The proposed algorithm reduces the set of rules which is used for classification model, to improve time complexity. Performance analysis shows an significant improvement in results as compare to other deep learning algorithm as well as random forest. Forecasting accuracy shows 8% improvement as compare to random forest with 26% reduced operation time.

scholarly journals Iraqi License Plate Detection and Segmentation based on Deep Learning

2021 ◽  
Vol 17 (2) ◽  
pp. 102-107
Author(s):  
Ghida Abbass ◽  
Ali Marhoon
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
License Plate ◽  
License Plate Recognition ◽  
K Nearest Neighbors ◽  
Deep Learning Algorithm ◽  
Learning Techniques ◽  
License Plate Detection ◽  
Accuracy Of Results ◽  
Speed And Accuracy

Nowadays, the trend has become to utilize Artificial Intelligence techniques to replace the human's mind in problem solving. Vehicle License Plate Recognition (VLPR) is one of these problems in which the computer outperforms the human being in terms of processing speed and accuracy of results. The emergence of deep learning techniques enhances and simplifies this task. This work emphasis on detecting the Iraqi License Plates based on SSD Deep Learning Algorithm. Then Segmenting the plate using horizontal and vertical shredding. Finally, the K-Nearest Neighbors (KNN) algorithm utilized to specify the type of car. The proposed system evaluated by using a group of 500 different Iraqi Vehicles. The successful results show that 98% regarding the plate detection, and 96% for segmenting operation.

scholarly journals A Deep-Learning Algorithm (ECG12Net) for Detecting Hypokalemia and Hyperkalemia by Electrocardiography: Algorithm Development

10.2196/15931 ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. e15931 ◽  
Author(s):  
Chin-Sheng Lin ◽  
Chin Lin ◽  
Wen-Hui Fang ◽  
Chia-Jung Hsu ◽  
Sy-Jou Chen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cardiac Tissue ◽  
Learning Algorithm ◽  
Area Under The Curve ◽  
Absolute Error ◽  
Learning Model ◽  
Cardiac Events ◽  
Deep Learning Algorithm ◽  
And Performance ◽  
Deep Learning Model

Background The detection of dyskalemias—hypokalemia and hyperkalemia—currently depends on laboratory tests. Since cardiac tissue is very sensitive to dyskalemia, electrocardiography (ECG) may be able to uncover clinically important dyskalemias before laboratory results. Objective Our study aimed to develop a deep-learning model, ECG12Net, to detect dyskalemias based on ECG presentations and to evaluate the logic and performance of this model. Methods Spanning from May 2011 to December 2016, 66,321 ECG records with corresponding serum potassium (K+) concentrations were obtained from 40,180 patients admitted to the emergency department. ECG12Net is an 82-layer convolutional neural network that estimates serum K+ concentration. Six clinicians—three emergency physicians and three cardiologists—participated in human-machine competition. Sensitivity, specificity, and balance accuracy were used to evaluate the performance of ECG12Net with that of these physicians. Results In a human-machine competition including 300 ECGs of different serum K+ concentrations, the area under the curve for detecting hypokalemia and hyperkalemia with ECG12Net was 0.926 and 0.958, respectively, which was significantly better than that of our best clinicians. Moreover, in detecting hypokalemia and hyperkalemia, the sensitivities were 96.7% and 83.3%, respectively, and the specificities were 93.3% and 97.8%, respectively. In a test set including 13,222 ECGs, ECG12Net had a similar performance in terms of sensitivity for severe hypokalemia (95.6%) and severe hyperkalemia (84.5%), with a mean absolute error of 0.531. The specificities for detecting hypokalemia and hyperkalemia were 81.6% and 96.0%, respectively. Conclusions A deep-learning model based on a 12-lead ECG may help physicians promptly recognize severe dyskalemias and thereby potentially reduce cardiac events.

scholarly journals Forecasting Gold Prices in India using Time series and Deep Learning Algorithms

2021 ◽  
Vol 10 (5) ◽  
pp. 21-27
Author(s):  
P Sai Shankar ◽  
M Krishna Reddy
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Arima Model ◽  
Absolute Error ◽  
Percentage Error ◽  
Deep Learning Algorithm ◽  
Primary Object ◽  
Daily Data ◽  
Forecasting Performance

The primary object of this paper is to compare the traditional time series models with deep learning algorithm. The ARIMA model is developed to forecast Indian Gold prices using daily data for the period 2016 to 2020 obtained from World Gold Council. We fitted the ARIMA (2,1,2) model which exhibited the least AIC values. In the meanwhile, MLP, CNN and LSTM models are also examined to forecast the gold prices in India. Mean absolute error, mean absolute percentage error and root mean squared errors used to evaluate the forecasting performance of the models. Hence, LSTM model superior than that of the other three models for forecasting the gold prices in India.

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