scholarly journals An Agricultural Monitoring System Based on Wireless Sensor and Depth Learning Algorithm

2017 ◽  
Vol 13 (12) ◽  
pp. 127 ◽  
Author(s):  
Liwei Geng ◽  
Tingting Dong
Keyword(s):  
Artificial Intelligence ◽  
Soil Moisture ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Future Trend ◽  
Classification Model ◽  
Wireless Sensor ◽  
Sensor Technology ◽  
Agricultural Modernization ◽  
Deep Learning Algorithm

<span style="font-family: 'Times New Roman',serif; font-size: 10pt; mso-fareast-font-family: SimSun; mso-ansi-language: EN-GB; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;" lang="EN-GB">The rise and development of the Internet of Things (IoT) have given birth to the frontier technology of the agricultural IoT, which marks the future trend in agriculture and the IoT. The agricultural IoT can be combined with Zigbee, a short-range wireless network technology for monitoring systems, to solve the excessively large planting area and other defects in agricultural production. Meanwhile, the modernization of planting and harvesting has set the stage for deep learning. Unlike the artificial neural network, the deep learning is an important intelligent algorithm, capable of solving many real-world problems. Therefore, this paper probes into the problems of modern automatic agriculture. First, the routing allocation technology and transmission mode were optimized to solve the energy consumption problem. Second, the classification model based on deep learning algorithm was put forward according to the application of the Wireless Sensor Network (WSN) in continuous monitoring of soil temperature and humidity. Despite the lack of upper soil humidity sensor in agriculture, the model can still classify the soil moisture of the nodes, and derive the main soil moisture content. Finally, a solution was presented based on agricultural ZigBee WSN technology. In addition to cheap cost and low power consumption, the solution has the functions of reminding and recognition due to the adoption of artificial intelligence algorithm. Suffice it to say that the solution is a successful attempt to integrate artificial intelligence and sensor technology into agricultural modernization.</span>

scholarly journals Using Slit-Lamp Images for Deep Learning-Based Identification of Bacterial and Fungal Keratitis: Model Development and Validation with Different Convolutional Neural Networks

Diagnostics ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1246
Author(s):  
Ning Hung ◽  
Andy Kuan-Yu Shih ◽  
Chihung Lin ◽  
Ming-Tse Kuo ◽  
Yih-Shiou Hwang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Diagnostic Accuracy ◽  
Convolutional Neural Networks ◽  
Learning Algorithm ◽  
Model Performance ◽  
Fungal Keratitis ◽  
Classification Model ◽  
Slit Lamp ◽  
Deep Learning Algorithm

In this study, we aimed to develop a deep learning model for identifying bacterial keratitis (BK) and fungal keratitis (FK) by using slit-lamp images. We retrospectively collected slit-lamp images of patients with culture-proven microbial keratitis between 1 January 2010 and 31 December 2019 from two medical centers in Taiwan. We constructed a deep learning algorithm consisting of a segmentation model for cropping cornea images and a classification model that applies different convolutional neural networks (CNNs) to differentiate between FK and BK. The CNNs included DenseNet121, DenseNet161, DenseNet169, DenseNet201, EfficientNetB3, InceptionV3, ResNet101, and ResNet50. The model performance was evaluated and presented as the area under the curve (AUC) of the receiver operating characteristic curves. A gradient-weighted class activation mapping technique was used to plot the heat map of the model. By using 1330 images from 580 patients, the deep learning algorithm achieved the highest average accuracy of 80.0%. Using different CNNs, the diagnostic accuracy for BK ranged from 79.6% to 95.9%, and that for FK ranged from 26.3% to 65.8%. The CNN of DenseNet161 showed the best model performance, with an AUC of 0.85 for both BK and FK. The heat maps revealed that the model was able to identify the corneal infiltrations. The model showed a better diagnostic accuracy than the previously reported diagnostic performance of both general ophthalmologists and corneal specialists.

scholarly journals A deep learning algorithm using CT images to screen for Corona virus disease (COVID-19)

2021 ◽  
Author(s):  
Shuai Wang ◽  
Bo Kang ◽  
Jinlu Ma ◽  
Xianjun Zeng ◽  
Mingming Xiao ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Virus Disease ◽  
Ct Images ◽  
Viral Pneumonia ◽  
Diagnostic Methods ◽  
Deep Learning Algorithm ◽  
Corona Virus ◽  
Total Accuracy

Abstract Objective The outbreak of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2) has caused more than 26 million cases of Corona virus disease (COVID-19) in the world so far. To control the spread of the disease, screening large numbers of suspected cases for appropriate quarantine and treatment are a priority. Pathogenic laboratory testing is typically the gold standard, but it bears the burden of significant false negativity, adding to the urgent need of alternative diagnostic methods to combat the disease. Based on COVID-19 radiographic changes in CT images, this study hypothesized that artificial intelligence methods might be able to extract specific graphical features of COVID-19 and provide a clinical diagnosis ahead of the pathogenic test, thus saving critical time for disease control. Methods We collected 1065 CT images of pathogen-confirmed COVID-19 cases along with those previously diagnosed with typical viral pneumonia. We modified the inception transfer-learning model to establish the algorithm, followed by internal and external validation. Results The internal validation achieved a total accuracy of 89.5% with a specificity of 0.88 and sensitivity of 0.87. The external testing dataset showed a total accuracy of 79.3% with a specificity of 0.83 and sensitivity of 0.67. In addition, in 54 COVID-19 images, the first two nucleic acid test results were negative, and 46 were predicted as COVID-19 positive by the algorithm, with an accuracy of 85.2%. Conclusion These results demonstrate the proof-of-principle for using artificial intelligence to extract radiological features for timely and accurate COVID-19 diagnosis. Key Points • The study evaluated the diagnostic performance of a deep learning algorithm using CT images to screen for COVID-19 during the influenza season. • As a screening method, our model achieved a relatively high sensitivity on internal and external CT image datasets. • The model was used to distinguish between COVID-19 and other typical viral pneumonia, both of which have quite similar radiologic characteristics.

scholarly journals Using Slit-Lamp Images for Deep Learning–Based Identification of Bacterial and Fungal Keratitis

2021 ◽  
Author(s):  
Ning Hung ◽  
Eugene Yu-Chuan Kang ◽  
Andy Guan-Yu Shih ◽  
Chi-Hung Lin ◽  
Ming‐Tse Kuo ◽  
...  
Keyword(s):  
Deep Learning ◽  
Diagnostic Accuracy ◽  
Learning Algorithm ◽  
Model Performance ◽  
Area Under The Curve ◽  
Fungal Keratitis ◽  
Classification Model ◽  
Mapping Technique ◽  
Slit Lamp ◽  
Deep Learning Algorithm

In this study, we aimed to develop a deep learning model for identifying bacterial keratitis (BK) and fungal keratitis (FK) by using slit-lamp images. We retrospectively collected slit-lamp images of patients with culture-proven microbial keratitis between January 1, 2010, and December 31, 2019, from two medical centers in Taiwan. We constructed a deep learning algorithm, consisting of a segmentation model for cropping cornea images and a classification model that applies convolutional neural networks to differentiate between FK and BK. The model performance was evaluated and presented as the area under the curve (AUC) of the receiver operating characteristic curves. A gradient-weighted class activation mapping technique was used to plot the heatmap of the model. By using 1330 images from 580 patients, the deep learning algorithm achieved an average diagnostic accuracy of 80.00%. The diagnostic accuracy for BK ranged from 79.59% to 95.91% and that for FK ranged from 26.31% to 63.15%. DenseNet169 showed the best model performance, with an AUC of 0.78 for both BK and FK. The heat maps revealed that the model was able to identify the corneal infiltrations. The model showed better diagnostic accuracy than the previously reported diagnostic performance of both general ophthalmologists and corneal specialists.

scholarly journals Selective Disinfection Based on Directional Ultraviolet Irradiation and Artificial Intelligence

Electronics ◽  
2021 ◽  
Vol 10 (20) ◽  
pp. 2557
Author(s):  
Ben Zierdt ◽  
Taichu Shi ◽  
Thomas DeGroat ◽  
Sam Furman ◽  
Nicholas Papas ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Ultraviolet Radiation ◽  
Ultraviolet Irradiation ◽  
Human Exposure ◽  
Large Scale ◽  
Learning Algorithm ◽  
Ultraviolet Disinfection ◽  
Time Efficiency ◽  
Deep Learning Algorithm

Ultraviolet disinfection has been proven to be effective for surface sanitation. Traditional ultraviolet disinfection systems generate omnidirectional radiation, which introduces safety concerns regarding human exposure. Large scale disinfection must be performed without humans present, which limits the time efficiency of disinfection. We propose and experimentally demonstrate a targeted ultraviolet disinfection system using a combination of robotics, lasers, and deep learning. The system uses a laser-galvo and a camera mounted on a two-axis gimbal running a custom deep learning algorithm. This allows ultraviolet radiation to be applied to any surface in the room where it is mounted, and the algorithm ensures that the laser targets the desired surfaces avoids others such as humans. Both the laser-galvo and the deep learning algorithm were tested for targeted disinfection.

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.

Sign in / Sign up

Export Citation Format

Share Document