Neural network for crop rotation and soil analysis in a Greenhouse

Currently, Artificial intelligence (AI) is a very important area, the way in which it has revolutionized has allowed it to be an essential part of technological evolution in different sectors of society such as agriculture, it is a fundamental activity in the development of our country, and one of the developing areas is implementation of greenhouse crop. This article describes the use of artificial intelligence for a greenhouse through an Artificial Neural Network (ANN) of the multilayer perceptron type using the BackPropagation algorithm. The main aim is obtain the most optimal type of crop to be sown by means of the crop rotation, which, supported by a data acquisition device through sensors, obtains the values of temperature and humidity of the environment and soil pH, with those data the ANN makes the soil analysis. Through the interfaces of the data analysis module and the measurement module, the data collection process, the calculation and the results produced by the artificial neural network are shown. For this project, the Prototype model was used using the Java programming language.

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Applications of Artificial Intelligence Techniques to Enhance Sustainability of Industry 4.0: Design of an Artificial Neural Network Model as Dynamic Behavior Optimizer of Robotic Arms

Complexity ◽

10.1155/2020/8564140 ◽

2020 ◽

Vol 2020 ◽

pp. 1-10 ◽

Cited By ~ 1

Author(s):

Aydin Azizi

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Artificial Neural Network ◽

Industrial Robots ◽

Optimal Parameters ◽

Ann Model ◽

Robotic Arms ◽

Revolute Joints ◽

Artificial Neural ◽

Artificial Neural Network Ann

Industrial robots have a great impact on increasing the productivity and reducing the time of the manufacturing process. To serve this purpose, in the past decade, many researchers have concentrated to optimize robotic models utilizing artificial intelligence (AI) techniques. Gimbal joints because of their adjustable mechanical advantages have been investigated as a replacement for traditional revolute joints, especially when they are supposed to have tiny motions. In this research, the genetic algorithm (GA), a well-known evolutionary technique, has been adopted to find optimal parameters of the gimbal joints. Since adopting the GA is a time-consuming process, an artificial neural network (ANN) architecture has been proposed to model the behavior of the GA. The result shows that the proposed ANN model can be used instead of the complex and time-consuming GA in the process of finding the optimal parameters of the gimbal joint.

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Modeling and Simulation of Artificial Neural Network Controlled Soft Switched Push–Pull DC to DC Converter

Journal of Computational and Theoretical Nanoscience ◽

10.1166/jctn.2017.6365 ◽

2017 ◽

Vol 14 (1) ◽

pp. 585-590 ◽

Cited By ~ 1

Author(s):

S Devikala ◽

V Sivachidambaranathan

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Prototype Model ◽

Switching Power ◽

Advantages And Disadvantages ◽

Unique System ◽

Control Scheme ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Storage Batteries

This paper presents the performance of DC/DC Push–Pull converter for storage batteries. Some of the DC/DC converters are analyzed for finding their advantages and disadvantages. Moreover, a unique system based on a Push–Pull converter associated with an active filter and superior controller is chosen. The main advantage is the possibility to minimize the ripple at the output, decrease the switching power losses, increase the power conversion efficiency and improve the transient and steady state response. This paper proposes a new filter, control scheme and Artificial Neural Network (ANN) controlled Push–Pull DC/DC converter. Simulation was done using MATLAB Simulink and designed biasing for the PIC 16F84 microcontroller. The performance of the proposed system has been verified through a 1 kW prototype model of the system for a 15 KHz, 48/12 V DC for battery. The simulation results are validated with experimental results.

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A Prototype Model to Detect Elephants near the Railway Tracks

Advances in Modelling and Analysis B ◽

10.18280/ama_b.631-402 ◽

2020 ◽

Vol 63 (1-4) ◽

pp. 7-9

Author(s):

Rakesh Kumar Mandal

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Expert System ◽

Prototype Model ◽

Ann Model ◽

Railway Tracks ◽

Research Areas ◽

Exact Position ◽

Artificial Neural ◽

Artificial Neural Network Ann

Artificial Neural Network (ANN) technologies are becoming very popular. It has been used almost in all the research areas. Approach in this paper is to develop an automated expert system to drive away Elephants found near the railway tracks to stop the casualties of these animals on the railway tracks. In this paper, a prototype model has been designed using Geophone Sensors which recognizes the vibrations of the Elephants roaming near the railway tracks. These vibrations are sent to the nearby servers with the help of Arduino. The server runs software based on the ANN model developed here. It detects the exact position of the Elephants present near the railway tracks and raises an alarm to drive them away.

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How to estimate and predict the expenses incurred by diabetes treatment using Artificial Neural Network (ANN)

Journal of Biology and Today s World ◽

10.15412/j.jbtw.01030101 ◽

2014 ◽

Vol 3 (1) ◽

Author(s):

M. Mahboubi ◽

M. Rahimi ◽

M. Mohebbi ◽

F. Ghahramani

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Diabetes Treatment ◽

Artificial Neural ◽

Artificial Neural Network Ann

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Application of the Artificial Neural Network (ANN) Method as MPPT Photovoltaic for DC Source Storage

International Review of Automatic Control (IREACO) ◽

10.15866/ireaco.v12i3.16455 ◽

2019 ◽

Vol 12 (3) ◽

pp. 145 ◽

Cited By ~ 1

Author(s):

Epyk Sunarno ◽

Ramadhan Bilal Assidiq ◽

Syechu Dwitya Nugraha ◽

Indhana Sudiharto ◽

Ony Asrarul Qudsi ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Artificial Neural ◽

Artificial Neural Network Ann

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LOAD SCHEDULING FOR SMART HOME USING DAY-AHEAD PREDICTION FROM ARTIFICIAL NEURAL NETWORK (ANN)

International Journal of Advanced Trends in Computer Science and Engineering ◽

10.30534/ijatcse/2020/9291.42020 ◽

2020 ◽

Vol 9 (1.4) ◽

pp. 658-663

Author(s):

Joharry S. H.

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Smart Home ◽

Load Scheduling ◽

Artificial Neural ◽

Artificial Neural Network Ann

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Random Forest (RF) and Artificial Neural Network (ANN) Algorithms for LULC Mapping

Engineering and Technology Journal ◽

10.30684/etj.v38i4a.399 ◽

2020 ◽

Vol 38 (4A) ◽

pp. 510-514

Author(s):

Tay H. Shihab ◽

Amjed N. Al-Hameedawi ◽

Ammar M. Hamza

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Artificial Neural Network ◽

Random Forest ◽

Satellite Image ◽

Landsat 8 ◽

Optical Remote Sensing ◽

Data Set ◽

Artificial Neural ◽

Artificial Neural Network Ann

In this paper to make use of complementary potential in the mapping of LULC spatial data is acquired from LandSat 8 OLI sensor images are taken in 2019. They have been rectified, enhanced and then classified according to Random forest (RF) and artificial neural network (ANN) methods. Optical remote sensing images have been used to get information on the status of LULC classification, and extraction details. The classification of both satellite image types is used to extract features and to analyse LULC of the study area. The results of the classification showed that the artificial neural network method outperforms the random forest method. The required image processing has been made for Optical Remote Sensing Data to be used in LULC mapping, include the geometric correction, Image Enhancements, The overall accuracy when using the ANN methods 0.91 and the kappa accuracy was found 0.89 for the training data set. While the overall accuracy and the kappa accuracy of the test dataset were found 0.89 and 0.87 respectively.

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Detection of Biomedical Images by Using Bio-inspired Artificial Intelligent

Engineering and Technology Journal ◽

10.30684/etj.v38i2a.319 ◽

2020 ◽

Vol 38 (2A) ◽

pp. 255-264

Author(s):

Hanan A. R. Akkar ◽

Sameem A. Salman

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Mean Square Error ◽

Average Rate ◽

Mean Square ◽

Performance Measurements ◽

Artificial Intelligent ◽

Average Mean Square Error ◽

Artificial Neural ◽

Artificial Neural Network Ann

Computer vision and image processing are extremely necessary for medical pictures analysis. During this paper, a method of Bio-inspired Artificial Intelligent (AI) optimization supported by an artificial neural network (ANN) has been widely used to detect pictures of skin carcinoma. A Moth Flame Optimization (MFO) is utilized to educate the artificial neural network (ANN). A different feature is an extract to train the classifier. The comparison has been formed with the projected sample and two Artificial Intelligent optimizations, primarily based on classifier especially with, ANN-ACO (ANN training with Ant Colony Optimization (ACO)) and ANN-PSO (training ANN with Particle Swarm Optimization (PSO)). The results were assessed using a variety of overall performance measurements to measure indicators such as Average Rate of Detection (ARD), Average Mean Square error (AMSTR) obtained from training, Average Mean Square error (AMSTE) obtained for testing the trained network, the Average Effective Processing Time (AEPT) in seconds, and the Average Effective Iteration Number (AEIN). Experimental results clearly show the superiority of the proposed (ANN-MFO) model with different features.

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