Robust features extraction for general fish classification

Image recognition process could be plagued by many problems including noise, overlap, distortion, errors in the outcomes of segmentation, and impediment of objects within the image. Based on feature selection and combination theory between major extracted features, this study attempts to establish a system that could recognize fish object within the image utilizing texture, anchor points, and statistical measurements. Then, a generic fish classification is executed with the application of an innovative classification evaluation through a meta-heuristic algorithm known as Memetic Algorithm (Genetic Algorithm with Simulated Annealing) with back-propagation algorithm (MA-B Classifier). Here, images of dangerous and non-dangerous fish are recognized. Images of dangerous fish are further recognized as Predatory or Poison fish family, whereas families of non-dangerous fish are classified into garden and food family. A total of 24 fish families were used in testing the proposed prototype, whereby each family encompasses different number of species. The process of classification was successfully undertaken by the proposed prototype, whereby 400 distinct fish images were used in the experimental tests. Of these fish images, 250 were used for training phase while 150 were used for testing phase. The back-propagation algorithm and the proposed MA-B Classifier produced a general accuracy recognition rate of 82.25 and 90% respectively.

Download Full-text

Two-Stage Water Jet Landing Point Prediction Model for Intelligent Water Shooting Robot

Sensors ◽

10.3390/s21082704 ◽

2021 ◽

Vol 21 (8) ◽

pp. 2704

Author(s):

Yunhan Lin ◽

Wenlong Ji ◽

Haowei He ◽

Yaojie Chen

Keyword(s):

Pitch Angle ◽

Back Propagation ◽

Water Jet ◽

The State ◽

Model Parameters ◽

Propagation Time ◽

Back Propagation Algorithm ◽

Angle Error ◽

Landing Point ◽

Propagation Algorithm

In this paper, an intelligent water shooting robot system for situations of carrier shake and target movement is designed, which uses a 2 DOF (degree of freedom) robot as an actuator, a photoelectric camera to detect and track the desired target, and a gyroscope to keep the robot’s body stable when it is mounted on the motion carriers. Particularly, for the accurate shooting of the designed system, an online tuning model of the water jet landing point based on the back-propagation algorithm was proposed. The model has two stages. In the first stage, the polyfit function of Matlab is used to fit a model that satisfies the law of jet motion in ideal conditions without interference. In the second stage, the model uses the back-propagation algorithm to update the parameters online according to the visual feedback of the landing point position. The model established by this method can dynamically eliminate the interference of external factors and realize precise on-target shooting. The simulation results show that the model can dynamically adjust the parameters according to the state relationship between the landing point and the desired target, which keeps the predicted pitch angle error within 0.1°. In the test on the actual platform, when the landing point is 0.5 m away from the position of the desired target, the model only needs 0.3 s to adjust the water jet to hit the target. Compared to the state-of-the-art method, GA-BP (genetic algorithm-back-propagation), the proposed method’s predicted pitch angle error is within 0.1 degree with 1/4 model parameters, while costing 1/7 forward propagation time and 1/200 back-propagation calculation time.

Download Full-text

APPENDIX 4. Learning with the Back-Propagation Algorithm

The Importance of Being Fuzzy ◽

10.1515/9780691187358-014 ◽

1999 ◽

pp. 166-170

Keyword(s):

Back Propagation ◽

Back Propagation Algorithm ◽

Propagation Algorithm

Download Full-text

A back-propagation algorithm applied to tool wear monitoring

International Journal of Machine Tools and Manufacture ◽

10.1016/0890-6955(94)90047-7 ◽

1994 ◽

Vol 34 (5) ◽

pp. 625-631 ◽

Cited By ~ 41

Author(s):

S. Purushothaman ◽

Y.G. Srinivasa

Keyword(s):

Tool Wear ◽

Back Propagation ◽

Back Propagation Algorithm ◽

Tool Wear Monitoring ◽

Propagation Algorithm ◽

Wear Monitoring

Download Full-text

Feed forward network with back propagation algorithm for detection of breast cancer

2013 8th International Conference on Computer Science & Education ◽

10.1109/iccse.2013.6553907 ◽

2013 ◽

Cited By ~ 1

Author(s):

G. Anjan Babu ◽

Sreenu Naik Bhukya ◽

R. Sendhil Kumar

Keyword(s):

Breast Cancer ◽

Back Propagation ◽

Back Propagation Algorithm ◽

Feed Forward ◽

Propagation Algorithm ◽

Feed Forward Network

Download Full-text

Forecasting Crude Oil price using Artificial Neural Network model

Asian Journal of Management ◽

10.52711/2321-5763.2021.00049 ◽

2021 ◽

pp. 321-326

Author(s):

Sivaprakash J. ◽

Manu K. S.

Keyword(s):

Neural Network ◽

Time Series ◽

Crude Oil ◽

Back Propagation ◽

Oil Prices ◽

Oil Price ◽

Back Propagation Algorithm ◽

Crude Oil Price ◽

Propagation Algorithm ◽

Crude Oil Prices

In the advanced global economy, crude oil is a commodity that plays a major role in every economy. As Crude oil is highly traded commodity it is essential for the investors, analysts, economists to forecast the future spot price of the crude oil appropriately. In the last year the crude oil faced a historic fall during the pandemic and reached all time low, but will this situation last? There was analysis such as fundamental analysis, technical analysis and time series analyses which were carried out for predicting the movement of the oil prices but the accuracy in such prediction is still a question. Thus, it is necessary to identify better methods to forecast the crude oil prices. This study is an empirical study to forecast crude oil prices using the neural networks. This study consists of 13 input variables with one target variable. The data are divided in the ratio 70:30. The 70% data is used for training the network and 30% is used for testing. The feed forward and back propagation algorithm are used to predict the crude oil price. The neural network proved to be efficient in forecasting in the modern era. A simple neural network performs better than the time series models. The study found that back propagation algorithm performs better while predicting the crude oil price. Hence, ANN can be used by the investors, forecasters and for future researchers.

Download Full-text