Thai Water Buffalo Disease Analysis with the Application of Feature Selection Technique and Multi-Layer Perceptron Neural Network

Int. J. Interact. Mob. Technol.

Poomhiran²

2022

This research proposes improving the aquarium environment for ornamental fish farming for small enterprises raising ornamental fish for sale during the COVID-19 Pandemic with an automatic aquarium system capable of forecasting the optimum environment for fish using Multi-Layer Perceptron Neural Network. Since the amount of the collected data was limited, it was also employed to adjust the imbalanced dataset by applying the Synthetic Minority OverSampling Technique to increase forecasting accuracy. Subsequently, the developed system is based on Internet of Things devices in conjunction with sensors for measuring the indicators that affect the aquarium environment, including temperature, turbidity, total dissolved solids, the potential of hydrogen ions, dissolved oxygen, and nitrate ion. Further, the mobile application was developed and collaborated with sensors and devices to facilitate entrepreneurs monitoring and controlling this automatic system. The results showed that the accuracy of the developed water environment forecasting model was 97.31% and gave the highest level of automation efficiency. Therefore, the developed automated aquarium system could be applied to reduce fish mortality and maintain environmental conditions to grow adequately over the fish life span.

Section: Multi-layer Perceptron Neural Networkmentioning

confidence: 99%

Multi-Layer Perceptron Neural Network and Internet of Things for Improving the Realtime Aquatic Ecosystem Quality Monitoring and Analysis

Int. J. Interact. Mob. Technol.

Poomhiran²

2022

“…Thus, all 12 models were evaluated by 10-fold cross-validation in this work. The performance of the models was validated on accuracy (ACC) [15], precision (PREC) [16], recall (REC) [17], and F1-Score.…”

Section: E Model Evaluationmentioning

confidence: 99%

A Hybrid Deep Learning and Optimized Machine Learning Approach for Rose Leaf Disease Classification

Eng. Technol. Appl. Sci. Res.

2021

Self Cite

Analysis of the symptoms of rose leaves can identify up to 15 different diseases. This research aims to develop Convolutional Neural Network models for classifying the diseases on rose leaves using hybrid deep learning techniques with Support Vector Machine (SVM). The developed models were based on the VGG16 architecture and early or late fusion techniques were applied to concatenate the output from a fully connected layer. The results showed that the developed models based on early fusion performed better than the developed models on either late fusion or VGG16 alone. In addition, it was found that the models using the SVM classifier had better efficiency in classifying the diseases appearing on rose leaves than the models using the softmax function classifier. In particular, a hybrid deep learning model based on early fusion and SVM, which applied the categorical hinge loss function, yielded a validation accuracy of 88.33% and a validation loss of 0.0679, which were higher than the ones of the other models. Moreover, this model was evaluated by 10-fold cross-validation with 90.26% accuracy, 90.59% precision, 92.44% recall, and 91.50% F1-score for disease classification on rose leaves.

“…The results showed that the model used to classify cardiac arrhythmias using Wrapper combined with MLP had the best performance. Authors in [14] developed an MLP model by applying the Correlation-based Feature Selection (CFS) and IG to analyze Thai water buffalo diseases. The experimental results showed that the developed model by CFS and MLP was efficient with accuracy, precision, and recall greater than 99.0%.…”

Section: E Related Workmentioning

confidence: 99%

Multi-Layer Perceptron Neural Network Model Development for Chili Pepper Disease Diagnosis Using Filter and Wrapper Feature Selection Methods

Eng. Technol. Appl. Sci. Res.

Sriurai

2021

Self Cite

The goal of the current study is to develop a diagnosis model for chili pepper disease diagnosis by applying filter and wrapper feature selection methods as well as a Multi-Layer Perceptron Neural Network (MLPNN). The data used for developing the model include 1) types, 2) causative agents, 3) areas of infection, 4) growth stages of infection, 5) conditions, 6) symptoms, and 7) 14 types of chili pepper diseases. These datasets were applied to the 3 feature selection techniques, including information gain, gain ratio, and wrapper. After selecting the key features, the selected datasets were utilized to develop the diagnosis model towards the application of MLPNN. According to the model’s effectiveness evaluation results, estimated by 10-fold cross-validation, it can be seen that the diagnosis model developed by applying the wrapper method along with MLPNN provided the highest level of effectiveness, with an accuracy of 98.91%, precision of 98.92%, and recall of 98.89%. The findings showed that the developed model is applicable.