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

Nuanmeesri, Sumitra; Sriurai, Wongkot

doi:10.48084/etasr.4383

Cited by 7 publications

(6 citation statements)

References 10 publications

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“…In [23], medical insurance data from a large urban area were engaged to build a Bayesian network model to predict future high-cost COPD patients. In [24], a chili pepper disease diagnosis model was developed implementing MLPNN, achieving 98.91% accuracy and suggesting its potential for disease prevention and treatment, aligning with previous studies showcasing MLP's effectiveness in disease diagnosis.…”

Section: Introductionsupporting

confidence: 55%

Chronic Obstructive Pulmonary Disease Diagnosis with Bagging Ensemble Learning and ANN Classifiers

Siddiqui,

Latif,

Farooq

et al. 2024

Eng. Technol. Appl. Sci. Res.

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Chronic Obstructive Pulmonary Disease (COPD) is a persistent respiratory disease that poses a significant threat to global human health with elevated incidence and mortality rates. Timely recognition and diagnosis of COPD play a pivotal role in efficiently managing and treating the condition. The incorporation of deep learning technologies into healthcare has significant potential to enhance diagnostics and treatment outcomes. This study proposes an innovative deep-learning approach along with an ensemble technique to address the imperative need for an effective predictive model in COPD disease classification, particularly in situations with limited available data. This was achieved by leveraging the ensemble bagging technique and incorporating ANN as a classifier within this framework. Training and evaluation of the proposed ensemble ANN model were performed on a dataset comprising a variety of attributes, including demographic information, medical history, diagnostic measurements, and pollution exposures. Data were collected from people aged 18 to 60 originating from Pakistan, encompassing patients, attendants, hospital staff, faculty, and students. The effectiveness of the model in classifying COPD was measured using F1 score, recall, precision, and accuracy. The evaluation of the model produced notable results, as it achieved a 90% F1 score, 96% recall, 84% precision, and 89% accuracy in identifying the presence of COPD in individuals. Furthermore, this study carried out a comparative analysis between a standalone ANN model and the proposed ensemble ANN model which revealed that the proposed Ensemble ANN model outperforms existing methods, particularly in scenarios with limited sample size. This research provides substantial contributions to healthcare technology, as it presents an efficient tool for COPD prediction, facilitates early intervention, and significantly increases the overall standard of patient care.

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Section: Introductionsupporting

confidence: 55%

Chronic Obstructive Pulmonary Disease Diagnosis with Bagging Ensemble Learning and ANN Classifiers

Siddiqui,

Latif,

Farooq

et al. 2024

Eng. Technol. Appl. Sci. Res.

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“…Through cross-validation, the accuracy, precision, and recall of the model reached 98.93%, 98.92%, and 98.89%, respectively. However, the key features of the disease may not be selected by filtering and packaging feature selection methods, so there may be a certain risk of overfitting the model [ 16 ]; the methods of Chen, W et al were conducive to processing pepper images in an HSV color space, enabling a convolutional neural network (CNN) to extract additional features. Compared with the RGB color space, this method improved its accuracy and other indicators, and it was more suitable for diseases with prominent color features, such as viral diseases, but for diseases that do not pay attention to color space, it has certain limitations [ 17 ]; Sharma, R et al proposed the detection of Pepper Leaf Blight Disease (PLBD) in pepper leaves based on the faster region-based convolutional neural network, R-CNN, and a multi-classification method to evaluate the model’s performance, the detection accuracy and multi-classification accuracy were 99.39% and 98.38%, respectively, and the computational efficiency of the model was evaluated.…”

Section: Introductionmentioning

confidence: 99%

Identification of Pepper Leaf Diseases Based on TPSAO-AMWNet

Wan,

Zhu,

Dai

et al. 2024

Plants

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Pepper is a high-economic-value agricultural crop that faces diverse disease challenges such as blight and anthracnose. These diseases not only reduce the yield of pepper but, in severe cases, can also cause significant economic losses and threaten food security. The timely and accurate identification of pepper diseases is crucial. Image recognition technology plays a key role in this aspect by automating and efficiently identifying pepper diseases, helping agricultural workers to adopt and implement effective control strategies, alleviating the impact of diseases, and being of great importance for improving agricultural production efficiency and promoting sustainable agricultural development. In response to issues such as edge-blurring and the extraction of minute features in pepper disease image recognition, as well as the difficulty in determining the optimal learning rate during the training process of traditional pepper disease identification networks, a new pepper disease recognition model based on the TPSAO-AMWNet is proposed. First, an Adaptive Residual Pyramid Convolution (ARPC) structure combined with a Squeeze-and-Excitation (SE) module is proposed to solve the problem of edge-blurring by utilizing adaptivity and channel attention; secondly, to address the issue of micro-feature extraction, Minor Triplet Disease Focus Attention (MTDFA) is proposed to enhance the capture of local details of pepper leaf disease features while maintaining attention to global features, reducing interference from irrelevant regions; then, a mixed loss function combining Weighted Focal Loss and L2 regularization (WfrLoss) is introduced to refine the learning strategy during dataset processing, enhancing the model’s performance and generalization capabilities while preventing overfitting. Subsequently, to tackle the challenge of determining the optimal learning rate, the tent particle snow ablation optimizer (TPSAO) is developed to accurately identify the most effective learning rate. The TPSAO-AMWNet model, trained on our custom datasets, is evaluated against other existing methods. The model attains an average accuracy of 93.52% and an F1 score of 93.15%, demonstrating robust effectiveness and practicality in classifying pepper diseases. These results also offer valuable insights for disease detection in various other crops.

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“…In addition, IoT and cloud computing technology are interdependent and have become beneficial areas for remotely managing the patient's condition to provide ongoing facilities by providing helpful information to patients and clinicians [7]. Many services use cloud solutions and machine learning algorithms to keep and process patient data [8][9][10][11]. In [2], a multi-objective successive approximation (EMSA) technique was introduced to maintain an adequate measure of privacy in healthcare clouds based on Euclidean L3P.…”

Section: Introductionmentioning

confidence: 99%

An Innovative Approach to Cardiovascular Disease Prediction: A Hybrid Deep Learning Model

Dhaka,

Sehrawat,

Bhutani

2023

Eng. Technol. Appl. Sci. Res.

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The increasing prevalence of cardiovascular disorders has created an imperative need for accurate diagnoses. Despite the emergence of numerous techniques for disease classification and secure data transmission, a prevailing shortcoming is the lack of precision in decision-making. This study aimed to address this critical issue by introducing an innovative disease prediction model that uses a hybrid classifier. The proposed hybrid classifier combined deep Bidirectional Long-Short-Term Memory (deep Bi LSTM) and deep Convolutional Neural Network (deep CNN).To further improve its performance, the proposed approach employed hybridized swarm optimization to fine-tune fusion parameters and optimize the learning model for enhanced accuracy. This study focused on heart disease as its central concern, strengthening data security through the implementation of Diffi-Huffman based on Elliptic Curve Cryptography (ECC) during data transmission. The resulting automatic disease prediction model adopted the hybrid deep classifier, which was born from the amalgamation of two components: the interactive hunt-deep CNN classifier and the WoM-deep Bi LSTM. The proposed hybrid learning model achieved impressive accuracy, F-measure, sensitivity, and specificity of 97.716%, 97.848%, 98.021%, and 97.807%, respectively, marking a significant advance in the realm of cardiovascular disease prediction.

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

Cited by 7 publications

References 10 publications

Chronic Obstructive Pulmonary Disease Diagnosis with Bagging Ensemble Learning and ANN Classifiers

Chronic Obstructive Pulmonary Disease Diagnosis with Bagging Ensemble Learning and ANN Classifiers

Identification of Pepper Leaf Diseases Based on TPSAO-AMWNet

An Innovative Approach to Cardiovascular Disease Prediction: A Hybrid Deep Learning Model

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