Early Disease Classification of Mango Leaves Using Feed-Forward Neural Network and Hybrid Metaheuristic Feature Selection

Pham, Tan Nhat; Tran, Ly Van; Dao, Son Vu Truong

doi:10.1109/access.2020.3031914

Cited by 147 publications

(52 citation statements)

References 32 publications

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“…Therefore, there exists scenarios in which using pre-trained models do not become an affordable solution. In 2020, some researchers have utilized same-domain TL and achieved excellent results [86][87][88]157]. Same-domain TL is an approach of using images that look similar to the target dataset for training.…”

Section: Transfer Learningmentioning

confidence: 99%

Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

et al. 2021

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In the last few years, the deep learning (DL) computing paradigm has been deemed the Gold Standard in the machine learning (ML) community. Moreover, it has gradually become the most widely used computational approach in the field of ML, thus achieving outstanding results on several complex cognitive tasks, matching or even beating those provided by human performance. One of the benefits of DL is the ability to learn massive amounts of data. The DL field has grown fast in the last few years and it has been extensively used to successfully address a wide range of traditional applications. More importantly, DL has outperformed well-known ML techniques in many domains, e.g., cybersecurity, natural language processing, bioinformatics, robotics and control, and medical information processing, among many others. Despite it has been contributed several works reviewing the State-of-the-Art on DL, all of them only tackled one aspect of the DL, which leads to an overall lack of knowledge about it. Therefore, in this contribution, we propose using a more holistic approach in order to provide a more suitable starting point from which to develop a full understanding of DL. Specifically, this review attempts to provide a more comprehensive survey of the most important aspects of DL and including those enhancements recently added to the field. In particular, this paper outlines the importance of DL, presents the types of DL techniques and networks. It then presents convolutional neural networks (CNNs) which the most utilized DL network type and describes the development of CNNs architectures together with their main features, e.g., starting with the AlexNet network and closing with the High-Resolution network (HR.Net). Finally, we further present the challenges and suggested solutions to help researchers understand the existing research gaps. It is followed by a list of the major DL applications. Computational tools including FPGA, GPU, and CPU are summarized along with a description of their influence on DL. The paper ends with the evolution matrix, benchmark datasets, and summary and conclusion.

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Section: Transfer Learningmentioning

confidence: 99%

Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

et al. 2021

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“…In this research, we used a feature selection method called Adaptive Particle -Grey Wolf Optimization (APGWO) [37], which is combined from Particle Swarm Optimization [38] and Grey Wolf Optimization [27]. The Particle Swarm Optimization algorithm published by Kennedy and Eberhart in [38] and its basic judgments are mainly inspired by animals' social behavior such as birds flocking while looking for food.…”

Section: ) Adaptive Particle -Grey Wolf Optimization (Apgwo)mentioning

confidence: 99%

A Novel Wrapper–Based Feature Selection for Early Diabetes Prediction Enhanced With a Metaheuristic

Pham

et al. 2021

IEEE Access

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Diabetes leads to health problems for hundreds of millions of people globally every year. Available medical records of patients quantify symptoms, body features, and clinical laboratory test values, which can be used to perform biostatistics analysis aimed at finding patterns or features undetectable by current practice. In this work, we proposed a machine learning model to predict the early onset of diabetes patients. It is a novel wrapper-based feature selection utilizing Grey Wolf Optimization (GWO) and an Adaptive Particle Swam Optimization (APSO) to optimize the Multilayer Perceptron (MLP) to reduce the number of required input attributes. Moreover, we also compared the results achieved using this method and several conventional machine learning algorithms approaches such as Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighbor (KNN), Naïve Bayesian Classifier (NBC), Random Forest Classifier (RFC), Logistic Regression (LR). Computational results of our proposed method show not only that much fewer features are needed, but also higher prediction accuracy can be achieved (96% for GWO -MLP and 97% for APGWO -MLP). This work has the potential to be applicable to clinical practice and become a supporting tool for doctors/physicians.

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“…In [11] have been organisms attributes, particularly demographic groups, community structure (plots), documents of organisms incidence, and environmental layouts such as remote sensing-based and also everyone else, which were needed for forecasting worldwide change's effect on plant different populations via the use of spatial variability designs. In [12] used, cotton insect information to implement three classifiers: Decision Tree (DT), Naive Bayes (NB), and K-Nearest Neighbour (K-NN). The DT classifier was the best classifier for estimates based on these installations.…”

Section: Related Workmentioning

confidence: 99%

Mango Pest Detection Using Entropy-ELM with Whale Optimization Algorithm

Muthaiah¹,

Chitra²

2023

Intelligent Automation &Amp; Soft Computing

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Image processing, agricultural production, and field monitoring are essential studies in the research field. Plant diseases have an impact on agricultural production and quality. Agricultural disease detection at a preliminary phase reduces economic losses and improves the quality of crops. Manually identifying the agricultural pests is usually evident in plants; also, it takes more time and is an expensive technique. A drone system has been developed to gather photographs over enormous regions such as farm areas and plantations. An atmosphere generates vast amounts of data as it is monitored closely; the evaluation of this big data would increase the production of agricultural production. This paper aims to identify pests in mango trees such as hoppers, mealybugs, inflorescence midges, fruit flies, and stem borers. Because of the massive volumes of large-scale high-dimensional big data collected, it is necessary to reduce the dimensionality of the input for classifying images. The community-based cumulative algorithm was used to classify the pests in the existing system. The proposed method uses the Entropy-ELM method with Whale Optimization to improve the classification in detecting pests in agriculture. The Entropy-ELM method with the Whale Optimization Algorithm (WOA) is used for feature selection, enhancing mango pests' classification accuracy. Support Vector Machines (SVMs) are especially effective for classifying while users get various classes in which they are interested. They are created as suitable classifiers to categorize any dataset in Big Data effectively. The proposed Entropy-ELM-WOA is more capable compared to the existing systems.

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Early Disease Classification of Mango Leaves Using Feed-Forward Neural Network and Hybrid Metaheuristic Feature Selection

Cited by 147 publications

References 32 publications

Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

A Novel Wrapper–Based Feature Selection for Early Diabetes Prediction Enhanced With a Metaheuristic

Mango Pest Detection Using Entropy-ELM with Whale Optimization Algorithm

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