Crop Disease Detection and Classification Using Deep Learning-Based Classifier Algorithm

Jha, Pradeep Kumar; Dembla, Deepak; Dubey, Widhi

doi:10.1007/978-981-99-1946-8_21

Cited by 2 publications

(3 citation statements)

References 10 publications

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“…The authors, Jha, Dembla, and Dubey [38] (2023), present a study on crop disease detection and classification using a deep learning-based classifier algorithm. Their approach achieves an accuracy of 96.7% and an F1 score of 0.95, surpassing traditional machine learning methods.…”

Section: Review Of Literaturementioning

confidence: 99%

Analysis and Enhancement of Prediction of Cardiovascular Disease Diagnosis using Machine Learning Models SVM, SGD, and XGBoost

Tomar,

Dembla,

Chaba

2024

IJACSA

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Cardiovascular disease (CVD), claiming 17.9 million lives annually, is exacerbated by factors like high blood pressure and obesity, prompting extensive data collection for deeper insights. Machine learning aids in accurate diagnosis, with techniques like SVM, SGD, and XGBoost proposed for heart disease prediction, addressing challenges such as data imbalance and optimizing diagnostic accuracy. This study integrates these algorithms to improve cardiovascular disease diagnosis, aiming to reduce mortality rates through timely interventions. This research investigates the efficacy of Support Vector Machine (SVM), Stochastic Gradient Descent (SGD), and XGBoost machine learning techniques for heart disease prediction. Analysis of the models' performance metrics reveals distinct characteristics and capabilities. SVM demonstrates robust performance with a training accuracy of 88.28% and a model accuracy score of 87.5%, exhibiting high precision and recall values across both classes. SGD, while commendable with a training accuracy of 83.65% and a model accuracy score of 84.24%, falls slightly behind SVM in accuracy and precision. XGBoost Classifier showcases perfect training accuracy but potential overfitting, yet demonstrates comparable precision and recall values to SVM. Overall, SVM emerges as the most effective model for heart disease prediction, followed by SGD and XGBoost Classifier. Further optimization and investigation into generalization capabilities are recommended to enhance the performance of SGD and XGBoost Classifier in clinical settings.

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Section: Review Of Literaturementioning

confidence: 99%

Analysis and Enhancement of Prediction of Cardiovascular Disease Diagnosis using Machine Learning Models SVM, SGD, and XGBoost

Tomar,

Dembla,

Chaba

2024

IJACSA

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“…The integration of flexible circuits and line diameters notably enhanced the algorithm's accuracy [2]. R. Udaiyakumar et al suggested the utilization of various machine learning (ML) methodologies [29] [30], including deep neural networks, KNN, SVM, decision trees, and random forest classifiers. Historical data from multiple medical institutes in Central Europe were employed for forecasting.…”

Section: Related Workmentioning

confidence: 99%

“…In addition to enhancing the predictive actuality of the dataset, 'Random Forest' employs multiple decision trees, each trained on different subsets of the input data. Rather than confide on an individual decision tree, 'Random Forest' aggregates predictions from individual trees and forecasts the outcome based on the predictions that receive the most support [25,26,27,28,29]. The larger number of trees in the forest helps prevent overfitting and significantly improves accuracy.…”

Section: ) Random Forestmentioning

confidence: 99%

Prediction of Cardiovascular Disease using Machine Learning Algorithms

Joshi,

Dembla,

Bhatia

2024

IJACSA

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Heart is the most critical organ of our body for being responsible for regulating and maintaining the blood circulation levels. Globally, heart disease cases are prevalent and constitute a significant cause of mortality. Manifestations such as chest discomfort and irregular heartbeat are notable symptoms. The healthcare sector has amassed substantial knowledge in this domain. Analyzing the research, this paper delves into the concept of utilizing ML algorithms to predict cardiac diseases. In this research will employ a diverse array of machine learning techniques, including decision tree, support vector classifier, random forest, K-NN, logistic regression and naive Bayes. These algorithms utilize specific characteristics to forecast cardiac diseases effectively. Leveraging machine learning algorithms to analyze and predict outcomes from the extensive healthcaregenerated data shows considerable promise. Recent advancements in machine learning models have incorporated numerous features, and in this study, propose the integration of these features in machine learning algorithms to forecast cardiovascular ailments. The main objective of this research is to identify the performance of the mentioned machine learning algorithms for predicting cardiovascular elements.

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Crop Disease Detection and Classification Using Deep Learning-Based Classifier Algorithm

Cited by 2 publications

References 10 publications

Analysis and Enhancement of Prediction of Cardiovascular Disease Diagnosis using Machine Learning Models SVM, SGD, and XGBoost

Analysis and Enhancement of Prediction of Cardiovascular Disease Diagnosis using Machine Learning Models SVM, SGD, and XGBoost

Prediction of Cardiovascular Disease using Machine Learning Algorithms

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