Efficient Method for Prediction Accuracy of Heart Diseases Using Machine Learning

Raman, Manoj; Sharma, Vijay Kumar; Hiranwal, Saroj; Bairwa, Amit Kumar

doi:10.1007/978-981-15-5077-5_10

Cited by 3 publications

(1 citation statement)

References 6 publications

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“…Here, sophisticated statistical analysis is used by agricultural experts in the field. Processing techniques expand the possibilities and help as an open source of knowledge processing for agriculture [3] There are many processor in ANN. It computes parallel in all layers.…”

Section: Related Workmentioning

confidence: 99%

Improving the Accuracy of Multi-Valued Datasets in Agriculture Using Logistic Regression and LSTM-RNN Method

Alameen¹

2022

TEM Journal

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Changes in environmental factors such as water quality, soil quality, and pollution factors lead to diseases in food producing plants. Identifying plant disease is a very difficult task in agriculture. Plant diseases are also mainly caused by many influences in agriculture which includes hybrid genetics, and the plant lifetime during the infection, environmental changes like climatic changes, soil, temperature, rain, wind, weather etc. The infections may be single or mixed, according to the infections the plants diseases spread. Early detection of plant diseases using recent technologies helps the plants growth. Therefore, Machine Learning techniques are used for early prediction of the diseases. This paper is used to improve the accuracy of detecting plant diseases using the prediction of the soil content in the field land. The techniques Nave Bayes (NB) and Neural Network (NN) were used in the existing system. The proposed system uses Logistic Regression method with Long Short- Term Memory (LSTM) in Neural Networks (NN) for predicting the soil content and also detects the plant diseases, improves the accuracy level in the plant growth.

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Section: Related Workmentioning

confidence: 99%

Improving the Accuracy of Multi-Valued Datasets in Agriculture Using Logistic Regression and LSTM-RNN Method

Alameen¹

2022

TEM Journal

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Revised Artificial Immune Recognition System

et al. 2021

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Artificial Immune Recognition System is a widely used bio-inspired algorithm that describes the recognition tasks of antigen by memory cells. Despite the success of the Artificial Immune Recognition System, the basic version has some drawbacks which have a direct impact on system efficiency in terms of the quality of the results, data explosion, and calculation cost. This paper investigates these disadvantages and proposes several modifications in the original version to overcome these problems. First, the concept of weight and lifetime counter was introduced for each memory cell to improve quality; second, a new mechanism was added to eliminate inactive memory cell models to reduce data explosion, and third, the structure of the memory cells set was replaced by a binary search tree to reduce processing time. Furthermore, this paper improves some algorithm functionalities especially in the mutation function and the memory cell introduction mechanism. The experimental results conducted on eleven public datasets show that the proposed method outperforms the original version, all the revised versions, and achieved a good rank compared to the other state-of-the-art methods with an average accuracy of 93.20 % on all tested datasets.

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Novel Supervised Machine Learning Classification Technique for Improve Accuracy of Multi-Valued Datasets in Agriculture

Agrawal

Patel

Sharma

2021

2021 6th International Conference on Inventive Computation Technologies (ICICT)

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Efficient Method for Prediction Accuracy of Heart Diseases Using Machine Learning

Cited by 3 publications

References 6 publications

Improving the Accuracy of Multi-Valued Datasets in Agriculture Using Logistic Regression and LSTM-RNN Method

Improving the Accuracy of Multi-Valued Datasets in Agriculture Using Logistic Regression and LSTM-RNN Method

Revised Artificial Immune Recognition System

Novel Supervised Machine Learning Classification Technique for Improve Accuracy of Multi-Valued Datasets in Agriculture

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