Advanced Deep Learning Models for Corn Leaf Disease Classification: A Field Study in Bangladesh

Mohanty, Sachi Nandan; Ghosh, Hritwik; Rahat, Irfan Sadiq; Reddy, Chirra Venkata Rami

doi:10.3390/engproc2023059069

Cited by 23 publications

(10 citation statements)

References 15 publications

(15 reference statements)

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“…The 2023 study by Mohanty, Ghosh, Rahat [2] and Reddy, "Advanced Deep Learning Models for Corn Leaf Disease Classification", focuses on the application of deep learning in classifying diseases in corn leaves based on a field study in order to perform the experimental evaluation, authors have considered the Pima Indians Diabetes dataset [3] that contains a variety of demographic and medical characteristics of female patients.…”

Section: Related Workmentioning

confidence: 99%

Modelling of Diabetic Cases for Effective Prevalence Classification

Shah,

Mangla,

Sharma

et al. 2024

EAI Endorsed Trans Perv Health Tech

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INTRODUCTION: This study compares and contrasts various machine learning algorithms for predicting diabetes. The study of current research work is to analyse the effectiveness of various machine learning algorithms for diabetes prediction. OBJECTIVES: To compare the efficacy of various machine learning algorithms for diabetic prediction. METHODS: For the same, a diabetic dataset was subjected to the application of various well-known machine learning algorithms. Unbalanced data was handled by pre-processing the dataset. The models were subsequently trained and assessed using different performance metrics namely F1-score, accuracy, sensitivity, and specificity. RESULTS: The experimental results show that the Decision Tree and ensemble model outperforms all other comparative models in terms of accuracy and other evaluation metrics. CONCLUSION: This study can help healthcare practitioners and researchers to choose the best machine learning model for diabetes prediction based on their specific needs and available data.

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Section: Related Workmentioning

confidence: 99%

Modelling of Diabetic Cases for Effective Prevalence Classification

Shah,

Mangla,

Sharma

et al. 2024

EAI Endorsed Trans Perv Health Tech

View full text Add to dashboard Cite

show abstract

“…However, during the exercise, it is necessary to consider the current forecasting error and all previous forecasts. This process, known as backpropagation over time [23], involves repeatedly multiplying the erroneous gradients by the network's weights until the slopes either explode or disappear, rendering the RNN untrainable.…”

Section: Ff-nnmentioning

confidence: 99%

“…A gang might alter transactions by mining "invalid" blocks or double-spending if it were to acquire this much power. Because most miners use ASICS mining rigs, they can only operate through pools [22,23]. The energy in some collections is so great that it might get abused.…”

Section: Introductionmentioning

confidence: 99%

Monitoring of operational conditions of fuel cells by using machine learning

Shrote,

Kumar,

Kaur

et al. 2024

EAI Endorsed Trans IoT

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The reliability of fuel cells during testing is crucial for their development on test benches. For the development of fuel cells on test benches, it is essential to maintain their dependability during testing. It is only possible for the alarm module of the control software to identify the most serious failures because of the large operating parameter range of a fuel cell. This study presents a novel approach to monitoring fuel cell stacks during testing that relies on machine learning to ensure precise outcomes. The use of machine learning to track fuel cell operating variables can achieve improvements in performance, economy, and reliability. ML enables intelligent decision-making for efficient fuel cell operation in varied and dynamic environments through the power of data analytics and pattern recognition. Evaluating the performance of fuel cells is the first and most important step in establishing their reliability and durability. This introduces methods that track the fuel cell's performance using digital twins and clustering-based approaches to monitor the test bench's operating circumstances. The only way to detect the rate of accelerated degradation in the test scenarios is by using the digital twin LSTM-NN model that is used to evaluate fuel cell performance. The proposed methods demonstrate their ability to detect discrepancies that the state-of-the-art test bench monitoring system overlooked, using real-world test data. An automated monitoring method can be used at a testing facility to accurately track the operation of fuel cells.

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“…In the present study, an interdisciplinary systematic approach was used to examine the phenomenon of anxiety, with a particular focus on its emergence and reinforcement [10]. This is the main and important point The article provides an overview of existing research studies investigating the relationship between anxiety and changes in brain function [11].…”

Section: Introductionmentioning

confidence: 99%

Anxiety Controlling Application using EEG Neurofeedback System

Kishore Kanna,

Athawale,

Naniwadekar

et al. 2024

EAI Endorsed Trans Perv Health Tech

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INTRODUCTION: This study aims to investigate the correlation between the oscillations of electroencephalography (EEG) bands and the level of anxiety in a sample of sixteen youth athletes aged 17–21. The research utilizes a mobile EEG system to collect data on EEG band oscillations. OBJECTIVES: The aim of this research study is to investigate the brain wave oscillations during relaxation, specifically comparing the contrast between eyes open and eyes closed state Electroencephalography (EEG) using a state-of-the-art wireless EEG headset system. METHODS: The system incorporates dry, non-interacting EEG sensor electrodes, developed exclusively by NeuroSky. In addition, the addition of the ThinkGear module and MindCap XL skull facilitated EEG recording. The aim of the present study was to investigate the effect of eyes open and eyes closed conditions on alpha-band activity in the prefrontal cortex The results showed a statistically significant difference (p≤0.006); appeared between these two states. The present study examined the relationship between the alpha band of the prefrontal cortex and anxiety levels. Specifically, we examined the relationship between these variables in the eyes-closed condition. RESULTS: Our analysis revealed a statistically significant correlation, with the alpha band showing a negative slope (p≤0.029). The present study examines the comparison of data obtained from single-channel wireless devices with data obtained from conventional laboratories The findings of this study show a striking similarity between the results obtained with both types of devices. The aim of the present study was to investigate the specific characteristics of the correlation between electroencephalographic (EEG) alphaband oscillations in the prefrontal cortex in relation to eye position and anxiety levels in young athletes. CONCLUSION: This study seeks to shed light on the possible relationship between this vibration and individuals' internal cognitive and affective states.

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Advanced Deep Learning Models for Corn Leaf Disease Classification: A Field Study in Bangladesh

Abstract: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

Cited by 23 publications

References 15 publications

Modelling of Diabetic Cases for Effective Prevalence Classification

Modelling of Diabetic Cases for Effective Prevalence Classification

Monitoring of operational conditions of fuel cells by using machine learning

Anxiety Controlling Application using EEG Neurofeedback System

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