A Fusion Method for Detection and Classification of Diseases in Tomato Plants Using Swarm-based Deep Learning DOI Creative Commons
Supriya Shrivastav, V. K. Jindal, Vassiliki Theodorou

и другие.

International Journal of experimental research and review, Год журнала: 2024, Номер 45(Spl Vol), С. 135 - 152

Опубликована: Ноя. 30, 2024

Precise identification and detection of ailments in tomato plants are essential for preserving crop vitality optimizing agricultural productivity. This promotes the use methods that can be maintained over time decreases financial losses caused by plant diseases. Detecting classifying diseases is critical ensuring health maximizing Utilizing advanced computer vision techniques this purpose enhances precision monitoring health, ultimately leading to more efficient targeted interventions. research work presents a novel framework Tomato Plant Disease Detection Classification (TPDDC) using fusion swarm-based deep-learning techniques. Our approach leverages K-means clustering with Grasshopper Optimization (GO) segmenting Regions Interest (ROI) from leaf images, followed feature extraction optimization Maximally Stable Extremal (MSER) GO. The optimized features then classified Convolutional Neural Network (CNN). proposed TPDDC model was evaluated Village Dataset, encompassing ten different Experimental results demonstrate significant improvements classification accuracy, achieving an average accuracy 97.6% GO-based compared 92.7% without These underscore effectiveness integrating deep learning robust precise disease plants.

Язык: Английский

A Novel Computer-Aided Approach for Predicting COVID-19 Severity Using Hyperparameters in ResNet50v2 from X-ray Images DOI Creative Commons
Rahul Deva, Arvind Dagur

International Journal of experimental research and review, Год журнала: 2024, Номер 42, С. 120 - 132

Опубликована: Авг. 30, 2024

This research has been globally impacted by COVID-19 virus, which was a very uncommon, highly contagious & dangerous respiratory illness demanding early detection for effective containment and further spread. In this research, we proposed an innovative methodology that utilizes images of X-rays at stage. By employing convolution neural network, enhance the accuracy performance via using ResNet50v2 hyperparameter. The achieves remarkable with average 99.12%. surpasses other available models based on different deep learning like VGG, Xception DenseNet COVID identification help X-ray images. scans are now preferably used modality COVID-19, given its widespread utilization effectiveness. However, manual treatment examination is challenging, specifically in field facing limitation skilled medical staff. Utilization demonstrated significant potential results automating diagnosis timely films. suggested architecture developed prediction analysis cases It firmly believes study holds alleviating workload frontline radiologists, expediting patient treatment, facilitating pandemic control efforts.

Язык: Английский

Процитировано

3
Automatic ECG Arrhythmia Recognition using ANN and CNN DOI Creative Commons
Ekta Soni, Arpita Nagpal, Sujata Bhutani

и другие.

International Journal of experimental research and review, Год журнала: 2024, Номер 45(Spl Vol), С. 01 - 14

Опубликована: Ноя. 30, 2024

Present research highlights the need for more patient-oriented monitoring systems cardiac health, especially in aftermath of COVID-19. The study introduces a contactless and affordable ECG device capable recording heart arrhythmias remote monitoring, which is vital managing rising incidence untimely attacks. Two deep learning algorithms have been developed to design system: RCANN (Real-time Compressed Artificial Neural Network) RCCNN Convolutional Network), respectively, based on ANN CNN. These methods are designed classify analyze three different forms datasets: raw, filtere filtered + compressed signals. were this identify most suitable type dataset that can be utilized regular/remote monitoring. This data prepared using online signals from Physionet(ONLINE) real-time Arduino sensor device. Performance analysed basis accuracy, sensitivity, specificity F1 score all kinds databases both RCANN. For raw data, accuracy 99.2%, sensitivity 99.7%, F1-Score 99.2%. RCCNN, 93.2%, 91.5%, 95.1%, 93.5% Filtered Data, 97.7%, 95.9%, 99.4%, 97.6%. 90.5%, 85.8%, 96.4%, 90.9% 96.6%, 97.6%, 95.7%, 96.5%. 85.2%, 79.2%, 94.5%, 86.7% performance evaluation shows with datasets outperforms other approaches telemonitoring makes it promising approach individualized health management.

Язык: Английский

Процитировано

0
A Fusion Method for Detection and Classification of Diseases in Tomato Plants Using Swarm-based Deep Learning DOI Creative Commons
Supriya Shrivastav, V. K. Jindal, Vassiliki Theodorou

и другие.

International Journal of experimental research and review, Год журнала: 2024, Номер 45(Spl Vol), С. 135 - 152

Опубликована: Ноя. 30, 2024

Precise identification and detection of ailments in tomato plants are essential for preserving crop vitality optimizing agricultural productivity. This promotes the use methods that can be maintained over time decreases financial losses caused by plant diseases. Detecting classifying diseases is critical ensuring health maximizing Utilizing advanced computer vision techniques this purpose enhances precision monitoring health, ultimately leading to more efficient targeted interventions. research work presents a novel framework Tomato Plant Disease Detection Classification (TPDDC) using fusion swarm-based deep-learning techniques. Our approach leverages K-means clustering with Grasshopper Optimization (GO) segmenting Regions Interest (ROI) from leaf images, followed feature extraction optimization Maximally Stable Extremal (MSER) GO. The optimized features then classified Convolutional Neural Network (CNN). proposed TPDDC model was evaluated Village Dataset, encompassing ten different Experimental results demonstrate significant improvements classification accuracy, achieving an average accuracy 97.6% GO-based compared 92.7% without These underscore effectiveness integrating deep learning robust precise disease plants.

Язык: Английский

Процитировано

0