Classification of Vegetative pests for cucumber plants using artificial neural networks

Waleed, Mohammed; Abdullah, Ahmed Saadi; Ahmed, Saadaldeen Rashid

doi:10.1109/iiceta50496.2020.9318890

Cited by 34 publications

(6 citation statements)

References 11 publications

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“…The reason for the emergence of deep learning applications clearly in the current period is due to the amount of data that exists at the present time because deep learning needs a large amount of data. In addition to the progress made in the hardware, especially the speed, because the operations within the deep learning algorithm are complex and need a group of operations, so speed will be required [8][9][10].…”

Section: Fig(1) Ai and Deep Learningmentioning

confidence: 99%

Still Rings Movements Recognition in Gymnastics Sport Based on Deep Learning

Abdullah¹,

Alsaif²

2023

wjps

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The methods of detecting objects and tracking their movements are among the methods that are relied upon in many fields, whether medical or industrial, and others. One of these areas that will rely on deep learning meth-ods in discovering and distinguishing the player's movements is the sports field and is very useful in games in which the player's degree depends on the accura-cy of the performance of the movement, such as the gymnastics game, where it was applied to the static ring gymnastics game, where the distinction of move-ments was discovered the stability in this game is based on a convolutional neu-ral network. Models. The neural network was trained on five of the most im-portant stability movements in this game after creating the data set based on a set of videos of tournaments held in the period from 2016-2022, where an aver-age of 1500 images were obtained for each stability movement, which was di-vided into 80% for training and 20 % for testing, after training the convolution-al neural network model, it was applied to a group of video clips for different tournaments. Many criteria were adopted to measure the efficiency of the model after training and practical application, which showed the efficiency of the pro-posed system.

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Section: Fig(1) Ai and Deep Learningmentioning

confidence: 99%

Still Rings Movements Recognition in Gymnastics Sport Based on Deep Learning

Abdullah¹,

Alsaif²

2023

wjps

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“…For combining four criteria to discriminate cancer, we find 92.3 percent, 96.2 percent, and 94.5 to 87.2 percent sensitivity in high sensitivity action. According to popular belief [20].…”

Section: [Deep Ab] [Deep Ab] [Deep Ab] [Deep 12]mentioning

confidence: 99%

Classification SINGLE-LEAD ECG by using conventional neural network algorithm

Jabbar¹,

Alwan²,

Ali³

2022

PEN

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Cardiac disease, including atrial fibrillation (AF), is one of the biggest causes of morbidity and mortality in the world, accounting for one third of all deaths. Cardiac modelling is now a well-established field. The Convolutional Neural Network (CNN) algorithm offer a valuable way of gaining insight into the dynamic behaviors of the heart, in normal and pathological conditions. Great efforts have been put into modelling the ventricles, whilst the atria have received less focus. This research therefore concentrates on developing models of the heart ECG atria using deep learning. The research developed an experimental result on MIT-BIH dataset for modelling myocyte electrophysiology and excitation waves in 1D & 2D tissues. It includes optimizations such as adaptive stimulus protocols. As examples of application, it is used to investigate effects of a novel anion bearing current on heart atrial excitation and the effect of remodeling on atrial myocyte electrical heterogeneity. A computationally efficient CNN anatomically based model of the heart atria is constructed. The 3D-CNN model includes heterogeneous, biophysically detailed electrophysiology and conduction anisotropy. The full model activates in 121 ms in heart rhythm, in close agreement with clinical ECG data. The model is used, with the toolkit, to investigate the function effects of S140G mutation in MIT-BIH dataset which is associated with familial. The 3D-CNN model forms the core of a boundary element model of the P-wave Body Surface Potential (BSP). The CNN model incorporates representations of the heart blood masses. Generated ECGs show qualitative agreement with clinical data. Their morphology is as expected for a healthy person, with a lead duration of 103 ms. The CNN model is used to verify an existing algorithm for focal atrial tachycardia location and in providing explanation for a novel clinical phenomenon, using CNN with 99.27% accuracy. Models of the human atria and body surface potential are constructed. The models are validated against both experimental and clinical data. These models are suitable to use as the platform for further research.

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“…These symptoms typically appear on cabbage leaves in the form of changes in the color or shape of the leaves [2]. To address this, early detection of pests attacking cabbage can be carried out by identifying the symptoms that appear on the outer parts of the cabbage [3]. However, the true complexity lies in addressing the diversity of diseases and pests affecting cabbage plants, necessitating a multiclass classification approach [2].…”

Section: Introductionmentioning

confidence: 99%

Ensemble CNN With Adasyn for Multiclass Classification on Cabbage Pests

Sovia,

Wardhani

2024

BAREKENG: J. Math. & App.

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Image classification is a complex process influenced by various factors, one of which is the amount of image data. In the context of cabbage pest classification, data often exhibits a significant class imbalance, where certain pests are more prevalent than others. This imbalance can pose challenges during model training and evaluation, potentially leading to biases in favor of the majority pests and reduced accuracy in identifying and classifying the less common ones. This research aims to enhance the classification performance for multiclass data specific to cabbage pests. We propose an ensemble learning approach that combines Convolutional Neural Network (CNN), Support Vector Machine (SVM), and Bagging methods. To address the imbalance issue inherent in cabbage pest data, we employ the Adaptive Synthetic Sampling (ADASYN) resampling technique. The CNN acts as the primary image identifier and classifier for various cabbage pests. Subsequently, the CNN model is integrated into SVM and Bagging models to mitigate the challenges of imbalanced data in pest classification. The research outcomes demonstrate that our ensemble approach, in conjunction with the ADASYN resampling technique, achieves an impressive accuracy rate of 97%, signifying its potential for improved cabbage pest detection and classification.

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Classification of Vegetative pests for cucumber plants using artificial neural networks

Cited by 34 publications

References 11 publications

Still Rings Movements Recognition in Gymnastics Sport Based on Deep Learning

Still Rings Movements Recognition in Gymnastics Sport Based on Deep Learning

Classification SINGLE-LEAD ECG by using conventional neural network algorithm

Ensemble CNN With Adasyn for Multiclass Classification on Cabbage Pests

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