Computational Prediction of Disease Detection and Insect Identification using Xception model

Cleetus, Lidia; Sukumar, A Raji; Hemalatha, N.

doi:10.1101/2021.08.10.455608

Cited by 4 publications

(1 citation statement)

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“…These convolutions occur not just in terms of space, but also in terms of depth as a result, with every additional filter. The researchers working on the Inception module were able to concatenate several layer modifications in parallel as a result of this reduction, resulting in a CNN that was wide and deep (Cleetus et al, 2021).…”

Section: 1mentioning

confidence: 99%

Hybrid deep learning model-based prediction of images related to cyberbullying

Elmezain,

Malki,

Gad

et al. 2022

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Cyberbullying has become more widespread as a result of the common use of social media, particularly among teenagers and young people. A lack of studies on the types of advice and support available to victims of bullying has a negative impact on individuals and society. This work proposes a hybrid model based on transformer models in conjunction with a support vector machine (SVM) to classify our own data set images. First, seven different convolutional neural network architectures are employed to decide which is best in terms of results. Second, feature extraction is performed using four top models, namely, ResNet50, EfficientNetB0, MobileNet and Xception architectures. In addition, each architecture extracts the same number of features as the number of images in the data set, and these features are concatenated. Finally, the features are optimized and then provided as input to the SVM classifier. The accuracy rate of the proposed merged models with the SVM classifier achieved 96.05%. Furthermore, the classification precision of the proposed merged model is 99% in the bullying class and 93% in the non-bullying class. According to these results, bullying has a negative impact on students' academic performance. The results help stakeholders to take necessary measures against bullies and increase the community's awareness of this phenomenon.

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Section: 1mentioning

confidence: 99%

Hybrid deep learning model-based prediction of images related to cyberbullying

Elmezain,

Malki,

Gad

et al. 2022

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Identification of Pest Attack on Corn Crops Using Machine Learning Techniques

Haq,

Raza,

Lan

et al. 2023

The 4th International Electronic Conference on Applied Sciences

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Poisonous Plants Species Prediction Using a Convolutional Neural Network and Support Vector Machine Hybrid Model

Noor

Elmezain

2022

Electronics

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The total number of discovered plant species is increasing yearly worldwide. Plant species differ from one region to another. Some of these discovered plant species are beneficial while others might be poisonous. Computer vision techniques can be an effective way to classify plant species and predict their poisonous status. However, the lack of comprehensive datasets that include not only plant images but also plant species’ scientific names, description, poisonous status, and local name make the issue of poisonous plants species prediction a very challenging issue. In this paper, we propose a hybrid model relying on transformers models in conjunction with support vector machine for plant species classification and poisonous status prediction. First, six different Convolutional Neural Network (CNN) architectures are used to determine which produces the best results. Second, the features are extracted using six different CNNs and then optimized and employed to Support Vector Machine (SVM) for testing. To prove the feasibility and benefits of our proposed approach, we used a real case study namely, plant species discovered in the Arabian Peninsula. We have gathered a dataset that contains 2500 images of 50 different Arabic plant species and includes plants images, plant species scientific name, description, local name, and poisonous status. This study on the types of Arabic plants species will help in the reduction of the number of poisonous plants victims and their negative impact on the individual and society. The results of our experiments for the CNN approach in conjunction SVM are favorable where the classifier scored 0.92, 0.94, and 0.95 in accuracy, precision, and F1-Score respectively.

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Computational Prediction of Disease Detection and Insect Identification using Xception model

Cited by 4 publications

References 4 publications

Hybrid deep learning model-based prediction of images related to cyberbullying

Hybrid deep learning model-based prediction of images related to cyberbullying

Identification of Pest Attack on Corn Crops Using Machine Learning Techniques

Poisonous Plants Species Prediction Using a Convolutional Neural Network and Support Vector Machine Hybrid Model

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