Automatic classification Infectious disease X-ray images based on Deep learning Algorithms

Makarovskikh, Tatiana; Salah, Amir; Badr, Amr; Kadi, Ammar; Alkattan, Hussein; Abotaleb, Mostafa

doi:10.1109/itnt55410.2022.9848538

Cited by 3 publications

(1 citation statement)

References 33 publications

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“…X-ray images for SARS-CoV-2 were diagnosed using neural networks. In [25], they created a system using five models and deep learning algorithms: Xception, VGG19, ResNet50, DenseNet121, and Inception for binary classification of X-ray images for SARS-CoV-2. In order to aid medical efforts and lessen the strain on medical professionals while dealing with SARS-CoV-2, they provided deep learning models and algorithms that have been developed and evaluated.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Deep Learning Algorithm for Forecasting SARS-CoV-2 Daily Infections and Death Cases

Alqahtani¹,

Abotaleb

Kadi

et al. 2022

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The prediction of new cases of infection is crucial for authorities to get ready for early handling of the virus spread. Methodology Analysis and forecasting of epidemic patterns in new SARS-CoV-2 positive patients are presented in this research using a hybrid deep learning algorithm. The hybrid deep learning method is employed for improving the parameters of long short-term memory (LSTM). To evaluate the effectiveness of the proposed methodology, a dataset was collected based on the recorded cases in the Russian Federation and Chelyabinsk region between 22 January 2020 and 23 August 2022. In addition, five regression models were included in the conducted experiments to show the effectiveness and superiority of the proposed approach. The achieved results show that the proposed approach could reduce the mean square error (RMSE), relative root mean square error (RRMSE), mean absolute error (MAE), coefficient of determination (R Square), coefficient of correlation (R), and mean bias error (MBE) when compared with the five base models. The achieved results confirm the effectiveness, superiority, and significance of the proposed approach in predicting the infection cases of SARS-CoV-2.

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Section: Related Workmentioning

confidence: 99%

Hybrid Deep Learning Algorithm for Forecasting SARS-CoV-2 Daily Infections and Death Cases

Alqahtani¹,

Abotaleb

Kadi

et al. 2022

Axioms

Self Cite

View full text Add to dashboard Cite

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A Two-Stage Hybrid Approach for Phishing Attack Detection Using URL and Content Analysis in IoT

Yousif Mohammed,

Aljanabi,

Mijwil

et al. 2024

BIO Web Conf.

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The goal of phishing assaults is to trick users into giving up personal information by making them believe they need to act quickly on critical information. The creation of efficient solutions, such as phishing attack detection systems backed by AI, is essential for the safety of users. This research suggests a two-stage hybrid strategy that uses both URL and content analysis to identify phishing assaults. In the first step of the suggested method, URL analysis is used to determine the legitimacy of suspected phishing assaults. If the site is still live, the second check uses content analysis to determine how serious the attack is. Both analysis' findings are taken into account in the decision-making procedure. As can be seen from the experiments, the hybrid system obtains an astounding 99.06% accuracy rate. This research adds to the existing body of knowledge by providing a massive dataset of over 14 million data samples that includes both legal and phishing URLs. Furthermore, when content analysis is required for phishing URL detection, the two-stage hybrid technique significantly outperforms URL analysis alone by 70.23 %. The proposed method provides better defense against phishing attempts and is practical enough for widespread use.

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Modeling and Forecasting of Coconut Area, Production, and Productivity Using a Time Series Model

Ramadhan,

Biswas,

Ray

et al. 2024

BIO Web Conf.

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The study aimed to compare ARIMA and Holt's models for predicting coconut metrics in Kerala. The coconut data series was collected from the period 1957 to 2019. Of this, 80% of the data (from 1957 to 2007) is treated as training data, and the rest (20% from 2008 to 2019) is treated as testing data. Ideal models were selected based on lower AIC and BIC values. Their accuracy was evaluated through error estimation on testing data, revealing Holt's exponential, linear, and ARIMA (0,1,0) models as the bestfit choices for predicting coconut area, production, and productivity respectively. After using the testing data, we tried for the forecasting for 2020-2024 using these models, and the DM test confirmed their significant forecasting accuracy. This comprehensive analysis provides valuable insights into effective prediction models for coconut-related metrics, offering a foundation for informed decision-making and future projections.

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Automatic classification Infectious disease X-ray images based on Deep learning Algorithms

Cited by 3 publications

References 33 publications

Hybrid Deep Learning Algorithm for Forecasting SARS-CoV-2 Daily Infections and Death Cases

Hybrid Deep Learning Algorithm for Forecasting SARS-CoV-2 Daily Infections and Death Cases

A Two-Stage Hybrid Approach for Phishing Attack Detection Using URL and Content Analysis in IoT

Modeling and Forecasting of Coconut Area, Production, and Productivity Using a Time Series Model

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