Recognition of mRNA N4 Acetylcytidine (ac4C) by Using Non-Deep vs. Deep Learning

Iqbal, Muhammad; Abbasi, Rashid; Heyat, Md Belal Bin; Akhtar, Faijan; Abdelgeliel, Asmaa Sayed; Albogami, Sarah; Fayad, Eman

doi:10.3390/app12031344

Cited by 18 publications

(13 citation statements)

References 49 publications

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“…The robustness of the suggested model was assessed using a variety of evaluation indicators. Accuracy, precision, specificity, F1_score, sensitivity, and area under a receiver operating characteristic curve are among the measurements [ 62 , 63 , 64 , 65 , 66 , 67 , 68 ]. TP stands for True Positive, FP for False Positive, TN for True Negative, and FN for False Negative.…”

Section: Methodsmentioning

confidence: 99%

Automated Lung-Related Pneumonia and COVID-19 Detection Based on Novel Feature Extraction Framework and Vision Transformer Approaches Using Chest X-ray Images

et al. 2022

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According to research, classifiers and detectors are less accurate when images are blurry, have low contrast, or have other flaws which raise questions about the machine learning model’s ability to recognize items effectively. The chest X-ray image has proven to be the preferred image modality for medical imaging as it contains more information about a patient. Its interpretation is quite difficult, nevertheless. The goal of this research is to construct a reliable deep-learning model capable of producing high classification accuracy on chest x-ray images for lung diseases. To enable a thorough study of the chest X-ray image, the suggested framework first derived richer features using an ensemble technique, then a global second-order pooling is applied to further derive higher global features of the images. Furthermore, the images are then separated into patches and position embedding before analyzing the patches individually via a vision transformer approach. The proposed model yielded 96.01% sensitivity, 96.20% precision, and 98.00% accuracy for the COVID-19 Radiography Dataset while achieving 97.84% accuracy, 96.76% sensitivity and 96.80% precision, for the Covid-ChestX-ray-15k dataset. The experimental findings reveal that the presented models outperform traditional deep learning models and other state-of-the-art approaches provided in the literature.

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Section: Methodsmentioning

confidence: 99%

Automated Lung-Related Pneumonia and COVID-19 Detection Based on Novel Feature Extraction Framework and Vision Transformer Approaches Using Chest X-ray Images

et al. 2022

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“…Inadequate data samples and the uneven distribution of classes are two problems that this model addresses and corrects. When compared to other network architectures, the proposed technique provides the highest accuracy for classifying WBC images [ 40 , 41 , 42 ]. Wijesinghe et al [ 43 ] have also presented a new method for the classification of WBC images.…”

Section: Related Workmentioning

confidence: 99%

BCNet: A Deep Learning Computer-Aided Diagnosis Framework for Human Peripheral Blood Cell Identification

Chola

Muaad²,

Heyat³

et al. 2022

Diagnostics

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Blood cells carry important information that can be used to represent a person’s current state of health. The identification of different types of blood cells in a timely and precise manner is essential to cutting the infection risks that people face on a daily basis. The BCNet is an artificial intelligence (AI)-based deep learning (DL) framework that was proposed based on the capability of transfer learning with a convolutional neural network to rapidly and automatically identify the blood cells in an eight-class identification scenario: Basophil, Eosinophil, Erythroblast, Immature Granulocytes, Lymphocyte, Monocyte, Neutrophil, and Platelet. For the purpose of establishing the dependability and viability of BCNet, exhaustive experiments consisting of five-fold cross-validation tests are carried out. Using the transfer learning strategy, we conducted in-depth comprehensive experiments on the proposed BCNet’s architecture and test it with three optimizers of ADAM, RMSprop (RMSP), and stochastic gradient descent (SGD). Meanwhile, the performance of the proposed BCNet is directly compared using the same dataset with the state-of-the-art deep learning models of DensNet, ResNet, Inception, and MobileNet. When employing the different optimizers, the BCNet framework demonstrated better classification performance with ADAM and RMSP optimizers. The best evaluation performance was achieved using the RMSP optimizer in terms of 98.51% accuracy and 96.24% F1-score. Compared with the baseline model, the BCNet clearly improved the prediction accuracy performance 1.94%, 3.33%, and 1.65% using the optimizers of ADAM, RMSP, and SGD, respectively. The proposed BCNet model outperformed the AI models of DenseNet, ResNet, Inception, and MobileNet in terms of the testing time of a single blood cell image by 10.98, 4.26, 2.03, and 0.21 msec. In comparison to the most recent deep learning models, the BCNet model could be able to generate encouraging outcomes. It is essential for the advancement of healthcare facilities to have such a recognition rate improving the detection performance of the blood cells.

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“…Ukwuoma et al [ 36 ] used different deep learning technique to classify and detect medical diseases. The Iqbal group applied intelligence techniques for the analysis of the experimental data [ 37 , 38 ]. However, in this present study, we used the computational intelligence technique to classify the PMS experimental data.…”

Section: Introductionmentioning

confidence: 99%

Role of Inflammation, Oxidative Stress, and Mitochondrial Changes in Premenstrual Psychosomatic Behavioral Symptoms with Anti-Inflammatory, Antioxidant Herbs, and Nutritional Supplements

Sultana

Rahman

Heyat

et al. 2022

Oxidative Medicine and Cellular Longevity

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Premenstrual syndrome (PMS) significantly lowers the quality of life and impairs personal and social relationships in reproductive-age women. Some recommendations are that inappropriate oxidative stress and inflammatory response are involved in PMS. Various nutritional supplements and herbs showed neuro-psycho-pharmacological activity with antioxidant and anti-inflammatory properties. This study aims to determine the systematic review of randomized controlled trials (RCTs) of herbal medicine and nutritional supplements in PMS. We also comprehensively highlighted the role of oxidative stress, inflammation, and mitochondrial changes on PMS with the application of computational intelligence. We used PRISMA and research question-based techniques to collect the data for evaluation of our study on different databases such as Scopus, PubMed, and PROSPERO from 1990 to 2022. The methodological quality of the published study was assessed by the modified Jadad scale. In addition, we used network visualization and word cloud techniques to find the closest terms of the study based on previous publications. While we also used computational intelligence techniques to give the idea for the classification of experimental data from PMS. We found 25 randomized controlled studies with 1949 participants ( mean ± SD : 77.96 ± 22.753 ) using the PRISMA technique, and all were high-quality studies. We also extracted the closest terms related to our study using network visualization techniques. This work has revealed the future direction and research gap on the role of oxidative stress and inflammation in PMS. In vitro and in vivo studies showed that bioactive molecules such as curcumin, allicin, anethole, thymoquinone, cyanidin 3-glucoside, gamma-linoleic acid, and various molecules not only have antioxidant and anti-inflammatory properties but also other various activities such as GABA-A receptor agonist, serotonergic, antidepressant, sedative, and analgesic. Traditional Unani Herbal medicine and nutritional supplements can effectively relieve PMS symptoms as they possess many bioactive molecules that are pharmacologically proven for the aforementioned properties. Hence, these biomolecules might influence a complex physical and psychological disease process like PMS. However, more rigorous research studies are recommended for in-depth knowledge of the efficacy of bioactive molecules on premenstrual syndrome in clinical trials.

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Recognition of mRNA N4 Acetylcytidine (ac4C) by Using Non-Deep vs. Deep Learning

Cited by 18 publications

References 49 publications

Automated Lung-Related Pneumonia and COVID-19 Detection Based on Novel Feature Extraction Framework and Vision Transformer Approaches Using Chest X-ray Images

Automated Lung-Related Pneumonia and COVID-19 Detection Based on Novel Feature Extraction Framework and Vision Transformer Approaches Using Chest X-ray Images

BCNet: A Deep Learning Computer-Aided Diagnosis Framework for Human Peripheral Blood Cell Identification

Role of Inflammation, Oxidative Stress, and Mitochondrial Changes in Premenstrual Psychosomatic Behavioral Symptoms with Anti-Inflammatory, Antioxidant Herbs, and Nutritional Supplements

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