Gender Identification and Classification of Drosophila melanogaster Flies Using Machine Learning Techniques

Chola, Channabasava; Benifa, J. V. Bibal; Guru, D. S.; Muaad, Abdullah Y.; Hanumanthappa, J.; Al-antari, Mugahed A.; AlSalman, Hussain; Gumaei, Abdu

doi:10.1155/2022/4593330

Cited by 13 publications

(8 citation statements)

References 20 publications

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“…For direct comparison using the same dataset, four deep learning models of DenseNet 201, ResNet50, Inception-V3, and Mobilenet-V2 are adopted and used. These AI models are selected to perform such direct comparison due to their promising classification performance in the research domain [ 21 , 37 , 47 , 58 , 59 , 64 , 65 ]. Such comparison is important to investigate the reliability of the proposed model with the trusted ones.…”

Section: Discussionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

“…Meanwhile, normalization of intensity is performed so that all pixel values fall inside the range [0, 255]. The augmentation strategy of the training set is performed to enlarge the dataset size and improve the overall performances, as performed in our previous works [ 47 , 48 ].…”

Section: Methodsmentioning

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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“…e proposed method performance is evaluated in terms of precision, recall, accuracy, and F-Measure as used subsequently [70][71][72][73][74][75].…”

Section: Evaluation Metricsmentioning

confidence: 99%

Arabic Document Classification: Performance Investigation of Preprocessing and Representation Techniques

Muaad

Hanumanthappa

Guru

et al. 2022

Mathematical Problems in Engineering

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With the increasing number of online social posts, review comments, and digital documentations, the Arabic text classification (ATC) task has been hugely required for many spontaneous natural language processing (NLP) applications, especially within the coronavirus pandemics. The variations in the meaning of the same Arabic words could directly affect the performance of any AI-based framework. This work aims to identify the effectiveness of machine learning (ML) algorithms through preprocessing and representation techniques. This effectiveness is measured via different AI-based classification techniques. Basically, the ATC process is influenced by several factors such as stemming in preprocessing, method of feature extraction and selection, nature of datasets, and classification algorithm. To improve the overall classification performance, preprocessing techniques are mainly used to convert each Arabic word into its root and decrease the representation dimension among the datasets. Feature extraction and selection always play crucial roles to represent the Arabic text in a meaningful way and improve the classification accuracy rate. The selected classifiers in this study are performed based on various feature selection algorithms. The overall classification evaluation results are compared using different classifiers such as multinomial Naive Bayes (MNB), Bernoulli Naive Bayes (BNB), Stochastic Gradient Descent (SGD), Support Vector Classifier (SVC), Logistic Regression (LR), and Linear SVC. All of these AI classifiers are evaluated using five balanced and unbalanced benchmark datasets: BBC Arabic corpus, CNN Arabic corpus, Open-Source Arabic corpus (OSAc), ArCovidVac, and AlKhaleej. The evaluation results show that the classification performance strongly depends on the preprocessing technique, representation methods and classification technique, and the nature of datasets used. For the considered benchmark datasets, the linear SVC has outperformed other classifiers overall when prominent features are selected.

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“…For deep learning techniques, we used a new technique called word embedding, in bidirectional encoder representations from Transformers (BERT). Instead of the basic language task, BERT was trained with two tasks to encourage bidirectional prediction and sentence-level understanding [20,21].…”

Section: Representationmentioning

confidence: 99%

“…AraBERT is an Arabic pre-trained language model based on Google's BERT architecture. AraBERT uses the same BERT-Base configuration [21] 4. Experimental Analysis 4.1.…”

Section: • Arabertv2mentioning

confidence: 99%

AI-Based Misogyny Detection from Arabic Levantine Twitter Tweets

Muaad

Hanumanthappa

Al-antari

et al. 2021

The 1st International Electronic Conference on Algorithms

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Gender Identification and Classification of Drosophila melanogaster Flies Using Machine Learning Techniques

Cited by 13 publications

References 20 publications

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

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

Arabic Document Classification: Performance Investigation of Preprocessing and Representation Techniques

AI-Based Misogyny Detection from Arabic Levantine Twitter Tweets

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