Arabic Speech Classification Method Based on Padding and Deep Learning Neural Network

Asroni, Asroni; Ku-Mahamud, Ku Ruhana; Damarjati, Cahya; Slamat, Hasan Basri

doi:10.21123/bsj.2021.18.2(suppl.).0925

Cited by 8 publications

(7 citation statements)

References 23 publications

(36 reference statements)

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“…The work of 25 used traditional convolutional neural networks, which include support vector machine, random forest, and K-nearest neighbour as machine learning classifiers to automatically label the hyperspectral images dataset for classification of spill from satellite images; the CNN has a global perspective in terms of its ability to perform object classification and recognition 26 . However, the classifiers achieved better accuracy in a specified region (Gulf of Mexico) selected for the research.…”

Section: Related Workmentioning

confidence: 99%

تصنيف الانسكابات النفطية على أساس الصور الفضائية باستخدام تقنيات التعلم العميق

Abba,

Mustaffa,

Hashim

et al. 2024

Baghdad Sci.J

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An oil spill is a leakage of pipelines, vessels, oil rigs, or tankers that leads to the release of petroleum products into the marine environment or on land that happened naturally or due to human action, which resulted in severe damages and financial loss. Satellite imagery is one of the powerful tools currently utilized for capturing and getting vital information from the Earth's surface. But the complexity and the vast amount of data make it challenging and time-consuming for humans to process. However, with the advancement of deep learning techniques, the processes are now computerized for finding vital information using real-time satellite images. This paper applied three deep-learning algorithms for satellite image classification, including ResNet50, VGG19, and InceptionV4; They were trained and tested on an open-source satellite image dataset to analyze the algorithms' efficiency and performance and correlated the classification accuracy, precisions, recall, and f1-score. The result shows that InceptionV4 gives the best classification accuracy of 97% for cloudy, desert, green areas, and water, followed by VGG19 with approximately 96% and ResNet50 with 93%. The findings proved that the InceptionV4 algorithm is suitable for classifying oil spills and no spill with satellite images on a validated dataset.

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Section: Related Workmentioning

confidence: 99%

تصنيف الانسكابات النفطية على أساس الصور الفضائية باستخدام تقنيات التعلم العميق

Abba,

Mustaffa,

Hashim

et al. 2024

Baghdad Sci.J

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show abstract

“…They can predict the outcome of every input not in the datasets, which makes the ANNs flexible in case some data is missing or wrong. Numerous studies on ML using ANNs have been conducted to recognize and classify patterns [15,16,17,18]. This study proposed a new criterion based on the arc of vision and the crescent width.…”

Section: Allawimentioning

confidence: 99%

Crescent Moon Visibility: A New Criterion using Deep learned Artificial Neural-Network

Allawi

2024

Iraqi Journal of Science

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Many authors investigated the problem of the early visibility of the new crescent moon after the conjunction and proposed many criteria addressing this issue in the literature. This article presented a proposed criterion for early crescent moon sighting based on a deep-learned pattern recognizer artificial neural network (ANN) performance. Moon sight datasets were collected from various sources and used to learn the ANN. The new criterion relied on the crescent width and the arc of vision from the edge of the crescent bright limb. The result of that criterion was a control value indicating the moon's visibility condition, which separated the datasets into four regions: invisible, telescope only, probably visible, and certainly visible. This criterion was used on the dataset for ANN learning to compare its efficiency with the actual moon visibility events.

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“…Long Short-Term Memory (LSTM) is the most important improvement for RNN which largely treated the vanishing gradient problem 19 . This network is better for dealing with long-distance sequences and therefore has better successes in NLP as compared to the RNN.…”

Section: Long-short Term Memory (Lstm)mentioning

confidence: 99%

An Effective Hybrid Deep Neural Network for Arabic Fake News Detection

Wotaifi

Dhannoon²

2023

Baghdad Sci.J

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Recently, the phenomenon of the spread of fake news or misinformation in most fields has taken on a wide resonance in societies. Combating this phenomenon and detecting misleading information manually is rather boring, takes a long time, and impractical. It is therefore necessary to rely on the fields of artificial intelligence to solve this problem. As such, this study aims to use deep learning techniques to detect Arabic fake news based on Arabic dataset called the AraNews dataset. This dataset contains news articles covering multiple fields such as politics, economy, culture, sports and others. A Hybrid Deep Neural Network has been proposed to improve accuracy. This network focuses on the properties of both the Text-Convolution Neural Network (Text-CNN) and Long Short-Term Memory (LSTM) architecture to produce efficient hybrid model. Text-CNN is used to identify the relevant features, whereas the LSTM is applied to deal with the long-term dependency of sequence. The results showed that when trained individually, the proposed model outperformed both the Text-CNN and the LSTM. Accuracy was used as a measure of model quality, whereby the accuracy of the Hybrid Deep Neural Network is (0.914), while the accuracy of both Text-CNN and LSTM is (0.859) and (0.878), respectively. Moreover, the results of our proposed model are better compared to previous work that used the same dataset (AraNews dataset).

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Arabic Speech Classification Method Based on Padding and Deep Learning Neural Network

Cited by 8 publications

References 23 publications

تصنيف الانسكابات النفطية على أساس الصور الفضائية باستخدام تقنيات التعلم العميق

تصنيف الانسكابات النفطية على أساس الصور الفضائية باستخدام تقنيات التعلم العميق

Crescent Moon Visibility: A New Criterion using Deep learned Artificial Neural-Network

An Effective Hybrid Deep Neural Network for Arabic Fake News Detection

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