A Modified Aquila-Based Optimized XGBoost Framework for Detecting Probable Seizure Status in Neonates

Mumenin, Khondoker Mirazul; Biswas, Prabir Kumar; Khan, Md. Al-Masrur; Alammary, Ali; Nahid, Abdullah-Al

doi:10.3390/s23167037

Cited by 6 publications

(2 citation statements)

References 36 publications

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“…The MAO algorithm [28] was used to determine the recommended classifier's k-value. The ability of the aquila to swoop down and seize its prey is crucial to the aquila optimizer (AO).…”

Section: Finding K Value Using Mao Algorithmmentioning

confidence: 99%

Advanced Dental Implant System Classification with Pre-trained CNN Models and Multi-branch Spectral Channel Attention Networks

Vemula,

Vatambeti

2024

ATAIML

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Dental implants (DIs) are prone to failure due to uncommon mechanical complications and fractures. Precise identification of implant fixture systems from periapical radiographs is imperative for accurate diagnosis and treatment, particularly in the absence of comprehensive medical records. Existing methods predominantly leverage spatial features derived from implant images using convolutional neural networks (CNNs). However, texture images exhibit distinctive patterns detectable as strong energy at specific frequencies in the frequency domain, a characteristic that motivates this study to employ frequency-domain analysis through a novel multi-branch spectral channel attention network (MBSCAN). High-frequency data obtained via a two-dimensional (2D) discrete cosine transform (DCT) are exploited to retain phase information and broaden the application of frequency-domain attention mechanisms. Fine-tuning of the multi-branch spectral channel attention (MBSCA) parameters is achieved through the modified aquila optimizer (MAO) algorithm, optimizing classification accuracy. Furthermore, pre-trained CNN architectures such as Visual Geometry Group (VGG) 16 and VGG19 are harnessed to extract features for classifying intact and fractured DIs from panoramic and periapical radiographs. The dataset comprises 251 radiographic images of intact DIs and 194 images of fractured DIs, meticulously selected from a pool of 21,398 DIs examined across two dental facilities. The proposed model has exhibited robust accuracy in detecting and classifying fractured DIs, particularly when relying exclusively on periapical images. The MBSCA-MAO scheme has demonstrated exceptional performance, achieving a classification accuracy of 95.7% with precision, recall, and F1-score values of 95.2%, 94.3%, and 95.6%, respectively. Comparative analysis indicates that the proposed model significantly surpasses existing methods, showcasing its superior efficacy in DI classification.

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“…The MAO algorithm [28] was used to determine the recommended classifier's k-value. The ability of the aquila to swoop down and seize its prey is crucial to the aquila optimizer (AO).…”

Section: Finding K Value Using Mao Algorithmmentioning

confidence: 99%

Advanced Dental Implant System Classification with Pre-trained CNN Models and Multi-branch Spectral Channel Attention Networks

Vemula,

Vatambeti

2024

ATAIML

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“…An MAO was introduced in this study [28]. By modifying the SCF from IAO, MAO was inspired to make further amendments to the AO.…”

Section: Hyperparameter Tuning Using Mao Algorithmmentioning

confidence: 99%

Modified Aquila Optimizer with Stacked Deep Learning-Based Sentiment Analysis of COVID-19 Tweets

Almasoud,

Alshahrani,

Hassan

et al. 2023

Electronics

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In recent times, global cities have been transforming from traditional cities to sustainable smart cities. In text sentiment analysis (SA), many people face critical issues namely urban traffic management, urban living quality, urban information security, urban energy usage, urban safety, etc. Artificial intelligence (AI)-based applications play important roles in dealing with these crucial challenges in text SA. In such scenarios, the classification of COVID-19-related tweets for text SA includes using natural language processing (NLP) and machine learning methodologies to classify tweet datasets based on their content. This assists in disseminating relevant information, understanding public sentiment, and promoting sustainable practices in urban areas during this pandemic. This article introduces a modified aquila optimizer with a stacked deep learning-based COVID-19 tweet Classification (MAOSDL-TC) technique for text SA. The presented MAOSDL-TC technique incorporates FastText, an effective and powerful text representation approach used for the generation of word embeddings. Furthermore, the MAOSDL-TC technique utilizes an attention-based stacked bidirectional long short-term memory (ASBiLSTM) model for the classification of sentiments that exist in tweets. To improve the detection results of the ASBiLSTM model, the MAO algorithm is applied for the hyperparameter tuning process. The presented MAOSDL-TC technique is validated on the benchmark tweets dataset. The experimental outcomes implied the promising results of the MAOSDL-TC technique compared to recent models in terms of different measures. This MAOSDL-TC technique improves accuracy and interpretability of sentiment prediction.

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Comprehensive database for food-gut microbiota-disease interactions (FGMDI) analysis and dietary recommendation applications

Zhang,

Zhao

et al. 2024

Food Bioscience

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A Modified Aquila-Based Optimized XGBoost Framework for Detecting Probable Seizure Status in Neonates

Cited by 6 publications

References 36 publications

Advanced Dental Implant System Classification with Pre-trained CNN Models and Multi-branch Spectral Channel Attention Networks

Advanced Dental Implant System Classification with Pre-trained CNN Models and Multi-branch Spectral Channel Attention Networks

Modified Aquila Optimizer with Stacked Deep Learning-Based Sentiment Analysis of COVID-19 Tweets

Comprehensive database for food-gut microbiota-disease interactions (FGMDI) analysis and dietary recommendation applications

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