Advanced Meta-Heuristic Algorithm Based on Particle Swarm and Al-Biruni Earth Radius Optimization Methods for Oral Cancer Detection

Hadjouni, Myriam; Abdelhamid, Abdelaziz A.; El-kenawy, El-Sayed M.; Ibrahim, Abdelhameed; Eid, Marwa M.; Jamjoom, Mona; Khafaga, Doaa Sami

doi:10.1109/access.2023.3253430

Cited by 16 publications

(9 citation statements)

References 67 publications

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“…The output, hidden, and visible layers are the three layers of DBN [17]. While learning, the feature goes through multiple hidden layers before getting the input or visible layers.…”

Section: Dbn-based Detection Modelmentioning

confidence: 99%

Enhancing authenticity and trust in social media: an automated approach for detecting fake profiles

Unni,

S.,

Kalapurackal

et al. 2024

IJEECS

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<p class="Abstract">Fake profile detection on social media is a critical task intended for detecting and alleviating the existence of deceptive or fraudulent user profiles. These fake profiles, frequently generated with malicious intent, could engage in different forms of spreading disinformation, online fraud, or spamming. A range of techniques is employed to solve these problems such as natural language processing (NLP), machine learning (ML), and behavioural analysis, to examine engagement patterns, user-generated content, and profile characteristics. This paper proposes an automated fake profile detection using the coyote optimization algorithm with deep learning (FPD-COADL) method on social media. This multifaceted approach scrutinizes user-generated content, engagement patterns, and profile attributes to differentiate genuine user accounts from deceptive ones, ultimately reinforcing the authenticity and trustworthiness of social networking platforms. The presented FPD-COADL method uses robust data pre-processing methods to enhance the uniformness and quality of data. Besides, the FPD-COADL method applies deep belief network (DBN) for the recognition and classification of fake accounts. Extensive experiments and evaluations on own collected social media datasets underscore the effectiveness of the approach, showcasing its potential to identify fake profiles with high scalability and precision.</p>

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“…The output, hidden, and visible layers are the three layers of DBN [17]. While learning, the feature goes through multiple hidden layers before getting the input or visible layers.…”

Section: Dbn-based Detection Modelmentioning

confidence: 99%

Enhancing authenticity and trust in social media: an automated approach for detecting fake profiles

Unni,

S.,

Kalapurackal

et al. 2024

IJEECS

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“…While these approaches hold promise, it's important to note that they come with high computational complexity, which may present practical challenges in real-world clinical applications. Myriam et al, (2023) introduced an innovative meta-heuristic algorithm for oral cancer detection, combining particle swarm optimization (PSO) and Al-Biruni earth radius optimization (BER) methods. This hybrid approach effectively initializes a deep belief network (DBN) with PSO and fine-tunes it using BER, achieving efficacy accuracy on a challenging dataset.…”

Section: Literature Reviewmentioning

confidence: 99%

Fuzzy Genetic Particle Swarm Optimization Convolution Neural Network Based On Oral Cancer Identification System

Dharani,

Revathy,

Danesh

2023

JAETS

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Oral cancer is the eighth most common type of cancer in the world. Every year, 130,000 people in India die from mouth cancer. Getting a diagnosis from a clinical exam by skilled doctors and a biopsy takes time. When a problem is found early, it is always easier to treat. The primary goal of this work is to recognise disease-affected oral regions in a given oral image and classify the oral cancer disorder. This study employs unique Deep Learning algorithms to detect the location of disease-affected oral areas. This work employs the most effective feature extraction techniques, including appearance and patter-based features. Following feature extraction, the Bee Pulse Couple Neural Network (BeePCNN) algorithm is used to choose the best feature. Finally, Deep Learning is used to classify these attributes. An innovative FGPSOCNN reduces the computational complexity of CNN. On an additional real-time data set from Arthi Scan Hospital, a secondary evaluation is conducted. The experimental results indicate that the innovative FGPSOCNN performs better than existing methods.

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“…The proposed method based on CNN and DBN is efficient and provides an accuracy of 97.35%. Features are extracted using optimized CNN and classification of oral cancer has been done using optimized DBN [69]. CNNs are providing significant improvements in the segmentation of biomedical images for brain tumor segmentation.…”

Section: Related Workmentioning

confidence: 99%

RNN-CNN Based Cancer Prediction Model for Gene Expression

Thakur,

Batra,

Malik

et al. 2023

IEEE Access

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One of those illnesses that is most deadly to people is cancer. The only way to prevent any harm to humanity is by its early discovery and treatment. Various types of tests are conducted in the medical labs for the detection of cancer. Cancer can also be detected at the genetic level. For this distinct machine learning and deep learning methods already exist. This paper proposes a hybrid method based on Recurrent Neural Network (RNN) and Convolution Neural Network (CNN) to predict different types of cancer such as Breast, Lung, Uterine, Kidney, Prostate and colon cancer from gene expression data. The bottleneck features are extracted using the sandwich stacked method based on VGG16 and VGG19 pre-trained models. Afterward, the proposed hybrid classifier based on RNN-CNN has been used to classify the data into various classes. The proposed model performs better than the other existing methods such as VGG16, VGG19, ResNet50, Inception V3 and MobileNet classifier in terms of various performance metrics such as accuracy, Mean Square Error (MSE), precision, recall, and F1 score. RNN-CNN classifier provides the highest accuracy of 0.978 among all the other existing methods for Dataset 1 and the highest accuracy of 0.994 for Dataset 2 at 80% training data. On the other hand, RNN-CNN classifier provides the lowest MSE of 0.101 among all the other existing methods for Dataset 1 and the lowest MSE of 0.006 for Dataset 2 at 80% training data.

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Advanced Meta-Heuristic Algorithm Based on Particle Swarm and Al-Biruni Earth Radius Optimization Methods for Oral Cancer Detection

Cited by 16 publications

References 67 publications

Enhancing authenticity and trust in social media: an automated approach for detecting fake profiles

Enhancing authenticity and trust in social media: an automated approach for detecting fake profiles

Fuzzy Genetic Particle Swarm Optimization Convolution Neural Network Based On Oral Cancer Identification System

RNN-CNN Based Cancer Prediction Model for Gene Expression

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