Efficient Q-learning Hyperparameter Tuning Using FOX Optimization Algorithm

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 104341 - 104341

Published: Feb. 1, 2025

Language: Английский

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Enhancing lung cancer diagnosis with data fusion and mobile edge computing using DenseNet and CNN

Journal of Cloud Computing Advances Systems and Applications, Journal Year: 2024, Volume and Issue: 13(1)

Published: April 19, 2024

Abstract The recent advancements in automated lung cancer diagnosis through the application of Convolutional Neural Networks (CNN) on Computed Tomography (CT) scans have marked a significant leap medical imaging and diagnostics. precision these CNN-based classifiers detecting analyzing symptoms has opened new avenues early detection treatment planning. However, despite technological strides, there are critical areas that require further exploration development. In this landscape, computer-aided diagnostic systems artificial intelligence, particularly deep learning methods like region proposal network, dual path local binary patterns, become pivotal. face challenges such as limited interpretability, data variability handling issues, insufficient generalization. Addressing is key to enhancing accurate diagnosis, fundamental for effective planning improving patient outcomes. This study introduces an advanced approach combines Network with DenseNet, leveraging fusion mobile edge computing identification classification. integration techniques enables system amalgamate information from multiple sources, robustness accuracy model. Mobile facilitates faster processing analysis CT scan images by bringing computational resources closer source, crucial real-time applications. undergo preprocessing, including resizing rescaling, optimize feature extraction. DenseNet-CNN model, strengthened capabilities, excels extracting features scans, effectively distinguishing between healthy cancerous tissues. classification categories include Normal, Benign, Malignant, latter sub-categorized into adenocarcinoma, squamous cell carcinoma, large carcinoma. controlled experiments, outperformed existing state-of-the-art methods, achieving impressive 99%. indicates its potential powerful tool cancer, advancement technology.

Language: Английский

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AI-Reinforced Wearable Sensors and Intelligent Point-of-Care Tests

Journal of Personalized Medicine, Journal Year: 2024, Volume and Issue: 14(11), P. 1088 - 1088

Published: Nov. 1, 2024

Artificial intelligence (AI) techniques offer great potential to advance point-of-care testing (POCT) and wearable sensors for personalized medicine applications. This review explores the recent advances transformative of use AI in improving wearables POCT. The integration significantly contributes empowering these tools enables continuous monitoring, real-time analysis, rapid diagnostics, thus enhancing patient outcomes healthcare efficiency. Wearable powered by models tremendous opportunities precise non-invasive tracking physiological conditions that are essential early disease detection treatments. AI-empowered POCT facilitates rapid, accurate making medical kits accessible available even resource-limited settings. discusses key applications data processing, sensor fusion, multivariate analytics, highlighting case examples exhibit their impact different scenarios. In addition, challenges associated with privacy, regulatory approvals, technology integrations into existing system have been overviewed. outlook emphasizes urgent need continued innovation AI-driven health technologies overcome fully achieve revolutionize medicine.

Language: Английский

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Predicting DoS and DDoS attacks in network security scenarios using a hybrid deep learning model

Journal of Intelligent Systems, Journal Year: 2024, Volume and Issue: 33(1)

Published: Jan. 1, 2024

Abstract Network security faces increasing threats from denial of service (DoS) and distributed (DDoS) attacks. The current solutions have not been able to predict mitigate these with enough accuracy. A novel effective solution for predicting DoS DDoS attacks in network scenarios is presented this work by employing an model, called CNN-LSTM-XGBoost, which innovative hybrid approach designed intrusion detection security. system applied analyzed three datasets: CICIDS-001, CIC-IDS2017, CIC-IDS2018. We preprocess the data removing null duplicate data, handling imbalanced selecting most relevant features using correlation-based feature selection. evaluated accuracy, precision, F 1 score, recall. achieves a higher accuracy 98.3% 99.2% CICIDS2017, 99.3% CIC-ID2018, compared other existing algorithms. also reduces overfitting model important features. This study shows that proposed efficient attack classification.

Language: Английский

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Dual-stage classification for lung cancer detection and staging using hybrid deep learning techniques

Neural Computing and Applications, Journal Year: 2024, Volume and Issue: 36(14), P. 8141 - 8161

Published: March 4, 2024

Language: Английский

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DKCN-Net: Deep Kronecker Convolutional Neural Network-based lung disease detection with Federated Learning

Computational Biology and Chemistry, Journal Year: 2025, Volume and Issue: 116, P. 108376 - 108376

Published: Feb. 8, 2025

Language: Английский

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Smart nanomedicines powered by artificial intelligence: a breakthrough in lung cancer diagnosis and treatment

Medical Oncology, Journal Year: 2025, Volume and Issue: 42(5)

Published: March 25, 2025

Language: Английский

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Optimizing SVM for Enhanced Lung Cancer Prediction: A Comparative Analysis with Traditional ML Models

Lecture notes in networks and systems, Journal Year: 2025, Volume and Issue: unknown, P. 143 - 155

Published: Jan. 1, 2025

Language: Английский

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Hybrid Metaheuristic Optimisation for Lung Cancer Image Classification: Leveraging MOEA, PSO, and ACO Algorithms

Procedia Computer Science, Journal Year: 2025, Volume and Issue: 258, P. 3781 - 3793

Published: Jan. 1, 2025

Language: Английский

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An Automated Lion-Butterfly Optimization (LBO) based Stacking Ensemble Learning Classification (SELC) Model for Lung Cancer Detection

Iraqi Journal for Computer Science and Mathematics, Journal Year: 2023, Volume and Issue: unknown, P. 87 - 100

Published: Aug. 9, 2023

Lung cancer is one of the most serious and prevalent cancers in globe. Early detection lung can increase a patient's chances life. Computed Tomography (CT) scan images are difficult for clinicians to utilize order determine stages cancer. Computer-aided systems assist researchers more precisely predicting recent times. This study demonstrates use technology that made possible by machine learning image processing accurately classify predict from CT images. The existing tumor frameworks have major difficulties terms high complexity, overfitting error prediction. Therefore, proposed work aims formulate simple accurate automated system prediction classification Before classifying input image, an adaptive median filtering approach used improve its contrast quality. From segmented parts, histogram texture features derived. relevant characteristics chosen using Lion-Butterfly Optimization (LBO) method training testing operations. Eventually, picture correctly predicted as either healthy or disease-affected Stacking Ensemble Learning Classification (SELC) algorithm. In this study, thorough performance evaluation conducted utilizing several measures analyze outcomes

Language: Английский

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