RETRACTED ARTICLE: Explainable context-aware IoT framework using human digital twin for healthcare

Multimedia Tools and Applications, Journal Year: 2023, Volume and Issue: 83(22), P. 62489 - 62490

Published: Sept. 29, 2023

Language: Английский

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A Survey of Internet of Things and Cyber-Physical Systems: Standards, Algorithms, Applications, Security, Challenges, and Future Directions

Information, Journal Year: 2023, Volume and Issue: 14(7), P. 388 - 388

Published: July 8, 2023

The smart city vision has driven the rapid development and advancement of interconnected technologies using Internet Things (IoT) cyber-physical systems (CPS). In this paper, various aspects IoT CPS in recent years (from 2013 to May 2023) are surveyed. It first begins with industry standards which ensure cost-effective solutions interoperability. With ever-growing big data, tremendous undiscovered knowledge can be mined transformed into useful applications. Machine learning algorithms taking lead achieve target applications formulations such as classification, clustering, regression, prediction, anomaly detection. Notably, attention shifted from traditional machine advanced algorithms, including deep learning, transfer data generation provide more accurate models. years, there been an increasing need for security techniques defense strategies detect prevent being attacked. Research challenges future directions summarized. We hope that researchers conduct studies on CPS.

Language: Английский

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Revolutionizing Healthcare Systems: Synergistic Multimodal Ensemble Learning & Knowledge Transfer for Lung Cancer Delineation & Taxonomy

2023 IEEE International Conference on Consumer Electronics (ICCE), Journal Year: 2024, Volume and Issue: unknown

Published: Jan. 6, 2024

Lung cancer presents a substantial global public health concern, underscoring the crucial role of early detection in enhancing patient prognosis and well-being. This paper novel deep ensemble model for classification lung cancer, addressing pressing issue high incidence mortality rates associated with disease, utilizing transfer learning (TL) Convolutional Neural Networks (CNNs) integrating modern technology form fitness trackers. The combines CNNs namely VGG16, VGG19, InceptionV3, Xception, DenseNet201 through weighted voting, achieving remarkable 97.2% accuracy. innovation extends beyond image analysis by trackers that continuously monitor metrics, engagement proactive management. framework's capacity to transform both diagnosis treatment is highlighted its heightened precision extensive monitoring capabilities, offering prospect better outcomes more efficient healthcare delivery.

Language: Английский

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Adversarial Attacks and Countermeasures on Image Classification-based Deep Learning Models in Autonomous Driving Systems: A Systematic Review

ACM Computing Surveys, Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 31, 2024

The rapid development of artificial intelligence (AI) and breakthroughs in Internet Things (IoT) technologies have driven the innovation advanced autonomous driving systems (ADSs). Image classification deep learning (DL) algorithms immensely contribute to decision-making process ADSs, showcasing their capabilities handling complex real-world scenarios, surpassing human intelligence. However, these are vulnerable adversarial attacks, which aim fool them real-time compromise reliability functions. This systematic review offers a comprehensive overview most recent literature on attacks countermeasures image DL models ADSs. highlights current challenges applying successful mitigating vulnerabilities. We also introduce taxonomies for categorizing provide recommendations guidelines help researchers design evaluate countermeasures. suggest interesting future research directions improve robustness against scenarios.

Language: Английский

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Ensemble deep learning and EfficientNet for accurate diagnosis of diabetic retinopathy

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Dec. 18, 2024

Diabetic Retinopathy (DR) stands as a significant global cause of vision impairment, underscoring the critical importance early detection in mitigating its impact. Addressing this challenge head-on, study introduces an innovative deep learning framework tailored for DR diagnosis. The proposed utilizes EfficientNetB0 architecture to classify diabetic retinopathy severity levels from retinal images. By harnessing advanced techniques computer and machine learning, model aims deliver precise dependable diagnoses. Continuous testing experimentation shows efficiency architecture, showcasing promising outcomes that could help transformation both diagnosing treatment DR. This takes EfficientNet Machine Learning algorithms employing CNN layering techniques. dataset utilized is titled 'Diagnosis Retinopathy' sourced Kaggle. It consists 35,108 images, classified into five categories: No (DR), Mild DR, Moderate Severe Proliferative Through rigorous testing, yields impressive results, boasting average accuracy 86.53% loss rate 0.5663. A comparison with alternative approaches underscores effectiveness handling classification tasks retinopathy, particularly highlighting high generalizability across levels. These findings highlight framework's potential significantly advance field diagnosis, given more datasets training resources which leads it be offering clinicians powerful tool intervention improved patient outcomes.

Language: Английский

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Improving Cancer Detection Classification Performance Using GANs in Breast Cancer Data

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 71594 - 71615

Published: Jan. 1, 2023

Breast cancer is one of the most prevalent cancers in women. In recent years, many studies have been conducted breast domain. Previous confirmed that timely and accurate detection allows patients to undergo early treatment. Recently, Generative Adversarial Networks applied medical domain synthetically generate image non-image data for diagnosis. However, development an effective classification model healthcare difficult owing limited datasets. To address this challenge, we propose a novel K-CGAN method trained different settings synthetic data. This study five methods feature selection Wisconsin Cancer 357 malignant 212 benign cases evaluation. Moreover, used recall, precision, accuracy, F1 Score on generated by verify performance our proposed K-CGAN. The empirical shows performed well with highest stability compared other GAN variants. Hence, findings indicate accurately represent original

Language: Английский

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A Convolutional Neural Network-Based Feature Extraction and Weighted Twin Support Vector Machine Algorithm for Context-Aware Human Activity Recognition

Electronics, Journal Year: 2023, Volume and Issue: 12(8), P. 1915 - 1915

Published: April 18, 2023

Human activity recognition (HAR) is crucial to infer the activities of human beings, and provide support in various aspects such as monitoring, alerting, security. Distinct may possess similar movements that need be further distinguished using contextual information. In this paper, we extract features for context-aware HAR a convolutional neural network (CNN). Instead traditional CNN, combined 3D-CNN, 2D-CNN, 1D-CNN was designed enhance effectiveness feature extraction. Regarding classification model, weighted twin vector machine (WTSVM) used, which had advantages reducing computational cost high-dimensional environment compared machine. A performance evaluation showed proposed algorithm achieves an average training accuracy 98.3% 5-fold cross-validation. Ablation studies analyzed contributions individual components 1D-CNN, samples SVM, strategy solving two hyperplanes. The corresponding improvements these five were 6.27%, 4.13%, 2.40%, 2.29%, 3.26%, respectively.

Language: Английский

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A Review of Deep Learning Techniques for Early Detection and Categorization of Lung Cancer

Lecture notes in networks and systems, Journal Year: 2025, Volume and Issue: unknown, P. 503 - 516

Published: Jan. 1, 2025

Language: Английский

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PCA-F-SHCNNet: Principal Component Analysis-Fused-Shepard Convolutional Neural Networks for lung cancer detection and severity level classification

Biomedical Signal Processing and Control, Journal Year: 2025, Volume and Issue: 107, P. 107843 - 107843

Published: March 30, 2025

Language: Английский

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GA-AGN: A generative adversarial network and attention gated network model for enhanced lung cancer detection using chest CT scans

Deleted Journal, Journal Year: 2025, Volume and Issue: 2(3), P. 100077 - 100077

Published: May 14, 2025

Language: Английский

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