An enhanced prediction of tumor using transfer learning based framework DOI

Surjeet Yadav,

T. Ramachandran,

Arvind Kumar Pandey

и другие.

Опубликована: Март 15, 2024

Switch mastering involves the transfer of understanding from one device version to every other, with purpose getting better performance being trained. This approach has currently been used improve effectiveness deep-gaining knowledge networks for most cancer detection. Specifically, switch getting-to-know method is applied best-song a pre-trained deep studying network medical imaging records research functions related datasets. The first-class-tuned learning then classify newly affected person pics analysis and prognosis cancers. switching know gain substantially decreasing amount information needed teach version, in addition offering multiplied accuracy model improving generalization capability. Moreover, may be investigate exclusive aspects cancers discover new As such, powerful

Язык: Английский

RETRACTED ARTICLE: Explainable context-aware IoT framework using human digital twin for healthcare DOI
Tarun Vats, Sunil K. Singh, Sudhakar Kumar

и другие.

Multimedia Tools and Applications, Год журнала: 2023, Номер 83(22), С. 62489 - 62490

Опубликована: Сен. 29, 2023

Язык: Английский

Процитировано

35

A Survey of Internet of Things and Cyber-Physical Systems: Standards, Algorithms, Applications, Security, Challenges, and Future Directions DOI Creative Commons
Kwok Tai Chui, Brij B. Gupta, Jiaqi Liu

и другие.

Information, Год журнала: 2023, Номер 14(7), С. 388 - 388

Опубликована: Июль 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.

Язык: Английский

Процитировано

24

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

Aishita Sharma,

Sunil K. Singh, Sudhakar Kumar

и другие.

2023 IEEE International Conference on Consumer Electronics (ICCE), Год журнала: 2024, Номер unknown

Опубликована: Янв. 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.

Язык: Английский

Процитировано

11

Adversarial Attacks and Countermeasures on Image Classification-based Deep Learning Models in Autonomous Driving Systems: A Systematic Review DOI Open Access
Bakary Badjie, José Cecílio, António Casimiro

и другие.

ACM Computing Surveys, Год журнала: 2024, Номер unknown

Опубликована: Авг. 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.

Язык: Английский

Процитировано

8

Ensemble deep learning and EfficientNet for accurate diagnosis of diabetic retinopathy DOI Creative Commons

Lakshay Arora,

Sunil K. Singh, Sudhakar Kumar

и другие.

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Дек. 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.

Язык: Английский

Процитировано

6

Improving Cancer Detection Classification Performance Using GANs in Breast Cancer Data DOI Creative Commons
Emilija Strelcenia, Simant Prakoonwit

IEEE Access, Год журнала: 2023, Номер 11, С. 71594 - 71615

Опубликована: Янв. 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

Язык: Английский

Процитировано

12

A Convolutional Neural Network-Based Feature Extraction and Weighted Twin Support Vector Machine Algorithm for Context-Aware Human Activity Recognition DOI Open Access
Kwok Tai Chui, Brij B. Gupta, Miguel Torres-Ruiz

и другие.

Electronics, Год журнала: 2023, Номер 12(8), С. 1915 - 1915

Опубликована: Апрель 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.

Язык: Английский

Процитировано

11

An optimized multi-head attention based fused depthwise convolutional model for lung cancer detection DOI

Sadam Kavitha,

Eswar Patnala,

Hrushikesava Raju Sangaraju

и другие.

Expert Systems with Applications, Год журнала: 2025, Номер unknown, С. 126596 - 126596

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0

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

Swati Joshi,

Raj Gaurav Mishra,

P. G. Sivagaminathan

и другие.

Lecture notes in networks and systems, Год журнала: 2025, Номер unknown, С. 503 - 516

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0

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

Sk Khader Basha,

Pravin R. Kshirsagar, P. Srinivasa Rao

и другие.

Biomedical Signal Processing and Control, Год журнала: 2025, Номер 107, С. 107843 - 107843

Опубликована: Март 30, 2025

Язык: Английский

Процитировано

0