Symmetrized Feature Selection with Stacked Generalization based Machine Learning Algorithm for the Early Diagnosis of Chronic Diseases

Hegde, Sandeep Kumar; Hegde, Rajalaxmi; Hombalimath, Veeranna S.; Palanikkumar, D; Patwari, Neha; Gowda, Vrushab

doi:10.1109/icssit55814.2023.10061062

Cited by 18 publications

(2 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When all the factors are considered, the RF forecast's accuracy rises to 80.84%. Using a unique genetic method based on stacking generalization, Dr. Sandeep Kumar Hegde et al [10] were able to predict chronic illnesses, such female breast cancer, cardiovascular disease, diabetes, and cancer of the lungs at an early stage. The researchers used the UCI sickness dataset for their study.…”

Section: Literature Surveymentioning

confidence: 99%

Unlocking The Potential Of Machine Learning For Diabetes Prediction

Atkotiya,

Ramniklal,

Zalavadia

2024

eatp

View full text Add to dashboard Cite

Section: Literature Surveymentioning

confidence: 99%

Unlocking The Potential Of Machine Learning For Diabetes Prediction

Atkotiya,

Ramniklal,

Zalavadia

2024

eatp

View full text Add to dashboard Cite

“…This growth can be attributed to the proliferation of digital cameras, smartphones, and internet-based platforms where users generate, share, and consume visual content incessantly. Such a massive influx of digital content underscores the need for highly efficient image matching techniques [12]. In applications like digital forensics or content-based image retrieval, speed and accuracy are paramount.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Perspectives on Efficient Image Matching in Natural Image Databases

Mirzanur Rahman

2023

IJRITCC

View full text Add to dashboard Cite

With the proliferation of digital content, efficient image matching in natural image databases has become paramount. Traditional image matching techniques, while effective to a certain extent, face challenges in dealing with the high variability inherent in natural images. This research delves into the application of deep learning models, particularly Convolutional Neural Networks (CNNs), Siamese Networks, and Triplet Networks, to address these challenges. We introduce various techniques to enhance efficiency, such as data augmentation, transfer learning, dimensionality reduction, efficient sampling, and the amalgamation of traditional computer vision strategies with deep learning. Our experimental results, garnered from specific dataset, demonstrate significant improvements in image matching efficiency, as quantified by metrics like precision, recall, F1-Score, and matching time. The findings underscore the potential of deep learning as a transformative tool for natural image database matching, setting the stage for further research and optimization in this domain.

show abstract