ArticleMultimedia tools and applications2023
The effect of choosing optimizer algorithms to improve computer vision tasks: a comparative study.
Article in Multimedia tools and applications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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31 citing papers in PubMed.
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- Deep learning algorithms for identifying developmental dysplasia of the hip based on sonographic images: a retrospective, prospective, multicenter study in China.EClinicalMedicine · 2025Article
- Intelligent fault diagnosis based on multi-source information fusion and attention-enhanced networks.Scientific reports · 2025Article
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- A novel model for expanding horizons in sign Language recognition.Scientific reports · 2025Article
- A novel framework for sentiment classification employing Bi-GRU optimized by enhanced human evolutionary optimization algorithm.Scientific reports · 2025Article
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- Cross-Domain Fault Diagnosis of Rotating Machinery Under Time-Varying Rotational Speed and Asymmetric Domain Label Condition.Sensors (Basel, Switzerland) · 2025Article
- Leveraging ensemble convolutional neural networks and metaheuristic strategies for advanced kidney disease screening and classification.Scientific reports · 2025Article
- Applications of Artificial Intelligence, Deep Learning, and Machine Learning to Support the Analysis of Microscopic Images of Cells and Tissues.Journal of imaging · 2025Review
- Enhanced hybrid attention deep learning for avocado ripeness classification on resource constrained devices.Scientific reports · 2025Article
- Artificial Intelligence in Cancer Oncology Through Comprehensive Bibliometric Mapping of Global Trends Impact and Conceptual Structures.Journal of healthcare leadership · 2025Review
- TransembleNet: Enhancing vector mosquito species classification through transfer learning-based ensemble model.PloS one · 2025Article
- Development and laboratory evaluation of a novel IoT-based electric-driven metering system for high precision garlic planter.PloS one · 2025Article
- Sustainable deep vision systems for date fruit quality assessment using attention-enhanced deep learning models.Frontiers in plant science · 2025Article
- Article
Corrections and comments
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4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Optimization algorithms are used to improve model accuracy. The optimization process undergoes multiple cycles until convergence. A variety of optimization strategies have been developed to overcome the obstacles involved in the learning process. Some of these strategies have been considered in this study to learn more about their complexities. It is crucial to analyse and summarise optimization techniques methodically from a machine learning standpoint since this can provide direction for future work in both machine learning and optimization. The approaches under consideration include the Stochastic Gradient Descent (SGD), Stochastic Optimization Descent with Momentum, Rung Kutta, Adaptive Learning Rate, Root Mean Square Propagation, Adaptive Moment Estimation, Deep Ensembles, Feedback Alignment, Direct Feedback Alignment, Adfactor, AMSGrad, and Gravity. prove the ability of each optimizer applied to machine learning models. Firstly, tests on a skin cancer using the ISIC standard dataset for skin cancer detection were applied using three common optimizers (Adaptive Moment, SGD, and Root Mean Square Propagation) to explore the effect of the algorithms on the skin images. The optimal training results from the analysis indicate that the performance values are enhanced using the Adam optimizer, which achieved 97.30% accuracy. The second dataset is COVIDx CT images, and the results achieved are 99.07% accuracy based on the Adam optimizer. The result indicated that the utilisation of optimizers such as SGD and Adam improved the accuracy in training, testing, and validation stages.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.