AIMC Topic: Support Vector Machine

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Predicting Brain Age Using Machine Learning Algorithms: A Comprehensive Evaluation.

IEEE journal of biomedical and health informatics
Machine learning (ML) algorithms play a vital role in the brain age estimation frameworks. The impact of regression algorithms on prediction accuracy in the brain age estimation frameworks have not been comprehensively evaluated. Here, we sought to a...

A Heterogeneous Ensemble Learning Method For Neuroblastoma Survival Prediction.

IEEE journal of biomedical and health informatics
Neuroblastoma is a pediatric cancer with high morbidity and mortality. Accurate survival prediction of patients with neuroblastoma plays an important role in the formulation of treatment plans. In this study, we proposed a heterogeneous ensemble lear...

DeepLumina: A Method Based on Deep Features and Luminance Information for Color Texture Classification.

Computational intelligence and neuroscience
Color texture classification is a significant computer vision task to identify and categorize textures that we often observe in natural visual scenes in the real world. Without color and texture, it remains a tedious task to identify and recognize ob...

Sensor Fault Diagnosis Using a Machine Fuzzy Lyapunov-Based Computed Ratio Algorithm.

Sensors (Basel, Switzerland)
Anomaly identification for internal combustion engine (ICE) sensors has become an important research area in recent years. In this work, a proposed indirect fuzzy Lyapunov-based computed ratio observer integrated with a support vector machine (SVM) w...

An Intelligent and Reliable Hyperparameter Optimization Machine Learning Model for Early Heart Disease Assessment Using Imperative Risk Attributes.

Journal of healthcare engineering
Heart disease is a severe disorder, which inflicts an adverse burden on all societies and leads to prolonged suffering and disability. We developed a risk evaluation model based on visible low-cost significant noninvasive attributes using hyperparame...

A Hybrid Model for Leaf Diseases Classification Based on the Modified Deep Transfer Learning and Ensemble Approach for Agricultural AIoT-Based Monitoring.

Computational intelligence and neuroscience
As possible diseases develop on plant leaves, classification is constantly hampered by obstacles such as overfitting and low accuracy. To distinguish healthy products from defective ones, the agricultural industry requires precise and error-free anal...

Extraction of low-dimensional features for single-channel common lung sound classification.

Medical & biological engineering & computing
In this study, feature extraction methods used in the classification of single-channel lung sounds obtained by automatic identification of respiratory cycles were examined in detail in order to extract distinctive features at the lowest size. In this...

Dynamically Generated Hierarchical Neural Networks Designed With the Aid of Multiple Support Vector Regressors and PNN Architecture With Probabilistic Selection.

IEEE transactions on neural networks and learning systems
The two issues on dynamically generated hierarchical neural networks such as the sort of basic neurons and how to compose a layer are considered in this article. On the first issue, a variant version of the least-square support vector regression (SVR...

Machine Learning in Injection Molding: An Industry 4.0 Method of Quality Prediction.

Sensors (Basel, Switzerland)
One of the essential requirements of injection molding is to ensure the stable quality of the parts produced. However, numerous processing conditions, which are often interrelated in quite a complex way, make this challenging. Machine learning (ML) a...

An Augmented Artificial Intelligence Approach for Chronic Diseases Prediction.

Frontiers in public health
Chronic diseases are increasing in prevalence and mortality worldwide. Early diagnosis has therefore become an important research area to enhance patient survival rates. Several research studies have reported classification approaches for specific di...