Comparative analysis of SVM and ANN classifiers for defective and non-defective fabric images classification

Anami, Basavaraj S.; Elemmi, Mahantesh C.

doi:10.1080/00405000.2021.1915559

Cited by 9 publications

(2 citation statements)

References 12 publications

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“…In this paper, a classification model for renewable energy increment based on ensemble learning is proposed. By training the renewable energy increment dataset with KNN, RF, SVM, and LR four traditional machine learning classification models, and obtaining the final classification result through weighted average soft voting, the experimental results show that: under the same hyperparameters, the accuracy and mean square error of the ensemble learning model are optimized [11] .…”

Section: Discussionmentioning

confidence: 99%

Research on renewable energy incremental classification model based on ensemble learning algorithm

Tian,

Lin,

Liu

2024

Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023)

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Renewable energy is a green and low-carbon energy source, which is of great significance for improving energy structure, protecting the ecological environment, addressing climate change, and achieving sustainable development. This article investigates the application of ensemble learning models in the prediction of renewable energy increment. Traditional prediction methods have problems due to their own limitations, such as imbalanced data and rough feature selection, resulting in low model prediction accuracy. This article proposes an ensemble learning based energy increment prediction model (ERLSK model). The model includes four traditional machine learning classification models: KNN, random forest, SVM, and logistic regression. The best hyperparameter of each model are obtained by cross validation and grid search. By using different feature selection and model training methods, different prediction results are obtained, and these results are weighted and fused to obtain the final classification result. The experimental results indicate that the model is effective and feasible.

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Section: Discussionmentioning

confidence: 99%

Research on renewable energy incremental classification model based on ensemble learning algorithm

Tian,

Lin,

Liu

2024

Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023)

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“…Compared with ANN, SVM is more generalizable and can obtain global optimal solutions. Among them, feature extraction is an important step ( Anami and Elemmi, 2022 ). It is useful to combine neural networks and intelligent agents for medical image analysis according to the research of Shi et al ( Sarvamangala and Kulkarni, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

A novel cascade machine learning pipeline for Alzheimer’s disease identification and prediction

Zhou

Piao²,

Liu³

et al. 2023

Front. Aging Neurosci.

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IntroductionAlzheimer’s disease (AD) is a progressive and irreversible brain degenerative disorder early. Among all diagnostic strategies, hippocampal atrophy is considered a promising diagnostic method. In order to proactively detect patients with early Alzheimer’s disease, we built an Alzheimer’s segmentation and classification (AL-SCF) pipeline based on machine learning.MethodsIn our study, we collected coronal T1 weighted images that include 187 patients with AD and 230 normal controls (NCs). Our pipeline began with the segmentation of the hippocampus by using a modified U2-net. Subsequently, we extracted 851 radiomics features and selected 37 features most relevant to AD by the Hierarchical clustering method and Least Absolute Shrinkage and Selection Operator (LASSO) algorithm. At last, four classifiers were implemented to distinguish AD from NCs, and the performance of the models was evaluated by accuracy, specificity, sensitivity, and area under the curve.ResultsOur proposed pipeline showed excellent discriminative performance of classification with AD vs NC in the training set (AUC=0.97, 95% CI: (0.96-0.98)). The model was also verified in the validation set with Dice=0.93 for segmentation and accuracy=0.95 for classification.DiscussionThe AL-SCF pipeline can automate the process from segmentation to classification, which may assist doctors with AD diagnosis and develop individualized medical plans for AD in clinical practice.

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Conditional image-to-image translation generative adversarial network (cGAN) for fabric defect data augmentation

Mohammed,

Clarke

2024

Neural Comput & Applic

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The availability of comprehensive datasets is a crucial challenge for developing artificial intelligence (AI) models in various applications and fields. The lack of large and diverse public fabric defect datasets forms a major obstacle to properly and accurately developing and training AI models for detecting and classifying fabric defects in real-life applications. Models trained on limited datasets struggle to identify underrepresented defects, reducing their practicality. To address these issues, this study suggests using a conditional generative adversarial network (cGAN) for fabric defect data augmentation. The proposed image-to-image translator GAN features a conditional U-Net generator and a 6-layered PatchGAN discriminator. The conditional U-Network (U-Net) generator can produce highly realistic synthetic defective samples and offers the ability to control various characteristics of the generated samples by taking two input images: a segmented defect mask and a clean fabric image. The segmented defect mask provides information about various aspects of the defects to be added to the clean fabric sample, including their type, shape, size, and location. By augmenting the training dataset with diverse and realistic synthetic samples, the AI models can learn to identify a broader range of defects more accurately. This technique helps overcome the limitations of small or unvaried datasets, leading to improved defect detection accuracy and generalizability. Moreover, this proposed augmentation method can find applications in other challenging fields, such as generating synthetic samples for medical imaging datasets related to brain and lung tumors.

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Comparative analysis of SVM and ANN classifiers for defective and non-defective fabric images classification

Cited by 9 publications

References 12 publications

Research on renewable energy incremental classification model based on ensemble learning algorithm

Research on renewable energy incremental classification model based on ensemble learning algorithm

A novel cascade machine learning pipeline for Alzheimer’s disease identification and prediction

Conditional image-to-image translation generative adversarial network (cGAN) for fabric defect data augmentation

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