A hybrid approach for Bangla sign language recognition using deep transfer learning model with random forest classifier

Das, Sunanda; Imtiaz, Md. Samir; Neom, Nieb Hasan; Siddique, Nazmul; Wang, Hui

doi:10.1016/j.eswa.2022.118914

Cited by 69 publications

(29 citation statements)

References 36 publications

(39 reference statements)

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“…In this study, the Google Earth Engine (GEE) remote-sensing cloud platform was utilized to implement three machine-learning classifiers: SVM, CART, and RF. The Random Forest (RF) [77] is an efficient machine-learning algorithm that achieves classification tasks by constructing multiple decision trees. The fundamental idea of this algorithm is to conduct random sampling and feature selection on the training data, create multiple decision trees, and then integrate them for classification, thereby enhancing the model's accuracy and stability.…”

Section: Classification Methodsmentioning

confidence: 99%

Eucalyptus Plantation Area Extraction Based on SLPSO-RFE Feature Selection and Multi-Temporal Sentinel-1/2 Data

Lin,

Ren,

et al. 2023

Forests

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An accurate and efficient estimation of eucalyptus plantation areas is of paramount significance for forestry resource management and ecological environment monitoring. Currently, combining multidimensional optical and SAR images with machine learning has become an important method for eucalyptus plantation classification, but there are still some challenges in feature selection. This study proposes a feature selection method that combines multi-temporal Sentinel-1 and Sentinel-2 data with SLPSO (social learning particle swarm optimization) and RFE (Recursive Feature Elimination), which reduces the impact of information redundancy and improves classification accuracy. Specifically, this paper first fuses multi-temporal Sentinel-1 and Sentinel-2 data, and then carries out feature selection by combining SLPSO and RFE to mitigate the effects of information redundancy. Next, based on features such as the spectrum, red-edge indices, texture characteristics, vegetation indices, and backscatter coefficients, the study employs the Simple Non-Iterative Clustering (SNIC) object-oriented method and three different types of machine-learning models: Random Forest (RF), Classification and Regression Trees (CART), and Support Vector Machines (SVM) for the extraction of eucalyptus plantation areas. Each model uses a supervised-learning method, with labeled training data guiding the classification of eucalyptus plantation regions. Lastly, to validate the efficacy of selecting multi-temporal data and the performance of the SLPSO–RFE model in classification, a comparative analysis is undertaken against the classification results derived from single-temporal data and the ReliefF–RFE feature selection scheme. The findings reveal that employing SLPSO–RFE for feature selection significantly elevates the classification precision of eucalyptus plantations across all three classifiers. The overall accuracy rates were noted at 95.48% for SVM, 96% for CART, and 97.97% for RF. When contrasted with classification outcomes from multi-temporal data and ReliefF–RFE, the overall accuracy for the trio of models saw an increase of 10%, 8%, and 8.54%, respectively. The accuracy enhancement was even more pronounced when juxtaposed with results from single-temporal data and ReliefF-RFE, at increments of 15.25%, 13.58%, and 14.54% respectively. The insights from this research carry profound theoretical implications and practical applications, particularly in identifying and extracting eucalyptus plantations leveraging multi-temporal data and feature selection.

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Section: Classification Methodsmentioning

confidence: 99%

Eucalyptus Plantation Area Extraction Based on SLPSO-RFE Feature Selection and Multi-Temporal Sentinel-1/2 Data

Lin,

Ren,

et al. 2023

Forests

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“… The users of Nepali Sign Language will greatly benefit out of this dataset as it is first of its kind and will be used by researchers to develop automatic Nepali sign language recognition application. There are many existing datasets for other languages [2] , [3] , [4] , [5] , 7 , 8 , [10] , [11] , [12] , [13] , 15 , 17 , [19] , [20] , [21] , [22] , [23] This dataset can be used to develop Nepali sign language translator a mean of communication between the deaf and non-deaf communities. This dataset will help researchers and developers to train and test machine learning, deep learning models [3 , 4 , 18] created specifically for automated Nepali Sign Language hand signs detection and recognition.…”

Section: Value Of the Datamentioning

confidence: 99%

“…There are many existing datasets for other languages [2] , [3] , [4] , [5] , 7 , 8 , [10] , [11] , [12] , [13] , 15 , 17 , [19] , [20] , [21] , [22] , [23] This dataset can be used to develop Nepali sign language translator a mean of communication between the deaf and non-deaf communities. This dataset will help researchers and developers to train and test machine learning, deep learning models [3 , 4 , 18] created specifically for automated Nepali Sign Language hand signs detection and recognition. The Nepali sign language dataset opens up a new avenue for future study and development of real-world gesture recognition for researchers as it comprises of real-world condition [3 , 6 , 8] where varying lighting conditions, backgrounds, and hand positioning has been considered.…”

Section: Value Of the Datamentioning

confidence: 99%

NSL23 dataset for alphabets of Nepali sign language

Sunuwar,

Borah,

Kharga

2024

Data in Brief

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“…In the ML field, RF is an algorithm that works as an ensemble. To make predictions, a large number of decision trees are used together to create the decision tree [46]. A decision tree is created using a random subset of the data, and then the results of each tree are combined to make a final prediction, based on the results of all the trees [47].…”

Section: F Random Forestmentioning

confidence: 99%

Breast Cancer Prediction using Machine Learning Models

Iparraguirre-Villanueva¹,

Epifanía-Huerta²,

Torres-Ceclén³

et al. 2023

IJACSA

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Breast cancer is a type of cancer that develops in the cells of the breast. Treatment for breast cancer usually involves X-ray, chemotherapy, or a combination of both treatments. Detecting cancer at an early stage can save a person's life. Artificial intelligence (AI) plays a very important role in this area. Therefore, predicting breast cancer remains a very challenging issue for clinicians and researchers. This work aims to predict the probability of breast cancer in patients. Using machine learning (ML) models such as Multilayer Perceptron (MLP), K-Nearest Neightbot (KNN), AdaBoost (AB), Bagging, Gradient Boosting (GB), and Random Forest (RF). The breast cancer diagnostic medical dataset from the Wisconsin repository has been used. The dataset includes 569 observations and 32 features. Following the data analysis methodology, data cleaning, exploratory analysis, training, testing, and validation were performed. The performance of the models was evaluated with the parameters: classification accuracy, specificity, sensitivity, F1 count, and precision. The training and results indicate that the six trained models can provide optimal classification and prediction results. The RF, GB, and AB models achieved 100% accuracy, outperforming the other models. Therefore, the suggested models for breast cancer identification, classification, and prediction are RF, GB, and AB. Likewise, the Bagging, KNN, and MLP models achieved a performance of 99.56%, 95.82%, and 96.92%, respectively. Similarly, the last three models achieved an optimal yield close to 100%. Finally, the results show a clear advantage of the RF, GB, and AB models, as they achieve more accurate results in breast cancer prediction.

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A hybrid approach for Bangla sign language recognition using deep transfer learning model with random forest classifier

Cited by 69 publications

References 36 publications

Eucalyptus Plantation Area Extraction Based on SLPSO-RFE Feature Selection and Multi-Temporal Sentinel-1/2 Data

Eucalyptus Plantation Area Extraction Based on SLPSO-RFE Feature Selection and Multi-Temporal Sentinel-1/2 Data

NSL23 dataset for alphabets of Nepali sign language

Breast Cancer Prediction using Machine Learning Models

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