An Integrated CNN-LSTM Model for Bangla Lexical Sign Language Recognition

Basnin, Nanziba; Nahar, Lutfun; Hossain, Mohammad Shahadat

doi:10.1007/978-981-33-4673-4_57

Cited by 34 publications

(11 citation statements)

References 22 publications

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“…In the current implementation of specific teaching, it has various names, such as General Linguistics, Introduction to Business English Linguistics, and Introduction to Business English Linguistics. e aim of its teaching is to make students aware of the rich achievements of human language research, to raise their awareness of the importance of language in social, humanistic, economic, technological and personal cultivation aspects, to cultivate linguistic awareness, and to develop rational thinking [1].…”

Section: Introductionmentioning

confidence: 99%

Construction of an Assessment System for Business English Linguistics Based on RNN Multidimensional Models

Shao

Shi

2022

Mathematical Problems in Engineering

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The current teaching evaluation system of the course “Introduction to Business English Linguistics” has many problems and should be reformed according to the requirements of the relevant professional syllabus. This paper proposes a recurrent neural network (RNN) modelling technique, which has achieved good results in modelling business English language models. The RNN modelling method is applied to Chinese language modelling according to the characteristics of Chinese language, and the advantages of the two models are combined to propose a fusion model. The experimental results demonstrate that this paper focuses on the process, diversity, and humanity of teaching evaluation and discusses the construction of the evaluation system model of the course “Introduction to Business English Linguistics” in terms of the content and criteria of teaching evaluation and the methods of teaching evaluation, so as to provide a new model for the teaching reform of the course “Introduction to Business English Linguistics”.

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Section: Introductionmentioning

confidence: 99%

Construction of an Assessment System for Business English Linguistics Based on RNN Multidimensional Models

Shao

Shi

2022

Mathematical Problems in Engineering

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“…The data of our Parkinson disease voice dataset is limited. In future we will collect more data to detect Parkinson disease and we will work with some deep learning method and other method [2][3][4]7,8,14,17,18,20,27,28,33,38]. In future, we will also work with Parkinson disease MRI Data.…”

Section: Discussionmentioning

confidence: 99%

Feature Selection Based Machine Learning to Improve Prediction of Parkinson Disease

Nahar

Ara

Neloy

et al. 2021

Lecture Notes in Computer Science

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Parkinson's disease (PD) is a kind of neurodegenerative disorder characterized by the loss of dopamine-producing cells in the brain. The disruption of brain cells that create dopamine, a chemical that allows brain cells to connect with one another, causes Parkinson's disease. Control, adaptability, and rapidity of movement are all controlled by dopamine-producing cells in the brain. Researchers have been investigating for techniques to identify non-motor symptoms that show early in the disease as soon as possible, slowing the disease's progression. A machine learning-based detection of Parkinson's disease is proposed in this research. Feature selection and classification techniques are used in the proposed detection technique. Boruta, Recursive Feature Elimination (RFE) and Random Forest (RF) Classifier have been used for the feature selection process. Four classification algorithms are considered to detect Parkinson disease which are gradient boosting, extreme gradient boosting, bagging and Extra Tree Classifier. Bagging with recursive feature elimination was found to outperform the other methods. The lowest number of voice characteristics for the diagnosis in Parkinson attained 82.35% accuracy.

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“…Further, this model can be enhanced to classify other syndromes like PD and we use other model to predict PD[?] [2,[4][5][6]8,9,14,15,20,22,29].…”

Section: Discussionmentioning

confidence: 99%

“…Figures 3,4,5, and,6 graphically represents the performance curve of all the state of the art model aplied to train the MRI samples for the classification of PD (Fig.7).…”

mentioning

confidence: 99%

Deep Learning Approach to Classify Parkinson’s Disease from MRI Samples

Basnin

Nahar

Anika

et al. 2021

Lecture Notes in Computer Science

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Perkinson's disease is a progressive degenerative disorder that comes from a recognized clinical parkinsonian syndrome. The manifestations of Parkinson's disease include both motor and nonmotor symptoms identified as tremor, bradykinesia (slowed movements), rigidity, and postural instability. PD is marked as one of the most prevalent disorders from various researches and surveys because it has been observed in 90% of people out of 100. It is imperative to design CAD to develop an advanced model for the determination of this disease with accuracy since up to date there is no accurate clinical intervention for the diagnosis of PD. In contrast to conventional methods. Deep learning convolutional neural network tools are implied for the faster and accurate identification of PD through MRI. The purpose of this research is to contribute to the development of an accurate PD detection method. To conduct the research a public dataset NTU (National Technical University of Athens) is used. The data samples are categorized into three sets (Training, Test, and Validation). A DenseNet integrated with LSTM is applied to the MRI data samples. DenseNet is used to strengthen the feature selection ability, as each layer selects features depending on the temporal closeness of the image. The output is then fed into the LSTM layer, for discovering the significant dependencies in temporal features. The performance of the proposed DenseNet-LSTM is compared to other CNN state-of-theart models. The proposed model outputs a training accuracy of 93.75%, testing accuracy of 90%, and validation accuracy of 93.8% respectively.

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An Integrated CNN-LSTM Model for Bangla Lexical Sign Language Recognition

Cited by 34 publications

References 22 publications

Construction of an Assessment System for Business English Linguistics Based on RNN Multidimensional Models

Construction of an Assessment System for Business English Linguistics Based on RNN Multidimensional Models

Feature Selection Based Machine Learning to Improve Prediction of Parkinson Disease

Deep Learning Approach to Classify Parkinson’s Disease from MRI Samples

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