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Computer vision-based six layered ConvNeural network to recognize sign language for both numeral and alphabet signs

Biomimetic Intelligence & Robotics 2024年第1期4卷 45-58页

作者： Muhammad Aminur Rahaman Kabiratun Ummi Oyshe Prothoma Khan Chowdhury tanoy debnath Anichur Rahman Md.Saikat Islam Khan Department of Computer Science and Engineering Green University of BangladeshDhakaBangladesh Department of Computer Science and Engineering National Institute of Textile Engineering and Research(NITER)Constituent Institute of Dhaka UniversityDhakaBangladesh Department of Computer Science and Engineering Mawlana Bhashani Science and Technology UniversityTangailBangladesh

People who have trouble communicating verbally are often dependent on sign language,which can be difficult for most people to understand,making interaction with them a difficult *** Sign Language Recognition(SLR)system takes an input expression from a hearing or speaking-impaired person and outputs it in the form of text or voice to a normal *** existing study related to the Sign Language Recognition system has some drawbacks,such as a lack of large datasets and datasets with a range of backgrounds,skin tones,and *** research efficiently focuses on Sign Language Recognition to overcome previous *** importantly,we use our proposed Convolutional Neural Network(CNN)model,“ConvNeural”,in order to train our ***,we develop our own datasets,“BdSL_OPSA22_STATIC1”and“BdSL_OPSA22_STATIC2”,both of which have ambiguous backgrounds.“BdSL_OPSA22_STATIC1”and“BdSL_OPSA22_STATIC2”both include images of Bangla characters and numerals,a total of 24,615 and 8437 images,***“ConvNeural”model outperforms the pre-trained models with accuracy of 98.38%for“BdSL_OPSA22_STATIC1”and 92.78%for“BdSL_OPSA22_STATIC2”.For“BdSL_OPSA22_STATIC1”dataset,we get precision,recall,F1-score,sensitivity and specificity of 96%,95%,95%,99.31%,and 95.78%***,in case of“BdSL_OPSA22_STATIC2”dataset,we achieve precision,recall,F1-score,sensitivity and specificity of 90%,88%,88%,100%,and 100%respectively.

关键词： Conv NeuralSign language CNN Static Feature extraction Convolution2D Fully connected layer Dropout

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Neuropharmacological evaluation of methanolic extract of Costus speciosus Linn. rhizome in Swiss albino mice

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Asian Pacific Journal of Tropical Biomedicine 2019年第5期9卷 217-221页

作者： Umay Chen Md.Saddam Hussain tanoy Mazumder S.M.Naim Uddin Sujan Banik Department of Pharmacy University of Chittagong Department of Pharmacy Noakhali Science and Technology University

Objective: To evaluate the neuropharmacological properties of Costus speciosus(C. speciosus) rhizome using different experimental mouse models. Methods: The anxiolytic effect was investigated by hole-board test, elevated plus maze and light/dark test, while central nervous system(CNS) depressant effect was evaluated by thiopental sodium-induced sleep test. Finally, antidepressant effect was evaluated by forced swimming test and tail suspension test. Results: In both elevated plus maze and hole board test, 400 mg/kg C. speciosus showed more significant CNS depressant effect than 1 mg/kg diazepam. Both 200 mg/kg and 400 mg/kg C. speciosus extract produced a significant dose-dependent decrease in onset of sleep. In forced swimming test, C. speciosus rhizome showed a decrease in duration of immobility in a dose-dependent manner. Imipramine(10 mg/kg) and C. speciosus extract at 400 mg/kg dose exhibited a significant reduction in duration of immobility in tail suspension test which provided additional evidence of antidepressant effect of C. speciosus rhizome. Conclusions: Our study indicates that C. speciosus rhizome possesses CNS depressant, anxiolytic and antidepressant-like activities. Further studies are warranted determine the exact phytoconstituents and mechanism of action responsible for the neuropharmacological effect.

关键词： Costus speciosus CNS depressant Anxiolytic Antidepressant

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