Implementation of back-propagation neural Network for isolated bangla speech recognition

Hossain, Md. Ali; Rahman, Mijanur; Prodhan, Uzzal Kumar; Khan, Md. Farukuzzaman

doi:10.5121/ijist.2013.3401

Cited by 13 publications

(4 citation statements)

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“…The methodology based on the machine learning (ML) model is summarized in Figure 7 [58]. The process of applying machine learning (ML), on renewable resource datasets to forecast values of the future, consists of three general phases [59], given as follows: The most amazing machine learning systems include neural network approaches that have the capability for learning, memorizing, and building relationships among non-linear data [110]. The systems can identify data with similar patterns, but not identical to each other which they have been trained.…”

Section: Prediction Methodologymentioning

confidence: 99%

Prospective Methodologies in Hybrid Renewable Energy Systems for Energy Prediction Using Artificial Neural Networks

et al. 2021

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This paper presents a comprehensive review of machine learning (ML) based approaches, especially artificial neural networks (ANNs) in time series data prediction problems. According to literature, around 80% of the world’s total energy demand is supplied either through fuel-based sources such as oil, gas, and coal or through nuclear-based sources. Literature also shows that a shortage of fossil fuels is inevitable and the world will face this problem sooner or later. Moreover, the remote and rural areas that suffer from not being able to reach traditional grid power electricity need alternative sources of energy. A “hybrid-renewable-energy system” (HRES) involving different renewable resources can be used to supply sustainable power in these areas. The uncertain nature of renewable energy resources and the intelligent ability of the neural network approach to process complex time series inputs have inspired the use of ANN methods in renewable energy forecasting. Thus, this study aims to study the different data driven models of ANN approaches that can provide accurate predictions of renewable energy, like solar, wind, or hydro-power generation. Various refinement architectures of neural networks, such as “multi-layer perception” (MLP), “recurrent-neural network” (RNN), and “convolutional-neural network” (CNN), as well as “long-short-term memory” (LSTM) models, have been offered in the applications of renewable energy forecasting. These models are able to perform short-term time-series prediction in renewable energy sources and to use prior information that influences its value in future prediction.

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Section: Prediction Methodologymentioning

confidence: 99%

Prospective Methodologies in Hybrid Renewable Energy Systems for Energy Prediction Using Artificial Neural Networks

et al. 2021

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“…Consequently, speech recognition research has evolved from laboratory demonstrations to copious real-life applications. But, research on human-computer interaction using Bangla language is still inadequate as for the understanding of the spoken form of this language, a systematic and scientific effort has not been started yet [1].…”

Section: Introductionmentioning

confidence: 99%

“…So it cannot be ensured that, a system that provides good recognition accuracy for English language will also provide good recognition accuracy for Bangla language [3]. Though speech recognition research work has been started since 1930 [4], but in regard to Bangla speech recognition, it's noteworthy that several works have been done starting from 2000 [1], [5] and they all worked on isolated word recognition. For Isolated word recognition, the assumption is that the speech to be recognized comprises of a single word or phrase and to be recognized as a complete entity with no explicit knowledge or regard for the phonetic content of the word or phrase [6].…”

Section: Introductionmentioning

confidence: 99%

Connected Bangla Speech Recognition using Artificial Neural Network

Ahammad¹,

Rahman²

2016

IJCA

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There are a plenty of research experiments and achieved results in various languages throughout the world regarding speech recognition. But, in Bangla language, early researchers in this field had qualified success, though the scenario is being changed in recent years. This research work aims at developing a neural network based connected digit recognition system in Bangla language. Firstly, a Bangla digit corpus has been developed comprising of male and female speakers. Speech is recorded in connected fashion and words are extracted through automatic segmentation. Then MFCC features of the segmented words are calculated and these feature values are sent as the input to the back-propagation neural network (BPNN). BPNN learning algorithm is used to train the network. The required time to train the network, number of hidden layers, error threshold and number of epochs are considered while training the network to reach the best possible recognition accuracy. This proposed system has been implemented using object oriented programming and the achieved recognition accuracy is very much satisfactory and consistent. The network has been tested for three different setups and the best recognition accuracy achieved for digit dataset is 98.46%.

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“…The following step is to compute the error for each hidden unit that sends data backward from the output unit. Finally, using a similar equation from the output layer, the weight of the input to the hidden unit is set (128).…”

Section: Elements Of Neural Networkmentioning

confidence: 99%

Cost effectiveness of oral health care program implementation in primary school in Bangkok, Thailand

Herman¹

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Background: Diagnosis of Pulmonary Rifampicin Resistant Tuberculosis (RR-TB) with the Drug-Susceptibility Test (DST) is costly and time-consuming, and the GeneXpert for rapid diagnosis is not widely available in Indonesia. This study aims to develop an Artificial Neural Network model and evaluate the deployed model performance for RR-TB screening. Methods: A cross-sectional study involved suspected RR-TB patients with complete sputum Lowenstein Jensen DST (reference) and 19 clinical, laboratory, and radiology parameter results, retrieved from medical records in hospitals under the Faculty of Medicine, Hasanuddin University Indonesia, from January 2015-December 2019. The Artificial Neural Network (ANN) models were built along with other classifiers. The model was tested on participants recruited from January 2020-October 2020 and deployed into CUHAS-ROBUST (index test) application. Sensitivity, specificity, and accuracy were obtained for assessment. A qualitative approach with content analysis was performed from September 2020 to October 2020. Medical staff from the primary care center were invited online for application trials and in-depth video call interviews. Transcripts were derived as a data source. An inductive thematic data saturation technique was conducted. Descriptive data of participants, user experience, and impact on the health service was summarized. Cost-effectiveness analysis of direct cost was made using the data of 330 participants who underwent Genexpert and Model, confirmed by DST. The Quality Adjusted Life years of TB being untreated was used as the approximation of the undiagnosed TB (acute morbidity, chronic morbidity, and mortality). The Incremental Cost-Effectiveness Ratio (ICER) was calculated. Results: A total of 487 participants (32 Multidrug-Resistant/MDR 57 RR-TB, 398 drug-sensitive) were recruited for model building and 157 participants (23 MDR and 21 RR) in prospective testing. The ANN full model yields 88% (95% CI 85-91) accuracy, 84% (95% CI 76-89) sensitivity, and 90% specificity (95% CI 86-93). This ANN model outperforms other classifiers and selected for the CUHAS-ROBUST application. A total of 33 participants (an average of 33.12 years old) were recruited from all parts of Indonesia. The findings show that DR-TB is a new threat, and its diagnosis faces obstacles particularly prolonged waiting time and inevitable delayed treatment. Despite overcoming the RR-TB screening problems with fast prediction, the dubious screening performance, and the reliability of data collection for input parameters were the main concerns of CUHAS-ROBUST. Nevertheless, this application increases confidence in decision making, promotes medical procedure compliance, active surveillance, and enhancing a low-cost screening approach. A cost-effectiveness analysis was made. The ICER Mortality value is -3601.706137. The ICER Acute Morbidity value is -17225.55 and The ICER Chronic Morbidity value is -825.391. The very minimum sensitivity of the model to not surpass the willingness to pay (WTP) of 100 USD per QALYs gained is 80.6%. The ideal prevalence of RR-TB according to the screening using model is 14.8% to 23.3%. Using the average cost, the results still consistent, showing the model as the dominant intervention Conclusions: Despite showing lower sensitivity than global GeneXpert results. The ANN-CUHAS ROBUST outperforms other AI classifier models, and by deploying it into the application, the health staff can utilize the tool for screening purposes particularly at the primary care level. Moreover, this study demonstrates AI's roles in enhancing healthcare quality and boost public health efforts against tuberculosis. The advantage of this device is cost-effective although it should need a bigger test expansion.

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Implementation of back-propagation neural Network for isolated bangla speech recognition

Cited by 13 publications

References 10 publications

Prospective Methodologies in Hybrid Renewable Energy Systems for Energy Prediction Using Artificial Neural Networks

Prospective Methodologies in Hybrid Renewable Energy Systems for Energy Prediction Using Artificial Neural Networks

Connected Bangla Speech Recognition using Artificial Neural Network

Cost effectiveness of oral health care program implementation in primary school in Bangkok, Thailand

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