Classification of Handwritten Devanagari Number – An analysis of Pattern Recognition Tool using Neural Network and CNN

Prashanth, Duddela Sai; Mehta, R. Vasanth Kumar; Sharma, Nisha

doi:10.1016/j.procs.2020.03.297

Cited by 49 publications

(16 citation statements)

References 9 publications

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“…The results presented in Table 6 has shown that increasing the number of dataset lead to increase in training accuracy. However, our results are in line with the study carried out by Prashanth et al (2020) based on data splits from 50%–90%. Moreover, 70:30 split is chosen as the best performing model which is “fit” compare to 80:20 and 90:10 which are relatively “overfit” due to testing on small number of datasets.…”

Section: Resultssupporting

confidence: 94%

Convolutional neural network for diagnosis of viral pneumonia and COVID‐19 alike diseases

Ibrahim¹,

Özsöz²,

Serte³

et al. 2021

Expert Systems

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Reverse‐Transcription Polymerase Chain Reaction (RT‐PCR) method is currently the gold standard method for detection of viral strains in human samples, but this technique is very expensive, take time and often leads to misdiagnosis. The recent outbreak of COVID‐19 has led scientists to explore other options such as the use of artificial intelligence driven tools as an alternative or a confirmatory approach for detection of viral pneumonia. In this paper, we utilized a Convolutional Neural Network (CNN) approach to detect viral pneumonia in x‐ray images using a pretrained AlexNet model thereby adopting a transfer learning approach. The dataset used for the study was obtained in the form of optical Coherence Tomography and chest X‐ray images made available by Kermany et al. (2018, https://doi.org/10.17632/rscbjbr9sj.3) with a total number of 5853 pneumonia (positive) and normal (negative) images. To evaluate the average efficiency of the model, the dataset was split into on 50:50, 60:40, 70:30, 80:20 and 90:10 for training and testing respectively. To evaluate the performance of the model, 10 K Cross‐validation was carried out. The performance of the model using overall dataset was compared with the means of cross‐validation and the currents state of arts. The classification model has shown high performance in terms of accuracy, sensitivity and specificity. 70:30 split performed better compare to other splits with accuracy of 98.73%, sensitivity of 98.59% and specificity of 99.84%.

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Section: Resultssupporting

confidence: 94%

Convolutional neural network for diagnosis of viral pneumonia and COVID‐19 alike diseases

Ibrahim¹,

Özsöz²,

Serte³

et al. 2021

Expert Systems

View full text Add to dashboard Cite

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“…1 , step 1. We chose an 80:20 ratio for our study and it has been shown to achieve the best results among other ratios elsewhere [ 34 ]. We randomly picked from a grid 50 sets of control values of the learning process (hyperparameters), and these were used in training and validation of data using each of Elastic Net (EN) [ 35 ], k-Nearest Neighbors (KNN) [ 36 ], RandomForest (RF) [ 37 ], Support Vector Machine (SVM) [ 38 ], XGBoost [ 39 ] and Light Gradient Boosting (LGBT) [ 40 ] algorithms, (Fig.…”

Section: Methodsmentioning

confidence: 99%

Use of machine learning techniques to identify HIV predictors for screening in sub-Saharan Africa

Mutai

McSharry

Ngaruye

et al. 2021

BMC Med Res Methodol

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Aim HIV prevention measures in sub-Saharan Africa are still short of attaining the UNAIDS 90–90-90 fast track targets set in 2014. Identifying predictors for HIV status may facilitate targeted screening interventions that improve health care. We aimed at identifying HIV predictors as well as predicting persons at high risk of the infection. Method We applied machine learning approaches for building models using population-based HIV Impact Assessment (PHIA) data for 41,939 male and 45,105 female respondents with 30 and 40 variables respectively from four countries in sub-Saharan countries. We trained and validated the algorithms on 80% of the data and tested on the remaining 20% where we rotated around the left-out country. An algorithm with the best mean f1 score was retained and trained on the most predictive variables. We used the model to identify people living with HIV and individuals with a higher likelihood of contracting the disease. Results Application of XGBoost algorithm appeared to significantly improve identification of HIV positivity over the other five algorithms by f1 scoring mean of 90% and 92% for males and females respectively. Amongst the eight most predictor features in both sexes were: age, relationship with family head, the highest level of education, highest grade at that school level, work for payment, avoiding pregnancy, age at the first experience of sex, and wealth quintile. Model performance using these variables increased significantly compared to having all the variables included. We identified five males and 19 females individuals that would require testing to find one HIV positive individual. We also predicted that 4·14% of males and 10.81% of females are at high risk of infection. Conclusion Our findings provide a potential use of the XGBoost algorithm with socio-behavioural-driven data at substantially identifying HIV predictors and predicting individuals at high risk of infection for targeted screening.

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“…It should be added that all 15 neurons were equipped with activation function in the form of Tank and there was also 1 neuron with activation function Softmax for output layer. In the recent research, during the process of network learning, algorithm Broyden-Fletcher-Goldfarb-Shanno (BFGS) [24][25][26][27][28][29] was used, which informed about the function of effective curvature and allowed to determine the direction of searching for the minimum destination point with effectiveness being part of the network.…”

Section: Resultsmentioning

confidence: 99%

Application of artificial neural networks in recognizing carrier based on the color of raspberry powders obtained in the spray-drying process

Przybył

Wawrzyniak²,

Samborska³

et al. 2022

Fourteenth International Conference on Digital Image Processing (ICDIP 2022)

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Fruit juices and vegetable and fruit juices are the products, which provide our bodies with a lot of valuable and nutritional ingredients and play a major role in prevention of numerous illnesses. Raspberries are the valuable source of bioactive compounds. As part of preserving food, whose main aim is to extend stability of products obtained only in season, the researchers took advantage of spray drying technique. In the research part of the study, research samples were prepared in the form of raspberry powders obtained from the process of dehumidified spray drying. Because of the research, a neural model was made, which supported the evaluation of the quality of detecting powder samples based on their color. The devised neural network reached classification accuracy at 0.924.

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Classification of Handwritten Devanagari Number – An analysis of Pattern Recognition Tool using Neural Network and CNN

Cited by 49 publications

References 9 publications

Convolutional neural network for diagnosis of viral pneumonia and COVID‐19 alike diseases

Convolutional neural network for diagnosis of viral pneumonia and COVID‐19 alike diseases

Use of machine learning techniques to identify HIV predictors for screening in sub-Saharan Africa

Application of artificial neural networks in recognizing carrier based on the color of raspberry powders obtained in the spray-drying process

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