Efficient Deep Learning model for de-husked Areca nut classification

Areca nut is a widely used agricultural product in India and even over the globe. Areca nut, a fruit of   areca palm (Areca catechu) is grown widely in the Asia-Pacific region.. Areca nut segregation is of prime importance in the areca nut industry. The quality segregation of peeled/de-husked nuts requires skilled workers. This process of manual segregation is time-consuming and can lead to erroneous classification. Recent deep learning (DL) advances have improved the performance in multi-class problems. The present  work presents the classification of de-husked areca nut among five classes using an efficient deep learning customized Convolutional Neural Network (CNN) and the results of this model were compared with the standard AlexNet architecture. The new CNN model was customized to obtain classification accuracy higher than the existing ones. A dataset of 300 nuts (60 per class) was created using a specially designed instrumentation setup. The areca nut images were then pre-processed and fed to these models to learn the features of the areca nut from different classes. The confusion matrix and Area Under the Curve - Receiver Operating Characteristics (AUC- ROC) were employed to assess the results of these models and cross-validated with 5 and 10-fold. The experimental results show that the CNN outperformed the AlexNet model with an average accuracy of 97.33% and 98.34%, F1 score of 97.48%, and 98.45% for 5 and 10 folds, respectively.  

Improving hemoglobin estimation accuracy through standardizing of light-emitting diode power

Nowadays, hemoglobin monitoring is essential during surgeries, blood donations, and dialysis. Which are normally done using invasive methods. To monitor hemoglobin, a non-invasive hemoglobin meter was developed with five fixed light-emitting diode (LED) wavelengths at 670 nm, 770 nm, 810 nm, 850 nm, 950 nm and controlled using an Arduino Uno embedded development board. A photodetector with an on-chip trans-impedance amplifier was utilized to acquire the transmitted signal through the finger using the photoplethysmography (PPG) principle. Before the standardization of LED power, we had tested the designed system on fifteen subjects for the five wavelengths and estimated the hemoglobin with an accuracy of 96.51% and root mean square error (RMSE) of 0.57 gm/dL. To further improve the accuracy, the LED power was standardized and the PPG signal was reacquired on the same subjects. With this, the accuracy improved to 98.29% and also reduced the RMSE to 0.36 gm/dL. The designed system with LED power standardization showed a good agreement with pathology results with the coefficient of determination R2=0.981. Also, Bland–Altman analysis was used to evaluate the designed system and it showed good agreement between the two measurements

SpringerLink.

Includes bibliographical references and index.Mode of access: World Wide Web.Book fair 2012.xv, 157 p.