Strong Formulation Finite Element Method Based on Differential Quadrature: A Survey

Special functions for engineers and applied mathematicians

A fourth order iterative method for solving nonlinear equations

An iterative method for solving nonlinear equations

Three-step iterative methods for nonlinear equations

On a class of quadratically convergent iteration formulae

Citrus fruit diseases have an egregious impact on both the quality and quantity of the citrus fruit production and market. Automatic detection of severity is essential for the high-quality production of fruit. In the current work, a citrus fruit dataset is preprocessed by rescaling and establishing bounding boxes with labeled image software. Then, a selective search, which combines the capabilities of both an extensive search and graph-based segmentation, is applied. The proposed deep neural network (DNN) model is trained to detect targeted areas of the disease with its severity level using citrus fruits that have been labeled with the help of a domain expert with four severity levels (high, medium, low and healthy) as ground truth. Transfer learning using VGGNet is applied to implement a multi-classification framework for each class of severity. The model predicts the low severity level with 99% accuracy, and the high severity level with 98% accuracy. The model demonstrates 96% accuracy in detecting healthy conditions and 97% accuracy in detecting medium severity levels. The result of the work shows that the proposed approach is valid, and it is efficient for detecting citrus fruit disease at four levels of severity.

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A Novel Deep Learning Model for Detection of Severity Level of the Disease in Citrus Fruits

On some third-order iterative methods for solving nonlinear equations

Solution of two point boundary value problems using orthogonal collocation on finite elements

Summary

Studies were carried out to determine the affect of different fruit plant sources viz., Litche chinensis (litchi), Citrus sinensis (sweet orange), Ziziphus mauritiana (ber) and Prunus persica (peach) on moisture content, pH, free acidity, reducing sugars and sucrose contents, fructose/glucose ratio, ash and proline content, invertase and diastase activities, hydroxymethylfurfural and mineral (sodium, potassium, iron, calcium, zinc, and copper) contents as well as on rheological properties of honey. The source of honey had a significant (P < 0.05) effect on moisture content, free acidity, reducing sugar, fructose/glucose ratio, hydroxymethylfurfural content, invertase and diastase activities, sucrose content, proline content, ash content, pH and mineral content. The honey from various sources exhibited Newtonian behaviour and the activation energy ranged from 63.63 to 81.48 kJ mol−1. Pattern recognition methods such as principal component analysis andlinear discriminate analysis were performed to classify honey on the basis of physico-chemical properties and mineral content. The variables proline, potassium and free acidity exhibited higher discrimination power.

V. K. Kukreja

Papers

On a class of quadratically convergent iteration formulae

A Novel Deep Learning Model for Detection of Severity Level of the Disease in Citrus Fruits

On some third-order iterative methods for solving nonlinear equations

Solution of two point boundary value problems using orthogonal collocation on finite elements

Characterisation of honey produced from different fruit plants of northern India