Tea Category Identification Using a Novel Fractional Fourier Entropy and Jaya Algorithm

This work proposes a tea-category identification (TCI) system, which can automatically determine tea category from images captured by a 3 charge-coupled device (CCD) digital camera. Three-hundred tea images were acquired as the dataset. Apart from the 64 traditional color histogram features that were extracted, we also introduced a relatively new feature as fractional Fourier entropy (FRFE) and extracted 25 FRFE features from each tea image. Furthermore, the kernel principal component analysis (KPCA) was harnessed to reduce 64 + 25 = 89 features. The four reduced features were fed into a feedforward neural network (FNN). Its optimal weights were obtained by Jaya algorithm. The 10 × 10-fold stratified cross-validation (SCV) showed that our TCI system obtains an overall average sensitivity rate of 97.9%, which was higher than seven existing approaches. In addition, we used only four features less than or equal to state-of-the-art approaches. Our proposed system is efficient in terms of tea-category identification

Multiple Sclerosis Identification Based on Fractional Fourier Entropy and a Modified Jaya Algorithm

Aim: Currently, identifying multiple sclerosis (MS) by human experts may come across the problem of “normal-appearing white matter”, which causes a low sensitivity. Methods: In this study, we presented a computer vision based approached to identify MS in an automatic way. This proposed method first extracted the fractional Fourier entropy map from a specified brain image. Afterwards, it sent the features to a multilayer perceptron trained by a proposed improved parameter-free Jaya algorithm. We used cost-sensitivity learning to handle the imbalanced data problem. Results: The 10 × 10-fold cross validation showed our method yielded a sensitivity of 97.40 ± 0.60%, a specificity of 97.39 ± 0.65%, and an accuracy of 97.39 ± 0.59%. Conclusions: We validated by experiments that the proposed improved Jaya performs better than plain Jaya algorithm and other latest bioinspired algorithms in terms of classification performance and training speed. In addition, our method is superior to four state-of-the-art MS identification approaches

Tea Verification Using Triplet Loss Convolutional Network

To solve tea image classification problems, this study focuses on triplet loss convolutional neural network to classify six high-mountain oolong tea classes. In the experiment, instead of using traditional deep learning training approach for local feature of tea images, an innovative image verification approach is proposed to learn the global feature of tea images by integrating the distributed tea leaves’ features of all tea sub-images and using a majority voting mechanism to do classification. The results show that the proposed approach can work for small sample size dataset and have higher accuracy than normal transfer learning approach. The average accuracy of the proposed approach achieves 99.54%

Hybrid Neural Network and Linear Model for Natural Produce Recognition Using Computer Vision

Natural produce recognition is a classification problem with various applications in the food industry. This paper proposes a natural produce recognition method using computer vision. The proposed method uses simple features consisting of statistical color features and the derivative of radius function. A hybrid neural network and linear model based on a Kalman filter (NN-LMKF) was employed as classifier. One thousand images from ten categories of natural produce were used to validate the proposed method by using 5-fold cross validation. The experimental result showed that the proposed method achieved classification accuracy of 98.40%. This means it performed better than the original neural network and k-nearest neighborhood

Intelligent Circuits and Systems

ICICS-2020 is the third conference initiated by the School of Electronics and Electrical Engineering at Lovely Professional University that explored recent innovations of researchers working for the development of smart and green technologies in the fields of Energy, Electronics, Communications, Computers, and Control. ICICS provides innovators to identify new opportunities for the social and economic benefits of society.　 This conference bridges the gap between academics and R&D institutions, social visionaries, and experts from all strata of society to present their ongoing research activities and foster research relations between them. It provides opportunities for the exchange of new ideas, applications, and experiences in the field of smart technologies and finding global partners for future collaboration. The ICICS-2020 was conducted in two broad categories, Intelligent Circuits & Intelligent Systems and Emerging Technologies in Electrical Engineering

The 2nd International Electronic Conference on Applied Sciences

This book is focused on the works presented at the 2nd International Electronic Conference on Applied Sciences, organized by Applied Sciences from 15 to 31 October 2021 on the MDPI Sciforum platform. Two decades have passed since the start of the 21st century. The development of sciences and technologies is growing ever faster today than in the previous century. The field of science is expanding, and the structure of science is becoming ever richer. Because of this expansion and fine structure growth, researchers may lose themselves in the deep forest of the ever-increasing frontiers and sub-fields being created. This international conference on the Applied Sciences was started to help scientists conduct their own research into the growth of these frontiers by breaking down barriers and connecting the many sub-fields to cut through this vast forest. These functions will allow researchers to see these frontiers and their surrounding (or quite distant) fields and sub-fields, and give them the opportunity to incubate and develop their knowledge even further with the aid of this multi-dimensional network