Journal of Intelligent & Fuzzy Systems - Volume 43, issue 5

Authors: Muruganandham, R. | Sheik Abdullah, A. | Selvakumar, S.

Article Type: Research Article

Abstract: The primary goal of this study is to optimize web content for a positive user experience and to develop a data-driven methodology to assess the success of visitor flow on a website for school children. Through Vision-Based Page Segmentation, the suggested study work intends to broaden the stated web approach’s reach and statistical inference. The improvisation has been made accordingly with the semantic structure observed from each node with the designated degree of coherence to indicate the content in spatial and block based on visual perception for each event. The click count (number of clicks) is calculated for all the …possibilities of Quest Software. The most frequently accessed event is displayed at the top to enhance usability and visibility with an accuracy of about 92.80%. From the experimental analysis, it has been observed that most of the students preferred events corresponding to drawing, rhymes, and rangoli with a willingness rate of above 80%, respectively. Statistical analysis has been made using chi-square analysis, and it has been found that the levels from A to D are significant for three years with a P -value < 0.001. Sentimental analysis of feedback collected from the participants about the events is also done, and the most preferred event is suggested for the upcoming years. Show more

Keywords: Data driven model, event analysis, optimization, page segmentation, web analytics

DOI: 10.3233/JIFS-221392

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6863-6875, 2022

Authors: Mohana Sundaram, K.D. | Shankar, T. | Sudhakar Reddy, N.

Article Type: Research Article

Abstract: Computer vision functions like object detection, image segmentation, and image classification were recently getting advance due to Convolutional Neural Networks (CNNs). In the food and agricultural industries, image classification plays a critical role in quality control. CNNs are made up of layers that alternate between convolutional, nonlinearity, and feature pooling. In this article, we proposed Fuzzy Pooling, a novel pooling approach that works based on fuzzy logic, that can increase the accuracy of the CNNs by replacing the conventional pooling layer. This proposed Fuzzy Pooling was put to the test with CIFAR-10 and SVHN data sets on single layer CNN, …and it outperformed previous pooling strategies by achieving 92% and 97% classification accuracy. This proposed Fuzzy Pooling layer was replaced the Max Pooling layer in the ThinNet architecture, and it was trained using the back propagation method. It was demonstrated experimentally on the Lemon fruit data set to classify the fruits into three categories such as Good, Medium, and Poor. In order to classify the lemon fruit into three categories, the 1000 Fully Connected layer in ThinNet architecture was replaced with three Fully Connected layers. The Modified ThinNet architecture called ThinNet_FP was trained with a learning rate of 0.001 and achieved 97% accuracy in classifying the images and outperformed previous CNN architectures when trained on the same data set. Show more

Keywords: Convolutional neural network (CNN), fuzzy logic, fuzzy pooling, back propagation, fruit classification

DOI: 10.3233/JIFS-221550

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6877-6891, 2022