P. Arunachalam¹, N. Janakiraman¹, Junaid Rashid², Jungeun Kim^2,*, Sovan Samanta³, Usman Naseem⁴, Arun Kumar Sivaraman⁵, A. Balasundaram⁶

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 2521-2543, 2022, DOI:10.32604/cmc.2022.025339

Abstract In this research work, we proposed a medical image analysis framework with two separate releases whether or not Synovial Sarcoma (SS) is the cell structure for cancer. Within this framework the histopathology images are decomposed into a third-level sub-band using a two-dimensional Discrete Wavelet Transform. Subsequently, the structure features (SFs) such as Principal Components Analysis (PCA), Independent Components Analysis (ICA) and Linear Discriminant Analysis (LDA) were extracted from this sub-band image representation with the distribution of wavelet coefficients. These SFs are used as inputs of the Support Vector Machine (SVM) classifier. Also, classification of PCA + SVM, ICA + SVM,… More >

Rongyu Chen, Lili Pan^*, Yan Zhou, Qianhui Lei

Journal of Information Hiding and Privacy Protection, Vol.2, No.2, pp. 67-76, 2020, DOI:10.32604/jihpp.2020.010472

Abstract With the rapid development of information technology, the speed and efficiency of image retrieval are increasingly required in many fields, and a compelling image retrieval method is critical for the development of information. Feature extraction based on deep learning has become dominant in image retrieval due to their discrimination more complete, information more complementary and higher precision. However, the high-dimension deep features extracted by CNNs (convolutional neural networks) limits the retrieval efficiency and makes it difficult to satisfy the requirements of existing image retrieval. To solving this problem, the high-dimension feature reduction technology is proposed with improved CNN and PCA… More >

Stephany C. de Rezende^1,2, João A. Pinto^1,3, Isabel P. Fernandes^1,3, Fernanda V. Leimann^1,2* and Maria-Filomena Barreiro^1,3*

Journal of Renewable Materials, Vol.6, No.7, pp. 715-723, 2018, DOI:10.32604/JRM.2018.00028

Abstract Pine-fruit shell (PFS) is a lignocellulosic residue derived from the fruit of Araucaria angustifolia, a coniferous tree native of South America, part of a whole vegetation of the Atlantic Forest, found in the South and Southwest of Brazil. In this work PFS will be characterized and used in the production of PFS-based polyols through oxypropylation. Three series were chosen (PFS/propylene oxide (PO) (w/v, g/mL) of 30/70, 20/80 and 10/90) with four catalyst levels (5%, 10%, 15% and 20%, (w/w, PFS based)). Oxypropylation occurred at moderate conditions of temperature, pressure and time giving rise to liquid polyols with a homopolymer content… More >