In this work, two types of silica functionalized monosodium glutamate (GMSG and VMSG)/ poly(vinyl alcohol) (PVA) were cross-linked by sol-gel process to prepare novel hybrid cation exchange membranes (CEMs). The prepared membranes were systematically characterized by FTIR, ion exchange capacity (IEC), TGA, water uptake, water swelling, mechanical strength and diffusion dialysis (DD) performance for alkali separation using NaOH/Na2WO4 solution. The FTIR peaks around 1260-1350 cm −1 confirmed the secondary C−N linkages. The cross-linking between GMSG/VMSG and PVA was verified by the presence of stretching peaks of Si−O−C, Si−O−Si, C−O−C, and −C(=O)−O−C groups between 1080-1120 cm −1 . TGA results indicated that GMSG membranes showed relatively high thermal stability as compared to VMSG membranes. Water uptake and degree of swelling decreased while IEC values increased with the increase of GMSG/VMSG content in membrane matrix. The mechanical properties of the membranes improved up to 40% GMSG/VMSG content. The NaOH dialysis coefficient (UOH)values improved while values of separation factor (S) declined with the increase of GMSG/VMSG content. Finally, the effect of temperature was studied and it was found that increase in temperature from 25 to 45 °C resulted in increase of diffusion coefficient and decrease of separation factor for both GMSG/VMSG crossed-linked with PVA membranes.
Histopathological image analysis is an examination of tissue under a light microscope for cancerous disease diagnosis. Computer-assisted diagnosis (CAD) systems work well by diagnosing cancer from histopathology images. However, stain variability in histopathology images is inevitable due to the use of different staining processes, operator ability, and scanner specifications. These stain variations present in histopathology images affect the accuracy of the CAD systems. Various stain normalization techniques have been developed to cope with inter-variability issues, allowing standardizing the appearance of images. However, in stain normalization, these methods rely on the single reference image rather than incorporate color distributions of the entire dataset. In this paper, we design a novel machine learning-based model that takes advantage of whole dataset distributions as well as color statistics of a single target image instead of relying only on a single target image. The proposed deep model, called stain acclimation generative adversarial network (SA-GAN), consists of one generator and two discriminators. The generator maps the input images from the source domain to the target domain. Among discriminators, the first discriminator forces the generated images to maintain the color patterns as of target domain. While second discriminator forces the generated images to preserve the structure contents as of source domain. The proposed model is trained using a color attribute metric, extracted from a selected template image. Therefore, the designed model not only learns dataset-specific staining properties but also image-specific textural contents. Evaluated results on four different histopathology datasets show the efficacy of SA-GAN to acclimate stain contents and enhance the quality of normalization by obtaining the highest values of performance metrics. Additionally, the proposed method is also evaluated for multiclass cancer type classification task, showing a 6.9% improvement in accuracy on ICIAR 2018 hidden test data.
The aim of the author profiling task is to automatically predict various traits of an author (e.g. age, gender, etc.) from written text. The problem of author profiling has been mainly treated as a supervised text classification task. Initially, traditional machine learning algorithms were used by the researchers to address the problem of author profiling. However, in recent years, deep learning has emerged as a state-of-the-art method for a range of classification problems related to image, audio, video, and text. No previous study has carried out a detailed comparison of deep learning methods to identify which method(s) are most suitable for same-genre and cross-genre author profiling. To fulfill this gap, the main aim of this study is to carry out an in-depth and detailed comparison of state-of-the-art deep learning methods, i.e. CNN, Bi-LSTM, GRU, and CRNN along with proposed ensemble methods, on four PAN Author Profiling corpora. PAN 2015 corpus, PAN 2017 corpus and PAN 2018 Author Profiling corpus were used for same-genre author profiling whereas PAN 2016 Author Profiling corpus was used for cross-genre author profiling. Our extensive experimentation showed that for same-genre author profiling, our proposed ensemble methods produced best results for gender identification task whereas CNN model performed best for age identification task. For cross-genre author profiling, the GRU model outperformed all other approaches for both age and gender.
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