Cervical cancer is the fourth most prevalent disease in women. Accurate and timely cancer detection can save lives. Automatic and reliable cervical cancer detection methods can be devised through the accurate segmentation and classification of Pap smear cell images. This paper presents an approach to whole cervical cell segmentation using a mask regional convolutional neural network (Mask R-CNN) and classifies this using a smaller Visual Geometry Group-like Network (VGG-like Net). ResNet10 is used to make full use of spatial information and prior knowledge as the backbone of the Mask R-CNN. We evaluate our proposed method on the Herlev Pap Smear dataset. In the segmentation phase, when Mask R-CNN is applied on the whole cell, it outperforms the previous segmentation method in precision (0.92±0.06), recall (0.91±0.05) and ZSI (0.91±0.04). In the classification phase, VGG-like Net is applied on the whole segmented cell and yields a sensitivity score of more than 96% with low standard deviation (±2.8%) for the binary classification problem and yields a higher result of more than 95% with low standard deviation (maximum 4.2% in accuracy measurement) for the 7-class problem in terms of sensitivity, specificity, accuracy, h-mean, and F1 score.
N-Myristoylation, an irreversible protein modification, occurs by the covalent attachment of myristate with the N-terminal glycine of the eukaryotic and viral proteins, and is associated with a variety of pathogens and disease-related proteins. Identification of myristoylation sites through experimental mechanisms can be costly, labour associated and time-consuming. Due to the association of N-myristoylation with various diseases, its timely prediction can help in diagnosing and controlling the associated fatal diseases. Herein, we present a method named N-MyristoylG-PseAAC in which we have incorporated PseAAC with statistical moments for the prediction of N-Myristoyl Glycine (NMG) sites. A benchmark dataset of 893 positive and 1093 negative samples was collected and used in this study. For feature vector, various position and composition relative features along with the statistical moments were calculated. Later on, a back propagation neural network was trained using feature vectors and scaled conjugate gradient descent with adaptive learning was used as an optimizer. Selfconsistency testing and 10-fold cross-validation were performed to evaluate the performance of N-MyristoylG-PseAAC, by using accuracy metrics. For self-consistency testing, 99.80% Acc, 99.78% Sp, 99.81% Sn and 0.99 MCC were observed, whereas, for 10-fold cross validation, 97.18% Acc, 98.54% Sp, 96.07% Sn and 0.94 MCC were observed. Thus, it was found that the proposed predictor can help in predicting the myristoylation sites in an efficient and accurate way.
Defining, measuring, and achieving quality of e-learning systems are not an easy task. Accordingly, one of the most essential goals for the higher educational institutes is how to reach a high and satisfied level of quality in their learning systems. Achieving such level needs adequate and continuous improvements for the whole e-learning environment elements. Therefore, we aim in our work to construct a unified framework for total quality management system (TQMS) that attempt to satisfy the quality requirements, needs, and standards. The objective of this paper is to present a quality control model for e-learning system that adopts the e-learning platform according to the on-line determination of both user's requirements and global standards. This paper proposed software architecture of quality Management framework for e-learning that could be adopted by different higher education institutes to control the quality of the e-learning process, and assure the quality of the e-learning process outcome. The proposed framework is based on a tri-dimensions quality model. The three dimensions are set of quality requirements for e-learning environment represented in Quality Assurance (QA) policies that will be formalized by using policy based approach, the specifications of e-learning platform that provide learning and teaching activities, and quality control process loop. The architecture for monitor and ensure quality control of the QA policies for e-learning system will deliver the whole learning services in an optimal way. It is also flexible and can be implemented over any e-learning system. Keyword:E-learning system Quality control model Total quality management system
A two-year study by the Ministry of Research, Technology and Education in Indonesia presented the evaluation of most universities in Indonesia. The findings of the evaluation are the peculiarities of various dissertation softcopies of doctoral students which are similar to any texts available on internet. The suspected plagiarism behavior has a negative effect on both students and faculty members. The main reason behind this behavior is the lack of standardized awareness among faculty members with regard to plagiarism. Therefore, this study proposes a computerized system that is able to detect plagiarism information by using K-means and cosine distance algorithm. The process starts from preprocessing process that includes a novel step of checking Indonesian big dictionary, vector space model design, and the combined calculation of K-means and cosine distance from 17 documents as test data. The result of this study generally shows that the documents have detection accuracy of 93.33%.
A crucial biological process called angiogenesis plays a vital role in migration, growth, and wound healing of endothelial cells and other processes that are controlled by chemical signals. Angiogenesis is the process that controls the growth of blood vessels within tissues while angiogenesis proteins play a significant role in the proper working of this process. The balancing of these signals is necessary for the proper working of angiogenesis. Unbalancing of these signals increases blood vessel formation, which causes abnormal growth or several diseases including cancer. The proposed work focuses on developing a two-layered prediction model using different classifiers like random forest (RF), neural network, and support vector machine. The first level performs in silico identification of angiogenesis proteins based on the primary structure. In the case the protein is an angiogenesis protein, then the second level predicts whether the protein is linked with tumor angiogenesis or not. The performance of the model is evaluated through various validation techniques. The model was evaluated using k -fold cross-validation, independent, self-consistency, and jackknife testing. The overall accuracy using an RF classifier for angiogenesis at the first level was 97.8% and for tumor angiogenesis at the second level was 99.5%, ANN showed 94.1% accuracy for angiogenesis and 79.9% for tumor angiogenesis, and the accuracy of SVM for angiogenesis was 78.8% and for tumor angiogenesis was 65.19%.
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