METHODS FOR INCREASING THE EFFICIENCY OF THE REMOTE USER AUTHENTICATION IN INTEGRATED SYSTEMS

Abstract: This paper analyzes the factors that affect the efficiency of a remote user’s authentication in integrated systems. Special attention is paid to increasing the performance of the authentication method. A new authentication method for remote users, which requires communication between users and verifiers in the system, is being proposed to overcome the weaknesses of the known methods. The presented method does not require a password list of the legal users, nor does it require searching operations. As a result, it does not impose restrictions on the number of users. The proposed method uses a combination of symmetric (Secret Key) and asymmetric (Public Key) cryptographic algorithms to protect integrated systems from unauthorized access. The information capacity required by the proposed authentication method is much lower compared to the known methods. Key-Words:- authentication, remote user, integrated system, information security.

Optimizing neuro-oncology imaging: A review of deep learning approaches for glioma imaging

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. Radiographic assessment with magnetic resonance imaging (MRI) is widely used to characterize gliomas, which represent 80% of all primary malignant brain tumors. Unfortunately, glioma biology is marked by heterogeneous angiogenesis, cellular proliferation, cellular invasion, and apoptosis. This translates into varying degrees of enhancement, edema, and necrosis, making reliable imaging assessment challenging. Deep learning, a subset of machine learning artificial intelligence, has gained traction as a method, which has seen effective employment in solving image-based problems, including those in medical imaging. This review seeks to summarize current deep learning applications used in the field of glioma detection and outcome prediction and will focus on (1) pre-and post-operative tumor segmentation, (2) genetic characterization of tissue, and (3) prognostication. We demonstrate that deep learning methods of segmenting, characterizing, grading, and predicting survival in gliomas are promising opportunities that may enhance both research and clinical activities

Deep-Learning Convolutional Neural Networks Accurately Classify Genetic Mutations in Gliomas.

Background and purpose: The World Health Organization has recently placed new emphasis on the integration of genetic information for gliomas. While tissue sampling remains the criterion standard, noninvasive imaging techniques may provide complimentary insight into clinically relevant genetic mutations. Our aim was to train a convolutional neural network to independently predict underlying molecular genetic mutation status in gliomas with high accuracy and identify the most predictive imaging features for each mutation.Materials and methods: MR imaging data and molecular information were retrospectively obtained from The Cancer Imaging Archives for 259 patients with either low- or high-grade gliomas. A convolutional neural network was trained to classify isocitrate dehydrogenase 1 (IDH1) mutation status, 1p/19q codeletion, and O6-methylguanine-DNA methyltransferase (MGMT) promotor methylation status. Principal component analysis of the final convolutional neural network layer was used to extract the key imaging features critical for successful classification.Results: Classification had high accuracy: IDH1 mutation status, 94%; 1p/19q codeletion, 92%; and MGMT promotor methylation status, 83%. Each genetic category was also associated with distinctive imaging features such as definition of tumor margins, T1 and FLAIR suppression, extent of edema, extent of necrosis, and textural features.Conclusions: Our results indicate that for The Cancer Imaging Archives dataset, machine-learning approaches allow classification of individual genetic mutations of both low- and high-grade gliomas. We show that relevant MR imaging features acquired from an added dimensionality-reduction technique demonstrate that neural networks are capable of learning key imaging components without prior feature selection or human-directed training

Deep-Learning Convolutional Neural Networks Accurately Classify Genetic Mutations in Gliomas

BACKGROUND AND PURPOSE: The World Health Organization has recently placed new emphasis on the integration of genetic information for gliomas. While tissue sampling remains the criterion standard, noninvasive imaging techniques may provide complimentary insight into clinically relevant genetic mutations. Our aim was to train a convolutional neural network to independently predict underlying molecular genetic mutation status in gliomas with high accuracy and identify the most predictive imaging features for each mutation. MATERIALS AND METHODS: MR imaging data and molecular information were retrospectively obtained from The Cancer Imaging Archives for 259 patients with either low- or high-grade gliomas. A convolutional neural network was trained to classify isocitrate dehydrogenase 1 (IDH1) mutation status, 1p/19q codeletion, and O6-methylguanine-DNA methyltransferase (MGMT) promotor methylation status. Principal component analysis of the final convolutional neural network layer was used to extract the key imaging features critical for successful classification. RESULTS: Classification had high accuracy: IDH1 mutation status, 94%; 1p/19q codeletion, 92%; and MGMT promotor methylation status, 83%. Each genetic category was also associated with distinctive imaging features such as definition of tumor margins, T1 and FLAIR suppression, extent of edema, extent of necrosis, and textural features. CONCLUSIONS: Our results indicate that for The Cancer Imaging Archives dataset, machine-learning approaches allow classification of individual genetic mutations of both low- and high-grade gliomas. We show that relevant MR imaging features acquired from an added dimensionality-reduction technique demonstrate that neural networks are capable of learning key imaging components without prior feature selection or human-directed training

Predictors of Adult Sibling Social Support for the Seriously Mentally III

Little research examines the reasons adult siblings might provide social support to unmarried, dependent brothers and sisters. This article examines how obligation, reciprocity, and the quality of personal relationships affect whether siblings provide social support to the seriously mentally ill. It uses a sample of 108 siblings of 85 participants in a treatment program for the seriously mentally ill to examine the factors that predict several aspects of help provision. Reciprocity is an important predictor of reported and projected support: The more help respondent siblings receive from ill siblings, the more willingness to help they show in return. The availability of parental and other sibling caregivers is also associated with reported help from siblings. Neither norms of family obligation nor relational quality are highly correlated with support. The results indicate that professionals should take into account the potential importance of siblings as providers of social support to the seriously mentally ill and encourage their clients to develop reciprocal interactions with their brothers and sisters.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/66728/2/10.1177_0192513X94015002007.pd