No abstract

Detection of mitotic tumor cells per tissue area is one of the critical markers of breast cancer prognosis. The aim of this paper is to develop a method for the automatic detection of mitotic figures from breast cancer histological slides using a partially supervised deep learning framework. Unlike the previous literature, which has focused on solving the problem of mitosis detection in the weakly annotated datasets using centroid pixel labels (weak labels) only without taking advantage of the available pixel-level labels (strong labels) of other datasets, in this paper, we design a novel partially supervised framework based on two parallel deep fully convolutional networks. One of them is trained using weak labels and the other is trained using strong labels, together with a weight transfer function. In the detection phase, we fuse the segmentation maps produced by the two networks to obtain the final mitosis detections. Our system exploits the available large sets of mitosis detection samples with mitosis centroid annotation, such as the 2014 ICPR dataset and the AMIDA13 dataset, and only a small set of samples with the annotation of all mitosis pixels, such as the 2012 ICPR dataset, to perform a more accurate mitosis detection on weakly labeled data. This enables us to outperform all previous mitosis detection systems by achieving F-scores of 0.575 and 0.698 on the 2014 ICPR dataset and the AMIDA13 dataset respectively.

Vehicles authentication, the integrity of messages exchanged, and privacy-preserving are essential features in vehicular ad hoc network (VANETs) security. Most of the previously proposed VANETs security solutions do not sufficiently satisfy the security and efficiency requirements. Besides, most of those solutions are heavily dependent on the system key and long-term sensitive data stored in an ideal tamperproof device, which may not be practical or ideal for resource-constrained onboard units (), especially in the case of an unexpected cloning or physical attack. Therefore, a robust authentication solution should consider those security issues and the nature of resource-constrained nodes. To satisfy all these requirements, we propose a lightweight multi-factor authentication and privacy-preserving security solution for VANETs. It employs a combination of physically unclonable functions () and one-time dynamic pseudo-identities as authentication factors. Furthermore, it eliminates the heavy dependency on the system key by decentralising the wide precinct of the certificate authority () into regional domains and achieves robust control of domains keys. A detailed analysis demonstrates that our scheme efficiently meets the VANETs security requirements, and offers more suitable communication and computation costs and features than existing schemes.

In this paper, we investigate the definition of Fintech and measure the extent of the impact of Fintech variables on the Cybersecurity as the dependent variable. The hypotheses developed by the authors which are based on research through the statistical results of the research variables and as all correlation coefficients were a positive relationship and at the level of significance 0.01 which made the authors accept the correlation hypotheses, and for the results of the influence factor between the research variables it also had a positive effect when the level of 0.05 for all sections of the independent variable, as we have also noticed that the influence of the independent variable of financial technology when linked to all of its sections with Cybersecurity. We find the influence of the influence coefficient significantly increased to 0.908, which explains that there is a complementarity between the sections of the independent variable.

According to the developments in financial liberalization and banking innovation, the bank risks have been changed in their nature which leads to use new financial instruments. Thus banks increasingly adopt risk assessment to avoid it. Therefore, this article describes a new model to assist financial risk management based on artificial intelligence. This entails using artificial neural networks to forecast financial risks and support the decision-makers and the consumers in making better risk management decisions. A real-world case study based on the Iraqi banking sector is presented to guarantee the applicability, accuracy, and efficiency of our proposed model. The sample was selected from a data of 16 banks for the period (2004-2018), taken from Iraq Securities Commission, regular market (https://www.isc.gov.iq/). The data were examined with an initial analysis and then converted to the formula compatible with neural networks. The authors describe the results obtained and compare them with previous studies. It confirmed the effectiveness of the proposed model for risk assessment by the results obtained from the approved form on artificial intelligence.