The Compound Prediction Analysis of Information Network Security Situation based on Support Vector Combined with BP Neural Network Learning Algorithm

In order to solve the problem of low security of data in network transmission and inaccurate prediction of future security situation, an improved neural network learning algorithm is proposed in this paper. The algorithm makes up for the shortcomings of the standard neural network learning algorithm, eliminates the redundant data by vector support, and realizes the effective clustering of information data. In addition, the improved neural network learning algorithm uses the order of data to optimize the "end" data in the standard neural network learning algorithm, so as to improve the accuracy and computational efficiency of network security situation prediction.MATLAB simulation results show that the data processing capacity of support vector combined BP neural network is consistent with the actual security situation data requirements, the consistency can reach 98%. the consistency of the security situation results can reach 99%, the composite prediction time of the whole security situation is less than 25s, the line segment slope change can reach 2.3% ,and the slope change range can reach 1.2%,, which is better than BP neural network algorithm.

Hubungan Kebijakan Pandemi COVID-19 Terhadap Faktor Pernikahan Usia Remaja Kecamatan Pringgabaya, Lombok Timur, NTB

The study aims to know the relationship of pandemic COVID-19 policy toward teenager marriage factor. The method used in this study is quantitative method with associative approch. Data collection techniques using questionnaires, while data analysis techniques use spearman correlation analysis the results showed that there was a significant relationship between COVID-19 Pandemic policy toward teenage marriage factor with relationship rate off 8,11%. Also Policy in pandemic covid-19 raises the awareners of healthy and clean life exercise a healthy lifestyle by following the healthy protocols applied by the goverment. But on the others hand, there is an event of latent or an uncological function of the policy of COVID-19 pandemic is one of the factor behind teenage marriage, where the policies are related to social restrinctions and network learning (online), socioeconomic, cultural and environment become the factor that cause teenagers in marriage.

The Role of Network Learning Capability in the Relationship between Open Mindedness and Innovation Performance

Learning, which is the main key of innovation, is an indispensable element for companies to gain sustainable competitive advantage. Although not being adequately studied in management literature, network learning capability, a type of organizational learning ability, is a determining factor in the innovation process. Likewise, open-mindedness is a component that accelerates the creation of knowledge in the organization as well as encouraging the organization to be open towards new opportunities and to value different opinions. In this study, a model including these variables was designed and the mediator role of network learning in the relationship between open-mindedness and innovation performance was explored. It is suggested that open-mindedness has a positive effect on innovation performance and that network learning capability possesses a mediator role in this relationship. The data were collected through surveys answered by the middle and senior managers of Turkey’s leading companies. As a result, it is pointed out that there is a positive and significant relationship between open-mindedness and innovation performance, and that network learning has a mediator effect on this relationship. This study adds value to the management literature by highlighting the momentousness of network learning capability in the innovation process as well as offering several avenues of future studies and implications for different stakeholders.

Cell-type–specific neuromodulation guides synaptic credit assignment in a spiking neural network

Brains learn tasks via experience-driven differential adjustment of their myriad individual synaptic connections, but the mechanisms that target appropriate adjustment to particular connections remain deeply enigmatic. While Hebbian synaptic plasticity, synaptic eligibility traces, and top-down feedback signals surely contribute to solving this synaptic credit-assignment problem, alone, they appear to be insufficient. Inspired by new genetic perspectives on neuronal signaling architectures, here, we present a normative theory for synaptic learning, where we predict that neurons communicate their contribution to the learning outcome to nearby neurons via cell-type–specific local neuromodulation. Computational tests suggest that neuron-type diversity and neuron-type–specific local neuromodulation may be critical pieces of the biological credit-assignment puzzle. They also suggest algorithms for improved artificial neural network learning efficiency.

Applications of Deep Learning on Topographic Images to Improve the Diagnosis for Dynamic Systems and Unconstrained Optimization

Studies carried out by researchers show that data growth can be exploited in such a way that the use of deep learning algorithms allow predictions with a high level of precision based on the data, which is why the latest studies are focused on the use of convolutional neural networks as the optimal algorithm for image classification. The present research work has focused on making the diagnosis of a disease that affects the cornea called keratoconus through the use of deep learning algorithms to detect patterns that will later be used to carry out preventive detections. The algorithm used to perform the classifications has been convolutional neural networks as well as image preprocessing to remove noise that can limit neural network learning, resulting in more than 1900 classified images out of a total of >2000 images distributed between normal eyes and those with keratoconus, which is equivalent to 92%.

STEM Netweaver Dialogues: Creating, Designing, and Managing Transformative Networks

Network of STEM Education Centers (NSEC) convened three 90-min network learning dialogues with four leading experts in network facilitation, systems change, and STEM education reform.