Perspective - (2022) Volume 9, Issue 4

The Development of Information System is Improved in the Network Parameters
Leilah Mahi*
Department of Social Sciences, University of Nantes, France
*Correspondence: Leilah Mahi, Department of Social Sciences, University of Nantes, France, Email:

Received: 01-Aug-2022, Manuscript No. TOSOCIAL-22-75856; Editor assigned: 03-Aug-2022, Pre QC No. TOSOCIAL-22-75856 (PQ); Reviewed: 17-Aug-2022, QC No. TOSOCIAL-22-75856; Revised: 22-Aug-2022, Manuscript No. TOSOCIAL-22-75856 (R); Published: 29-Aug-2022


The deepening of big data and the development of information technology, countries, enterprises, organizations and even individuals are increasingly dependent on information systems. In recent years, all kinds of network attacks have occurred endlessly, and the losses are immeasurable. Protecting the security of information systems is therefore a sensitive issue in the new context. The existing BP neural network algorithm is improved as the core algorithm of safety intelligent evaluation of evaluation information system. Input nodes are optimized. During the risk factor identification phase, most of the redundant information is filtered out and the core factors are extracted. In the risk determination phase, the particle swarm optimization algorithm is used to optimize the initial network parameters of the BP neural network algorithm to overcome the dependence of the network on the initial threshold. At the same time, the performance of the improved algorithm is verified through simulation experiments. Experimental results show that the PSO-BP algorithm has a faster convergence speed and a higher prediction accuracy of risk values compared to the conventional BP algorithm. The error value of the PSO-BP evaluation method is almost zero, and there is no error variation even after 100 sample tests. The maximum error value is only 0.34 and the average error value is 0.21, proving the excellent performance of the PSO-BP algorithm.


This paper is blended with the wise nursing records machine to construct the emergency nursing platform architecture, from the machine emergency procedures, machine functionality, community surroundings deployment, and database layout factors of the discussion. Based on health center records security, the nursing tracking machine of the wise nursing records machine is built to understand community communique, which is apparent and intuitive. The wise records machine is carried out to protection control, clinical order records, circumstance records, and records inquiry, which could shop operating time and whole the speedy transmission and correct execution of clinical order, making the community communique of hospital therapy extra brief and handy and maximizing the general efficiency. Based at the disordered phenomenon of registration triage, the Relief set of rules is used to categorise the aetiology and triage, and the mixture of clinical advice, records query, and IT era is optimized, so that it will take away the phenomenon of spherical diagnosis, insert number, and enhance the clinical surroundings of watching for diagnosis, taking medicine, examination, and trying out. Finally, thru the trying out of machine records security, records traceability, and speedy records query, the issues in nursing control were essentially solved.


Mild cognitive impairment (MCI) is often the first sign of dementia and affects an increasing number of older people worldwide. Early detection and intervention through cognitive assessment, training, and rehabilitation can help patients with MCI maintain or improve cognitive function. In particular, the cognitive status of patients with MCI should be assessed regularly due to the uncertainty of disability progression. Technological advances in mobile devices and the development of specialized applications are providing new methods for cognitive training and assessment in healthy older adults and those with MCI. Attempts are being made to provide comfortable and easy-to-use cognitive interventions aimed at user engagement through user-friendly and engaging applications. These specialized applications offer the potential for long-term remote and autonomous monitoring of a user’s cognitive state, combined with sensors integrated into mobile devices, to provide clinicians with new data streams and timely, personalized information. This chapter provides an overview of existing mobile applications for cognitive assessment, training, and improvement. In addition, we describe the design of information systems aimed at supporting memory training and cognitive enhancement. This information system is intended for adults with or without cognitive impairment who wish to maintain or improve their cognitive status using the possibilities and perspectives of mobile digital technology.

Copyright: This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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