Artificial Intelligence for Media Ecological Integration and Knowledge Management

Abstract

Information Technology’s development increases day by day, making life easier in terms of work and progress. In these developments, knowledge management is becoming mandatory in all the developing sectors. However, the conventional model for growth analysis in organizations is tedious as data are maintained in ledgers, making the process time consuming. Media Ecology, a new trending technology, overcomes this drawback by being integrated with artificial intelligence. Various sectors implement this integrated technology. The marketing strategy of Huawei Technologies Co. Ltd. is analyzed in this research to examine the advantages of Media Ecology Technology in integration with artificial intelligence and a Knowledge Management Model. This combined model supports sensor technology by considering each medium, the data processing zone, and user location as nodes. A Q-R hybrid simulation methodology is implemented to analyze the data collected through Media Ecology. The proposed method is compared with the inventory model, and the results show that the proposed system provides increased profit to the organization. Paying complete attention to Artificial intelligence without the help of lightweight deep learning models is impossible. Thus, lightweight deep models have been introduced in most situations, such as healthcare management, maintenance systems, and controlling a few IoT devices. With the support of high-power consumption as computational energy, it adapts to lightweight devices such as mobile phones. One common expectation from the deep learning concept is to develop an optimal structure in case time management.

Proceedings of the 7th International Conference on European Integration 2024

“…From this point of view, the term Society 4.0 can be associated with economic activities bordering on the quaternary sector. This sector contains the services related to the generation and sharing of information, education and scientific research are typical of the quaternary sector and include the whole area of development and use of technology, whether it is information, communication, manufacturing, computing, environmental or biotechnology (see also Balaram, 2023). This main group is complemented by advisory and consultancy services, financial services and activities relating to health care and medical research.…”

The challenges of integrating explainable artificial intelligence into GeoAI

“…P/PGIS lays out various approaches to engage marginalized peoples and can be viewed as a step towards co‐producing explainability as well as trust (Sieber, 2006). Integrating XAI into GeoAI presumably would incorporate the lessons learned from subfields like P/PGIS and also borrow from emergent participatory methods in AI Ethics (Balaram et al, 2018).…”