{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T11:05:49Z","timestamp":1779102349361,"version":"3.51.4"},"reference-count":19,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2023,4,26]],"date-time":"2023-04-26T00:00:00Z","timestamp":1682467200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Systems"],"abstract":"<jats:p>Information Technology\u2019s 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.<\/jats:p>","DOI":"10.3390\/systems11050222","type":"journal-article","created":{"date-parts":[[2023,4,27]],"date-time":"2023-04-27T01:28:28Z","timestamp":1682558908000},"page":"222","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Artificial Intelligence for Media Ecological Integration and Knowledge Management"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2312-2700","authenticated-orcid":false,"given":"Allam","family":"Balaram","sequence":"first","affiliation":[{"name":"Department of Information Technology, MLR Institute of Technology, Hyderabad 500 043, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3587-9022","authenticated-orcid":false,"given":"K Nattar","family":"Kannan","sequence":"additional","affiliation":[{"name":"Department of Artificial intelligence and Machine Learning, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Chennai 600 124, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7328-9445","authenticated-orcid":false,"given":"Lenka","family":"\u010cepov\u00e1","sequence":"additional","affiliation":[{"name":"Department of Machining, Assembly and Engineering Metrology, Faculty of Mechanical Engineering, VSB-Technical University of Ostrava, 17. Listopadu 2172\/15, 70800 Ostrava, Czech Republic"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kishore","family":"Kumar M","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering (Data Science), CMR Technical Campus, Hyderabad 501 401, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Swaroopa","family":"Rani B","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering (AI&ML), CMR Technical Campus, Hyderabad 501 401, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vladimira","family":"Schindlerova","sequence":"additional","affiliation":[{"name":"Department of Machining, Assembly and Engineering Metrology, Faculty of Mechanical Engineering, VSB-Technical University of Ostrava, 17. Listopadu 2172\/15, 70800 Ostrava, Czech Republic"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,26]]},"reference":[{"key":"ref_1","first-page":"673","article-title":"Artificial intelligence and journalism","volume":"96","author":"Broussard","year":"2019","journal-title":"J. Mass Commun. Q."},{"key":"ref_2","first-page":"739","article-title":"Prospective Directions of Using Multimedia Technologies in the Training of Future Specialists","volume":"22","author":"Shunkov","year":"2022","journal-title":"IJCSNS Int. J. Comput. Sci. Netw. Secur."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Wang, H., Cui, Z., Liu, R., Fang, L., and Sha, Y. (2023). A multi-type transferable method for missing link prediction in heterogeneous social networks. IEEE Trans. Knowl. 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