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Artificial intelligence (AI) of Things (AIoT) devices are anticipated to possess human\u2010like decision\u2010making, reasoning, perception, and other capacities with the combination of AI and IoT. AIoT gadgets are expected to be extensively utilized across several domains, as anticipated by 6G networks. With AI's steady advancements in speech recognition, computer vision, and natural language processing\u2014not to mention its ability to analyze large amounts of data\u2014semantic communication is now feasible. A new paradigm in wireless communication is opened by semantic communication, which seeks to explore the meaning behind the bits and only transmits the information that may be used, as opposed to attaining error\u2010free transmission. The combination of IoT with AI provides prominent features to overcome various important issues in cloud computing networks. However, there is bottleneck of delay and precision. Therefore, this paper proposed a new method to overcome this problem. First, the network slicing feature maps were extracted by convolutional neural networks. Next, the processing delay is reduced by semantic compression. Simulation results show that the proposed approach makes 99.2% reduction in communication complexity and an 80% reduction in transmission delay as compared with traditional methods. Taking the Resnet18 network as an example, the running time of the semantic communication method is only 0.8% of the traditional method.<\/jats:p>","DOI":"10.1002\/nem.70007","type":"journal-article","created":{"date-parts":[[2025,2,24]],"date-time":"2025-02-24T00:04:41Z","timestamp":1740355481000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Computationally Efficient Approach for 6G\u2010AI\u2010IoT Network Slicing and Error\u2010Free Transmission"],"prefix":"10.1002","volume":"35","author":[{"given":"Yunxiang","family":"Qi","sequence":"first","affiliation":[{"name":"Faculty of Humanities and Arts Macau University of Science and Technology  Taipa China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,2,23]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2019.05.001"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2892455"},{"issue":"11","key":"e_1_2_9_4_1","first-page":"1","article-title":"A Model for Identification of piwiRNA Using Deep Neural Learning","volume":"22","author":"Adnan A.","year":"2023","journal-title":"Journal of Biomolecular Structure and Dynamics"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10822-019-00207-x"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.105006"},{"key":"e_1_2_9_7_1","doi-asserted-by":"crossref","unstructured":"F.Ali W.Alghamdi A. 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