###### Abstract  
This research proposes an intelligent classification framework for quality of service (QoS) performance improvement in information-centric networking (ICN). The proposal works towards keyword classification techniques to obtain the most valuable information via suitable content prefixes in ICN. In this study, we have achieved the intelligent function using Artificial Intelligence (AI) implementation. Particularly, to find the most suitable and promising intelligent approach for maintaining QoS matrices, we have evaluated various AI algorithms, including evolutionary algorithms (EA), swarm intelligence (SI), and machine learning (ML) by using the cost function to assess their classification performances. With the goal of enabling a complete ICN prefix classification solution, we also propose a hybrid implementation to optimize classification performances by integration of relevant AI algorithms. This hybrid mechanism searches for a final minimum structure to prevent the local optima from happening. By simulation, the evaluation results show that the proposal outperforms EA and ML in terms of network resource utilization and response delay for QoS performance optimization. information-centric networking (ICN); Intelligent classifications; artificial intelligence (AI); quality of service (QoS) +
Footnote †: journal: Computer Science and Engineering  
0000-0001-5000-5000]C. Sa. The major difference between IP and ICN is that the latter applies content name prefix format as the identifier for the forwarding process. Name prefix is a hierarchical segment that is concatenated by \"/\" as of the well-known uniform resource identifier (URI) full name representation. The general format of the content prefix in ICN can be depicted as '/publisherID/routerID /content_name/content_type'. The content name, itself, is composed of keywords that can be registered and bonded as the name identifier of the content. Nevertheless, as of the current IP-based Internet, users still cannot express the intended content request with exact key terms. Thus, to obtain the desired data, users need to try several keywords using the search engine then choose the content that is most related to them [4]. As current network devices require fast and efficient services, it is important to dynamically learn from user's inputs, like a string of keywords as content attributes then estimate and return the most important name prefix to ensure quality of service (QoS). This motivates the need for an intelligent classification mechanism to obtain the most appropriate content prefix component for content discovery improvement then accomplish QoS optimization. We focus on a generic framework which performs content prefix classification by utilizing artificial intelligence (AI) approach to evaluate the relevance between the inputted keywords and the desired content. This is a potential approach given that up to now, AI approach in ICN is still at early stage, though prior studies in this area suggest opportunities for intelligent processing of content prefix in ICN. Different from other notable related work in this field, our study focuses on finding the most suitable intelligent approach that supports efficient classification in the application and network levels. In this paper, we evaluate and observe the performance of various AI algorithms, then discuss their classification performances with our selected QoS matrices. Specifically, the selected algorithms are well-known AI algorithms including evolutionary algorithm (EA), swarm intelligence (SI), and machine learning (ML) methods. Their performances are then evaluated under four different criteria, which are number of function evaluation, cost function, standard deviation, and computation time. From the extensive investigation using MATLAB, we select the most suitable intelligent classification methods for the proposed framework. Then, we show how the framework can optimize ICN efficiency by solving the localization problem in relevant comparable related AI-based work for classification.