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In today’s rapidly evolving digital landscape, businesses are constantly under pressure to produce high-quality, engaging content for various marketing channels, including blog posts, social media updates, and email campaigns. However, the traditional manual content generation process is often time-consuming, resource-intensive, and inconsistent in maintaining the desired messaging and tone. As a result, the content production process can become a bottleneck, delay marketing campaigns, and reduce organizational agility. Furthermore, manual content generation introduces the risk of inconsistencies in tone, style, and messaging across different platforms and pieces of content. These inconsistencies can confuse the audience and dilute the message. We propose a hybrid approach for content generation based on the integration of Markov Chains with Semantic Technology (CGMCST). Based on the probabilistic nature of Markov chains, this approach allows an automated system to predict sequences of words and phrases, thereby generating coherent and contextually accurate content. Moreover, the application of semantic technology ensures that the generated content is semantically rich and maintains a consistent tone and style. Consistency across all marketing materials strengthens the message and enhances audience engagement. Automated content generation can scale effortlessly to meet increasing demands. The algorithm obtained an entropy of 9.6896 for the stationary distribution, indicating that the model can accurately predict the next word in sequences and generate coherent, contextually appropriate content that supports the efficacy of this novel CGMCST approach. The simulation was executed for a fixed time of 10,000 cycles, considering the weights based on the top three topics. These weights are determined both by the global document index and by term. The stationary distribution of the Markov chain for the top keywords, by stationary probability, includes a stationary distribution of “people” with a 0.004398 stationary distribution. Full article

For evaluating the classification model of an information system, a proper measure is usually needed to determine if the model is appropriate for dealing with the specific domain task. Though many performance measures have been proposed, few measures were specially defined for multi-class problems, which tend to be more complicated than two-class problems, especially in addressing the issue of class discrimination power. Confusion entropy was proposed for evaluating classifiers in the multi-class case. Nevertheless, it makes no use of the probabilities of samples classified into different classes. In this paper, we propose to calculate confusion entropy based on a probabilistic confusion matrix. Besides inheriting the merit of measuring if a classifier can classify with high accuracy and class discrimination power, probabilistic confusion entropy also tends to measure if samples are classified into true classes and separated from others with high probabilities. Analysis and experimental comparisons show the feasibility of the simply improved measure and demonstrate that the measure does not stand or fall over the classifiers on different datasets in comparison with the compared measures. Full article