Polysemy in Advertising

The article reviews the conceptual foundations of advertising polysemy – the occurrence of different interpretations for the same advertising message. We discuss how disciplines as diverse as psychology, semiotics and literary theory have dealt with the issue of polysemy, and provide translations and integration among these multiple perspectives. From such review we draw recurrent themes to foster future research in the area and to show how seemingly opposed methodological and theoretical perspectives complement and extend each other. Implications for advertising research and practice are discussed.Advertising;Polysemy;Semiotics

Polysemy in Advertising

The article reviews the conceptual foundations of advertising polysemy – the occurrence of different interpretations for the same advertising message. We discuss how disciplines as diverse as psychology, semiotics and literary theory have dealt with the issue of polysemy, and provide translations and integration among these multiple perspectives. From such review we draw recurrent themes to foster future research in the area and to show how seemingly opposed methodological and theoretical perspectives complement and extend each other. Implications for advertising research and practice are discussed

CaSePer: An efficient model for personalized web page change detection based on segmentation

AbstractUsers who visit a web page repeatedly at frequent intervals are more interested in knowing the recent changes that have occurred on the page than the entire contents of the web page. Because of the increased dynamism of web pages, it would be difficult for the user to identify the changes manually. This paper proposes an enhanced model for detecting changes in the pages, which is called CaSePer (Change detection based on Segmentation with Personalization). The change detection is micro-managed by introducing web page segmentation. The web page change detection process is made efficient by having it perform a dual-step process. The proposed method reduces the complexity of the change-detection by focusing only on the segments in which the changes have occurred. The user-specific personalized change detection is also incorporated in the proposed model. The model is validated with the help of a prototype implementation. The experiments conducted on the prototype implementation confirm a 77.8% improvement and a 97.45% accuracy rate

Performance Comparison of Turkish Web Pages Classification

Nowadays., web page classification is essential for efficient and fast search engines. There is an ever-increasing need for automatic classification techniques with higher classification accuracy. In this article., a performance comparison of existing Turkish language CNN models for web pages classification systems is performed. In more detail., the content of web pages is extracted first., then preprocessing steps that aim to detect the important parts and eliminate useless contents are used. Next., Bert word embedding is integrated to represent the texts by efficient numerical vectors. Finally., three state-of-the-art CNN models that fully support the Turkish language are investigated to find the best classifier. Overall., the three studied models obtained an acceptable performance while classifying the Turkish webpages., however., the third model was able to achieve slightly better than the other two models. © 2021 IEEE

Texture classification of proteins using support vector machines and bio-inspired metaheuristics

6th International Joint Conference, BIOSTEC 2013, Barcelona, Spain, February 11-14, 2013[Abstract] In this paper, a novel classification method of two-dimensional polyacrylamide gel electrophoresis images is presented. Such a method uses textural features obtained by means of a feature selection process for whose implementation we compare Genetic Algorithms and Particle Swarm Optimization. Then, the selected features, among which the most decisive and representative ones appear to be those related to the second order co-occurrence matrix, are used as inputs for a Support Vector Machine. The accuracy of the proposed method is around 94 %, a statistically better performance than the classification based on the entire feature set. This classification step can be very useful for discarding over-segmented areas after a protein segmentation or identification process