One of the most complex tasks in digital image processing is image segmentation. This paper proposes a novel image segmentation algorithm that uses a biologically inspired technique based on swarm intelligence and a cellular automata model. The proposed swarm intelligence-based algorithm operates on the image pixel data and a region/neighborhood map to form a context in which they can merge. The swarm intelligent algorithm also tries to find similar pixels using a sensor function, which is then utilized by swarm agents to determine the next appreciate pixel in the region/segment area. In addition, the paper introduces a cellular automata-based dynamic flow algorithm to guide swarm agents to choose the best possible advancing direction to avoid traffic jam and inconsistency. The suggested image segmentation strategy is tested on a set of dental radiographs

In this paper we explore the task of mood classification for blog postings. We propose a novel approach that uses the hierarchy of possible moods to achieve better results than a standard machine learning approach. We also show that using sentiment orientation features improves the performance of classification. We used the Livejournal blog corpus as a dataset to train and evaluate our method.

In this article, we explore the task of mood classification for blog postings. We propose a novel approach that uses the hierarchy of possible moods to achieve better results than a standard machine learning approach. We also show that using sentiment orientation features improves the performance of classification. We used the Livejournal blog corpus as a data set to train and evaluate our method. We present extensive error analysis and discuss the difficulty of the task.