Emotion modeling has gained attention for almost two decades now due to the rapid growth of affective computing (AC). AC aims to detect and respond to the end-user's emotions by devices and... Show More

Emotion modeling has gained attention for almost two decades now due to the rapid growth of affective computing (AC). AC aims to detect and respond to the end-user's emotions by devices and computers. Despite the hard efforts being directed to emotion modeling with numerous tries to build different models of emotions, emotion modeling remains an art with a lack of consistency and clarity regarding the exact meaning of emotion modeling. This review deconstructs the vagueness of the term ‘emotion modeling' by discussing the various types and categories of emotion modeling, including computational models and its categories—emotion generation and emotion effects—and emotion representation models and its categories—categorical, dimensional, and componential models. This review deals with applications associated with each type of emotion model including artificial intelligence and robotics architecture, computer-human interaction applications of the computational models, and emotion classification and affect-aware applications such as video games and tutoring systems applications of emotion representation models.

Evolution of data in the data stream environment generates patterns at different time instances. The cluster formation changes with respect to time because of the behaviour and members of clusters.... Show More

Evolution of data in the data stream environment generates patterns at different time instances. The cluster formation changes with respect to time because of the behaviour and members of clusters. Data stream clustering (DSC) allows us to investigate the changes of the group behaviour. These changes in the behaviour of the group members over time lead to formation of new clusters and may make old clusters extinct. Also, these extinct old clusters may recur over time. The problem is to identify and record these change patterns of evolving data streams. The knowledge obtained from these change patterns is then used for trends analysis over evolving data streams. In order to address this flexible clustering requirement, density-based clustering method is proposed to dynamically cluster evolving data streams. The decay factor identifies formation of new clusters and diminishing of older clusters on arrival of data points. This indicates trends in evolving data streams.

This paper proffers an overview of neural network, coupled with early neural network architecture, learning methods, and applications. Basically, neural networks are simplified models of biological... Show More

This paper proffers an overview of neural network, coupled with early neural network architecture, learning methods, and applications. Basically, neural networks are simplified models of biological nervous systems and that's why they have drawn crucial attention of research community in the domain of artificial intelligence. Basically, such networks are highly interconnected networks possessing a huge number of processing elements known as neurons. Such networks learn by examples and exhibit the mapping capabilities, generalization, fault resilience conjointly with escalated rate of information processing. In the current paper, various types of learning methods employed in case of neural networks are discussed. Subsequently, the paper details the deep neural network (DNN), its key concepts, optimization strategies, activation functions used. Afterwards, logistic regression and conventional optimization approaches are described in the paper. Finally, various applications of neural networks in various domains are included in the paper before concluding it.