International Conference on Knowledge-Based and Intelligent Information and Engineering Systems 

About: International Conference on Knowledge-Based and Intelligent Information and Engineering Systems is an academic conference. The conference publishes majorly in the area(s): Fuzzy logic & Artificial neural network. Over the lifetime, 4278 publications have been published by the conference receiving 26657 citations.

Prediction of Information Diffusion Probabilities for Independent Cascade Model

Abstract: We address a problem of predicting diffusion probabilities in complex networks. As one approach to this problem, we focus on the independent cascade (IC) model, and define the likelihood for information diffusion episodes, where an episode means a sequence of newly active nodes. Then, we present a method for predicting diffusion probabilities by using the EM algorithm. Our experiments using a real network data set show the proposed method works well.

Parallel genetic algorithm taxonomy

Abstract: Genetic algorithms (GAs) are powerful search techniques that are used to solve difficult problems in many disciplines. Unfortunately, they can be very demanding in terms of computation load and memory. Parallel genetic algorithms (PGAs) are parallel implementations of GAs which can provide considerable gains in terms of performance and scalability. PGAs can easily be implemented on networks of heterogeneous computers or on parallel mainframes. We review the state of the art on PGAs and propose a new taxonomy also including a new form of PGA (the dynamic deme model) which was recently developed.

Classifying environmental sounds using image recognition networks

Abstract: Automatic classification of environmental sounds, such as dog barking and glass breaking, is becoming increasingly interesting, especially for mobile devices. Most mobile devices contain both camer ...

Preventing Student Dropout in Distance Learning Using Machine Learning Techniques

Abstract: Student dropout occurs quite often in universities providing distance education. The scope of this research is to study whether the usage of machine learning techniques can be useful in dealing with this problem. Subsequently, an attempt was made to identifying the most appropriate learning algorithm for the prediction of students’ dropout. A number of experiments have taken place with data provided by the ‘informatics’ course of the Hellenic Open University and a quite interesting conclusion is that the Naive Bayes algorithm can be successfully used. A prototype web based support tool, which can automatically recognize students with high probability of dropout, has been constructed by implementing this algorithm.

Clustering-and-selection model for classifier combination

Abstract: We devise a simple clustering-and-selection algorithm based on a probabilistic interpretation of classifier selection. First, the data set is clustered into K clusters, and then the most successful classifier for a given cluster is nominated to label the inputs in the Voronoi cell of the cluster centroid. The proposed method is compared experimentally with the minimum, maximum, product and average. Also given are the results from the naive Bayes method, the behaviour-knowledge space (BKS) method, the best individual and the oracle.