Applied Intelligence

The prediction of numbers of newborns is an important issue in hospital management. Relying on the inherent non-aftereffect property, discrete-time Markov chain (DTMC) is a candidate for solving the problem. But the classical DTMC is unable to handle ...

Temporal difference and eligibility traces are of the most common approaches to solve reinforcement learning problems. However, except in the case of Q-learning, there are no studies about using these two approaches in a cooperative multi-agent learning ...

Despite many years of research, breast cancer detection is still a difficult, but very important problem to be solved. An automatic diagnosis system could establish whether a mammography presents tumours or belongs to a healthy patient and could offer, ...

This paper presents a hybrid evolutionary algorithm with guided mutation (EA/G) to solve the minimum weight dominating set problem (MWDS) which is $\mathcal {N}\mathcal {P}$-hard in nature not only for general graphs, but also for unit disk graphs (UDG)...

Dempster-Shafer evidence theory is an efficient tool in knowledge reasoning and decision-making under uncertain environments. Conflict management is an open issue in Dempster-Shafer evidence theory. In past decades, a large amount of research has ...

Model-based Diagnosis is a well known AI technique that has been applied to software debugging for senior programmers, called Model-Based Software Debugging (MBSD). In this paper we describe the basis of MBSD and show how it can be used for educational ...

In many resource allocation problems, economy efficiency must be taken into consideration together with social equality, and price rigidities are often made according to some economic and social needs. We investigate the computational issues of dynamic ...

Traditional machine learning algorithms depend heavily on the assumption that there is sufficient data to learn a reliable model. This is not always the case, and in situations where data is limited, transfer learning can be applied to compensate for ...

Many previous studies have focused on the extraction of association rules from transaction data. Unfortunately, customer purchasing intentions tend to be uncertain during the decision making process. That is, they cannot be obtained from business ...

Treatment of missing data has become increasingly significant in scientific research and engineering applications. The classic imputation strategy based on the K nearest neighbours (KNN) has been widely used to solve the plague problem. However, former ...

Semi-supervised clustering techniques have been proposed in the literature to overcome the problems associated with unsupervised and supervised classification. It considers a small amount of labeled data and the whole data distribution during the ...

Enabled to provide pervasive access to distributed resources in parallel ways, heterogeneous scheduling is extensively applied in large-scaled computing system for high performance. Conventional real-time scheduling algorithms, however, either disregard ...

Hybrid interval multi-objective optimization problems are common in real-world applications. These problems involve both explicit and implicit objectives, and the values of these objectives are intervals. Few previous methods are suitable for them. An ...

All types of recommender systems have been thoroughly explored and developed in industry and academia with the advent of online social networks. However, current studies ignore the trust relationships among users and the time sequence among items, which ...