The development of new technologies has confronted the entire domain of science and industry with issues of big data's scalability as well as its integration with the purpose of forecasting analytics in its life cycle. In predictive analytics, the forecast of near-future and recent past - or in other words, the now-casting - is the continuous study of real-time events and constantly updated whe...

The evolution of telecommunications systems towards complex 5G networks implies changes on many levels. In this paper, we concentrate on the management landscape of current and future networks, unfolding a vision of advanced analytics and machine learning approaches to mitigate the unavoidable complexity of future networks. We explore proficient Deep Learning mechanisms and Cognitive Frameworks...

Culture is widely thought to be beneficial when social learning is less costly than individual learning and thus may explain the enormous ecological success of humans. Rogers (1988. Does biology constrain culture. Am. Anthropol. 90: 819-831) contradicted this common view by showing that the evolution of social learning does not necessarily increase the net benefits of learned behaviours in a va...

This article proposes new approach to the development of student’s individual learning path (ILP). The result of student’s learning is defined as a set of competencies levels. The use of the multi-level system of generalized professional competencies allows to model ILP effectively and to automate the development of an optimal ILP. Each competency level in our model introduces some substantial ...

Since knowledge in expert system is vague and modified frequently, expert systems are fuzzy and dynamic systems. It is very important to design a dynamic knowledge inference framework which is adjustable according to knowledge variation as human cognition and thinking. Aiming at this object, a generalized fuzzy Petri net model is proposed in this paper, it is called adaptive fuzzy Petri net (AF...

Although Hopfield neural network is one of the most commonly used neural network models for auto-association and optimization tasks, it has several limitations. For example, it is well known that Hopfield neural networks has limited stored patterns, local minimum problems, limited noise ratio, retrieve reverse value of pattern, and shifting and scaling problems. This research will propose multi...

Although Hopfield neural network is one of the most commonly used neural network models for auto-association and optimization tasks, it has several limitations. For example, it is well known that Hopfield neural networks has limited stored patterns, local minimum problems, limited noise ratio, retrieve reverse value of pattern, and shifting and scaling problems. This research will propose multi...

In this paper, we consider the multi-task sparse learning problem under the assumption that the dimensionality diverges with the sample size. The traditional l1/l2 multi-task lasso does not enjoy the oracle property unless a rather strong condition is enforced. Inspired by adaptive lasso, we propose a multi-stage procedure, adaptive multi-task lasso, to simultaneously conduct model estimation a...