Knowledge Extraction Using Probabilistic Reasoning: An Artificial Neural Network Approach

The World Wide Web (WWW) has radically changed the way in which we access, generate and disseminate information. Its presence is felt daily and with more internet-enabled devices being connected the web of knowledge is growing. We are now moving into era where the WWW is capable of ‘understanding’ the actual/intended meaning of our content. This is being achieved by creating links between distributed data sources using the Resource Description Framework (RDF). In order to find information in this web of interconnected sources, complex query languages are often employed, e.g. SPARQL. However, this approach is limited as exact query matches are often required. In order to overcome this challenge, this paper presents a probabilistic approach to searching RDF documents. The developed algorithm converts RDF data into a matrix of features and treats searching as a machine learning problem. Using a number of artificial neural network algorithms, a successfully developed prototype has been developed that demonstrates the applicability of the approach. The results illustrate that the Voted Perceptron classifier (VPC), perceptron linear classifier (PERLC) and random neural network classifier (RNNC) performed particularly well, with accuracies of 100%, 98% and 93% respectively

Is swarm intelligence able to create mazes?

In this paper, the idea of applying Computational Intelligence in the process of creation board games, in particular mazes, is presented. For two different algorithms the proposed idea has been examined. The results of the experiments are shown and discussed to present advantages and disadvantages

Real-Time Cloud-based Game Management System via Cuckoo Search Algorithm

This paper analyses the idea of applying Swarm Intelligence in the process of managing the entire 2D board game in a real-time environment. For the proposed solution Game Management System is used as a cloud resource with a dedicated intelligent control agent. The described approach has been analysed on the basis of board games like mazes. The model and the control algorithm of the system is described and examined. The results of the experiments are presented and discussed to show possible advantages and disadvantages of the proposed method.

Survey on the Family of the Recursive-Rule Extraction Algorithm

In this paper, we first review the theoretical and historical backgrounds on rule extraction from neural network ensembles. Because the structures of previous neural network ensembles were quite complicated, research on an efficient rule extraction algorithm from neural network ensembles has been sparse, even though a practical need exists for rule extraction in Big Data datasets. We describe the Recursive-Rule extraction (Re-RX) algorithm, which is an important step toward handling large datasets. Then we survey the family of the Recursive-Rule extraction algorithm, i.e. the Multiple-MLP Ensemble Re-RX algorithm, and present concrete applications in financial and medical domains that require extremely high accuracy for classification rules. Finally, we mention two promising ideas to considerably enhance the accuracy of the Multiple-MLP Ensemble Re-RX algorithm. We also discuss developments in the near future that will make the Multiple-MLP Ensemble Re-RX algorithm much more accurate, concise, and comprehensible rule extraction from mixed datasets

Prediction in Photovoltaic Power by Neural Networks

The ability to forecast the power produced by renewable energy plants in the short and middle term is a key issue to allow a high-level penetration of the distributed generation into the grid infrastructure. Forecasting energy production is mandatory for dispatching and distribution issues, at the transmission system operator level, as well as the electrical distributor and power system operator levels. In this paper, we present three techniques based on neural and fuzzy neural networks, namely the radial basis function, the adaptive neuro-fuzzy inference system and the higher-order neuro-fuzzy inference system, which are well suited to predict data sequences stemming from real-world applications. The preliminary results concerning the prediction of the power generated by a large-scale photovoltaic plant in Italy confirm the reliability and accuracy of the proposed approaches

Enhancing competitive island cooperative neuro - evolution through backpropagation for pattern classification

Cooperative coevolution is a promising method for training neural networks which is also known as cooperative neuro-evolution. Cooperative neuro-evolution has been used for pattern classification, time series prediction and global optimisation problems. In the past, competitive island based cooperative coevolution has been proposed that employed different instances of problem decomposition methods for competition. Neuro-evolution has limitations in terms of training time although they are known as global search methods. Backpropagation algorithm employs gradient descent which helps in faster convergence which is needed for neuro-evolution. Backpropagation suffers from premature convergence and its combination with neuro-evolution can help eliminate the weakness of both the approaches. In this paper, we propose a competitive island cooperative neuro-evolutionary method that takes advantage of the strengths of gradient descent and neuro-evolution. We use feedforward neural networks on benchmark pattern classification problems to evaluate the performance of the proposed algorithm. The results show improved performance when compared to related methods

A pilot study to evaluate the efficacy of self-attachment to treat chronic anxiety and/or depression in Iranian women

The aim of this pilot study was to evaluate the efficacy of the new Self-Attachment Technique (SAT) in treating resistant anxiety and depression, lasting at least three years, among Iranian women from different social backgrounds. In this intervention, the participant, using their childhood photos, imaginatively creates an affectional bond with their childhood self, vows to consistently support and lovingly re-raise this child to emotional well-being. We conducted a longitudinal study with repeated measurement to evaluate the efficacy of SAT using ANOVA. Thirty-eight women (N=30) satisfying the inclusion and exclusion criteria were recruited from different parts of Tehran. To describe the SAT protocols, a total of eight one-to-one sessions were offered to the recruits, the first four were weekly while the last four were fortnightly. The participants were expected to practice the protocols for twenty minutes twice a day. Two questionnaires, GAD-7 and PHQ-9, were used to measure anxiety and depression levels before and after the intervention and in a three-month follow-up. Thirty women completed the course. The change in the anxiety level between the pre-test and the post-test was significant at p<0.001 with effect size 2.6. The change in anxiety between pre-test and follow-up test was also significant at p<0.001 with effect size 3.0 respectively. The change in anxiety between the post-test and the follow-up was significant at p<0.05 with effect size 0.6. For depression, the change between the pre-test and the post-test or the follow-up was significant at p<0.001 with effect size 2.5 for each