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Production of electricity from the burning of fossil fuels has caused an increase in the emission of greenhouse gases. In the long run, greenhouse gases cause harm to the environment. To reduce these gases, it is important to accurately forecast electricity production, supply and consumption. Forecasting of electricity consumption is, in particular, useful for minimizing problems of overproduction and oversupply of electricity. This research study focuses on forecasting electricity consumption based on time series data using different artificial intelligence and metaheuristic methods. The aim of the study is to determine which model among the artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), least squares support vector machines (LSSVMs) and fuzzy time series (FTS) produces the highest level of accuracy in forecasting electricity consumption. The variables considered in this research include the monthly electricity consumption over the years for different countries. The monthly electricity consumption data for seven countries, namely, Norway, Switzerland, Malaysia, Egypt, Algeria, Bulgaria and Kenya, for 10 years were used in this research. The performance of all of the models was evaluated and compared using error metrics such as the root mean squared error (RMSE), average forecasting error (AFE) and performance parameter (PP). The differences in the results obtained via the different methods are analyzed and discussed, and it is shown that the different models performed better for different countries in different forecasting periods. Overall, it was found that the FTS model performed the best for most of the countries studied compared to the other three models. The research results can allow electricity management companies to have better strategic planning when deciding on the optimal levels of electricity production and supply, with the overall aim of preventing surpluses or shortages in the electricity supply. [ABSTRACT FROM AUTHOR]

Inaccurate prediction would cause the insurance company encounter catastrophic losses and may lead to overpriced premiums where low-earning consumers cannot afford to insure themselves. The ability to forecast mortality rates accurately can allow the insurance company to take preventive measures to introduce new policies with reasonable prices. In this paper, several Lee–Carter (LC) based models are used to forecast the mortality rates in a case study of the Malaysian population. The LC-ARIMA model and also a combination of the LC model with two machine learning (ML) methods, namely the random forest (RF) and artificial neural network (ANN) methods are utilized on the prediction of mortality rates for males and females in Malaysia, whereby the LC-Random Forest (LC-RF) hybrid model is a new model that is introduced in this paper. Seventeen years of mortality data in Malaysia are selected as the dataset for this research. To analyze how the forecasting models perform for other countries, we have determined the model that has the best fit and produced the best forecasted mortality rates for all the other countries that are studied. This research has showed that LC-ANN and LC-ARIMA are the best model in predicting the mortality rates of males and females in Malaysia, respectively. This study has also found that the LC-ARIMA model is the best performing model in forecasting the mortality rates in countries that have longer life expectancy and a good healthcare system such as Sweden, Ireland, Japan, Hong Kong, Norway, Switzerland and Czechia. In contrast, the LC-ANN model is the best performing model in forecasting the mortality rates in countries that have a less efficiency, less accessibility healthcare system, and bad personal behavior such as Malaysia, Canada and Latvia. [ABSTRACT FROM AUTHOR]

The International Roughness Index (IRI) is the one of the most important roughness indexes to quantify road surface roughness. In this paper, we propose a new hybrid approach between adaptive network based fuzzy inference system (ANFIS) and various meta-heuristic optimizations such as the genetic algorithm (GA), particle swarm optimization (PSO), and the firefly algorithm (FA) to develop several hybrid models namely GA based ANGIS (GANFIS), PSO based ANFIS (PSOANFIS), FA based ANFIS (FAANFIS), respectively, for the prediction of the IRI. A benchmark model named artificial neural networks (ANN) was also used to compare with those hybrid models. To do this, a total of 2811 samples in the case study of the north of Vietnam (Northwest region, Northeast region, and the Red River Delta Area) within the scope of management of the DRM-I Department were used to validate the models in terms of various criteria like coefficient of determination (R) and the root mean square error (RMSE). Experimental results affirmed the potentiality and effectiveness of the proposed prediction models whereas the PSOANFIS (RMSE = 0.145 and R = 0.888) is better than the other models named GANFIS (RMSE = 0.155 and R = 0.872), FAANFIS (RMSE = 0.170 and R = 0.849), and ANN (RMSE = 0.186 and R = 0.804). The results of this study are helpful for accurate prediction of the IRI for evaluation of quality of road surface roughness. [ABSTRACT FROM AUTHOR]

In this paper, a new method for handling the Multi-Input Multi-Output Student Academic Performance Prediction (MIMO SAPP) problem is proposed. The MIMO SAPP aims to predict the future performance of a student after being enrolled into a university. The existing methods have limitations of using a parameter set and an unsuitable training strategy. Thus, the new method called MANFIS-S (Multi Adaptive Neuro-Fuzzy Inference System with Representative Sets) uses multiple parameter sets and a special learning strategy to resolve those weaknesses. Specifically, the idea of multiple parameter sets is to approximate the MANFIS-S model with many meaningful parameters to ensure the performance of system. This is regarded as the representative problem, which is mathematically formulated and theoretically validated. The idea for the special learning strategy is to use global and local training. In global training, a random parameter set is trained from the first to the last record of the database. Each time of training results in a set of parameters. Global training will rectify and achieve a meaningful subset of parameters by the last training process. In local training, there are 2 types of parameters in MANFIS-S namely premise and consequent that are trained by the gradient descent and Particle Swarm Optimization in a hybrid way. Lastly, for a new record in the testing set, Fuzzy K-Nearest Neighbor is used to find which group it belongs to. The proposed MANFIS-S model is experimentally validated against ANFIS, MANFIS, OneR and Random Tree in a benchmark student performance dataset from UCI, a real student performance dataset from VNU University of Science, Vietnam, and 3 educational datasets taken from KDD Cup. The experiments demonstrated the superiority of MANFIS-S over the related algorithms in term of accuracy. [ABSTRACT FROM AUTHOR]

Link prediction in online social networks is used to determine new interactions among its members which are likely to occur in the future. Link prediction in the co-authorship network has been regarded as one of the main targets in link prediction researches so far. Researchers have focused on analyzing and proposing solutions to give efficient recommendation for authors who can work together in a science project. In order to give precise prediction of links between two ubiquitous authors in a co-authorship network, it is preferable to design a similarity metric between them and then utilizing it to determine the most possible co-author(s). However, the relevant researches did not regard the integration of paper’s content in the metric itself. This is important when considering the collaboration between scientists since it is possible that authors having same research interests are more likely to have a joint paper than those in different researches. In this paper, we propose a new metric for link prediction in the co-authorship network based on the content similarity named as LDAcosin. Mathematical notions of the link prediction in the co-authorship network and a link prediction algorithm based on topic modeling are proposed. The new metric is experimentally validated on the public bibliographic collection. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we propose an integrated approach between fuzzy C-means (FCM) and multi-active neuro fuzzy inference system (MANFIS) for the lossless DEM compression for fast retrieval (DCR) problem, aiming to compress digital elevation model (DEM) data with the priority of fast retrieval from the client machine over the Internet environment. Previous researches of this problem either used the float wavelet transforms integrated with the SPIHT coding or constructed a predictor model using statistical correlation of DEM data in local neighborhoods; thus giving large-sized compressed data and slow transferring time of data between the server and the client. Based on the observation that different non-linear transforms for predictive values in the sliding windows may increase the compression ratio, we herein present a novel approach for DCR problem and validated it experimentally on the benchmark DEM datasets. The comparative results show that our method produces better compression ratio than the relevant ones. [Copyright &y& Elsevier]

Human emotion analysis is one of the challenging tasks in today's scenario. The success rate of human emotion recognition has high implication in practical applications such as Human Machine Interaction, anomaly detection, surveillance, etc. Artificial Neural Networks (ANN) is one of the highly favored computational intelligence techniques for human emotion recognition. However, the performance of traditional ANN is not satisfactory in case of applications such as human emotion analysis. This leads to the necessity of modified ANN with better performance than the conventional systems. In this paper, we propose Circular Back Propagation Neural Network (CBPN) and Deep Kohonen Neural Network (DKNN) to overcome drawbacks of the traditional neural networks regarding computational complexity and accuracy. Performance of the proposals is explored in classifying different emotions of humans using Electroencephalography (EEG) signals. It has been validated that the proposals have better performance than the related methods. [ABSTRACT FROM AUTHOR]