This paper reports the use of simulated annealing to design more efficient fuzzy logic\ud systems to model problems with associated uncertainties. Simulated annealing is used\ud within this work as a method for learning the best configurations of interval and general\ud type-2 fuzzy logic systems to maximize their modeling ability. The combination\ud of simulated annealing with these models is presented in the modeling of four benchmark\ud problems including real-world problems. The type-2 fuzzy logic system models\ud are compared in their ability to model uncertainties associated with these problems.\ud Issues related to this combination between simulated annealing and fuzzy logic systems,\ud including type-2 fuzzy logic systems, are discussed. The results demonstrate that\ud learning the third dimension in type-2 fuzzy sets with a deterministic defuzzifier can\ud add more capability to modeling than interval type-2 fuzzy logic systems. This finding\ud can be seen as an important advance in type-2 fuzzy logic systems research and should\ud increase the level of interest in the modeling applications of general type-2 fuzzy logic\ud systems, despite their greater computational load

Twitter sentiment analysis is a challenging problem in natural language processing. For this purpose, supervised learning techniques have mostly been employed, which require labeled data for training. However, it is very time consuming to label datasets of large size. To address this issue, unsupervised learning techniques such as clustering can be used. In this study, we explore the possibility of using hierarchical clustering for twitter sentiment analysis. Three hierarchical-clustering techniques, namely single linkage (SL), complete linkage (CL) and average linkage (AL), are examined. A cooperative framework of SL, CL and AL is built to select the optimal cluster for tweets wherein the notion of optimalcluster selection is operationalized using majority voting. The hierarchical clustering techniques are also compared with k-means and two state-of-the-art classifiers (SVM and Naïve Bayes). The performance of clustering and classification is measured in terms of accuracy and time efficiency. The experimental results indicate that cooperative clustering based on majority voting approach is robust in terms of good quality clusters with tradeoff of poor time efficiency. The results also suggest that the accuracy of the proposed clustering framework is comparable to classifiers which is encouraging.

Solar energy is considered as one of the main sources for renewable energy in the near future. However, solar energy and other renewable energy sources have a drawback related to the difficulty in predicting their availability in the near future. This problem affects optimal exploitation of solar energy, especially in connection with other resources. Therefore, reliable solar energy prediction models are essential to solar energy management and economics. This paper presents work aimed at designing reliable models to predict the global horizontal irradiance (GHI) for the next day in 8 stations in Saudi Arabia. The designed models are based on computational intelligence methods of automated-design fuzzy logic systems. The fuzzy logic systems are designed and optimized with two models using fuzzy c-means clustering (FCM) and simulated annealing (SA) algorithms. The first model uses FCM based on the subtractive clustering algorithm to automatically design the predictor fuzzy rules from data. The second model is using FCM followed by simulated annealing algorithm to enhance the prediction accuracy of the fuzzy logic system. The objective of the predictor is to accurately predict next-day global horizontal irradiance (GHI) using previous-day meteorological and solar radiation observations. The proposed models use observations of 10 variables of measured meteorological and solar radiation data to build the model. The experimentation and results of the prediction are detailed where the root mean square error of the prediction was approximately 88% for the second model tuned by simulated annealing compared to 79.75% accuracy using the first model. This results demonstrate a good modeling accuracy of the second model despite that the training and testing of the proposed models were carried out using spatially and temporally independent data.

Solar radiation prediction is an important process in ensuring optimal exploitation of solar energy power. Numerous models have been applied to this problem, such as numerical weather prediction models and artificial intelligence models. However, well-designed hybridization approaches that combine numerical models with artificial intelligence models to yield a more powerful model can provide a significant improvement in prediction accuracy. In this paper, novel hybrid machine learning approaches that exploit auxiliary numerical data are proposed. The proposed hybrid methods invoke different machine learning paradigms, including feature selection, classification, and regression. Additionally, numerical weather prediction (NWP) models are used in the proposed hybrid models. Feature selection is used for feature space dimension reduction to reduce the large number of recorded parameters that affect estimation and prediction processes. The rough set theory is applied for attribute reduction and the dependency degree is used as a fitness function. The effect of the attribute reduction process is investigated using thirty different classification and prediction models in addition to the proposed hybrid model. Then, different machine learning models are constructed based on classification and regression techniques to predict solar radiation. Moreover, other hybrid prediction models are formulated to use the output of the numerical model of Weather Research and Forecasting (WRF) as learning elements in order to improve the prediction accuracy. The proposed methodologies are evaluated using a data set that is collected from different regions in Saudi Arabia. The feature-reduction has achieved higher classification rates up to 8.5% for the best classifiers and up to 15% for other classifiers, for the different data collection regions. Additionally, in the regression, it achieved improvements of average root mean square error up to $5.6\%$ and in mean absolute error values up to 8.3%. The hybrid models could reduce the root mean square errors by 70.2% and 4.3% than the numerical and machine learning models, respectively, when these models are applied to some dataset. For some reduced feature data, the hybrid models could reduce the root mean square errors by 47.3% and 14.4% than the numerical and machine learning models, respectively.

In the present study, based on the multivariate nature of images from lung cancer, we extracted autoencoder, reconstruction independent component analysis (RICA) and sparse filters features along with traditional texture and morphological features. The machine learning techniques such as Support Vector Machine (SVM) with Gaussian, radial base function (RBF) and polynomial kernels, decision tree and Naïve Bayes were used classification purpose. The Jackknife 10-fold cross validation (CV) was used for training/testing data formulation and performance was evaluated in terms of sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), total accuracy (TA), false positive rate (FPR) and area under the receiving operating curve (AUC) to distinguish the small cell lung cancer (SCLC) from non-small cell lung cancer (NSCLC) images. The highest detection performance was obtained by extracting RICA and Sparse filters with 100% of sensitivity, specificity, PPV, NPV and TA using SVM with RBF, polynomial kernels and Naïve Bayes followed by Sparse filter with decision tree with TA (99.78%). The highest AUC of 1.00 was obtained by extracting RICA and Sparse filters using decision tree, SVM Gaussian, RBF and polynomial and Naïve Bayes. The results showed the good potential of these methods for lung cancer detection (LCD).