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Measuring brain activity through Electroencephalogram (EEG) analysis for eye state prediction has attracted attention from machine learning researchers. There have been many methods for EEG analysis using supervised and unsupervised machine learning techniques. The tradeoff between the accuracy and computation time of these methods in performing the analysis is an important issue that is rarely investigated in the previous research. This paper accordingly proposes a new method for EEG signal analysis through Self-Organizing Map (SOM) clustering and Deep Belief Network (DBN) approaches to efficiently improve the computation and accuracy of the previous methods. The method is developed using SOM clustering and DBN, which is a deep layer neural network with multiple layers of Restricted Boltzmann Machines (RBMs). The results on a dataset with 14980 instances and 15 attributes representing the values of the electrodes demonstrated that the method is efficient for EEG analysis. In addition, compared with the other supervised methods, the proposed method was able to significantly improve the accuracy of the EEG prediction. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) is a complex neurodegenerative disease. Accurate diagnosis of this disease in the early stages is crucial for its initial treatment. This paper aims to present a comparative study on the methods developed by machine learning techniques in PD diagnosis. We rely on clustering and prediction learning approaches to perform the comparative study. Specifically, we use different clustering techniques for PD data clustering and support vector regression ensembles to predict Motor-UPDRS and Total-UPDRS. The results are then compared with the other prediction learning approaches, multiple linear regression, neurofuzzy, and support vector regression techniques. The comparative study is performed on a real-world PD dataset. The prediction results of data analysis on a PD real-world dataset revealed that expectation-maximization with the aid of SVR ensembles can provide better prediction accuracy in relation to decision trees, deep belief network, neurofuzzy, and support vector regression combined with other clustering techniques in the prediction of Motor-UPDRS and Total-UPDRS. [ABSTRACT FROM AUTHOR]

A genetic-based neural network ensemble (GNNE) is applied for estimation of daily soil temperatures (DST) at distinct depths. A sequential genetic-based negative correlation learning algorithm (SGNCL) is adopted to train the GNNE parameters. CLMS algorithm is used to achieve the optimum weights of components. Recorded data for two different stations located in Iran are used for the development of the GNNE models. Furthermore, the GNNE predictions are compared with the existing machine-learning models. The results demonstrate that GNNE outperforms other methods for the prediction of DSTs. [ABSTRACT FROM AUTHOR]

Programmers strive to design programs that are flexible, updateable, and maintainable. However, several factors such as lack of time, high costs, and workload lead to the creation of software with inadequacies known as anti-patterns. To identify and refactor software anti-patterns, many research studies have been conducted using machine learning. Even though some of the previous works were very accurate in identifying anti-patterns, a method that takes into account the relationships between different structures is still needed. Furthermore, a practical method is needed that is trained according to the characteristics of each program. This method should be able to identify anti-patterns and perform the necessary refactorings. This paper proposes a framework based on probabilistic graphical models for identifying and refactoring anti-patterns. A graphical model is created by extracting the class properties from the source code. As a final step, a Bayesian network is trained, which determines whether anti-patterns are present or not based on the characteristics of neighboring classes. To evaluate the proposed approach, the model is trained on six different anti-patterns and six different Java programs. The proposed model has identified these anti-patterns with a mean accuracy of 85.16 percent and a mean recall of 79%. Additionally, this model has been used to introduce several methods for refactoring, and it has been shown that these refactoring methods will ultimately create a system with less coupling and higher cohesion. • One of the major aims of programmers is to produce flexible programs that can be updated quickly and maintained easily. • A framework based on probabilistic graphical models is proposed for the identification and refactoring of anti-patterns. • The classes and their relationships are extracted from the source code. Then, they are mapped to a graphical model. • A Bayesian network is trained, which determines the probability of the presence or absence of anti-patterns. • The evaluations show that the proposed model has identified anti-patterns with an accuracy of 85.16% and a recall of 79%. [ABSTRACT FROM AUTHOR]

• A multi-criteria collaborative recommender system is proposed for MOOCs. • Text mining and fuzzy logic approaches are used for method development. • Data collection is performed in Udemy.com. • Numerical And textual data are analyzed for method evaluation. • The method is effective for MOOCs websites for course recommendations. Massive Open Online Courses (MOOCs) provide learners with high-quality and flexible online courses with no limitations regarding time and location. Detecting users' behavior in MOOCs is an important task for course recommendations. Collaborative Filtering (CF) is considered the widely approach in recommender systems to provide a online learner courses according to similar learners' preferences in an e-learning environment. The current research provides a novel framework through machine learning techniques to propose course recommendations in MOOCs according to the uses' preferences and behavior. The method is developed using multi-criteria ratings extracted from users' online reviews. We use Latent Dirichlet Allocation (LDA) for text mining, Decision Trees for decision rule generation, Self-Organizing Map (SOM) for users' reviews on courses and the fuzzy rule-based system for users' preferences prediction. We also adopt a feature selection method to select the most important criteria for users' preferences prediction. The method is evaluated using the data collected from an online learning platform, Udemy. The results showed that the method is able to accurately provide relevant courses to the users tailored to their preferences. The method has the potential to be implemented as a recommendation agent in the MOOC websites for course recommendations. [ABSTRACT FROM AUTHOR]

• Introducing combined fuzzy firefly algorithm and random forest (named FFA-RF). • Collecting a dataset by utilizing FFA for offline clustering in different applications. • Performing RF to learn the behavioral pattern of FFA in proper cluster-head selection. • Applying the trained FFA-RF model for online clustering in new unseen WSNs. Clustering in wireless sensor networks (WSNs) has proved to be one of the most efficient ways to hierarchically organize the network topology for the purposes of load-balancing and elongating the network lifetime. However, achieving optimal clustering in WSNs is an NP-hard problem, and consequently, heuristics and metaheuristics have been widely adopted. In this paper, a combined clustering technique based on a fuzzy-firefly algorithm (FFA) and random forest (RF), shortly named as: FFA-RF, is presented as an application-specific routing protocol for WSNs. Our FFA-RF protocol entails offline tuning and online routing phases: the offline phase consists of data collection using FFA, training and test of the RF, while the online phase is the actual application of the FFA-RF model to new network instances. In the offline phase, we construct an optimized fuzzy inference system based on FFA and apply it on different network topologies, to collect a comprehensive dataset. We then divide the resulting dataset into training and test sets to train and test the RF model. In the online phase, the trained RF model is used as an online clustering algorithm to estimate the fuzzy priority factor of the nodes for being cluster heads (CHs) in new network instances. To increase the generalizability of the RF for different configurations, node features as well as application features are used as inputs of the RF model. Simulation results for different network topologies demonstrate the superiority of the proposed FFA-RF protocol in prolonging the application-specific lifetime when compared against existing crisp heuristic, fuzzy heuristic, metaheuristic, and combined fuzzy-metaheuristic protocols. [ABSTRACT FROM AUTHOR]