Water quality prediction is very important for both water resource scheduling and management. Simple linear regression analysis and artificial neural network models cannot accurately forecast water quality because of complicated linear and nonlinear relationships in the water quality dataset. An adaptive neuro-fuzzy inference system (ANFIS) that can integrate linear and nonlinear relationships has been proposed to address the problem. However, the ANFIS model can only work in scenarios where input and target parameters have strong correlations. In this paper, a fuzzy model integrated with a time series data analysis method is proposed to address the water quality prediction problem when the correlation between the input and target parameters is weak. The water quality datasets collected from the Las Vegas Wash between the years 2005 and 2010, and the Boulder Basin, Nevada-Arizona from the years 2011 to 2016 are used to test the proposed model. The prediction accuracy of the proposed model is measured by three different statistical indices: mean average percentage error, root mean square error, and coefficient of determination. The experimental results have proven that the ANFIS model combined with a time series analysis method achieves the best prediction accuracy for predicting electrical conductivity and total dissolved solids in the Las Vegas Wash, with the testing value of coefficient of determination reaching 0.999 and 0.997, respectively. The fuzzy time series analysis has the best performance for dissolved oxygen and electrical conductivity prediction in the Boulder Basin, and dissolved oxygen prediction in the Las Vegas Wash, with testing value of coefficients of determination equal to 0.990, 90975, and 0.960, respectively.

Recommender Systems (RS) help users in making appropriate decisions. In the area of RS research, many researchers focused on improving the performances of the existing methods, but most of them have not considered the potential of their employed methods in reaching the ultimate solution. In our view, the Machine Learning supervised approach as one of the existing techniques to create an RS can reach higher degrees of success in this field. Thus, we implemented a Collaborative Filtering recommender system using various Machine Learning supervised classifiers to study their performances. These classifiers implemented not only on a traditional platform but also on the Apache Spark platforms. The Caret package is used to implement the algorithms in the classical computational platform, and the H2O and Sparklyr are used to run the algorithms on the Spark Machine. Accordingly, we compared the performance of our algorithms with each other and with other algorithms from recent literature. Our experiments indicate the Caret-based algorithms are significantly slower than the Sparklyr and H2O based algorithms. Also, in the Spark platform, the runtime of the Sparklyr-based algorithm decreases with increasing the cluster size. However, the H2O-based algorithms run slower with increasing the cluster size. Moreover, the comparison of the results of our implemented algorithms with each other and with other algorithms from recent literature shows the Bayesian network is the fastest classifier between our implemented classifiers, and the Gradient Boost Model is the most accurate algorithm in our research. Therefore, the supervised approach is better than the other methods to create a collaborative filtering recommender system.

The tremendous advancements in digital technology pertaining to diverse application areas like medical diagnostics, crime detection, defense etc., has led to an exceptional increase in the multimedia image content. This bears an acute requirement of an effectual retrieval system to cope up with the human demands. Therefore, Content-based image retrieval (CBIR) is among the renowned retrieval systems which uses color, texture, shape, edge and other spatial information to extract the basic image features. This paper proposes an efficient and unexcelled hybrid color descriptor which is an amalgamation of color histogram, color moment and color auto-correlogram. In order to determine the predominance between machine learning and deep learning, two machine learning models, Support vector machine (SVM) and Extreme learning machine (ELM) have been tested. Whereas from deep learning category, Cascade forward back propagation neural network (CFBPNN) and Patternnet have been utilized. Finally, from these divergent tested algorithms, CFBPNN attains the highest accuracy and has been selected to enhance the retrieval accuracy of the proposed system. Numerous standard benchmark datasets namely Corel-1K, Corel-5K, Corel-10K, Oxford flower, Coil-100 and Zurich buildings have been tested here and average precision of 97.1%, 90.3%, 87.9%, 98.4%, 98.9% and 82.7% is obtained respectively which is significantly higher than many state-of-the-art related techniques.

The problem encountered in most metaheuristic methods is the choice of the good control parameters of the algorithm. That is the objective of this work by using an efficient sine cosine algorithm (ESCA) in optimal power flow problem. The sine-cosine algorithm (SCA) is a modern method applied in numerical optimization problems. It consists of search randomly the best vector of control variables from the initial group of elements and oscillates to converge to the global optimum or diverge from it, functioning with a simple formulation based on sine and cosine mathematical functions with few setting parameters. In the proposed efficient sine cosine Algorithm (ESCA) the best values of setting parameters are chosen to give the best optimum solution with fast convergence. This technique improves the quality of the solution by exploring more search domain than the SCA method. The modified algorithm has been applied to the classical IEEE 30-Bus network with various objective functions and constraints. To make the comparison of ESCA and different recent algorithms, present results show the importance of ESCA to give the best and effective solution to the multi-objective optimal power flow problem.

Recommender Systems (RSs) work as a personal agent for individuals who are not able to make decisions from the potentially overwhelming number of alternatives available on the World Wide Web (or simply Web). Neighborhood-based algorithms are traditional approaches for collaborative recommendations and are very popular due to their simplicity and efficiency. Neighborhood-based recommender systems use numerous kinds of similarity measures between users or items in order to achieve diverse goals for designing an RS such as accuracy, novelty, diversity etc. However, the existing similarity measures cannot manage well the data sparsity problems, which results in either very few co-rated items or absolutely no co-rated items. Furthermore, there are also situations where only the associations between users and items, such as buying/browsing behaviors, exist in form of unary ratings, a special case of ratings. In such situations, the existing similarity measures are either undefined or provide extreme values such as either 0 or 1. Thus, there is a compelling need to define a similarity measure that can deal with data sparsity problem and/or unary rating data. This article proposes a new similarity measure for neighborhood-based collaborative recommender systems based on Newton's law of universal gravitation. In order to achieve this, a new way of interpreting the relative mass as well as the relative distance has been taken into consideration by using the rating data from the user-item matrix. Finally, for evaluating the proposed approach against baseline approaches, several experiments have been conducted using standardized benchmark datasets such as MovieLens-100K and MovieLens-1M. Results obtained demonstrate that the proposed method provides better predictive accuracy in terms of RMSE and significantly improves the classification accuracy in terms of precision-recall.