Artificial neural networks (ANNs) have been successfully trained to model and predict the acidity constants (pK(a)) of 128 various phenols with diverse chemical structures using a quantitative structure-activity relationship. An ANN with 6-14-1 architecture was generated using six molecular descriptors that appear in the multi-parameter linear regression (MLR) model. The polarizability term (pi (I)), most positive charge of acidic hydrogen atom (q+), molecular weight (MW), most negative charge of the phenolic oxygen atom (q-), the hydrogen-bond accepting ability (epsilon(B)) and partial-charge weighted topological electronic (PCWTE) descriptors are inputs and its output is pK(a). It was found that a properly selected and trained neural network with 106 phenols could represent the dependence of the acidity constant on molecular descriptors fairly well. For evaluation of the predictive power of the ANN, an optimized network was used to predict the pK(a)s of 22 compounds in the prediction set, which were not used in the optimization procedure. A squared correlation coefficient (R2) and root mean square error (RMSE) of 0.8950 and 0.5621 for the prediction set by the MLR model should be compared with the values of 0.99996 and 0.0114 by the ANN model. These improvements are due to the fact that the pK(a) of phenols shows non-linear correlations with the molecular descriptors. [Figure: see text].

The objective of this study is to develop a method for identifying and discriminating ten potato varieties by combining machine vision and artificial neural network methods. The potato varieties include Agria, Emrad. A total number of 72 characteristic parameters specifying color, textural and morphological features are found among these varieties. By using principal component analysis (PCA), 16 principal features are selected for identifying and discriminating potato varieties. The data obtained from image processing were classified using linear discriminant analysis and nonlinear artificial neural network method. The accuracy of discriminant analysis were 73.3%, 93.3%, 73.3%, 40%, 73.3%, 73.3%, 66.7%, 80%, 40% and 53.3%, respectively for the varieties used in this study. The classification accuracy was improved by 100% for all the varieties using neural network analysis and the Correct Classification Ratio (CCR) was 100% using this method.A c c e p t e d M a n u s c r i p t 2 It is revealed from the results that machine vision technique and neural network analysis could identify potato varieties with acceptable accuracy.

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Network functions virtualization (NFV) is a new concept that has received the attention of both researchers and network providers. NFV decouples network functions from specialized hardware devices and virtualizes these network functions as software instances called virtualized network functions (VNFs). NFV leads to various benefits, including more flexibility, high resource utilization, and easy upgrades and maintenances. Despite recent works in this field, placement and chaining of VNFs need more attention. More specifically, some of the existing works have considered only the placement of VNFs and ignored the chaining part. So, they have not provided an integrated view of host or bandwidth resources and propagation delay of paths. In this paper, we solve the VNF placement and chaining problem as an optimization problem based on the particle swarm optimization (PSO) algorithm. Our goal is to minimize the required number of used servers, the average propagation delay of paths, and the average utilization of links while meeting network demands and constraints. Based on the obtained results, the algorithm proposed in this study can find feasible and high-quality solutions.

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