The purpose of this study was to evaluate the performance of artificial intelligence tools for the prediction of Salmonella presence and absence in agricultural surface waters based on the population of microbiological indicators (total coliform, generic Escherichia coli, and enterococci) and physicochemical attributes of water (air and water temperature, conductivity, ORP, pH, and turbidity). Previously collected data set from six agricultural ponds monitored for two growing seasons were used for analysis. Classification algorithms including artificial neural networks (ANNs), the nearest neighborhood algorithm (kNN), and support vector machines (SVM) were trained and tested with a 539-point data set for optimum prediction accuracy. Classification accuracy performances were validated with data set (400 samples) collected from different agricultural surface water sources. All tested algorithms yielded the highest accuracy around 75 ± 1% for generic E. coli followed by enterococci (65 ± 5%) and total coliform (60 ± 10%). Classifiers calculated 6-15% higher accuracy ranging from 62 to 66% for turbidity than all other tested physicochemical attributes.Based on E. coli populations measured in other water sources, trained algorithms predicted the presence and absence of Salmonella with an accuracy between 58.15 and 59.23%. The classification performance of ANN, kNN, and SVM algorithms are encouraging for the prediction of Salmonella in agricultural surface waters.

Emotion plays an important role in human interaction. People can explain their emotions in terms of word, voice intonation, facial expression, and body language. However, brain–computer interface (BCI) systems have not reached the desired level to interpret emotions. Automatic emotion recognition based on BCI systems has been a topic of great research in the last few decades. Electroencephalogram (EEG) signals are one of the most crucial resources for these systems. The main advantage of using EEG signals is that it reflects real emotion and can easily be processed by computer systems. In this study, EEG signals related to positive and negative emotions have been classified with preprocessing of channel selection. Self-Assessment Manikins was used to determine emotional states. We have employed discrete wavelet transform and machine learning techniques such as multilayer perceptron neural network (MLPNN) and k-nearest neighborhood (kNN) algorithm to classify EEG signals. The classifier algorithms were initially used for channel selection. EEG channels for each participant were evaluated separately, and five EEG channels that offered the best classification performance were determined. Thus, final feature vectors were obtained by combining the features of EEG segments belonging to these channels. The final feature vectors with related positive and negative emotions were classified separately using MLPNN and kNN algorithms. The classification performance obtained with both the algorithms are computed and compared. The average overall accuracies were obtained as 77.14 and 72.92% by using MLPNN and kNN, respectively.

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