This study evaluates the use of the Artificial Bee Colony (ABC) algorithm to optimize the Recurrent Neural Network (RNN) that is used to analyze traffic volume. Related studies have shown that Deep Neural Networks are superseding the Shallow Neural Networks especially in terms of performance. Here we show that using the ABC algorithm in training the Recurrent Neural Network yields better results, compared to several other algorithms that are based on statistical or heuristic techniques that were preferred in earlier studies. The ABC algorithm is an example of swarm intelligence algorithms which are inspired by nature. Therefore, this study evaluates the performance of the RNN trained using the ABC algorithm for the purpose of forecasting. The performance metric used in this study is the Mean Squared Error (MSE) and ultimately, the outcome of the study may be generalized and extended to suit other domains.Povzetek: Ocena uspešnosti algoritma umetne kolonije čebelje pri optimizaciji ponavljajoče se nevronske mreže.

Autotheft is a crime that can be mitigated using artificial intelligence as a scientific approach. In this case, we assess the drivers driving pattern using both deep neural network and swarm intelligence algorithms. From the analysis we are able to obtain the driving signature of the driver which can be associated with the vehicle. The vehicle is then tracked and monitored. Next, a deviation from the usual driving signature of the owner or assigned driver would signify a possible instance of autotheft. Subsequently, the vehicle can be traced and reclaimed by the owner. The algorithms are evaluated based on their performance in analysing the datasets bearing variable features. The variations in features enable us to verify the efficacy and accuracy levels of the various algorithms that are used in the study. The metrics used for evaluation are the Mean Squared Error and the F1 Score for precision, accuracy and recall functionality.

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