## 3 Traffic-Volume-Monitoring System
The initial stage of the algorithmic path realized over the resources of the WUoT mainframe was dedicated to image classification. Visual data processing procedures were developed using the Mathworks MATLAB programming environment. The core flow was dedicated to image processing with the use of the Artificial Intelligence (AI) algorithm of a Deep Convolutional Neural Network, [29] based on a native AlexNET plug-in for the MATLAB environment. In that stage, each individual vehicle's image was processed to address the ingest requirements of the AlexNET [30]. Next, each data module was processed as an individual classification flow that resulted in a list of the top five labels describing the analyzed vehicle (with a percentage of the credibility of classification). Amongst those labels were the following classes of vehicles: car, truck (lorry), bus, and others; moreover, optional sub-classes of certain vehicles were also stored. Finally, after the counting of categories, a proper, fully detailed data log file was written, as well as the crucial data depicted on-screen for the user. Table 1 presents the traffic-volume-monitoring-system efficiency. As for the presented efficiency metrics, one can perceive the deployed system to have real-time characteristics, with a reasonably high effectiveness when it came to the success rates of the appropriate classification of objects. The efficiency mentioned above was evaluated over a long-term, real-life-conditions testing campaign, with over four months' worth of measurement data. Table 1 presents measurement effectiveness data by means of tabular positioning. The table depicts both the time and accuracy averages of the specific parameters, namely the computational effectiveness and success rates of the video data stream analysis. The presented values (time averages) were calculated based upon a timestamp analysis (i.e., synchronized network data ingestion over specific network nodes) and algorithmic path logging (the final step performed on the WUoT Server). By means of the MATLAB environment's built-in metrics (tic-toc paradigm), the aggregated values for the time and success rates averages were calculated. As per the video data stream analysis, the performance of the algorithmic flow followed the real-time characteristics and simultaneously featured high effectiveness in terms of the success rates. Correlating data gathered via probability metrics (i.e., AlexNET preprocessing data stream for classification and tagging) with the base data layer (i.e., ground truth for the training set where detection and classification was performed manually), the average success rates were calculated. Both the successful detection of objects and vehicle classification achieved a level of over 95% in terms of the success-rate coefficient within the aforementioned data measurement window. Concerning the efficiency of the designed algorithm, a decision was made to utilize a deep learning approach. As an evolution of machine learning algorithms, a deep learning approach is based on end-to-end learning, where learning tasks follows directly from sets \begin{table}
\begin{tabular}{c c} \hline \hline
**Efficiency Features** & **Value** \\ \hline Average time for a single video frame processing & 28 ms \\ Average time for a complete detection and classification process & 980 ms \\ Average success rate for object detection within video stream & 97.1\% \\ Average success rate for vehicle classification within detected objects & 95.5\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Traffic-volume-monitoring system efficiency (pivotal parameters). of data images. Data is transformed into tasks (results of classification) based on learned features over the convolutional neural network (CNN) [31]. As the overall performance of the classification procedures in the contemporary deep learning mechanisms outperforms average human accuracy [32], it was of the utmost importance to empower the designed algorithm by means of the AlexNET module for the MATLAB environment.