Vision-based vehicle classification is a very challenging task due to vehicle pose and angle variations, weather conditions, lighting quality, and limited number of available datasets for training. It can be applied for driver assistance system and autonomous vehicles. This paper conducted a performance evaluation for this task based on three Convolutional Neural Network (CNN) models, which are simple CNN, and pre-trained CNN models that are AlexNet and GoogleNet. A dataset of more than 7000 images from the Image Processing Group (IPG) has been used for training and testing and the results indicate that AlexNet achieves the best classification result that is 65.09%. This result is obtained because of the variations of the quality of the images.
The small and medium enterprise (SME) companies optimize performance using different automated systems to highlight the operations concerns. However, lack of efficient visualization in reporting results in slow feedbacks, difficulties in extracting root cause, and minimal corrective actions. To complicate matters, the data heterogeneity has intensely increased, and it is produced in a fast manner making it unmanageable if the traditional methods of analytics are applied. Hence, we propose the use of a dashboard that can summarize the operational events using real-time data based on the data visualization approach. This proposed solution summarizes the raw data, which allows the user to make informed decisions that can give a positive impact on business performance. An interactive intelligent dashboard for SME (iid-SME) is developed to tackle issues such as measurement of cases completed, the duration of time needed to solve a case, the individual performance of handling cases and other tasks as a proof of concept. From the result, the implementation of the iid-SME approach simplifies the conveyance of the message and helps the SME personnel to make decisions. With the positive feedback obtained, it is envisaged that such a solution can be further employed for SME improvement for better profit and decision making.
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