Since the personal computer usage and internet bandwidth are increasing, e-learning systems are also widely spreading. Although e-learning has some advantages in terms of information accessibility, time and place flexibility compared to the formal learning, it does not provide enough face-to-face interactivity between an educator and learners. In this study, we are proposing a hybrid information system, which is combining computer vision and machine learning technologies for visual and interactive e-learning systems. The proposed information system detects emotional states of the learners and gives feedback to an educator about their instant and weighted emotional states based on facial expressions. In this way, the educator will be aware of the general emotional state of the virtual classroom and the system will create a formal learning-like interactive environment. Herein, several classification algorithms were applied to learn instant emotional state and the best accuracy rates were obtained using kNN and SVM algorithms.

Wireless Sensor Networks (WSN) are self-configurable adhoc networks. To set up a seamlessly interoperable a WSN, a wireless sensor device technology called ZigBee is used. Moreover, ZigBee is an IEEE 802.15.4 based specification for a group of high-level communication protocols used to build area networks having both small range and wide range coverage. The design of the communication infrastructure and hardware components are crucial in the large-scale ZigBee networks in order to ensure communication efficiency in the real world applications. This study presents a performance analysis of a large-scale ZigBee networks for three different topologies comparatively. The performance of ZigBee devices has been analyzed in the network simulator OPNET in terms of total end-to-end delay, MAC throughput and MAC load.