The robotics and automation industry which is ruled the sectors from manufacturing to household entertainments. It is widely used because of its simplicity and ability to modify to meet changes of needs. The project is designed to develop a robotic vehicle using android application for remote operation attached with wireless camera for monitoring purpose. The robot along with camera can wirelessly transmit real time video with night vision capabilities. This is kind of robot can be helpful for spying purpose in war fields.
User satisfaction on web sites depends on many factors and usability is one of these factors. A web site should be organized in a logical manner to aid the user in accomplishing their goal. This paper proposes an automated method for usability testing of a web site using machine-learning techniques to uncover usability issues related to the organization of the pages (information architecture). The information architecture was represented as a weighted directed graph. The graph features are used to train several machine learning models that can then be used to predict the usability score of another website. Models evaluated include classifiers of support vector, random forest, decision tree, regression models, etc. A number of machine learning models are evaluated to determine the best possible model for this specific use case, using 10-fold cross validation on various sized datasets. We also demonstrate a way of extracting a ranked list of prominent features to that can be improved.1
In recent years Deep Learning reached significant results in many practical problems, such as computer vision, natural language processing, speech recognition and many others. For many years the main goal of the research was to improve the quality of models, even if the complexity was impractically high. However, for the production solutions, which often require real-time work, the latency of the model plays a very important role. Current state-of-the-art architectures are found with neural architecture search (NAS) taking model complexity into account. However, designing of the search space suitable for specific hardware is still a challenging task. To address this problem we propose a measure of hardware efficiency of neural architecture search space -matrix efficiency measure (MEM); a search space comprising of hardware-efficient operations; a latency-aware scaling method; and ISyNet -a set of architectures designed to be fast on the specialized neural processing unit (NPU) hardware and accurate at the same time. We show the advantage of the designed architectures for the NPU devices on ImageNet (Figure 1) and the generalization ability for the downstream classification and detection tasks.• NPU-efficient search space design having high MEM value;
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