Recently, by using the deep learning models, it has become easier to recognize the human activity with more accuracy than before by categorizing the activities that people are doing daily. Nowadays, with the extensive use of modern smartphones that have sensors, it has become easier to capture the data in raw format that has the movement details in three dimensions (X-Y-Z). In this paper, we utilized the open source WIreless Sensor Data Mining (WISDM) dataset which has six activities that are walking, jogging, standing, sitting, upstairs and downstairs. Each type of those activities consists of values in terms of (X, Y and Z) axes. We employed two types of deep learning algorithms that are Convolutional Neural Network (CNN) and Recurrent Neural Network-Long Short-Term Memory (RNN-LSTM). Our objective is to make a comparison between accuracy and loss after implementing the two models. We discovered that, when using the Convolutional Neural Network (CNN), the accuracy was 81%. However, the accuracy was 91% when using Recurrent Neural Network-Long Short-Term Memory (RNN-LSTM) and applying it on the same database. As a result, the Recurrent Neural Network-Long Short-Term Memory (RNN-LSTM) model outperformed the Convolutional Neural Network (CNN) model.
Recently, scientists are paying more attention to the Organic Light Emitting Diode (OLED) technology as it is being used in devices and displays to play videos and show photos with high resolution. This technology is used in products such as mobile phones, televisions, laptops, etc. To make the energy consumed less, new methods were shown up to prevent high energy consumption while presenting videos and photos on OLED devices and displays without losing their details and quality, one of the methods is a deep learning-based technique which is related to artificial intelligence. In this review paper, the last methods were discussed as well as their results. Saturation, brightness, contrast, and luminance are factors that impacting energy consumption. In terms of OLED mobile phones, there were a few studies that concentrated on turning off the unnecessary pixels which will be black as default, and as a result, the lifetime of batteries will be extended. Also, for OLED mobile phones, a web browser called Chameleon was presented as it has some modes to save the energy consumed while surfing the internet by remapping the displayed colors of the website.
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