<p class="0abstract">Smart devices like smartphones and smartwatches have made this world smarter. These wearable devices are created through complex research methodologies to make them more usable and interactive with its user. Various interactive mobile applications such as augmented reality (AR), virtual reality (VR) or mixed reality (MR) applications solely depend on the in-built sensors of the smart devices. A lot of facilities can be taken from these devices with sensors such as accelerometer and gyroscope. Different physical activities such as walking, jogging, sitting, etc., can be important for analysis like health state prediction and duration of exercise by using those sensors based on artificial intelligence. In this paper, we have implemented machine learning and deep learning algorithms to detect and recognize eight activities namely, walking, jogging, standing, walking upstairs, walking downstairs, sitting, sitting-in-a-car and cycling; with a maximum of 99.3% accuracy. A few activities are almost similar in action, such as sitting and sitting-in-a-car, but difficult to distinguish; which makes it more challenging to predict tasks. In this paper, we have hypothesized that with more sensors (sensor fusion) and data collection points (sensor-body positions) a wide range of activities can be recognized and the recognition accuracies can be increased. Finally, we showed that the combination of all the sensors data of both pocket/waist and wrist can be used to recognize a wide range of activities accurately. The possibility of using the proposed methodologies for futuristic mobile technologies is quite significant. The adaptation of most recent deep learning algorithms such as convolutional neural network (CNN) and bi-directional Long Short Time Memory (Bi-LSTM) demonstrated high credibility of the methods presented as experimentation.<strong></strong></p>
{ 1 shovon10, 2 shaon007, 3 samcit41, 4 hasank, 5 hasan, 6 mohiuddin}@iut-dhaka.ed Abstract-Social network sites (SNS's) have connected millions of users creating the social revolution. Users' social behavior influences them to connect with others with same mentality. Social networks are constituted because of its user or organizations common interest in some social emerging issues. The popular social networking sites are Facebook, Twitter, MySpace, Orkut, LinkedIn, Google plus etc. which are actually online social networking (OSN) sites. However, the large amount of online users and their diverse and dynamic interests possess great challenges to support recommendation of friends on SNS's for each of the users. In this paper, we proposed a novel friend recommendation framework (FRF) based on the behavior of users on particular SNS's. The proposed method is consisted of the following stages: measuring the frequency of the activities done by the users and updating the dataset according to the activities, applying FP-Growth algorithm to classify the user behavior with some criteria, then apply multilayer thresholding for friend recommendation. The proposed framework shows good accuracy for social graphs used as model dataset.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.