2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)

Conference Information
Name: 2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)
Location: Vancouver, Canada
Date: 2018-11-1 - 2018-11-3

Articles from this conference

Jing Zhang, Dongdong An, Tianchi Zhang, Xiang Gao
The swinging posture of floating objects plays an important role in improving the simulation degree of oceanic scenes. In this paper, we study the influence factors of swinging posture of floating objects in oceanic scenes, including the environment and its own influences. For the problem that the traditional floating model only applies to regular objects, such as boxes, we propose a floating model with multi-center force, including the confirmation of the center of the corner block, the distribution of weights, the vector synthesis of forces, and the determination of resistance problems. This model analyzes the shape of floating objects and allocates a branch center of gravity for the more prominent corner blocks, which makes the corner block contribute to the swing posture. In this way, the swing posture of irregular floating objects is more in line with reality. Through experimental comparison, a floating model based on multi-center force shows swinging posture which is more consistent with practical mechanics than the traditional single-center floating model on the irregular floating objects with more corner blocks.
Siddhartha Haldar, Ruptirtha Mukherjee, Pushpak Chakraborty, Shayan Banerjee, Shreyaasha Chaudhury, Sankhadeep Chatterjee
Early and reliable detection of neurological disorders is important for effective treatment of patients. In spite of reasonable amount of research done in the field of early detection of epileptic seizure, still an effective model for predicting the same is absent. Motivated by this, in the current study the class imbalance problem associated with classification of patients into healthy and epilepsy affected ones is addressed. Two well established algorithms namely Synthetic Minority Oversampling Technique (SMOTE) and Selective Pre-Processing of Imbalanced Data Algorithm (SPIDER) have been used in order to combat the imbalanced classes. Afterwards, three different classifiers namely KNN, SVM and MLP-FFN have been used for the classification task. Experimental results revealed that addressing imbalances classes improved the classification accuracy to a greater extent.
, , Farhana Zulkernine, Pete Nicholls
Detecting patterns in real time streaming data has been an interesting and challenging data analytics problem. With the proliferation of a variety of sensor devices, real-time analytics of data from the Internet of Things (IoT) to learn regular and irregular patterns has become an important machine learning problem to enable predictive analytics for automated notification and decision support. In this work, we address the problem of learning an irregular human activity pattern, fall, from streaming IoT data from wearable sensors. We present a deep neural network model for detecting fall based on accelerometer data giving 98.75 percent accuracy using an online physical activity monitoring dataset called “MobiAct”, which was published by Vavoulas et al. The initial model was developed using IBM Watson studio and then later transferred and deployed on IBM Cloud with the streaming analytics service supported by IBM Streams for monitoring real-time IoT data. We also present the systems architecture of the real-time fall detection framework that we intend to use with Mbientlab's wearable health monitoring sensors for real time patient monitoring at retirement homes or rehabilitation clinics.
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