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The advancements of technology continuously rising over the years has seen many applications that are useful in providing users with sufficient information to make better journey plans on their own. However, commuters still find themselves going through congested routes every day to get to their destinations. This paper attempts to delineate the possibilities of improving urban mobility through big...
Forecasting the returns of stock markets is gaining importance nowadays in finance. For this aim, in the last decade, Artificial Neural Networks (ANN) have been widely used to forecast stock market movements. In Baltic countries, artificial neural networks are not commonly used in predicting financial failures. This study aims using artificial neural networks to predict OMX Baltic Benchmark GI (OMXBBGI)...
In this work, we conduct a systematic exploration on the promise and challenges of deep learning for the sparse matrix format selection. We propose a set of novel techniques to solve special challenges to deep learning, including input matrix representations, a late-merging deep neural network structure design, and the use of transfer learning to alleviate cross-architecture portability issues.
A spatial analysis of magnitude distribution is presented in this paper to identify the optimal number of clusters based on seismic data of all region in Indonesia. The data were obtained from Indonesian Agency for Meteorological, Climatological and Geophysics (BMKG) and United States Geological Survey's (USGS). Clustering process consist of two steps: finding the global optimum number of clusters...
Step change is a key factor affecting the user trajectory and distance, to determine the trajectory of the user is a common indoor positioning method based on inertial navigation line calculation model, the prediction step is mainly based on the linear sensor, acceleration sensor data and the movement of the periodic estimation of pedestrians every step of the displacement distance. In order to improve...
Anaerobic ammonium oxidation (anammox) process has been recognized as efficient biological nitrogen removal process, which has the advantages of cost-effective and low energy compared to the conventional nitrification and denitrification processes. However, the efficient operation and control is limited due to the complexity of nonlinear and biochemical phenomena involved. This paper proposes an appropriate...
With the incredible growth of OSNs (online social networks), users have numerous choices every moment. However, due to the limit of time and resources, only a small part of OSNs are chosen to remain social and active by users. The dynamic changes of users' interests entail user migration. Understanding user migration behavior is important to improve business intelligence and retain users. In this...
Network traffic prediction aims at predicting the subsequent network traffic by using the previous network traffic data. This can serve as a proactive approach for network management and planning tasks. The family of recurrent neural network (RNN) approaches is known for time series data modeling which aims to predict the future time series based on the past information with long time lags of unrevealed...
Tagging provides a convenient means to assign tokens of identification to research papers which facilitate recommendation, search and disposition process of research papers. This paper contributes a document centered approach for auto-tagging of research papers. The auto-tagging method mainly comprises of two processes:- classification and tag selection. The classification process involves automatic...
We study the task of unsupervised domain adaptation, where no labeled data from the target domain is provided during training time. To deal with the potential discrepancy between the source and target distributions, both in features and labels, we exploit a copula-based regression framework. The benefits of this approach are two-fold: (a) it allows us to model a broader range of conditional predictive...
The design of effective financial early warning algorithm is of great significance to the financial management of the company. The weak classification algorithm can be improved to a high classification algorithm with high recognition rate through the ensemble learning. The algorithm can overcome the drawback of low classification accuracy of single classifier. Therefore, this paper combines decision...
This paper presented the research on heat load prediction method of central heating system. The combined simulation data at Xi'an in January was used as the samples for training and predicting. This paper selected the daily average outdoor wind speed, the daily average outdoor temperature, date type, sunshine duration as input variables and the heating load value as output variable. After preprocessing...
This article presents a survey of 278 intelligence analysts' views of fully operational analytic technologies and their newly developed replacements. It was found that usability was an important concept in analysts' reasons for and against using analytic tools. The perceived usability of a tool was not necessarily indicative of its perceived usefulness. Analysts' decisions to recommend an analytic...
The electricity price is an uncertain and changeable entity, mostly depends on power generating source and consumer's power demands behavior. The problem arises when all the consumers try to avail specific low price time slot to activate their power demands. It ends up with energy congestion or system destabilization. A better strategy is, to forecast a day ahead price and update it instantly, whenever...
Recent work in video compression has shown that using multiple 2D transforms instead of a single transform in order to de-correlate residuals provides better compression efficiency. These transforms are tested competitively inside a video encoder and the optimal transform is selected based on the Rate Distortion Optimization (RDO) cost. However, one needs to encode a syntax to indicate the chosen...
In this work, the potential application of Artificial Neural Network (ANN) was studied to predict the absorption of Carbon Dioxide (CO2) in Ionic Liquid (IL) solutions over wide-ranging operating conditions. A few physical properties had been chosen as input data which were temperature, partial pressure of CO2, molecular weight, acentric value, critical temperature and critical pressure of IL. A sample...
Wearable electronic systems have been and will continue to be utilized in both civil and military uses. Strong noise environments derived from large vehicles (e.g. ships, aircrafts, or military tanks) seriously affect the quality of speech communications especially for wearable systems without hermetic packaging. Bone conduction technology through the acquisition of skull vibration is able to obtain...
A variety of applications (App) installed on mobile systems such as smartphones enrich our lives, but make it more difficult to the system management. For example, finding the specific Apps becomes more inconvenient due to more Apps installed on smartphones, and App response time could become longer because of the gap between more, larger Apps and limited memory capacity. Recent work has proposed...
Alzheimer's disease (AD) cannot be cured or slowed down with today's medication. Scientific studies have found that 1) the progression of AD is highly correlated to a cognition decline, 2) cognition drop is a precursor of Alzheimer's disease, and 3) making lifestyle changes and training the brain can slow down AD progression. This project aims to develop a predictive model to know the progression...
In this paper, we present artificial neural network (ANN) models to predict hard and soft-responses of three configurations of arbiter based physical unclonable functions (PUFs): standard, feed-forward (FF) and modified feed-forward (MFF). The models are trained using data extracted from 32-stage arbiter PUF circuits fabricated using IBM 32 nm HKMG process. The contributions of this paper are two-fold...
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