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Friend recommendation has been one of the most challenging problems as the social networks grow rapidly, due to the needs of seeking people who are acquaintances in real life or share the common interests. In this paper, we tackle the problem by treating it as a link prediction task and propose a hybrid algorithm that exploits the existing friendship links, users' history ratings and the tags annotated...
For online car-hailing dispatch, we have presented Long Short-Term Memory neural networks (called LSTM) to forecast supply-demand gap. It is a new creative thinking to apply deep networks to model gap volatility, incorporating weather information, traffic condition and point of interest (POI) data, as well as twelve previous returns. As far as we know, this paper is the first attempt to train LSTM...
In this paper, the Elastic Net method is applied to longitudinal data model which appears in network marketing. It not only makes us better understand the impact of big data on a variety of marketing activities, but also allows companies to better play its effectiveness. The Elastic Net estimation of longitudinal data model is established and proved that this model has the nature of group effect....
In this paper, we propose a novel image interpolation algorithm suitable for general scale enlargement. Different from previous AR-based interpolation algorithms which employ predetermined reference configuration to predict pixel values, we consider the context information when building AR models. Optimal references are selected by incorporating nonlocal-based correlation coefficient and the indicator...
Due to the significant contribution of air-conditioning load towards total energy consumption in residential buildings, accurate modelling and forecasting of such load is key to effective demand-side energy management programmes. This paper suggests a data driven framework for 15 min-ahead AC load forecasting based on modern machine learning techniques that includes Support Vector Regression, Ensemble...
Predicting change-prone object-oriented software using source code metrics is an area that has attracted several researchers attention. However, predicting change-prone web services in terms of changes in the WSDL (Web Service Description Language) Interface using source code metrics implementing the services is a relatively unexplored area. We conduct a case-study on change proneness prediction on...
Emergence of big data is directly proportional to the data shared in social media. Audio, video, text or the combination of all the above are the data shared in social media. Social networking is achieved by Social Networking Sites (SNS). In real world business, analysts use software tools to analyze product sales, promotion of brand and also tend to identify influential factors that impact their...
The predictive maintenance of industrial machines is one of the challenging applications in the new era of Industry 4.0. Thanks to the predictive capabilities offered by the emerging smart data analytics, data-driven approaches for condition monitoring are becoming widely used for early detection of anomalies on production machines. The aim of this paper is to provide insights on the predictive maintenance...
The goal of this paper is to study how friendship clusters evolve in online social networks. Results obtained from our work on Facebook data indicate that the set of friends who actively interact during a particular time interval is only a fraction of the total number of listed friends and this set of active friends tends to evolve with time. Interaction footprints on the timeline are used to cluster...
In developing countries, the issue of road accidents are a major concern. Increasing road traffic/vehicle occupancy could be the reason behind this. There is an increase in accidents over the years. It is very important to regulate traffic on roads to reduce accidents in accident prone zones. To reduce accidents, it is very important to analyze and identify such road accident prone features. Based...
Data mining is an emerging field of research in Information Technology as well as in agriculture. The present study focus on the applications of data mining techniques in tea plantations in the face of climatic change to help the farmer in taking decision for farming and achieving the expected economic return. This paper presents an analysis using data mining techniques for estimating the future yield...
Each and every company are interested to know about the status of performance in their business. Bankruptcy prediction is an important issue and it plays a vital role in creating decisions in the field of corporate and financial organization. It has its influence on both economic as well as social factors, e.g., investors, creditors, government, managers, employees, etc. So, it is important and useful...
The result of Chinese housing market continues to prosper or not is related to the development of China, and further it also has an impact on the world finance. Thus forecasting the house price index is very important and challenging. In this paper we propose an unsupervised learnable neuron model (DNM) by including the nonlinear interactions between excitation and inhibition on dendrites. We use...
Because of the volatility of memory, nodes in in-memory storage system crashing down would lead to data lost. One solution to this problem is backing data up. However, if we backup data to a node which is about to fail down, the data should be recopied again. That would lead to a large amount of backup data, and in turn reduce the system reliability. We first establish a correlated failure model with...
Stereotactic radiotherapy such as Cyberknife is one of the main methods of treatment for lung cancer, but tumor location change caused by human respiration has brought great difficulties to accurate radiation therapy. The main method to reduce the effect of respiratory motion in the process of radiotherapy is respiratory motion real-time tracking technology. The basis of real-time tracking is establishing...
Since the performance of educational institutions depends critically on their students, it is imperative that educational institutions deploy an efficient and reliable admission criteria. In the context of Pakistan, a variety of admission criteria has been developed—mostly in isolation—by different universities. Despite the importance of these admission criteria, limited systematic information exists...
Fixing some security failures are difficult because they cannot be easily reproduced. To address Hardly Reproducible Vulnerabilities (HRVs), security experts spend a significant amount of time, effort, and budget. Sometimes they do not succeed in the reproduction step and ignore some security failures. The exploitation of a vulnerability due to its irreproducibility may cause severe consequences....
We propose a traffic jam prediction method based on mining frequent patterns correlated to traffic jams. For traffic jam prediction at a given sensor, first, we apply a one-dimensional clustering scheme to identify automatically which sensors are and in what degree correlated to the given sensor in terms that certain volume values with a compact distribution co-occur frequently with the traffic jams...
User number prediction in cell phone base station is a very important problem for cell phone communication system design and base station location selection. Recent years, we have witnessed the encouraging potentials of deep neural networks for real-life applications of various domains. User number prediction, however, is still in its initial stage. In this paper, we propose a wavelet-based stacked...
Adverse effects, such as voice change and fatigue, are prevalent in cancer treatment duration. These adverse effects have been significant burden for patients physically and emotionally. Predicting multiple adverse effects becomes important for patients and oncologists. In this paper, we formulate the prediction of multiple adverse effects in cancer treatment as a longitudinal multiple-output regression...
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