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Predictive Analytics analyze the present and the historical informations and make future predictions utilizing data mining or machine learning techniques. Predictive models usually check for some patterns and relationships leading to certain behaviours based on the dependent variables. This paper proposes a mechanism named Analysis and Prediction of Application Usage (APAU) in Android Phones for providing...
With the completion of the Human Genome Project, proteomics research has become one of the most important topics in the fields of life science and natural science. The project determined that proteins participate in life activities mainly in the form of complexes. At present, research on protein-protein interaction networks (PPINs) have mainly focused on detecting protein complexes or function modules...
Information needs of the users have grown exponentially with the advent of advancements in information and communication technology. The traditional ways of searching information from the online resources has been evolved and the tendency is geared more towards getting quality contents. In healthcare domain, the clinical researchers and physicians are even more interested to find quality information...
Over the last few years, deep learning has produced breakthrough results in many application fields including speech recognition, image understanding and so on. We try to deep learning techniques for real-time facial expression recognition instead of hand-crafted feature-based methods. The proposed system can recognize human emotions based on facial expressions using a webcam. It can detect faces...
Objective image quality assessment plays an important role in various image processing applications, where the goal of this process is to automatically evaluate the image quality in agreement with human visual perception. In this paper, we propose three different nonlinear learning approaches in order to design image quality assessment models, which serve to predict the perceived image quality. The...
Analysis of emotion in speech is manifested by the analysis of the vocal behavior of the nonverbal aspect of the speech. The basic assumption is that there is a set of objectively measurable voice parameters called prosodic aspects of speech, which can be assessed through computerized acoustical analysis. In this paper, we report results on recognizing emotional states (happy, sad, angry) from a corpus...
Automated human facial image de-identification is a much needed technology for privacy-preserving social media and intelligent surveillance applications. Other than the usual face blurring techniques, in this work, we propose to achieve facial anonymity by slightly modifying existing facial images into "averaged faces" so that the corresponding identities are difficult to uncover. This approach...
Social networking websites are considered as major sources of opinions and views of the public on the prevalent social issues at a given point in time. Websites like the Twitter1 reflect the public views through its millions of messages posted by its users world wide, whenever a controversial issue arises in the society. It is during this time that we observe significant amount of malicious, violent...
The production difficulty of the DRAM family has rapidly increasing. Several factors can be adduced to explain. For example, increasing of step in the production process by the miniaturization of semiconductor process, challenges of Low-power designed due to the growth of mobile family. And the two are connected by a combination of the result. It may increase the difficulty of an analysis of the test...
Over the past decade, the field of Cloud Computing has been the focus of intensive research. This paper proposes 'A Public Cloud based SOA Workflow for Machine Learning based Recommendation Algorithms' to build a framework that will simulate the architectural setup of a cloud environment and examine how it can leverage Apriori and sequential pattern based recommendation algorithms using R. Furthermore,...
Recent automatic facial expression recognition research has focused on optimizing performance on a few databases that were collected under controlled pose and lighting conditions, and has produced nearly perfect accuracy. This paper explores the necessary characteristics of the training dataset, feature representations and machine learning algorithms for a system that operates reliably in more realistic...
Every day numerous new vulnerabilities and exploits are reported for a wide variety of different software configurations. There is a big need to be able to quickly assess associated risks and sort out which vulnerabilities that are likely to be exploited in real-world attacks. A small percentage of all vulnerabilities account for almost all the observed attack volume. We use machine learning to make...
Mining frequent patterns is a crucial task in data mining. Most of the existing frequent pattern mining methods find the complete set of frequent patterns from a given dataset. However, in real-life scenarios we often need to predict the future frequent patterns for different tasks such as business policy making, web page recommendation, stock-market behavior and road traffic analysis. Predicting...
In this paper we address the challenge of performing face recognition on human faces that are wearing glasses. This is a common problem for face recognition and automatic identity checking at airports, as passengers frequently forget to remove their glasses when passing through customs. In order to solve this problem, we first propose an automatic glasses presence detection model based on the tree-pictorial-structured...
We demonstrate a computational method to predict theclinical phenotypes of a patient from raw metagenomic sequenceread data. We compared two state of the art programs forannotating the sequence data, UCLUST and Kraken, and usingtheir output for feature generation. We apply these programs to aset of over 1.3 million reads from 904 patients, some of whom haveliver cirrhosis, encephalopathy due to liver...
Deep learning is a multilayer neural network learning algorithm which emerged in recent years. It has brought a new wave to machine learning, and making artificial intelligence and human-computer interaction advance with big strides. We applied deep learning to handwritten character recognition, and explored the two mainstream algorithm of deep learning: the Convolutional Neural Network (CNN) and...
In recent times, the availability of inexpensive image capturing devices such as smartphones/tablets has led to an exponential increase in the number of images/videos captured. However, sometimes the amateur photographer is hindered by fences in the scene which have to be removed after the image has been captured. Conventional approaches to image de-fencing suffer from inaccurate and non-robust fence...
Although there has been much work done in the industry and academia on developing the theory and application of social networks as well as recommender systems, the relation between these research areas is still unclear. An innovative idea, which enables to integrate these areas, and applies recommendation systems to the big data systems, is proposed in this paper. Recommendation systems for machine...
Although rigorous clinical studies are required before a drug is placed on the market, it is impossible to predict all side effects for the approved medication. The United States Food and Drug Administration actively monitors approved drugs to identify adverse events. The FDA Adverse Event Reporting System (FAERS) contains a database of adverse drug events reported by the healthcare providers and...
the objective to develop clinical decision support system (CDSS) tools is to help physicians making faster and more reliable clinical decisions. The first step in their development is choose a machine learning classifier as the system core. Previous works reported implementation of artificial neural networks, support vector machines, genetic algorithms, etc. as core classifiers for CDSS; however,...
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