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Consumer reviews play an important role in various e-commerce sites like hotel reservation and app stores. Online consumer reviews are informative because they convey consumers' actual experiences and evaluations to the products and services they received. In this paper, we leverage the consumer reviews to develop a review-driven recommender service for e-commerce websites. We semantically explore...
Improving the quality of end-of-life care for hospitalized patients is a priority for healthcare organizations. Studies have shown that physicians tend to over-estimate prognoses, which in combination with treatment inertia results in a mismatch between patients wishes and actual care at the end of life. We describe a method to address this problem using Deep Learning and Electronic Health Record...
Continuous training is crucial for creating and maintaining the right skill-profile for the industrial organization's workforce. There is a tremendous variety in the available trainings within an organization: technical, project management, quality, leadership, domain-specific, soft-skills etc. Hence it is important to assist the employee in choosing the best trainings, which perfectly suits her background,...
Given a collection of basic customer demographics (e.g., age and gender) andtheir behavioral data (e.g., item purchase histories), how can we predictsensitive demographics (e.g., income and occupation) that not every customermakes available?This demographics prediction problem is modeled as a classification task inwhich a customer's sensitive demographic y is predicted from his featurevector x. So...
We propose a novel personalized recommendation model for social network users based on location computing. The novelty of our model is that we deal with the location based recommendation by combing logistic regression with collaborative filtering method. The logistic regression is used to train the weights of items' features, i.e., the recommendation sort list. On the other hand, the collaborative...
Nowadays, deep learning is very popular in a variety of research field due to its outperformance over the existing machine learning methods and its high generality over raw inputs. According to recent surveys, deep learning can give high performance in visual object recognition system. Human Action Recognition (HAR) is a promising research area over the computer vision research field due to its enormous...
Clinical research registries need to be driven by data quality to improve the outcome of clinical trials and to provide the possibility to facilitate new research initiatives. The International Niemann-Pick Disease Registry (INPDR) is one such example of a clinical research registry. Unlike other registries where data quality is largely based around best effort manual data entry, the INPDR registry...
This Paper reveals the information about Deep Neural Network (DNN) and concept of deep learning in field of natural language processing i.e. machine translation. Now day's DNN is playing major role in machine leaning technics. Recursive recurrent neural network (R2NN) is a best technic for machine learning. It is the combination of recurrent neural network and recursive neural network (such as Recursive...
The availability of mobile access has shifted social media use. With that phenomenon, what users shared on social media and where they visited is naturally an excellent resource to learn their visiting behavior. Knowing visit behaviors would help market survey and customer relationship management, e.g., sending customers coupons of the businesses that they visit frequently. Most prior studies leverage...
The research training that technical communication undergraduates receive remains an under examined but never more timely topic of discussion. The skills a practicing technical communicator must possess is quickly expanding. In particular, technical communicators require data collection, curation, and analysis competences. In our paper, we offer three case histories that illustrate how to increase...
Action recognition from video streams is among the active research topics in computer vision. The challenge is on the identification of the actions robustly regardless of the variations imposed by appearances of actions performed by different people. The challenge increases when the data is gathered from an outdoor environment, i.e. background and illumination variations. This paper proposes a Hidden...
Impacts are one of the main causes of damage in composite panels. The determination of the impact location and the reconstruction of impact force are necessary to evaluate the health of the structure. These data may be measured indirectly from the measurements of responses of sensors located on the system subjected to the impact. In this study, a composite panel model developed in Abaqus/CAE is first...
Opponent modeling is an essential approach for building competitive computer agents in imperfect information games. This paper presents a novel approach to accelerate the convergence process in opponent modeling. The approach applies neural network (ANN) to abstract and build an endgame data set of imperfect information game. Based on a labeled database of author's previous work, several parameters...
The Biodiversity Heritage Library (BHL) is a consortium of major natural history museum libraries, botanical libraries, and research institutions that cooperate to digitize and make accessible the legacy biodiversity literature. Through an Institute of Museum and Library Services (IMLS)-funded grant called Expanding Access to Biodiversity Literature (EABL), BHL has adapted its digitization and metadata...
The budgeted information gathering problem — where a robot with a fixed fuel budget is required to maximize the amount of information gathered from the world — appears in practice across a wide range of applications in autonomous exploration and inspection with mobile robots. Although there is an extensive amount of prior work investigating effective approximations of the problem, these methods do...
The research on computer game with complete information theory has been mature. The computer has established a more obvious advantage in the competition with human in game projects such as Chess and Go. Computer game with incomplete information mainly refers to the name of the other party or the position of the pieces is not clear, or the number of points in the other hand is unknown, which makes...
Stream multi-class imbalance learning in smart home applications is an evolving learning area that incorporates the challenges of both multi-class imbalance and stream learning. Moreover, another argument in the learning from the imbalanced multi-class distributions that cause misleading classification outcomes, is the imbalanced ratio in a sensor data stream which is vigorously changing. Due to the...
Software developers use continuous integration to find defects in the early stage and reduce risk. But this process can be resource and time consuming, which decreases the efficiency of development. In this work, we adopt cascaded classifiers to predict the build outcome and study what kinds of attributes are potentially useful for this process. We emphasize on the "failed" instances which...
Bitcoin is the most famous cryptocurrency currently operating with a total marketcap of almost 7 billion USD. This innovation stands strong on the feature of pseudo anonymity and strives on its innovative de-centralized architecture based on the Blockchain. The Blockchain is a distributed ledger that keeps a public record of all the transactions processed on the bitcoin protocol network in full transparency...
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