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The design of a recommender system is largely influenced by its domain of application. A recommender system for niche application requires more accuracy as it targets a specific audience or a specific genre of products to recommend. Certain examples of niche domains include course recommendation for university courses, text recommendation for translators etc. In this paper, we address the problem...
Hospital readmissions within 30 days after discharge are costly and it has been a prior for researchers to identify patients at risk of early readmission. Most of the reported hospital readmission prediction models have been built with historical data and thus can outdate over time. In this work, a self-adaptive 30-day diabetic hospital readmission prediction model has been developed. A diabetic inpatient...
The software composition using high-granularity entities nowadays is a common practice. The process of software composition is supported by various CASE tools. First tools were made on the basis of very simple formalisms (e.g. intuitionistic propositional logic). During the years the tools evolved to more efficient ones, which are able to deal with concurrency, multiparty sessions and other advanced...
Personalized recommendations can effectively solve the data explosion problem in network. Most existing works utilize rating information to reduce the score prediction error, e.g. MAE; however, users prefer a list of top-k items and minimizing MAE does not always result in better top-k item lists. Meanwhile, because of data sparse problem, social connections among users play an increasingly important...
We develop a cache-efficient RNA folding algorithm, ByBox, that is based on Zuker's method. Using a simple LRU cache model, we show that the traditional implementation, Zuker, of Zuker's method has a much higher number of cache misses than ByBox. Extensive experiments conducted on the Xeon E5 server show that cache efficiency translates into time and energy efficiency. Our benchmarking shows that,...
We consider the problem of link prediction in dynamic networks under the condition of a set of snapshots of the networks. To address the nonlinear transitional patterns in network structures, we propose an approach that incorporates the historical linkage and neighboring information into the restricted Boltzmann machine (RBM) model by adding temporal and neighboring connections between the hidden...
This paper presents a novel method to predict future human activities from partially observed RGB-D videos. Human activity prediction is generally difficult due to its non-Markovian property and the rich context between human and environments. We use a stochastic grammar model to capture the compositional structure of events, integrating human actions, objects, and their affordances. We represent...
Filtering recommendation system is always key and hot point in electronic commerce research; to obtain recommendation result with high accuracy, performance, universality and strong adaptation, improve recommended efficiency and veracity of collaborative filtering recommendation system and provide more personalized recommendation service for users, a kind of collaborative filtering recommendation...
Selecting relevant features in data modeling is critical to ensure effective and accurate prediction of future effects. The problem becomes compounded when the relevance of previously selected features cannot be guaranteed due to changes in the underlying dataset. We propose an algorithm based on the statistical plaid model for the discovery and tracking of feature relevance scores in datasets that...
Cooperative spectrum sensing is a powerful sensing approach which is based on sharing information about channel activities among secondary users (SUs). Cooperative spectrum sensing aims to overcome hidden node problem, shadowing and fading problems, it also enhances sensing accuracy. However, sensing accuracy may degrade due to various reasons: if environmental properties are poor or intra-node characteristics...
A cyber-physical system (CPS) approach for branch and bound algorithm based direct model predictive control (BnB-DMPC) for grid-tied Active Front End (AFE) power converters is proposed in this work. The proposed control strategy deals with both the physical states and a cyber state, i.e. the sampling frequency. With the help of cyber controller, the cyber resource and sampling rate are regulated based...
Based on Cloud Computing, Networked Control Systems and Internet of Things, the concept of Cloud Control Systems is proposed to handle complex and high-intelligent systems with mass data. This paper is concerned with cloud control systems based on data-driven predictive control, which is designed by applying the subspace matrixes method. The proposed algorithm can provide accurate real-time control...
MicroRNAs (miRNAs) are a class of small noncoding RNAs which have close relations with human diseases. Herein, predicting the novel associations between human diseases and miRNAS is urgently needed. However, only use of the experimental approaches to identity such relations have many choke points such as time-consumption and high cost. In this study, we adopt a network-based inference (NBI) based...
Recent meta-learning approaches are oriented towards algorithm selection, optimization or recommendation of existing algorithms. In this paper we show how Inductive algorithms constructed from building blocks on small data sub-sample can be scaled up to model large data sets. We demonstrate how one particular template (simple ensemble of fast sigmoidal regression models) outperforms state-of-the-art...
Web browsing response times are affected by the heterogeneous nature of Internet: links with disparate bandwidth, servers and routers with diverse specifications and network segments with particular traffic shaping policies. Also, on a daily basis, several new information systems get connected to the Internet consequently increasing traffic. Aiming to reduce response times, prefetching systems predict...
Patient admitted with acute decompensated heart failure (ADHF) facing with high risk of mortality where 30 day mortality rates are reaching 10%. Identifying patient with high and low risk of mortality could improve clinical outcomes and hospital resources allocation. This paper proposed the use of artificial neural network to predict mortality for the patient admitted with ADHF. Results show that...
Crowd-sourced reviews are used daily by potential customers to learn relevant information about a business. While textual reviews have become prominent in many recommendation-based systems, the inclusion of images can significantly increase the effectiveness of a review. However, it is difficult to verify the accuracy and usefulness of the information provided by a contributing user. In this paper,...
We address the problem of selecting, from a given dictionary, a subset of predictors whose linear combination provides the best description for the vector of measurements. To this end, we apply the well-known matching pursuit algorithm (MPA). Even if there are theoretical results on the performance of MPA, there is no widely accepted rule for stopping the algorithm. In this work, we focus on stopping...
The efficient market hypothesis (EMH) affirms that asset prices should reveal all available information. Therefore, it is impossible to "beat the market" always on a risk-adjusted basis since market prices should only respond to new information. Here, we propose a new model to validate the EMH that is inspired on an elastic network model. More specifically, we apply this comparison to Foreign...
Machine Learning or Artificial Intelligence basically involves tasks of modifying and supervising problems taken as vectors in multi-dimensional space. The Primitive algorithms which are used take Polynomial Time for computing such vector problems which are not fruitful for us, on the other hand, Quantum algorithms have the capability to solve such vector problems in a considerable amount of time...
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