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Algorithms, Volume 11, Issue 12 (December 2018)

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Open AccessArticle Decision Support Software for Forecasting Patient’s Length of Stay
Algorithms 2018, 11(12), 199; https://doi.org/10.3390/a11120199
Received: 11 October 2018 / Revised: 4 December 2018 / Accepted: 4 December 2018 / Published: 6 December 2018
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Abstract
Length of stay of hospitalized patients is generally considered to be a significant and critical factor for healthcare policy planning which consequently affects the hospital management plan and resources. Its reliable prediction in the preadmission stage could further assist in identifying abnormality or
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Length of stay of hospitalized patients is generally considered to be a significant and critical factor for healthcare policy planning which consequently affects the hospital management plan and resources. Its reliable prediction in the preadmission stage could further assist in identifying abnormality or potential medical risks to trigger additional attention for individual cases. Recently, data mining and machine learning constitute significant tools in the healthcare domain. In this work, we introduce a new decision support software for the accurate prediction of hospitalized patients’ length of stay which incorporates a novel two-level classification algorithm. Our numerical experiments indicate that the proposed algorithm exhibits better classification performance than any examined single learning algorithm. The proposed software was developed to provide assistance to the hospital management and strengthen the service system by offering customized assistance according to patients’ predicted hospitalization time. Full article
(This article belongs to the Special Issue Humanistic Data Mining: Tools and Applications)
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Open AccessArticle Damage Identification Algorithm of Hinged Joints for Simply Supported Slab Bridges Based on Modified Hinge Plate Method and Artificial Bee Colony Algorithms
Algorithms 2018, 11(12), 198; https://doi.org/10.3390/a11120198
Received: 12 November 2018 / Revised: 30 November 2018 / Accepted: 2 December 2018 / Published: 4 December 2018
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Abstract
Hinge joint damage is a typical form of damage occurring in simply supported slab bridges, which can present adverse effects on the overall force distribution of the structure. However, damage identification methods of hinge joint damage are still limited. In this study, a
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Hinge joint damage is a typical form of damage occurring in simply supported slab bridges, which can present adverse effects on the overall force distribution of the structure. However, damage identification methods of hinge joint damage are still limited. In this study, a damage identification algorithm for simply supported hinged-slab bridges based on the modified hinge plate method (MHPM) and artificial bee colony (ABC) algorithms was proposed by considering the effect of hinge damage conditions on the lateral load distribution (LLD) of structures. Firstly, MHPM was proposed and demonstrated, which is based on a traditional hinge plate method by introducing relative displacement as a damage factor to simulate hinge joint damage. The effectiveness of MHPM was verified through comparison with the finite element method (FEM). Secondly, damage identification was treated as the inverse problem of calculating the LLD in damage conditions of simply supported slab bridges. Four ABC algorithms were chosen to solve the problem due to its simple structure, ease of implementation, and robustness. Comparisons of convergence speed and identification accuracy with genetic algorithm and particle swarm optimization were also conducted. Finally, hinged bridges composed of four and seven slabs were studied as numerical examples to account for the feasibility and correctness of the proposed method. The simulation results revealed that the proposed algorithm could identify the location and degree of damaged joints efficiently and precisely. Full article
Open AccessArticle Parametric Estimation in the Vasicek-Type Model Driven by Sub-Fractional Brownian Motion
Algorithms 2018, 11(12), 197; https://doi.org/10.3390/a11120197
Received: 15 November 2018 / Revised: 30 November 2018 / Accepted: 30 November 2018 / Published: 4 December 2018
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Abstract
In the paper, we tackle the least squares estimators of the Vasicek-type model driven by sub-fractional Brownian motion: dXt=(μ+θXt)dt+dStH,t0 with X0
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In the paper, we tackle the least squares estimators of the Vasicek-type model driven by sub-fractional Brownian motion: d X t = ( μ + θ X t ) d t + d S t H , t 0 with X 0 = 0 , where S H is a sub-fractional Brownian motion whose Hurst index H is greater than 1 2 , and μ R , θ R + are two unknown parameters. Based on the so-called continuous observations, we suggest the least square estimators of μ and θ and discuss the consistency and asymptotic distributions of the two estimators. Full article
(This article belongs to the Special Issue Parameter Estimation Algorithms and Its Applications)
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Open AccessArticle Solon: A Holistic Approach for Modelling, Managing and Mining Legal Sources
Algorithms 2018, 11(12), 196; https://doi.org/10.3390/a11120196
Received: 31 October 2018 / Revised: 25 November 2018 / Accepted: 30 November 2018 / Published: 3 December 2018
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Abstract
Recently there has been an exponential growth of the number of publicly available legal resources. Portals allowing users to search legal documents, through keyword queries, are now widespread. However, legal documents are mainly stored and offered in different sources and formats that do
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Recently there has been an exponential growth of the number of publicly available legal resources. Portals allowing users to search legal documents, through keyword queries, are now widespread. However, legal documents are mainly stored and offered in different sources and formats that do not facilitate semantic machine-readable techniques, thus making difficult for legal stakeholders to acquire, modify or interlink legal knowledge. In this paper, we describe Solon, a legal document management platform. It offers advanced modelling, managing and mining functions over legal sources, so as to facilitate access to legal knowledge. It utilizes a novel method for extracting semantic representations of legal sources from unstructured formats, such as PDF and HTML text files, interlinking and enhancing them with classification features. At the same time, utilizing the structure and specific features of legal sources, it provides refined search results. Finally, it allows users to connect and explore legal resources according to their individual needs. To demonstrate the applicability and usefulness of our approach, Solon has been successfully deployed in a public sector production environment, making Greek tax legislation easily accessible to the public. Opening up legislation in this way will help increase transparency and make governments more accountable to citizens. Full article
(This article belongs to the Special Issue Humanistic Data Mining: Tools and Applications)
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Open AccessArticle Convex-Hull Algorithms: Implementation, Testing, and Experimentation
Algorithms 2018, 11(12), 195; https://doi.org/10.3390/a11120195
Received: 2 October 2018 / Revised: 22 November 2018 / Accepted: 23 November 2018 / Published: 28 November 2018
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Abstract
From a broad perspective, we study issues related to implementation, testing, and experimentation in the context of geometric algorithms. Our focus is on the effect of quality of implementation on experimental results. More concisely, we study algorithms that compute convex hulls for a
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From a broad perspective, we study issues related to implementation, testing, and experimentation in the context of geometric algorithms. Our focus is on the effect of quality of implementation on experimental results. More concisely, we study algorithms that compute convex hulls for a multiset of points in the plane. We introduce several improvements to the implementations of the studied algorithms: plane-sweep, torch, quickhull, and throw-away. With a new set of space-efficient implementations, the experimental results—in the integer-arithmetic setting—are different from those of earlier studies. From this, we conclude that utmost care is needed when doing experiments and when trying to draw solid conclusions upon them. Full article
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Open AccessArticle New and Efficient Algorithms for Producing Frequent Itemsets with the Map-Reduce Framework
Algorithms 2018, 11(12), 194; https://doi.org/10.3390/a11120194
Received: 26 September 2018 / Revised: 19 November 2018 / Accepted: 23 November 2018 / Published: 28 November 2018
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Abstract
The Map-Reduce (MR) framework has become a popular framework for developing new parallel algorithms for Big Data. Efficient algorithms for data mining of big data and distributed databases has become an important problem. In this paper we focus on algorithms producing association rules
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The Map-Reduce (MR) framework has become a popular framework for developing new parallel algorithms for Big Data. Efficient algorithms for data mining of big data and distributed databases has become an important problem. In this paper we focus on algorithms producing association rules and frequent itemsets. After reviewing the most recent algorithms that perform this task within the MR framework, we present two new algorithms: one algorithm for producing closed frequent itemsets, and the second one for producing frequent itemsets when the database is updated and new data is added to the old database. Both algorithms include novel optimizations which are suitable to the MR framework, as well as to other parallel architectures. A detailed experimental evaluation shows the effectiveness and advantages of the algorithms over existing methods when it comes to large distributed databases. Full article
(This article belongs to the Special Issue MapReduce for Big Data)
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Open AccessArticle A Forecast Model of the Number of Containers for Containership Voyage
Algorithms 2018, 11(12), 193; https://doi.org/10.3390/a11120193
Received: 20 October 2018 / Revised: 23 November 2018 / Accepted: 26 November 2018 / Published: 28 November 2018
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Abstract
Container ships must pass through multiple ports of call during a voyage. Therefore, forecasting container volume information at the port of origin followed by sending such information to subsequent ports is crucial for container terminal management and container stowage personnel. Numerous factors influence
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Container ships must pass through multiple ports of call during a voyage. Therefore, forecasting container volume information at the port of origin followed by sending such information to subsequent ports is crucial for container terminal management and container stowage personnel. Numerous factors influence container allocation to container ships for a voyage, and the degree of influence varies, engendering a complex nonlinearity. Therefore, this paper proposes a model based on gray relational analysis (GRA) and mixed kernel support vector machine (SVM) for predicting container allocation to a container ship for a voyage. First, in this model, the weights of influencing factors are determined through GRA. Then, the weighted factors serve as the input of the SVM model, and SVM model parameters are optimized through a genetic algorithm. Numerical simulations revealed that the proposed model could effectively predict the number of containers for container ship voyage and that it exhibited strong generalization ability and high accuracy. Accordingly, this model provides a new method for predicting container volume for a voyage. Full article
(This article belongs to the Special Issue Modeling Computing and Data Handling for Marine Transportation)
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Open AccessArticle A Study on Faster R-CNN-Based Subway Pedestrian Detection with ACE Enhancement
Algorithms 2018, 11(12), 192; https://doi.org/10.3390/a11120192
Received: 13 September 2018 / Revised: 10 November 2018 / Accepted: 21 November 2018 / Published: 26 November 2018
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Abstract
At present, the problem of pedestrian detection has attracted increasing attention in the field of computer vision. The faster regions with convolutional neural network features (Faster R-CNN) are regarded as one of the most important techniques for studying this problem. However, the detection
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At present, the problem of pedestrian detection has attracted increasing attention in the field of computer vision. The faster regions with convolutional neural network features (Faster R-CNN) are regarded as one of the most important techniques for studying this problem. However, the detection capability of the model trained by faster R-CNN is susceptible to the diversity of pedestrians’ appearance and the light intensity in specific scenarios, such as in a subway, which can lead to the decline in recognition rate and the offset of target selection for pedestrians. In this paper, we propose the modified faster R-CNN method with automatic color enhancement (ACE), which can improve sample contrast by calculating the relative light and dark relationship to correct the final pixel value. In addition, a calibration method based on sample categories reduction is presented to accurately locate the target for detection. Then, we choose the faster R-CNN target detection framework on the experimental dataset. Finally, the effectiveness of this method is verified with the actual data sample collected from the subway passenger monitoring video. Full article
(This article belongs to the Special Issue Deep Learning for Image and Video Understanding)
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Open AccessArticle MapReduce Algorithm for Location Recommendation by Using Area Skyline Query
Algorithms 2018, 11(12), 191; https://doi.org/10.3390/a11120191
Received: 29 October 2018 / Revised: 22 November 2018 / Accepted: 22 November 2018 / Published: 25 November 2018
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Abstract
Location recommendation is essential for various map-based mobile applications. However, it is not easy to generate location-based recommendations with the changing contexts and locations of mobile users. Skyline operation is one of the most well-established techniques for location-based services. Our previous work proposed
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Location recommendation is essential for various map-based mobile applications. However, it is not easy to generate location-based recommendations with the changing contexts and locations of mobile users. Skyline operation is one of the most well-established techniques for location-based services. Our previous work proposed a new query method, called “area skyline query”, to select areas in a map. However, it is not efficient for large-scale data. In this paper, we propose a parallel algorithm for processing the area skyline using MapReduce. Intensive experiments on both synthetic and real data confirm that our proposed algorithm is sufficiently efficient for large-scale data. Full article
(This article belongs to the Special Issue Algorithms for Decision Making)
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Open AccessArticle Best Trade-Off Point Method for Efficient Resource Provisioning in Spark
Algorithms 2018, 11(12), 190; https://doi.org/10.3390/a11120190
Received: 20 September 2018 / Revised: 11 November 2018 / Accepted: 16 November 2018 / Published: 22 November 2018
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Abstract
Considering the recent exponential growth in the amount of information processed in Big Data, the high energy consumed by data processing engines in datacenters has become a major issue, underlining the need for efficient resource allocation for more energy-efficient computing. We previously proposed
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Considering the recent exponential growth in the amount of information processed in Big Data, the high energy consumed by data processing engines in datacenters has become a major issue, underlining the need for efficient resource allocation for more energy-efficient computing. We previously proposed the Best Trade-off Point (BToP) method, which provides a general approach and techniques based on an algorithm with mathematical formulas to find the best trade-off point on an elbow curve of performance vs. resources for efficient resource provisioning in Hadoop MapReduce. The BToP method is expected to work for any application or system which relies on a trade-off elbow curve, non-inverted or inverted, for making good decisions. In this paper, we apply the BToP method to the emerging cluster computing framework, Apache Spark, and show that its performance and energy consumption are better than Spark with its built-in dynamic resource allocation enabled. Our Spark-Bench tests confirm the effectiveness of using the BToP method with Spark to determine the optimal number of executors for any workload in production environments where job profiling for behavioral replication will lead to the most efficient resource provisioning. Full article
(This article belongs to the Special Issue MapReduce for Big Data)
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