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A new algorithm for a compact mobile four-hydrophone array “RAPID Array” that displays the dolphin's real-time direction data on a graphical user interface (GUI) was proposed to accurately estimate the number of Ganges river dolphins from acoustic data recorded during the census. By considering the trajectory changes in dolphins' click data depending on the time (t) and azimuth (θ) in the coordinate...
With the rapid growth of online content consumption, knowing end-users and having actionable content insights has become extremely important for any online content provider. Insights from user segment identification could help in developing a content recommendation as well as new content acquisition. For advertisers, identifying segments could assist in designing ad campaigns with greater target accuracy...
With the rapid advances in digital technology, the multimedia documents have been growing ubiquitously. The analysis of this huge repository of multimedia documents requires efficient organization of documents. Multimedia document clustering organizes the multimedia documents with common multimedia topics. The important step of multimedia document clustering is computing the similarity between multimedia...
Incorporating user characteristics and contextual information has shown to be essential when it comes to personalized music retrieval and recommendation. To this end, the current location of a user is often exploited. However, relying solely on GPS coordinates neglects the cultural background of users, which does not necessarily coincide with political borders. In this paper, we analyze culture-specific...
The paper presents research in usability of web technologies for implementation of machine learning and clustering algorithms into embedded systems. The research work is divided into two main parts. The first part is devoted to designing backend system with fast C++ application for learning execution model. The second part of application is frontend based web application with PHP and AJAX to provide...
Constrained spectral clustering is an important area with many applications. However, most previous work has only been applied to relatively small data sets: graphs with thousands of points. This prevents this work from being applied to the large data sets found in application domains such as medical imaging and document data. Recent work on constrained and unconstrained spectral clustering has explored...
We present an accelerated algorithm for hierarchical density based clustering. Our new algorithm improves upon HDBSCAN*, which itself provided a significant qualitative improvement over the popular DBSCAN algorithm. The accelerated HDBSCAN* algorithm provides comparable performance to DBSCAN, while supporting variable density clusters, and eliminating the need for the difficult to tune distance scale...
Supply chain management aims at delivering goods in the shortest time at the lowest possible price while ensuring the best possible quality and is now vital to the success of the online retail business. Executing effective warehouse site selection has been one of the key challenges in the development of a successful supply chain system. While some effective strategies for warehouse site selection...
We propose EC3, a novel algorithm that merges classification and clustering together in order to support both binary and multi-class classification. EC3 is based on a principled combination of multiple classification and multiple clustering methods using a convex optimization function. We additionally propose iEC3, a variant of EC3 that handles imbalanced training data. We perform an extensive experimental...
In order to yield a more balanced partitioning, we investigate the use of additive regularizations for the Min Cut cost function, instead of normalization. In particular, we study the case where the regularization term is the sum of the squared size of the clusters, which then leads to shifting (adaptively) the pairwise similarities. We study the connection of such a model with Correlation Clustering...
We consider the problem of partitioning clinical services in hospitals into groups with the goal of efficiently allocating existing inpatient beds. At the strategic level, there are two major possibilities: pooling versus focusing. Pooling the bed capacity allows one to achieve an overall high occupancy level for a fixed number of beds. On the other hand, focusing by dividing the capacity into groups...
Mutual information clustering is an agglomerative hierarchical clustering method that has been used to group random variables or sets thereof. Some researchers have found that the normalization method used can lead to oddly-sized clusters that do not line up with expected results. We introduce a new normalization parameter to control the size of the clusters, and apply it to food allergy data from...
HDBSCAN*, a state-of-the-art density-based hierarchical clustering method, produces a hierarchical organization of clusters in a dataset w.r.t. a parameter mpts. While the performance of HDBSCAN* is robust w.r.t. mpts, choosing a "good" value for it can be challenging: depending on the data distribution, a high or low value for mpts may be more appropriate, and certain data clusters may...
We describe a dynamic graph generator with overlapping communities that is capable of simulating community scale events while at the same time maintaining crucial graph properties. Such a benchmark generator is useful to measure and compare the responsiveness and efficiency of dynamic community detection algorithms. Since the generator allows the user to tune multiple parameters, it can also be used...
The Internet of Things (IoT) for agriculture is a rapidly emerging technology where seamless connected sensors device make it possible to monitor and control crop parameters to get quality and quantity of food. This research proposes a new dynamic clustering and data gathering scheme for harnessing the IoT in agriculture. In this paper, an Unmanned Aerial Vehicle (UAV) is used to locate and assist...
Energy hole is regarded as one of the challenging issues in wireless sensor networks (WSNs). According to this problem, nodes that are closer to the sink lose their energy earlier than other nodes. Consequently, it leads to the early breakdown of the network. One approach for preventing this problem is to use mobile sink rather than one or more sinks with fixed positions. Also, using fuzzy logic and...
Millimeter wave (MMW) readily penetrates fabrics, thus it can be used to detect objects concealed under clothing. A passive MMW imaging system can operate as a stand-off type sensor that scans people away from the system. However, the image often suffers from low image quality due to the diffraction limit and low signal level. In the paper, we discuss four image interpolation methods to recognize...
We suggested a method of clustering, which allows to build a model of conceptual clustering for objects of fuzzy nature, and also to increase the accuracy of clustering for such objects. We used Cobweb clustering method as a base. We modified the formula of assessing the utility of conceptual clustering for objects with fuzzy parameter values. Then we suggested a modified Cobweb version for working...
There has been a continuing demand for traditional and complementary medicine worldwide. A fundamental and important topic in Traditional Chinese Medicine (TCM) is to optimize the prescription and to detect herb regularities from TCM data. In this paper, we propose a novel clustering model to solve this general problem of herb categorization, a pivotal task of prescription optimization and herb regularities...
Chronic wounds present a significant risk to the patient and a substantial drain on health budgets, with the problem likely to worsen markedly with increased incidence of type II diabetes. The wound fluid microbiome is known to influence wound healing outcomes, but is poorly characterised. Next Generation Sequencing approaches yield abundant data from wound samples, but progress in understanding these...
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