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In underwater sensor networks (UWSNs), information transmission is extremely challenging because of the underwater channel. Underwater acoustic channel suffers from long propagation delay and low bandwidth is inclined to packet losses. Hence, the design of an energy efficient communication system is challenging under the water. In this paper, we present an energy efficient clustered multi-hop routing...
The Krylov subspace based information retrieval (IR) approach has been shown to provide comparable accuracy to latent semantic indexing (LSI), while providing some computational advantages. Recently, in the area of numerical linear algebra, attention has been drawn to the block Krylov subspace methods, which are shown to be more efficient than the classic Krylov subspace methods in solving linear...
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...
The data anonymization landscape has become quite complex in the last decades. On the methodology side, the statistical disclosure control methods designed in official statistics have been supplemented by a number of privacy models proposed by computer scientists. On the data side, static data sets now coexist with big data, and particularly data streams. In the quest for a unified and conceptually...
There has been a surge in research interest in learning feature representation of networks in recent times. Researchers, motivated by the recent successes of embeddings in natural language processing and advances in deep learning, have explored various means for network embedding. Network embedding is useful as it can exploit off-the-shelf machine learning algorithms for network mining tasks like...
Network clustering is an essential approach to finding latent clusters in real-world networks. As the scale of real-world networks becomes increasingly larger, the existing network clustering algorithms fail to discover meaningful clusters efficiently. In this paper, we propose a framework called AnySCAN, which applies anytime theory to the structural clustering algorithm for networks (SCAN). Moreover,...
Currently, the demand on portable medical electronic systems are increasing, for they provide services and information to both patients and doctors that the traditional medical methods cannot achieve. However, the development of portable medical electronic systems has many limitations, i.e., one has to find a sweet spot among performance, power consumption, size, and cost. For instance, if one only...
As an effective method in dealing with the massive data, the serial processing, aiming to obtain the useful information quickly, cannot satisfy our calculation requirements with high-performance. However, both distributed computing and parallel computing are good choices in calculating high-volume data with high-performance. As a parallel computing framework based on memory computing large data, Spark...
The optimal sub-pattern assignment (OSPA) metric is a distance between two sets of points that jointly accounts for the dissimilarity in the number of points and the values of the points in the respective sets. The OSPA metric is often used for measuring the distance between two sets of points in Euclidean space. A common example is in multi-target filtering, where the aim is to estimate the set of...
We consider the problem of finding consistent matches across multiple images. Current state-of-the-art solutions use constraints on cycles of matches together with convex optimization, leading to computationally intensive iterative algorithms. In this paper, we instead propose a clustering-based formulation: we first rigorously show its equivalence with traditional approaches, and then propose QuickMatch,...
LoRa-based (Long Range Communication based) localization systems make use of RSSI (Received Signal Strength Indicator) to locate an object properly in an outdoor environment for IoT (Internet of Things) applications in smart environment and urban networking. The accuracy of localization is highly degraded, however, by noisy environments (e.g., electronic interference, blocking). To address this issue,...
For text clustering task, distinctive text features selection is important due to feature space high dimensionality. It is essential to reduce the feature space dimension to increase accuracy and decrease processing time. In this work, for text clustering task, we introduce a novel hybrid feature selection model. This method measures the term importance based on the correlation coefficient among four...
Spectral clustering is one of the most effective methods of data mining, in which the adjacency matrix is constructed by using the similarity matrix. In this paper, to extend spectral clustering method for uncertain data clustering, we propose a new spectral clustering method based on JS-divergence. In the proposed method, the JS-divergence is used to construct the adjacency matrix in the spectral...
With the growing public interest in health today, people are rapidly increasing their use of sleep sensing devices and smartphone apps in their daily lives to check and manage their sleeping health. However, some of the current sleep monitoring services are void of technical reliability in terms of data collection and analytic methodologies. In this research, for the purpose of robust representativeness,...
Connected vehicles will likely use hybrid communication networks. Presumably a licence-free radio access technology (RAT) will be used for vehicle-to-vehicle (V2V) contact, complemented by a cellular network, with an associated usage cost. In previous work, we developed a self-adaptive clustering algorithm for reducing cellular access costs, while ensuring that clustering overheads do not saturate...
A solid and practical approach for designing an optimal secondary distribution network is proposed. The methodology starts by optimally locating and sizing medium voltage/low voltage transformers then finding the optimal path for secondary circuits. The optimal number of transformers and their location are determined by k-means clustering algorithm, and validated using Davies-Bouldin index. In addition,...
Travel route recommendation can strongly influence users' satisfaction and the success of touristic businesses. This paper proposes a personalized travel recommendation algorithm with time planning. We use landmark categorization and region clustering to obtain effective elements. Then we build a travel map to generate all possible travel routes. Our proposed algorithm has higher precision in landmark...
There is a plenty of unorganized data available in various information repositories and examining this data is very necessary for some future analysis. Clustering this kind of data plays a vital role in knowing about formerly unknown and possibly useful data and also the concerns should be widely examined. Here, we are proposing a high level methodology for clustering the data. First of all the proposed...
In many applications, such as data integration and big data analytics, one has to integrate data from multiple sources without detailed and accurate schema information. The state of the art focuses on matching attributes among sources based on the information derived from the data in those sources. However, a best join result according to a method's own pre-determined criteria may not fit a user's...
In recent technological advancement such as smart grid applications, security surveillance & border protection, internet of things, disaster management & other smart home applications exhilarate the deployment of autonomous, self-configured, large-scale wireless sensor networks. Efficient power conservation is crucial concerns for sensor networks to operate in the hostile environment. Therefore...
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