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Sleep quality impacts virtually all aspects of life, including health, mood, emotions, cognition, memory, behavior, and performance. Actigraphy offers a lower-cost alternative to conventional polysomnography (PSG), the gold standard for measuring sleep quality. Effective use of actigraphy for assessing sleep quality requires reliable methods for detecting sleep/wake states from actigraphy measurements...
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...
Sparse subspace clustering (SSC) is an effective approach to cluster high-dimensional data. However, how to adaptively select the number of clusters/eigenvectors for different data sets, especially when the data are corrupted by noise, is a big challenge in SSC and also an open problem in field of data mining. In this paper, considering the fact that the eigenvectors are robust to noise, we develop...
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...
Genes participating in a common module may cause clinically similar diseases and shares the common genetic origin of their associated disease phenotypes. Identifying such modules may be helpful in system level understanding of biological and cellular processes or their disruption caused in associated diseases. The choose dofthe appropriate method for gene selection is a difficult task. In this work...
Clustering is an important branch in the field of data mining as well as statistical analysis and is widely used in exploratory analysis. Many algorithms exist for clustering in the Euclidean space. However, time series clustering introduces new problems, such as inadequate distance measure, inaccurate cluster center description, lack of efficient and accurate clustering techniques. When dealing with...
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,...
Due to the advances of wireless sensor networks, radiofrequency identification (RFID) and Web-based services, large volume of devices have been interconnected to the Internet of Things (IoT). In addition, the tremendous number of IoT services provided by service providers arises an urgent need to propose effective recommendation methods to discover suitable services to users. In this paper, we propose...
We develop a new density-based clustering algorithm named CRAD which is based on a new neighbor searching function with a robust data depth as the dissimilarity measure. Our experiments prove that the new CRAD is highly competitive at detecting clusters with varying densities, compared with the existing algorithms such as DBSCAN, OPTICS and DBCA. Furthermore, a new effective parameter selection procedure...
Diabetes mellitus and obesity are becoming some of the most serious public health challenges in the world. To help researchers more quickly reveal the complex relationships existing between diabetes mellitus, obesity, and related diseases in the literature, and give them an inspiration to search the effective treatments for these diseases, we propose a novel model named as representative latent Dirichlet...
Nowadays with enlargement of power grid and increasing renewable energy integration, uncertainties and randomness challenge traditional power system analysis methods. Scenarios are widely applied to deal with power system uncertainties. Especially power flow examination in transmission expansion planning needs typical scenarios to verify normal power flow, and provide useful information for auxiliary...
Computer Vision and Machine Learning are the key to develop autonomous robots. While engaged with a IEEE Open Challenge, in which the robots need to recognize a miniature of a cow, we saw a solution in these areas. The main contribution of this paper is the algorithm implemented to identify and follow a known object, the miniature of a cow. We are constructing an application based on Image Processing...
The paper presents an approach for tracking a variable number of objects by using a multi-layer particle filter combined with an extended Expectation Maximization (EM) clustering. The approach works on basis of binary foreground images coming from previous background subtraction. The multi-layer particle filter is an improvement of a conventional particle filter approach. It uses an introduced adaptive...
Computer Network has great effect on improving the efficiency of the communication system and application requirements in our life. In order to establish transmission path with movement characteristics and improve the efficiency of information transmission in Computer Network, the optimized clustering routing protocol based on node position using the minimum distance routing competition mechanism...
Biological data is often represented as networks, as in the case of protein-protein interactions and metabolic pathways. Modeling, analyzing, and visualizing networks can help make sense of large volumes of data generated by high-throughput experiments. However, due to their size and complex structure, biological networks can be difficult to interpret without further processing. Cluster analysis is...
One of the main challenges in genome-wide association studies is finding gene-gene interactions for complex diseases. For binary traits, multifactor dimensionality reduction (MDR) has been proposed as a non-parametric approach for identifying gene-gene interactions. However, few statistical methods are currently available for determining the genetic interactions associated with quantitative traits...
Structural variations are a complex collection of mutations, many of which are reported to associated to complex traits. Recent research reports a rare case of structural variants, complex indels, which may contribute to carcinogenesis. A complex indel often presents multiple inserted nucleotides in a deleted region. Due to the limitations on both data and algorithm, existing approaches could only...
This paper presents detailed anomaly detection evaluation on operational time-series data of Internet of Things (IoT) based household devices in general and Heating, Ventilation and Air Conditioning (HVAC) systems in specific. Due to the number of issues observed during evaluation of widely used distance-based, statistical-based, and cluster-based anomaly detection techniques, we also present a pattern-based...
Time series motifs are approximately repeating patterns in real-valued time series data. They are useful for exploratory data mining and are often used as inputs for various time series clustering, classification, segmentation, rule discovery, and visualization algorithms. Since the introduction of the first motif discovery algorithm for univariate time series in 2002, multiple efforts have been made...
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