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In this paper, bootstrap percolation is introduced to control the information propagation for efficient cooperative positioning in wireless networks. Particularly, we obtain a novel linear least square (LLS) estimator for the localization of agent nodes. Exploiting the idea of bootstrap percolation, agent nodes sequentially get activated and estimate their positions with an adaptive location updating...
Triple negative breast cancers (TNBC) are clinically heterogeneous, an aggressive subtype with poor diagnosis and strong resistance to therapy. There is a need to identify novel robust biomarkers with high specificity for early detection and therapeutic intervention. Microarray gene expression-based studies have offered significant advances in molecular classification and identification of diagnostic/prognostic...
Many governments and institutions have guidelines for health-enhancing physical activity. Additionally, according to recent studies, the amount of time spent on sitting is a highly important determinant of health and wellbeing. In fact, sedentary lifestyle can lead to many diseases and, what is more, it is even found to be associated with increased mortality.
As one tool for structuring a massive volume of archived news videos based on their semantic contents, this paper proposes a method to detect scene duplicates from news videos. A scene duplicate is a pair of video segments taken at the same event from different viewpoints. Referring to the audio channel is effective to detect scene duplicates regardless of viewpoints, but it cannot be relied on when...
This paper presents an effective method for improving the accuracy of sleep detection using actigraphic algorithm. We have collected 2 sets of comparative data, wrist movement acceleration and brain wave analysis. In the first mode, we use the acceleration on x-axis as the input of actigraphic algorithm, in imitation of uni-axial accelerometer used in former study. In the second mode, we calculate...
Many real-world applications involve multi-label data streams, so effective concept drift detection methods should be able to consider the unique properties of multi-label stream data, such as label dependence. To deal with these challenges, we proposed an efficient and effective method to detect concept drift based on label grouping and entropy for multi-label data. Two methods are proposed to group...
Incomplete data clustering plays an important role in the big data analysis and processing. Existing algorithms for clustering incomplete high-dimensional big data have low performances in both efficiency and effectiveness. The paper proposes an incomplete high-dimensional big data clustering algorithm based on feature selection and partial distance strategy. First, a hierarchical clustering-based...
There are many examples in the literature of scorecards derived from clinical data. These scorecards are proposed for use by health professionals to stratify patients into risk categories and are often compared using receiver operating characteristic (ROC) curves and their associated areas (AUC). This paper analyses random scorecards and shows that the underlying distributions and therefore statistical...
Wrapper based gene selection methods tend to obtain better classification accuracy than filter methods, while it is much more time consuming. Accelerating this process without degrading the high accuracy is of great value for researchers to better analyze gene expression profiles. In this paper, we explore to reduce the time complexity of wrapper based gene selection method with K-Nearest-Neighbor...
There is an important relationship between the stability of protein complex and hot region. Research has shown that in protein-protein interaction (PPI), residues are denser around the hot region. Therefore, this paper proposed an algorithm based on Gi statistics, regional division rule and regional amplification principle to form residue dense region (RDR); Then, according to the results of cascade...
Existing personalized recommendation systems are facing many problems such as cold start, data sparseness and high complexity. Users' interests exist more widely and are more personalized compared with purchasing history in traditional recommendation systems. Thus, applying the interest graph in the recommendation process can make up certain shortages. This paper builds the mechanism of a user-interest-goods...
This paper describes SPICE-compatible macromodels to model the nonlinear behavior of operational amplifiers (Op Amp) and current feedback operational amplifiers (CFOA). The proposed macro-models include not only those performance parameters more important of the Op Amp and CFOA like the dynamic range, slew-rate, DC gain and gain-bandwidth product, but parasitic resistors and capacitors associated...
The Texture Feature Extraction (TFE) plays an important role in satellite image processing application. This paper proposes a novel method for Satellite Imagery Classification. Our proposed method is a combination of Local Binary Pattern (LBP) and Fuzzy c-means classification algorithm. Local Binary Pattern is calculated by thresholding a 3 × 3 neighborhood of each pixel by the center pixel value...
Texas A&M University Corpus-Christi is undergoing a major university expansion on its island campus. The university's Conrad Blucher Institute for Surveying and Science was tasked with providing the spatial information needs related to this expansion. To meet this objective, a small-scale Unmanned Aircraft System (UAS) equipped with a small-format digital camera is utilized to acquire aerial imagery...
Big data is a set of very large and complex data that is hard to load on computers. The main challenge in big data world is related to their search, categorize and analyze specially, when they are unbalanced. Despite, there are a lot of works in the field of big data but analyzing unbalanced big data is still a fundamental challenge in this area. In this paper we try to solve the problem of RSIO-LFCM...
The objective of the present paper is to demonstrate the potential of Computational Intelligence in applications pertaining to the automatic identification - categorisation of Cardiotocograms using Machine Learning Algorithms and Artificial Neural Networks whose purpose is to distinguish between healthy or pathological cases leading to mortality during birth or fetal cerebral palsy. Interest is also...
In this paper, an algorithm is presented for extracting fuzzy rules from the Breast Cancer dataset. To extract fuzzy rules, an imitation based evolutionary algorithm is used called Krill Herd (KH). The KH algorithm is converted to a binary algorithm here, and is used for the classification problem with innovation, named Binary Krill Herd-based Fuzzy Rule Miner (BKH-FRM). Choosing the best krill and...
Malicious program or malware is a computer program which was written intentionally to harm computing system. Malware protection involves several sub-tasks namely Monitoring, Prevention, Analysis, Detection, Removal and Recovery. This paper proposes a static heuristic based scoring system that gives a maliciousness score to portable executable files. Malicious score can be used at different stage of...
Sentence similarity measures play an increasingly important role in text-related research and applications in areas such as text mining, Web page retrieval, and dialogue systems. Existing methods for computing sentence similarity have been adopted from approaches used for long text documents. These methods process sentences in a very high-dimensional space and are consequently inefficient, require...
An improved classification method based on KMeans using HSV color feature is introduced in this paper. It is implemented by extracting three color features (hue, saturation, value) for K-Means clustering. Compared with the traditional K-Means clustering, the experimental results turn out that our proposed method is better than K-Means in classification accuracy and performance.
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