MB Algorithm Research Articles

Interactive Mining of Probabilistic Frequent Patterns in Uncertain Databases

Many modern applications such as sensor networks produce probabilistic data. These data are collected into an uncertain database. To interpret uncertainty and to mine frequent patterns in an uncertain database, all possible certain databases are considered, which generates an exponential number of combinations and makes the mining problem highly complicated. In practice, mining is interactive, which makes the discovery of frequent itemsets in an uncertain database even more challenging. The objective of interactive mining is to shorten the time that is required to obtain the desired patterns in the iterated lengthy mining process. The time-consuming mining process in an uncertain database is exacerbated by repeated processing if the mining is performed from scratch. Therefore, we propose an interactive mining algorithm called iDIP to solve this problem. The iDIP algorithm adopts an approximation mechanism to mine the patterns and prunes candidates by using the existing patterns. Comprehensive experiments using both real and synthetic datasets show that iDIP outperforms the well-known re-mining-based MB algorithm for 4.3 times faster on average. In addition, iDIP has good linear scalability.

New algorithm for clustering unlabeled big data

The clustering analysis techniques play an important role in the area of data mining. Although from existence several clustering techniques. However, it still to their tries to improve the clustering process efficiently or propose new techniques seeks to allocate objects into clusters so that two objects in the same cluster are more similar than two objects in different clusters and careful not to duplicate the same objects in different groups with the ability to cover all data as much as possible. This paper presents two directions. The first is to propose a new algorithm that coined a name (MB Algorithm) to collect unlabeled data and put them into appropriate groups. The second is the creation of a lexical sequence sentence (LCS) based on similar semantic sentences which are different from the traditional lexical word chain (LCW) based on words. The results showed that the performance of the MB algorithm has generally outperformed the two algorithms the hierarchical clustering algorithm and the K-mean algorithm.

Development and Validation of Open-Source Activity Intensity Count and Activity Intensity Classification Algorithms from Raw Acceleration Signals of Wearable Sensors.

Background: A popular outcome in rehabilitation studies is the activity intensity count, which is typically measured from commercially available accelerometers. However, the algorithms are not openly available, which impairs long-term follow-ups and restricts the potential to adapt the algorithms for pathological populations. The objectives of this research are to design and validate open-source algorithms for activity intensity quantification and classification. Methods: Two versions of a quantification algorithm are proposed (fixed [FB] and modifiable bandwidth [MB]) along with two versions of a classification algorithm (discrete [DM] vs. continuous methods [CM]). The results of these algorithms were compared to those of a commercial activity intensity count solution (ActiLife) with datasets from four activities (n = 24 participants). Results: The FB and MB algorithms gave similar results as ActiLife (r > 0.96). The DM algorithm is similar to a ActiLife (r ≥ 0.99). The CM algorithm differs (r ≥ 0.89) but is more precise. Conclusion: The combination of the FB algorithm with the DM results is a solution close to that of ActiLife. However, the MB version remains valid while being more adaptable, and the CM is more precise. This paper proposes an open-source alternative for rehabilitation that is compatible with several wearable devices and not dependent on manufacturer commercial decisions.

Calibration of exponential Hawkes processes using a Modified Bionomic Algorithm

The aim of this research is to develop a fast and robust variant of the evolutionary heuristic Bionomic algorithm and assess its contribution to solving complex parametric estimation problems, in conjunction with other traditional optimization techniques. We introduce a modified version of the Bionomic Algorithm (MB), designed to efficiently compute the MLE of self-exciting exponential Hawkes processes with increasing dimensionality of the solution space. Performance tests, performed on simulated and historical S&P 500 financial data, show that the MB algorithm, with its solutions locally improved by either the standard Nelder Mead (NM) or Expectation Maximization (EM) algorithm, converges significantly faster and more frequently to a near-global solution than the NM or EM algorithms operating alone. These test results illustrate the robustness and computational efficiency of the MB algorithm, combined with traditional optimization methods, in the optimization of complex objective functions of high dimensionality.

Experimental evaluation of efficiency of algorithms for direction finding of unmanned aerial vehicles by acoustic radiation

The results of an experimental evaluation of Bartlett, Capon algorithms and cross-correlation function method effectiveness for direction finding of unmanned aerial vehicles are presented. Full-scale studies were carried out using a harmonic signal and acoustic radiation of a prototype of a propeller-driven UAV group indoors and in open space, as well as in real-world conditions of flight modes and UAV hovering. The acoustic emission spectrum of the electric propeller-motor group of modern UAVs includes narrow-band tonal and broadband noise-like components with predominant propeller radiation. The use of algorithms for determining the direction of arrival of acoustic radiation from the spatial spectrum by the methods of Bartlett and Capon involves the use of narrow-band signals. It is shown that to work with UAV acoustic radiation, the MB, MK, and cross-correlation function algorithms require some adaptation (including the use of high-pass filters). The Capon method has a significantly higher resolution than the classic Bartlett method, and a lower level of side lobes. The presence of significant reflections from local objects leads to the appearance of abnormal bearing estimates for the acoustic signal source of the propeller-driven UAV group. The results of determining the bearing of the source of acoustic radiation in a wide frequency band in open space show good agreement with the specified values of the bearing when applying the method of mutual correlation function. To increase the efficiency of the mutual correlation function algorithm when processing UAV acoustic signals, it is necessary to reduce the influence of low-frequency interference caused by anthropogenic and natural acoustic noise.

Node localization algorithm of wireless sensor networks with mobile beacon node

When maximum movement distance of beacon node is limited, in order to improve the localization accuracy of sensor nodes, node localization algorithm of wireless sensor networks with mobile beacon node (NLA_MB) is proposed. NLA_MB algorithm divides the movement area of beacon node into some hexagonal grids, and establishes optimization model of node localization errors according to the movement path constraint, movement distance constraint and other constraints of beacon node. Beacon node uses heuristic method to solve the model approximately based on virtual force theory and location information of guide sensor nodes, and obtains a moving path which is suitable for the current node distribution. According to location information of beacon node or anchor sensor nodes, sensor nodes use maximum likelihood estimation algorithm to calculate their own location coordinates. Simulation results show that NLA_MB algorithm is suitable for the distribution scenarios of sensor nodes, such as full coverage distribution, L-type distribution, Tian-type distribution and U-type distribution. It can increase the number of sojourn locations of beacon node and average number of anchor points of sensor nodes, and reduce average localization error of sensor nodes. Under certain conditions, NLA_MB algorithm outperforms DOUBLE_SCAN, ZSCAN, MLE, RSCAN and DBMS algorithms.

Statistical evaluation of dosimetric differences changes between the Modified Batho's density correction method and the Anisotropic Analytical Algorithm for clinical practice

Purpose: The aim of this work was to assess and to quantify, for clinical practice, the differences in computed doses using two types of dose calculation algorithm for the heterogeneity correction including target volumes and organs at risk (OARs). Methods: 35 patients having lung, breast, spine, head & neck, brain and pelvic tumors, were studied. For each patient, 2 treatment plans were generated. In plan 1, the dose was calculated using the Modified Batho's (MB) density correction method integrated in the Pencil Beam Convolution algorithm. In plan 2, the dose was calculated using the Anisotropic Analytical Algorithm (AAA). To compare the two plans a dosimetric analysis was carried out including cumulative and differential dose volume histograms (DVH), coverage index, and conformity index. Wilcoxon signed rank and Spearman’s tests were used to calculate p-values and correlation coefficients (r), respectively. Bootstrap simulation with 1000 random samplings was used to calculate the 95% confidence interval (95% CI). Results: The analysis of DVH showed that the AAA method calculated significantly higher doses for OARs for all cancer sites and lower doses for target volumes, especially for targets located in lung, with p 0.9. Conclusion: This study confirms that using the AAA integrated into Eclipse ® TPS, the calculated dose will be increased to OARs, and reduced to target volumes. Thus, when changing from the MB algorithm to AAA, attention should be paid to avoid any bias of over/under estimating the dose given by AAA and to hold discussions between physicists and oncologists regarding any necessary modification in the prescription method.

Implementation of MB algorithm for nonlinear magnetostatics

The iteration strategy in the MB formulation for nonlinear magnetostatic problems is discussed. The difference between MH and MB iteration is examined, and two methods for implementating the constitutive M(B) relation are presented.