ABSTRACT
In this paper, an adaptive artefact canceller is designed using the bounded range artificial bee colony (BR-ABC) optimization technique. The results of proposed method are compared with recursive least square and other evolutionary algorithms. The performance of these algorithms is evaluated in terms of signal-to-noise ratio (SNR), mean square error (MSE), maximum error (ME) mean, standard deviation (SD) and correlation factor (r). The noise attenuation capability is tested on EMG signal contaminated with power line and ECG noise at different SNR levels. A comparative study of various techniques reveals that the performance of BR-ABC algorithm is better in noisy environment. Our simulation results show that the ANC filter using BR-ABC technique provides 15 dB improvement in output average SNR, 63 and 83% reduction in MSE and ME, respectively as compared to ANC filter based on PSO technique. Further, the ANC filter designed using BR-ABC technique enhances the correlation between output and pure EMG signal.
Subject(s)
Artifacts , Bees , Electrocardiography , Methods , Noise , Signal-To-Noise RatioABSTRACT
Este artículo propone un algoritmo de aprendizaje de clases de equivalencia de redes bayesianas basado en un algoritmo de búsqueda Greedy y modelos de búsqueda inspirados en hormigas competitivas. Específicamente para el algoritmo propuesto, se obtuvo una mejor aproximación entre la red predicha y la red bayesiana teórica de ejemplo ASIA, con respecto a algoritmos anteriores, para conjuntos de datos con 20 y 500 muestras. En promedio el algoritmo desarrollado obtuvo una aproximación con respecto a la distancia estructural de hamming de 10.7% y 5.3% menor comparada con la obtenida por los algoritmos Greedy y de colonia de hormigas (ACO-E) respectivamente para 20 muestras, y de hasta el 6.8% menor con respecto al algoritmo ACO-E para 500 muestras. Además, para 500 muestras el número de llamadas a la función de puntaje realizadas por el algoritmo propuesto fue menor que las realizadas por el algoritmo ACO-E en el 90% de las combinaciones, concluyendo que hubo una reducción de la complejidad computacional. Finalmente se presentan los resultados de la aplicación del algoritmo propuesto a un microarreglo obtenido por muestras de pacientes con Leucemia Mieloide Aguda (LMA) con 6 nuevas interacciones con dependencias estadísticas como potenciales interacciones biológicas con alta probabilidad.
This article proposes an algorithm for learning equivalence classes of Bayesian networks based on a Greed search algorithm and search patterns inspired by competitive ants. Specifically, for the proposed algorithm, we obtained a better approximation between the predicted network and the theoretical network ASIA with respect to previous algorithms for data sets with 20 and 500 samples. On average, the algorithm developed an approximation with respect to Structural Hamming Distance of 10.7% and 5.3% lower than Greedy algorithms and ACO-E respectively to 20 samples, and up to 6.8% lower tan ACO-E algorithm for 500 samples. Furthermore, for 500 samples the number of calls to the scoring function performed by the algorithm proposed was smaller than in the ACO-E algorithm in 90% of the combinations, concluding that there was a reduction in the computational complexity. Finally, we present the results of applying the proposed algorithm to a microarray samples obtained from patients with acute myeloid leukemia (AML) with 6 new interactions with statistical dependencies as potential biological interactions with high probability.
ABSTRACT
We propose a new distance algorithm for phylogenetic estimation based on Ant Colony Optimization (ACO), named Ant-Based Phylogenetic Reconstruction (ABPR). ABPR joins two taxa iteratively based on evolutionary distance among sequences, while also accounting for the quality of the phylogenetic tree built according to the total length of the tree. Similar to optimization algorithms for phylogenetic estimation, the algorithm allows exploration of a larger set of nearly optimal solutions. We applied the algorithm to four empirical data sets of mitochondrial DNA ranging from 12 to 186 sequences, and from 898 to 16,608 base pairs, and covering taxonomic levels from populations to orders. We show that ABPR performs better than the commonly used Neighbor-Joining algorithm, except when sequences are too closely related (e.g., population-level sequences). The phylogenetic relationships recovered at and above species level by ABPR agree with conventional views. However, like other algorithms of phylogenetic estimation, the proposed algorithm failed to recover expected relationships when distances are too similar or when rates of evolution are very variable, leading to the problem of long-branch attraction. ABPR, as well as other ACO-based algorithms, is emerging as a fast and accurate alternative method of phylogenetic estimation for large data sets.