Sensibly highlighting the hidden structures of many real-world networks has attracted growing interest and triggered a vast array of techniques on what is called nowadays community detection (CD) problem. Non-deterministic metaheuristics are proved to competitively transcending the limits of the counterpart deterministic heuristics in solving community detection problem. Despite the increasing interest, most of the existing metaheuristic based community detection (MCD) algorithms reflect one traditional language. Generally, they tend to explicitly project some features of real communities into different definitions of single or multi-objective optimization functions. The design of other operators, however, remains canonical lacking any inte

This paper study two stratified quantile regression models of the marginal and the conditional varieties. We estimate the quantile functions of these models by using two nonparametric methods of smoothing spline (B-spline) and kernel regression (Nadaraya-Watson). The estimates can be obtained by solve nonparametric quantile regression problem which means minimizing the quantile regression objective functions and using the approach of varying coefficient models. The main goal is discussing the comparison between the estimators of the two nonparametric methods and adopting the best one between them

This paper proposes a better solution for EEG-based brain language signals classification, it is using machine learning and optimization algorithms. This project aims to replace the brain signal classification for language processing tasks by achieving the higher accuracy and speed process. Features extraction is performed using a modified Discrete Wavelet Transform (DWT) in this study which increases the capability of capturing signal characteristics appropriately by decomposing EEG signals into significant frequency components. A Gray Wolf Optimization (GWO) algorithm method is applied to improve the results and select the optimal features which achieves more accurate results by selecting impactful features with maximum relevance

A fluorescence microscopy considered as a powerful imaging tool in biology and medicine. In addition to useful signal obtained from fluorescence microscopy, there are some defects in its images such as random variation in brightness, noise that caused by photon detection and some background pixels in the acquired fluorescence microscopic images appear wrongly auto-fluorescence property. All these practical limitations have a negative impact on the correct vision and analysis of the fluorescent microscope users. Our research enters the field of automation of image processing and image analysis using image processing techniques and applying this processing and analysis on one of the very important experiments in biology science. This research

Survival analysis is one of the types of data analysis that describes the time period until the occurrence of an event of interest such as death or other events of importance in determining what will happen to the phenomenon studied. There may be more than one endpoint for the event, in which case it is called Competing risks. The purpose of this research is to apply the dynamic approach in the analysis of discrete survival time in order to estimate the effect of covariates over time, as well as modeling the nonlinear relationship between the covariates and the discrete hazard function through the use of the multinomial logistic model and the multivariate Cox model. For the purpose of conducting the estimation process for both the discrete

Pots experiment was conducted in the green house of Biology Department, College of Education ( Ibn-AL-Haithum), University of Baghdad , during the growing season of 2005-2006 , to study the effect of five levels of urea fertilizer (0,50,100,150and 200) mg/pot in on vegetative growth of one wheat cultivar (Adanania) upon randomized complete block design with four replications. The results showed that significant difference between urea fertilizer levels above in vegetative growth characteristics ( plant height , leaf area , chlorophyll content, dry matter weight , relative efficiency and relative yield ) It was also showed that 200 mg/pot level was superior to urea fertilizer levels others in above charac

To explore the potential for in vitro rapid regeneration of Spanish dagger (Yucca gloriosa 'Variegata'), different concentrations of 6-Benzyladenine (BA), 1-naphthaleneacetic acid (NAA) and combinations of both were evaluated for callus induction initiated on leaf and bud (terminal and axillary buds) explants using Murashige and Skoog (MS) medium. Callus response induction percentage, fresh weight, color and texture of the callus were assessed after 1.5 and 6.0 months in culture. The appropriate medium for callus initiation on leaf explants was MS medium supplemented with 6.0 mg/L NAA. A combination of 0.2 mg/L BA and 1.5 mg/L NAA also exhibited a remarkable callus induction on bud explants. Effect of thidiazuron (TDZ) addition to the cultu