Codes of red, green, and blue data (RGB) extracted from a lab-fabricated colorimeter device were used to build a proposed classifier with the objective of classifying colors of objects based on defined categories of fundamental colors. Primary, secondary, and tertiary colors namely red, green, orange, yellow, pink, purple, blue, brown, grey, white, and black, were employed in machine learning (ML) by applying an artificial neural network (ANN) algorithm using Python. The classifier, which was based on the ANN algorithm, required a definition of the mentioned eleven colors in the form of RGB codes in order to acquire the capability of classification. The software's capacity to forecast the color of the code that belongs to an ob

Codes of red, green, and blue data (RGB) extracted from a lab-fabricated colorimeter device were used to build a proposed classifier with the objective of classifying colors of objects based on defined categories of fundamental colors. Primary, secondary, and tertiary colors namely red, green, orange, yellow, pink, purple, blue, brown, grey, white, and black, were employed in machine learning (ML) by applying an artificial neural network (ANN) algorithm using Python. The classifier, which was based on the ANN algorithm, required a definition of the mentioned eleven colors in the form of RGB codes in order to acquire the capability of classification. The software's capacity to forecast the color of the code that belongs to an object under de

The electric quadrupole moments for some scandium isotopes (41, 43, 44, 45, 46, 47Sc) have been calculated using the shell model in the proton-neutron formalism. Excitations out of major shell model space were taken into account through a microscopic theory which is called core polarization effectives. The set of effective charges adopted in the theoretical calculations emerging about the core polarization effect. NushellX@MSU code was used to calculate one body density matrix (OBDM). The simple harmonic oscillator potential has been used to generate the single particle matrix elements. Our theoretical calculations for the quadrupole moments used the two types of effective interactions to obtain the best interaction compared with the exp

The development of a reversed phase high performance liquid chromatography fluorescence method for the determination of the mycotoxins fumonisin B1 and fumonisin B2 by using silica-based monolithic column is described. The samples were first extracted using acetonitrile:water (50:50, v/v) and purified by using a C18 solid phase extraction-based clean-up column. Then, pre-column derivatization for the analyte using ortho-phthaldialdehyde in the presence of 2-mercaptoethanol was carried out. The developed method involved optimization of mobile phase composition using methanol and phosphate buffer, injection volume, temperature and flow rate. The liquid chromatographic separation was performed using a reversed phase Chromolith® RP-18e column

In the present work, heterojunction diode detectors will be prepared using germanium wafers as a substrate material and 200 nm tin sulfide thickness will be evaporated by using thermal evaporation method as thin film on the substrate. Nd:YAG laser (λ=532 nm) with different energy densities (5.66 J/cm2 and 11.32 J/cm2) is used to diffuse the SnS inside the surface of the germanium samples with 10 laser shots in different environments (vacuum and distilled water). I-V characteristics in the dark illumination, C-V characteristics, transmission measurements, spectral responsivity and quantum efficiency were investigated at 300K. The C-V measurements have shown that the heterojunction were of abrupt type and the maximum value of build-in pot

    Identification of complex communities in biological networks is a critical and ongoing challenge since lots of network-related problems correspond to the subgraph isomorphism problem known in the literature as NP-hard. Several optimization algorithms have been dedicated and applied to solve this problem. The main challenge regarding the application of optimization algorithms, specifically to handle large-scale complex networks, is their relatively long execution time. Thus, this paper proposes a parallel extension of the PSO algorithm to detect communities in complex biological networks. The main contribution of this study is summarized in three- fold; Firstly, a modified PSO algorithm with a local search operator is proposed

Researchers are increasingly using multimodal biometrics to strengthen the security of biometric applications. In this study, a strong multimodal human identification model was developed to address the growing problem of spoofing attacks in biometric security systems. Through the use of metaheuristic optimization methods, such as the Genetic Algorithm(GA), Ant Colony Optimization(ACO), and Particle Swarm Optimization (PSO) for feature selection, this unique model incorporates three biometric modalities: face, iris, and fingerprint. Image pre-processing, feature extraction, critical image feature selection, and multibiometric recognition are the four main steps in the workflow of the system. To determine its performance, the model wa