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

Catalytic removal of the S-content from thiophene is a central step in efforts aiming to reduce the environmental burdens of transportation fuels. In this contribution, we investigate the hydrodesulfurization (HDS) mechanisms of thiophene (C4H4S) over γ-Mo2N catalyst by means of density functional theory (DFT) calculations. The thiophene molecule preferentially adsorbs in a flat mode over 3-fold fcc nitrogen hollow sites. The HDS mechanism may potentially proceed either unimolecularly (direct desulfurization) or via H-assisted reactions (hydrogenation). Due to a sizable activation barrier required for the first Csingle bondS bond scission of 54.6 kcal/mol, we predict that the direct desulfurization to contribute rather very insignificant

Physical adsorption by nitrogen gas was studied on seven commercial platinum reforming catalysts (RG-402, RG-412, RG-432, RG-451, RG 422,RG-482, PS-10), four prepared platinum catalysts (0.1%Pt/alumina, 0.2 %Pt/alumina, 0.45 %Pt/alumina and 0.55% Pt/alumina), and -alumina support. Physical adsorption was carried out by using Accelerated Surface Area and Porosimetry (ASAP 2400 device) at 77 K . The results indicate that the surface area in genaral decreases with increasing platinum percentage, high platinum loaded (0.45% and 0.55%) it was found that the percent increasing in surface area was lower than those obtained for low platinum loaded catalysts , and at very higher platinum loading 0.6 %Pt , some reduction in surface area was

Data <span>transmission in orthogonal frequency division multiplexing (OFDM) system needs source and channel coding, the transmitted data suffers from the bad effect of large peak to average power ratio (PAPR). Source code and channel codes can be joined using different joined codes. Variable length error correcting code (VLEC) is one of these joined codes. VLEC is used in mat lab simulation for image transmission in OFDM system, different VLEC code length is used and compared to find that the PAPR decreased with increasing the code length. Several techniques are used and compared for PAPR reduction. The PAPR of OFDM signal is measured for image coding with VLEC and compared with image coded by Huffman source coding and Bose-

This paper introduces a non-conventional approach with multi-dimensional random sampling to solve a cocaine abuse model with statistical probability. The mean Latin hypercube finite difference (MLHFD) method is proposed for the first time via hybrid integration of the classical numerical finite difference (FD) formula with Latin hypercube sampling (LHS) technique to create a random distribution for the model parameters which are dependent on time t . The LHS technique gives advantage to MLHFD method to produce fast variation of the parameters’ values via number of multidimensional simulations (100, 1000 and 5000). The generated Latin hypercube sample which is random or non-deterministic in nature is further integrated with the FD method t

In this research thin films from SnO2 semiconductor have been prepared by using chemical pyrolysis spray method from solution SnCl2.2H2O at 0.125M concentration on glass at substrate temperature (723K ).Annealing was preformed for prepared thin film at (823K) temperature. The structural and sensing properties of SnO2 thin films for CO2 gas was studied before and after annealing ,as well as we studied the effect temperature annealing on grain size for prepared thin films .

Nowadays, cloud computing has attracted the attention of large companies due to its high potential, flexibility, and profitability in providing multi-sources of hardware and software to serve the connected users. Given the scale of modern data centers and the dynamic nature of their resource provisioning, we need effective scheduling techniques to manage these resources while satisfying both the cloud providers and cloud users goals. Task scheduling in cloud computing is considered as NP-hard problem which cannot be easily solved by classical optimization methods. Thus, both heuristic and meta-heuristic techniques have been utilized to provide optimal or near-optimal solutions within an acceptable time frame for such problems. In th

Future wireless networks will require advance physical-layer techniques to meet the requirements of Internet of Everything (IoE) applications and massive communication systems. To this end, a massive MIMO (m-MIMO) system is to date considered one of the key technologies for future wireless networks. This is due to the capability of m-MIMO to bring a significant improvement in the spectral efficiency and energy efficiency. However, designing an efficient downlink (DL) training sequence for fast channel state information (CSI) estimation, i.e., with limited coherence time, in a frequency division duplex (FDD) m-MIMO system when users exhibit different correlation patterns, i.e., span distinct channel covariance matrices, is to date ve