Abstract:

           The great importance that distinguish these factorial experiments made ​​them subject a desirable for use and application in many fields, particularly in the field of agriculture, which is considered the broad area for experimental designs applications.

            And the second case for the factorial  experiment, which faces researchers have great difficulty in dealing with the case unbalance we mean that frequencies treatments factorial  are not equal meaning (that is allocated a number unequal of blocks or units experimental per tre

Sentiment analysis is one of the major fields in natural language processing whose main task is to extract sentiments, opinions, attitudes, and emotions from a subjective text. And for its importance in decision making and in people's trust with reviews on web sites, there are many academic researches to address sentiment analysis problems. Deep Learning (DL) is a powerful Machine Learning (ML) technique that has emerged with its ability of feature representation and differentiating data, leading to state-of-the-art prediction results. In recent years, DL has been widely used in sentiment analysis, however, there is scarce in its implementation in the Arabic language field. Most of the previous researches address other l

In this paper, a new hybridization of supervised principal component analysis (SPCA) and stochastic gradient descent techniques is proposed, and called as SGD-SPCA, for real large datasets that have a small number of samples in high dimensional space. SGD-SPCA is proposed to become an important tool that can be used to diagnose and treat cancer accurately. When we have large datasets that require many parameters, SGD-SPCA is an excellent method, and it can easily update the parameters when a new observation shows up. Two cancer datasets are used, the first is for Leukemia and the second is for small round blue cell tumors. Also, simulation datasets are used to compare principal component analysis (PCA), SPCA, and SGD-SPCA. The results sh

This study was performed on the Tigris River (Baghdad city section) during the period between December 2016 and December 2018 to assess seasonal variation in water quality using the Overall Index of Pollution (OIP). The OIP is one of the reliable tools for the assessment of surface water quality. To calculate OIP-values, eight parameters were measured ( pH, Dissolved Oxygen "DO", Biological Oxygen Demand "BOD", Total Dissolved Solid "TDS", Total Hardness "TH", calcium "Ca", Sulphate "SO4" and Alkalinity). The results showed the anthropogenic activities impact of Baghdad population that directly discharge of "inadequate treated" waste water to the river.  OIP values were acceptable (1˃OIP˃ 1.7) in 2011, 2012, 2013 and 2018. However, in

A newly flow injection-turbidimetric method characterized by it is speed and sensitivity has been developed for the determination of Amiloride in pure and pharmaceutical preparations. It is based on the formation of yellowish white precipitate for the Amiloride-phosphomolybidic acid ion pair in aqueous medium. Turbidity was measured by Ayah 6Sx1-T-1D solar cell CFI analyser via the attenuation of incident light from the surfaces precipitated particles at 0-180. The Chemical and physical parameters were investigated. Linear dynamic range for the attenuation of incident light versus Amiloride concentration was of 0.005-10 mmol.L-1, with the correlation coefficient (r) of 0.9986 , while the percentage linearity (r2%) was 99.71%. The L.O.

A newly flow injection-turbidimetric method characterized by it is speed and sensitivity has been developed for the determination of Amiloride in pure and pharmaceutical preparations. It is based on the formation of yellowish white precipitate for the Amiloride-phosphomolybidic acid ion pair in aqueous medium. Turbidity was measured by Ayah 6Sx1-T-1D solar cell CFI analyser via the attenuation of incident light from the surfaces precipitated particles at 0-180. The Chemical and physical parameters were investigated. Linear dynamic range for the attenuation of incident light versus Amiloride concentration was of 0.005-10 mmol.L-1, with the correlation coefficient (r) of 0.9986 , while the percentage linearity (r2%) was 99.71%. The L.O.

In this article, new Schiff base ligand LH-prepared Mn(II), Co(II), Ni(II), Cu(II), Zn(II), Cd(II), Hg(II), Pd(II), and Pt(II) materials were analyzed using spectroscopy (1 Metal: 2 LH). The ligand was identified using techniques such as FTIR, UV-vis, 1H-13C-NMR, and mass spectra, and their complexes were identified using CHN microanalysis, UV-vis and FTIR spectral studies, atomic absorption, chloride content, molar conductivity measurements, and magnetic susceptibility. According to the measurements, the ligand was bound to the divalent metal ions as a bidentate through oxygen and nitrogen atoms. The complexes that were created had microbicide activity against two different bacterial species and one type of fungus. DPPH techniques were bei

     This research aims to predict new COVID-19 cases in Bandung, Indonesia. The system implemented two types of deep learning methods to predict this. They were the recurrent neural networks (RNN) and long-short-term memory (LSTM) algorithms. The data used in this study were the numbers of confirmed COVID-19 cases in Bandung from March 2020 to December 2020. Pre-processing of the data was carried out, namely data splitting and scaling, to get optimal results. During model training, the hyperparameter tuning stage was carried out on the sequence length and the number of layers. The results showed that RNN gave a better performance. The test used the RMSE, MAE, and R2 evaluation methods, with the best numbers being  0.66975075, 0.470