Journal of Kufa for Mathematics and Computer

Data preprocessing in general and data reduction in specific represent the main steps in data mining
techniques and algorithms since data in real world due to its vastness, the analysis will take a long time to complete
.Almost all mining techniques including classification, clustering, association and others have high time and space
complexities due to the huge amount of data and the algorithm behavior itself. That is the reason why data reduction
represent an important phase in Knowledge Discovery in Databases (KDD) process. Many researchers introduced
important solutions in this field. The study in this paper represents a comparative study for about 22 research papers
in data reduction fields that covers different data reduction techniques such as dimensionality reduction, numerisoty
reduction, sampling, clustering data cube aggregation and other techniques. From the conducted study, it can be
concluded that the appropriate technique that can be used in data reduction is highly dependent on the data type, the
dataset size, the application goal, the availability of noise and outliers and the compromise between the reduced data
and the knowledge required from the analysis.

We introduce the notions of an algebra-BH , Fuzzy of an algebra-BH , Pseudo algebra-BH , Solve
Differential Equations in pseudo BH-algebra , Solve Differential Equations in fuzzy pseudo BH-algebra . Also we
prove and stated some properties , theorems and examples with some Remarks that explain the relationship with
the definitions.

The rapid development of Unmanned Aerial Vehicles UAVs technology opens many new application
areas. One of these applications is remote monitoring for living entities (Humans or Animals) within a known area for
safety and protection purposes. UAVs are programmable to carry missions according to a specified algorithm. Their
ability of movements and actions provide solutions with a reasonable cost. However, how a set of UAVs doing a
mission behave cooperatively need to be tested and evaluated in a lab with simulation before moving to operation
field. In this paper, a model for scanning determined area using a set of cooperative UAVs is developed and tested.
The objective is reducing operation time. The set of affecting parameters is examined. The model is simulated using
NetLogo agent-based simulation tool. The results of the experiment of a various settings are conducted.

Alzheimer's disease (AD) is a major productive neurological illness with complicated genetic
architecture. One of the main aims of biomedical research is to identify risk genes and then explain how
these genes contribute to disease development. As a result, it is required to increase the list of genes
linked to Alzheimer's disease. Genes play a crucial role in every biological activity. Microarray
technology has given genes access to a large number of genes, allowing them to evaluate several levels of
expression at the same time. Microarray datasets are categorized by a huge number of genes and small
sample sizes. This reality is referred to as a multidimensional curse with a difficult task. A promising
technology known as gene selection is addressing this issue and has the potential to revolutionize
Alzheimer's disease diagnosis. In this work, gene selection approaches such as Singular Value
Decomposition (SVD) and Principle Component Analysis (PCA) were used. Techniques can help to
minimize an amount from trivial and redundant gene in the unique datasets. Then, using the
Convolutional Neural Network (CNN) as a classifier, deep learning (DL) is used to predict AD. The
dataset was processed using a CNN with seven layers and varied settings. With the AD dataset, the
empirical findings reveal that the PCA-CNN model has 96.60 percent accuracy and a loss of 0.3503,
while the SVD-CNN model has 97.08% accuracy and a loss of 0.2466. As a result, the suggested
approach is suited for reducing gene dimensions and improving classification accuracy by choosing a
subset of relevant genes.

In this research, we applied actual data related to the number of infections with the Corona
virus for some Arab countries for the month of February of the year 2022, where the method of least
significant difference (LSD) was used to find out the significant differences in the number of infections
between any two Arab countries. First, the completely random design method (CRD) was used to obtain a
table of analysis of variance (ANOVA) and to know in general whether there are significant differences in
the number of injuries between Arab countries or not, by rejecting the null hypothesis ????0, the method of
least significance LSD was used to compare the number of injuries between Arab countries (Is there a
moral difference or not).

In this study, two applied statistical tests were used, namely, the Tukey test and the Scheffe test,
to study the significant differences in the number of infections with the Corona virus for some selected Arab
countries (Iraq, Kuwait, UAE, Oman, Bahrain, Qatar, Morocco, Egypt, Algeria, Syria, Lebanon, Libya)
for two months, which are January and February of the year 2022. In the beginning, analysis of variance
was conducted to find out the presence of significant differences in the number of injuries or not, by
applying a completely randomized design. After that, the two statistical tests were used, Scheffe test and a
Tukey test, to study the significant differences in the rate of injuries between each of the two Arab countries
selected under study. After conducting the statistical tests, it was concluded that there are significant
differences between the number of infections with the Corona virus for most of the selected Arab countries
under study.