Al-Salam Journal for Engineering and Technology

There are many ways can be utilized to figure out optimal solutions the in-operation research that
left off the traditional way network. The research goes over the building operation of integrated computer database
for College of Dentistry students for their patients, and the database functions on a LAN connected with a
server/client as the dentist clinic calculator functions as a server and the ones in the doctors’ room of the three
clinics are as clients, when a patient comes into the dentist clinic undergo examinations and accordingly, the doctor
makes a decision to get into the clinic for treatment, or expel him/her to another clinic for treating. After that,
returns to the main clinic after finishing the treatment in these clinics. Utilizing cloud computing and neural
networks for flexible database.

In this study, the relationships between matrix division and some matrix properties such as
transpose, simplification, and expansion are discussed. Unless otherwise stated, our matrices are real number
matrices. The relationship between our definitions of column co-divisor and row co-divisor of a matrix on another
matrix is examined. Equality and differences of these connections are observed. The validity of the provided
features is discussed under which conditions. These features have been determined. Examples are given for
features that do not. Some of the obtained properties bring different solutions to the solution of linear equation
systems, which are encountered in the literature and are more used. This led us to examine the relationship between
the defined division operation and transpose. Investigations on this subject enabled the presentation of new
definitions, lemmas and theorems. Examination of these offered a broader perspective on the given mathematical
expressions.

One of the most common machine learning approaches is linear classification. Multithreading used
in LIBLINEAR Library has become an important research topic for processing large datasets. Working in a
multithreaded environment accelerates the training of large datasets and increases classification accuracy. This
study examines multiple linear support vector machine implementations for binary classification of Landsat-8
satellite image datasets to determine the optimal model. Four datasets have been created for different scenes in
Iraq. Each dataset contains millions of pixels to be classified optimally. This article selects an appropriate
algorithm for such a large dataset of current types in the LIBLINEAR library optimal for organizing large datasets
using the linear SVM algorithm. Each dataset containing about 4.5 million samples was used to test the
performance. The seven linear SVM techniques have statically examined the most effective SVM implementation
method. Accuracy, F-Score, and Kappa are used as model assessment measures to evaluate and rank the models'
performances. Based on the results, the LibLINEAR library with type 4 (LINLINEAR4) classifier was the best
classifier for satellite image classification in remote sensing when applied to large datasets. The accuracy, Kappa,
and f-score of the LIBLINEAR 4 classifier are as follows: (92.89 %), (73.53 %), and (77.77%) with dataset1,
(96.25 %), (59.97 %), and (61.88%) with dataset2, (96.65%), (68.33%), and (70.03%), and (94.72%), (53.67%),
and (56.25%), respectively, with dataset4.

The amount of today's image collections has exploded to petabytes of data. A fair amount of time
cannot be allotted for the analysis of such massive datasets using a personal computer. As a result, distributed
computing is required for current image collection mining. This work used multi-nodes-computers for image
mining in order to improve reliability, fall tolerance, and time efficiency. Because of this, the data was divided up
across the nodes in the Hadoop multi-node cluster, and the results were then compiled to create an image clustering
algorithm. One master node and two slave nodes were used to test our technique on a huge dataset. We found that
Using multi-node Hadoop (48%) faster than traditional image mining implementation, and (24%) faster than
single-node Hadoop implementation.

Particle reduction in planetary mills is mainly due to impact and abrasion or shear. In our opinion,
graphene nanostructures are formed most intensively when shear forces act on particles of graphite or graphite
oxide. With regard to a planetary mill, shear effects on graphite particles are possible when the balls slide relative
to each other, or when the balls slide relative to the inner surface of the drum. The most intense sliding of particles
relative to each other occurs in the circulation mode of movement of the material in the cross section of a smooth
rotating drum.

In order to safeguard the information and data that must be protected from any interference or
incursion, security is one of the most significant and prominent concepts at the moment. There are several
techniques to ensure security and the protection of our data, but cryptography is the most crucial. Because of the
importance of the documents and information that institutions must deal with or transport over the Internet, where
unauthorized users or hackers can read and manipulate them, these documents must be encrypted using cipher
methods and techniques.