Journal of Kufa for Mathematics and Computer

Abstract. Security is one of the significant difficulties that individuals are confronting when the
information exchanged over the internet in today's world. Cryptography is the way to achieve security
of network or data by hiding the important information from unauthorized individuals. Encryption is a
mechanism of converting plain-text into cipher-text in order to be unreadable by unwanted people. In
this review, we explain some important cryptography primitives ( symmetric and asymmetric
cryptography algorithms) with examples for each type. Comparative study is making among this
cryptography primitives according to many parameters such as structure, speed, block size and others
parameters.

Abstract- The Internet of Things (IoT) is an emerging network paradigm that understands the
connections between all things and is the foundation of the intelligent community.

Because IoT is
always about everyday life or daily work of the user, privacy and security are important.

The pervasive,
complex and heterogeneous properties of the IoT make its security issues very challenging.

In other
words, how to achieve data, while data privacy is a challenging task. In addition, resource constraints

In spite of the attribute based
encryption like (CP-ABE), can provide access control to data by allowing specific users who their
attributes in accordance with the policy of access to decoded text.

of node creates a lightweight requirement for IoT security mechanisms.
This paper provides a method of a
scalable and secure access control based on attribute with effective revocation based on allow users to
access and as well as the possibility of expiring some users' access. Security and efficiency evaluations
show that the proposed scheme can achieve the desired security goals, while keeping comparable
computation overhead.

Abstract— there is a considerable advancement in research that concern brain-computer interfaces (BCI).BCI can be defined as a communication system that is developed for allowing individuals experiencingcomplete paralysis sending commands or messages with no need to send them via normal output pathwaysof brain.
EEG recording are Affected by cardiac noise, blinks, eye movement, in addition to non-biologicalsources (such as power-line noise).There will be an obstacle if the subject generates an artifact since willviolate the specification of BCI as a non-muscular communication channel and the ability of subjectssuffering degenerative diseases could be lost and This artifacts(noise) leads to incorrect classificationaccuracy .The presented study has the aim of being a sufficient reference in BCI system and also emphasizealgorithms which are capable of separating and removing the noise that interferes with the task-relatedElectroencephalography (EEG) signal for the best features . The task is the motions of the index finger ofright or left .The separation process based BSS technique ,This separating would be having an effectivespeeding impact on classifying patterns of EEG. and classified using classifier ( Hoeffding Tree). Theproposed algorithm is tested and trained with the use of real recorded signals of EEG . Experiments revealthat the proposed classifier with the stone algorithm leads to high classification results up to theclassification accuracy 79%.

Abstract— The applications of digitally modulated signals are still in progress and expansion. Automatic
Modulation Identification (AMI) is important to classify the digitally modulated signals ..To get better results of the
system suggested optimization the features to discard weak or irrelevant features in the system and keep only strong
relevant features .In this work, present hybrid intelligent system for the recognition related to the digitally
modulated signals where used . The proposed (AMI) had been built to classify ten most popular schemes of digitally
modulated signals, namely (2ASK, , 2PSK, 4PSK, 8PSK, 8QAM,16QAM ,32QAM, 64 QAM, 128QAM, and 256QAM),
with the signal to noise ratio ranging from (-2 to 13) dB. High-order cumulants (HOCs) as well as high-order
moments (HOMs) were utilized. .In this thesis used , Bat Swarm Optimization (BA).The Random Forest ( RF)
classifier was introduced for the first time in this work. Simulation results of the System proposed , under additive
white Gaussian noise channel, show that .While algorithm ( BA Swarm Optimization ) for the modulated signals we
obtained a classification accuracy of around 92% for the SNR between (-2....12 ) dB.

Abstract— Integral equation plays very important tools in a developed science like numerous troubles in an
engineering and mechanical sciences . a cubic spline method renowned to begin with one powerful role to solve many
functional equations like integral equations ,ordinary and partial differential equation .In our paper, a spline method
is used to solve the problem linear integral equation of Fredholm. Integral function is turned to polynomial by basis
of cubic spline . efficiency test of the proposed method , the comparison is check together with method of power
series (least square method) and quadrature(nystrom) method,Laguerre,Legendre and Hermite series method . In
MATLAB, all programs are written.