IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING

Face detection technology is the first and essential step for facial-based
analysis algorithms such as face recognition, face feature extraction, face alignment, face
enhancement, and face parsing. That is besides serving other applications related to human
intention and act analysis such as facial expression recognition, gender recognition, and
age classification. Face detection is used to detect faces in digital images. It is a special
case of object detection and can be used in many areas such as biometrics, security, law
enforcement, entertainment, and personal safety. There are various methods proposed in
the field of face detection, and they all compete to make it more advanced and accurate.
These methods belong to two main approaches; feature-based approach and image-based
approach. This paper reviews the face detection methods that belong to the feature-based
approach. Moreover, their work concepts, strengths, and limitations are mentioned. This
paper concentrates on the feature-based approach due to its simplicity and high
applicability in real-time detection compared to the image-based approach.

The World Wide Web (WWW) is a vast repository of knowledge,
including intellectual, social, financial, and security-related data. Online
information is typically accessed for instructional purposes. On the internet,
information is accessible in a variety of formats and access interfaces. Because
of this, indexing or semantic processing of the data via websites may be
difficult. The method that seeks to resolve this issue is web data scraping.
Unstructured web data can be converted into structured data using web data
scraping so that it can be stored and examined in a central local database or
spreadsheet. This paper offers a metadata scraping using a programmable
Customized Search Engine (CSE) system, which can extract metadata from web
pages (HTML pages) in the Google database and save it in an XML format for
later analysis and retrieval. Documents that contain metadata are a relatively
recent phenomenon on the web and increase the likelihood that users will find
the information they need

decentralization within IoT eliminates the need to use distributed networks
within IoT to communicate only with servers that may face difficulties related to the
internet, vulnerabilities, DDOS, or hijacks, merging blockchain with IoT converted the IoT
system into a decentralized with many benefits and outcomes from this conversion. An
encryption scheme homomorphic technique (HE) is a method that encrypts the cipher data
without the need to decrypt it,paillier encryption method is used. This paper aims to
propose a system that integrates Paillier cryptosystem homomorphic technology with IoT
and lightweight blockchain technology to provide decentralization to the IoT environment
and improve security. The proposed system results in improving the IoT device’s work
environment by solving the main challenges of security using blockchain, privacy using
homomorphism, and data volume using blockchain. The data set used to implement and
evaluate the proposed system is industrial internet of things data The dataset used in this
paper is generated via machine industry 4.0 Storage System status which represents the
system failure and work status. this system is evaluated using standard metrics used to
evaluate the blockchain effectiveness and time, resources consumed and shows better
results in time and power consumption.

Electrical energy is one of the most important components of life today where
different fields depend on it. The field of electrical energy distribution (electricity
network), which transmits electrical energy from sources to consumers, is one of the most
important areas that need to be developed and improved. In addition to analyzing
electrical energy consumption, it needs to forecast consumption and determine consumer
behavior in terms of consumption and how to balance supply and demand. The research
aims to analyze weather data and find the relation between the weather factors and
energy consumption in order to prepare data to use as a suitable data in machine learning
model for future use. This model analyzes the building consumption rate for a particular
area and takes into account the weather factors that affect electrical energy consumption,
where (temperature, dew point, ultraviolet index) are selects based on the correlation
confidence and then divided these factors into a set of categories using the K-Means
algorithm to show the effect of each factors on the other.

The artificial intelligence techniques such as neural networks and fuzzy systems
play an important role to disconnect flexion & expansion of the swing leg, the earth
response force of the other foot has been redesigned. Underthat paper, we think the fuzzy
controller plan issue for yield following flawed genuine investigation of nonlinear systems.
For examination, an essential fuzzy control plot has been bristly developed dependent on
a current methodology delegate under the field.In this paper, the Feedforward Neural
Network has been implemented with integer, fixed point and floating point data
representations. Additionally, The Fuzzy Logic Controllers in both analog and digital
forms has been implemented in hardware. Both designs use less hardware resources and
operate with reasonable speed compared to other existing designs. The digital
implementation of Fuzzy Logic Controller has been tested for a simulated first order liquid
level process and the performance results have been compared with those of the Matlab
version of Fuzzy Logic Controller. Here, Fuzzy Logic Controller is used as the controller
and is trained adaptively for the changes in process parameters using recursive k-means
clustering algorithm for updating the centers of the hidden layer and Recursive Least
Square algorithm for updating the weights of the output layer, the result of the settling time
about 20ms and takes 20 iterations, and the squared error reaches zero at approximately
20 µs.

Traditional network abilities have a drastic shortage in the current networking
world. Software-Defined Networking (SDN) is a revival development in the networking
domain that provides separation of control and data planes, enlarges the data plane
granularity, and simplifies the network devices. All these factors accelerate and automate
the evolution of new services. However, when the SDN network topology becomes large, it
poses new challenges in security, traffic management, and scalability due to the vast
amounts of traffic data generated and the need for additional controllers to manage the
significant number of networking devices. On the other hand, big data has become an
attractive trend that can enhance network performance in general, specifically SDN. Both
SDN and big data have gained great attraction from industry and academia. Traditionally,
these two subjects have been studied separately in most of the preceding works. However,
big data can impact the design and implementation of SDN thoroughly. This paper presents
how big data can support SDN in various aspects, including intrusion detection, traffic
monitoring, and controller scalability and resiliency. We suggest several approaches
toward deeper cooperation between big data and SDN.

Microsoft Common Objects in Context (COCO) is a huge image dataset that has
over 300 k images belonging to more than ninety-one classes. COCO has valuable
information in the field of detection, segmentation, classification, and tagging; but the
COCO dataset suffers from being unorganized, and classes in COCO interfere with each
other. Dealing with it gives very low and unsatisfying results whether when calculating
accuracy or intersection over the union in classification and segmentation algorithms. A
simple method is proposed to create a customized subset from the COCO dataset by
determining the class or class numbers. The suggested method is very useful as
preprocessing step for any detection or segmentation algorithms such as YOLO, SSPNET,
RCNN, etc. The proposed method was validated using the link net architecture for semantic
segmentation. The results after applying the preprocessing were presented and compared
to the state of art methods. The comparison demonstrates the exceptional effectiveness of
transfer learning with our preprocessing model.

Linear and non-linear impairments are the major problems that
affected the dense wavelength division multiplexing (DWDM) system
performance. In this simulation designs, two different compensation techniques
have been proposed to eliminate signal impairments. The repeater technique
was proposed to reduce non-linear effects like four-wave mixing (FWM) which
cause transfer power from DWDM channels to another new channels. The
dispersion compensation fiber (DCF) technique was used to reduce dispersion
effects which cause signal pulse broadening over long transmission distances.
Also, three different modulation formats and four frequency separations were
used to determine which modulation scheme is more compatible with the
compensation techniques. The results is calculated in term of quality factor
against power and noetic that the carrier suppressed return to zero (CSRZ)
format offers the best performance with the DCF technique with 32.1804 Q-
factor at 15dBm threshold power then, the differential phase shift keying
(DPSK) format is compatible with the repeater technique with 27.7959 quality
factor and 14dBm threshold power.

Iris identification is a well‐ known technology used to detect striking biometric
identification techniques for recognizing human beings based on physical behavior. The
texture of the iris is essential and its anatomy varies from individual to individual. Humans
have distinctive physical characteristics that never change. This has resulted in a
considerable advancement in the field of iris identification, which inherits the random
variation of the data and is often a dependable technological area. This research proposes
three algorithms to examine the classifications in machine learning approaches using
feature extraction for the iris image. The applied recognition system used many methods to
enhance the input images for iris recognition using the Multimedia University (MMU)
database. Linear Discriminant Analysis (LDA) feature extraction method is applied as an
input of three algorithms of machine learning approaches that are OneR, J48, and JRip
classifiers. The result indicates that the OneR classifier with LDA achieves the highest
performance with 94.387 % accuracy, while J48 and JRip reached to 90.151% and 86.885%
respectively.

Modelling computer networks in general, particularly Software
Defined Networking (SDN) as a graph, is beneficial in network planning and
design, configuration management, traffic analysis, and security. According to
the dynamic nature of SDN, it needs a fast response due to the rapid changes in
the network state. The SDN network topology can be modelled as a graph and
stored in a graph database, and the traffic load of each switch is stored in the
created graph. Consequently, a graph processing framework can be used to
process the stored traffic data, and the results are utilized in traffic engineering
to assist the SDN controller in network management. This paper provides a
comprehensive literature survey involving graph techniques applied to SDN.
Then, a summary of graph algorithms is presented. In addition, an overview of
graph databases and graph processing frameworks is displayed. Finally, a
model is suggested to integrate the graph database and graph processing
framework in SDN traffic analysis.

Securing information is difficult in the modern internet era, as
terabytes of data are generated daily online and online transactions occur
virtually every second. The current world's information security relies heavily on
cryptography, which makes the internet a safer environment. Making information
incoherent to an unauthorized person is done through the use of cryptography.
Providing legitimate users with confidentiality as a result. There are a wide
variety of cryptographic algorithms suitable for this purpose. An ideal
cryptography method would allow the user to do their job without breaking the
bank. Unfortunately, there is no magic formula that can address every
issue.Several algorithms balance cost and performance. A banking application
needs robust security at a high cost, while a gaming software that sends user
patterns for analytics cares more about speed and cost. Thus, choosing the
appropriate encryption technique for the user. This study offers important
insights in the process of selecting cryptographic algorithms in terms of each
algorithm's strengths, weaknesses, cost, and performance . In order to
demonstrate an entire performance analysis in this article, as opposed to just
theoretical comparisons, this research developed and thoroughly examined the
cost and performance of commonly used cryptographic algorithms, including
DES, 3DES, AES, RSA, and blowfish. According to the findings, blowfish
requires the smallest amount of time to decrypt files of various sizes (25K, 50K,
1M, 2M, 3M, and 4M), and it also consumes the smallest amount of memory. This
makes it approximately three times faster than other cryptographic algorithms.

The Internet of things (IoT) is one of those emerging technologies, which are
going to rule the world in the next few decades. The IoT environment not only enables
Human to Machine interaction but also fosters Machine to Machine connectivity.
Numerous IoT devices have poor security and insufficient computing power, making them
prime targets for hackers. The IoT environment uses lightweight cryptographic techniques
to address security requirements. Another security method for IoT devices is
steganography. In the contemporary Internet era, the ability to secure private information
is crucial, and steganography offers this capability. Due to its great ability to conceal
sensitive data, video has drawn the attention of numerous academics among all forms of
digital media. The main goal of this work is to examine several methods for fusing video
steganography and cryptography techniques. Additionally, a thorough investigation and
evaluation of a variety of video steganography methods in both compressed and raw
domains are also emphasized. The comprehensive analysis of prior material makes it
easier to have in-depth knowledge while creating approaches that combine cryptography
with steganography.

A client-server network is one of the most important topics in a computer network.
In this work, a real-time computer control system is designed based on the Client-Server
Model for a Multi-Agent Mobile Robot System (CSM-MAMRS) and is applied to a building
that consists of (N) floors and uses one mobile robot on each floor that has specific actions
in different types of environments. The new proposed CSM-MAMRS consists of four layers.
The first stage manages the network communication for each agent. The second stage solves
the major problems of path planning by using a proposed hybrid algorithm that combines
the Rapidly Exploring Random Tree Star (RRT*) and the Particle Swarm Optimization
(PSO) algorithms in order to provide the shortest and smoothest path with collision
avoidance between the starting and the target points in a static and dynamic robot
environments are used.. In the third stage, a velocity planner controller is based on an
inverse kinematic mobile robot model. In the fourth stage, the Hypertext Transfer Protocol
HTTP is used to send velocity values to real mobile robots via the Wireless Network Control
Administration. The simulation results and experimental works successfully achieved to
significant improvement in real-time when using three missions of three mobile robots on
different static map floors in the building. The maximum tracking pose errors for three robots
in the static enviormnets are 0.39 cm, 0.02 cm, 0.32 cm respectively, but the maximum
tracking pose error in the dynamic environments are 2.94 cm and 2.8 cm only for two mobile
robots along the maximum distance of 250 cm.

The expert system and artificial intelligence are still important modern
technologies. A structure can modify its behavior under dynamic stresses by using active
controls. The term "intelligent" or "smart" structures refers to these self-modifying
structures. The structural engineering discipline may experience a revolution thanks to
smart structure technologies. Particularly for huge structures, It is anticipated to have
major effects. Effects with regard to the avoidance of fatalities and damage to the
structure and its contents, particularly with regard to huge buildings that have thousands
of elements. An efficient control algorithm to establish the magnitude of the actual forces
to be applied to the construction is one of the most critical components in the successful
use of smart active control technology. An overview of the primary active control
approaches for the reduction of vibration in intelligent mechanical and civil structures
that are subject to external dynamic loads is provided in this study. Different control
algorithms' benefits and drawbacks are examined. Finally, recent advances in the study
of control algorithms are highlighted, including the use of multiparadigm strategies,
decentralized control, deep learning techniques applied to neural networks design of
controls for sustainability, and a merging of the domains of vibration control and
structural health monitoring.

Since the Internet has been more widely used and more people have access to
multimedia content, copyright hacking, and piracy have risen. By the use of watermarking
techniques, security, asset protection, and authentication have all been made possible. In
this paper, a comparison between fragile and robust watermarking techniques has been
presented to benefit them in recent studies to increase the level of security of critical media.
A new technique has been suggested when adding an embedded value (129) to each pixel
of the cover image and representing it as a key to thwart the attacker, increase security,
rise imperceptibility, and make the system faster in detecting the tamper from unauthorized
users. Using the two watermarking types in the same system reaches better results and
increases the power of the system and makes it robust against any attack and revel the
modification if any at the same time. PSNR has been used as a performance metric to
evaluate the study. The result of the new proposed watermark is 54. It is preferable to utilize
both a fragile and a robust watermark simultaneously.