IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING

t is known that many individuals suffer from chronic diseases such
as heart disease, high blood pressure, and sleep apnea, which requires constant
monitoring, which is only found in the hospital, and the high cost that the
individual cannot afford, especially at present. This paper proposes a system
through which an individual can monitor vital signs and can use the wearable
device by himself without the need for assistance. Wearable devices have been
used from the sensors where the proposed system uses six sensors which are the
electrocardiogram, the pulse oximeter, heart rate, blood pressure, skin
temperature, temperature, and humidity as it collects data and then transfers it
via Wi-Fi to the microcontroller on the Internet of things. The results of the
sensors have been successfully obtained. Finally, linked them to the Blynk
platform that displays the desired results for the individual.

Plant diseases are a severe threat to the environment, economy, and health. Early
disease identification remains a challenging task in Iraq due to the scarce of the necessary
resources and infrastructure. This paper uses various deep learning algorithms to detect different
diseases on plant leaves and detect healthy ones, using an RGB camera as a crucial part of our
real-time autonomous greenhouses' robot along with using two datasets, plant-village and cotton
dataset, to investigate the best convolutional neural network architecture. The first dataset
contained 10,190 images from the plant-village open datasets; it includes four crops with ten
distinct classes of diseased and healthy leaves. Moreover, the cotton dataset contained 2,204
images for training and 106 images for testing; it has four classes of diseased and healthy plants
and leaves. Different network architectures were tested in this paper for the best suitable
lightweight architecture for our mobile robot. Results show that the best performance is 99.908%
which achieved by the VGG16 network. The highest accuracy of VGG16 obtained in our research
makes it the best tool for our autonomous plant disease detection robot.

With the increasing use of media in communications, both academia
and industry pay attention to the content security of digital images. This
research presents a Homomorphic cryptosystem-based asymmetric picture
encryption technique (Paillier). The algorithm is used for securing images that
transmit over public unsecured channels. The Homomorphic property is used in
this paper, which is comprised of three steps: key generation, encryption, and
decryption. To realize such approach, the encryption cryptosystem must support
additional operation over encrypted data. This cryptosystem can be effective in
protecting images and supporting the construction of programs that can process
encrypted input and produce encrypted output

Steganography includes hiding text, image, or any sentient information inside another
image, video, or audio. It aims to increase individuals’ use of social media, the internet and web
networks to securely transmit information between sender and receiver and an attacker will not be
able to detect its information. The current article deals with steganography that can be used as
machine learning method, it suggests a new method to hide data by using unsupervised machine
learning (clustering k-mean algorithm). This system uses hidden data into the cover image, it
consists of three steps: the first step divides the cover image into three clusterings that more contrast
by using k-means cluster, the selects a text or image to be converted to binary by using ASCII
code, the third step hides a binary message or binary image in the cover image by using sequential
LSB method. After that, the system is implemented. The objective of the suggested system is obtained,
using Unsupervised Machine Learning (K-mean technique) to securely send sensitive information
without worrying about man-in-the-middle attack was proposed. Such a method is characterized by
high security and capacity. Through evaluation, the system uses PSNR, MSE, Entropy, and
Histogram to hide the secret message and secret image in the spatial domain in the cover image.

Software Defined Network (SDN) is a modern network architecture that has a
centralized controller. It is more flexible, and programmable due to the separation of the control
plane from the data plane. However, Distributed Denial of Service (DDoS) attacks is one of the
dangers that the SDN network is facing. It could attack and stop the controller from working,
causing the whole system to be down. Moreover, DDoS attacks can target the hosts and the
switches to stop the services for a long time as they could cause more damage to the network or
datacenter. In this work, a proposed approach is utilized to protect datacenter networks and
servers from DDoS attacks using entropy and real SDN-controller Python Network Operating
system (POX) by redirect traffic to the edge of the datacenter to minimize the damage. The results
of this experiment show how to detect abnormal traffics in an early stage and isolate them in a
server outside the datacenter to distribute the huge amount of traffic in more than one server and
avoid congestion on switches. Also, the throughput of the server was increased by about %16
during the suspected attack, this means maintaining the service until further analysis to be done on
the traffic. These results are compared with the direct block mitigation method which was mostly
used with the entropy detection method in previous researches. Moreover, this work is done to
confirm whether the suspected traffic is an actual attack or not. Therefore, this method will
decrease the false positives of detection.

In recent two years, information systems have been adopted in most fields of
life due to the health state around the world. At the same time, the risk factor of
security attacks is increased sharply as well. These attacks consider different actions
toward damaging the data of systems or even making down their work. In this paper, a
study of cyber-threats (attacks) on Wireless Sensor Network (WSN) is presented. This
study illustrates the effects of the cyber-threats on the WSN according to the network
layers, as well as their privacy concerns. The outcome of this study is the classification
of these attacks that can lead to produce cyber-security systems which can prevent
them from damaging the involved information systems.

Biometrics signs are the most important factor in the human
recognition field and considered an effective technique for person
authentication systems. Voice recognition is a popular method to use due to its
ease of implementation and acceptable effectiveness. This research paper will
introduce a speaker recognition system that consists of preprocessing
techniques to eliminate noise and make the sound smoother. For the feature
extraction stage, the method Mel Frequency Cepstral Coefficient (MFCC) is
used, and in the second step, the four features (FF) Mean, Standard Division,
Zero-Cross and Amplitude, which added to (MFCC) to improve the results. For
data representation, vector quantization has been used. The evaluation method
(k-fold cross-validation) has been used. Supervised machine learning (SML) is
Department of Computer Science, University of Technology- Iraq, Baghdad, Iraq
1 cs.19.12@grad.uotechnology.edu.iq, 2110014@uotechnology.edu.iq,
3 mohammad.n.fadhil@uotechnology.edu.iq
Quadratic Discriminant Analysis (QDA) classification
algorithms. And the results obtained by the algorithm (QDA) varied between 98
percent and 98.43 percent, depending on the way of features extraction that was
used. These results are satisfactory and reliable.

The widespread of recent multimedia, including various 3D model
applications in different domains of areas, may lead to 3D models being stolen and
attacked by hackers. Moreover, 3D models must be protected from unauthorized
users and when transmitting over the internet. Nowadays the 3D model protection
is a very important issue. This paper proposed a scheme that provides high
protection for the textured 3D model by implementing multiple levels of security.
The first level of security is achieved by encrypting the texture map based on a key
generated by a 2D Logistic chaotic map. The second level of security is
implemented by modifying the vertices values of the 3D mesh based on keys
generated by the 3D Lorenz chaotic map. The proposed scheme was implemented
on various 3D textured models varying in the number of vertices and faces. The
experimental results show that the proposed scheme has a good encryption and
provides high security by completely deforms the whole texture and 3D mesh of the
textured 3D model into the two levels. The encryption scheme has a large key space
10135 making the scheme resists violent attacks. The Hausdorff Distance (HD) and
histogram metrics are adopted to calculate the matching degree between the
original and extracted model. The results show that the original and extracted
model are identical through the values of HD, which are approximate to zero, and
the histogram visually is similar.

The university courses timetabling problem (UCTP) is a big topic
among academics and institutions since it occurs every academic year. In
general, UCTP is the distribution of events across slots time for each room
based on a list of restrictions provided in one semester, such as (hard constraint
and soft constraint), with the objective of avoiding conflicts in such
assignments. Hard constraints should never be breached when striving to
satisfy as many soft constraints as possible. There are many different methods
used in automating the problems of the university timetabling course in higher
education institutions. This paper presents an improved algorithm for the
Meerkat Clan to solve the UCTP. This is done by studying the behavior of the
Meerkat Clan Algorithm and Specifying the points that are able to improve
without changing the main behavior of the Meerkat Clan Algorithm. And by
testing with four datasets of different sizes, good results were obtained by
optimizing this algorithm.