Iraqi Journal for Electrical And Electronic Engineering

Energy constraint has become the major challenge for designing wireless sensor networks. Network lifetime is considered
as the most substantial metric in these networks. Routing technique is one of the best choices for maintaining network
lifetime. This paper demonstrates implementation of new methodology of routing in WSN using firefly swarm intelligence.
Energy consumption is the dominant issue in wireless sensor networks routing. For network cutoff avoidance while
maximize net lifetime energy exhaustion must be balanced. Balancing energy consumption is the key feature for rising
nets lifetime of WSNs. This routing technique involves determination of optimal route from node toward sink to make
energy exhaustion balance in network and in the same time maximize network throughput and lifetime. The proposed
technique show that it is better than other some routing techniques like Dijkstra routing, Fuzzy routing, and ant colony
(ACO) routing technique. Results demonstrate that the proposed routing technique has beat the three routing techniques
in throughput and extend net lifetime.

Address Resolution Protocol (ARP) is used to resolve a host’s MAC address, given its IP address. ARP is stateless, as
there is no authentication when exchanging a MAC address between the hosts. Hacking tactics using ARP spoofing are
constantly being abused differently; many previous studies have prevented such attacks. However, prevention requires
modification of the underlying network protocol or additional expensive equipment, so applying these methods to the
existing network can be challenging. In this paper, we examine the limitations of previous research in preventing ARP
spoofing. In addition, we propose a defence mechanism that does not require network protocol changes or expensive
equipment. Before sending or receiving a packet to or from any device on the network, our method checks the MAC and
IP addresses to ensure they are correct. It protects users from ARP spoofing. The findings demonstrate that the proposed
method is secure, efficient, and very efficient against various threat scenarios. It also makes authentication safe and easy
and ensures data and users’ privacy, integrity, and anonymity through strong encryption techniques.

According to the characteristic of HVS (Human Visual System) and the association memory ability of neural network,
an adaptive image watermarking algorithm based on neural network is proposed invisible image watermarking is
secret embedding scheme for hiding of secret image into cover image file and the purpose of invisible watermarking
is copyrights protection. Wavelet transformation-based image watermarking techniques provide better robustness for
statistical attacks in comparison to Discrete Cosine Transform domain-based image watermarking. The joined method
of IWT (Integer Wavelet Transform) and DCT (Discrete Cosine Transform) gives benefits of the two procedures. The
IWT have impediment of portion misfortune in embedding which increments mean square estimate as SIM and results
diminishing PSNR. The capacity of drawing in is improved by pretreatment and re-treatment of image scrambling
and Hopfield neural network. The proposed algorithm presents hybrid integer wavelet transform and discrete cosine
transform based watermarking technique to obtain increased imperceptibility and robustness compared to IWT-DCT
based watermarking technique. The proposed watermarking technique reduces the fractional loss compared to DWT
based watermarking.

The learning process in online lectures through the Learning Management System (LMS) will produce a learning flow
according to the event log. Assessment in a group of parallel classes is expected to produce the same assessment point
of view based on the semester lesson plan. However, it does not rule out the implementation of each class to produce
unequal fairness. Some of the factors considered to influence the assessment in the classroom include the flow of
learning, different lecturers, class composition, time and type of assessment, and student attendance. The implementation
of process mining in fairness assessment is used to determine the extent to which the learning flow plays a role in the
assessment of ten parallel classes, including international classes. Moreover, a decision tree algorithm will also be
applied to determine the root cause of the student assessment analysis based on the causal factors. As a result, there
are three variables that have effects on student graduation and assessment, i.e attendance, class and gender. Variable
lecturer does not have much impact on the assessment, but has an influence on the learning flow.

The decline in the marketing volume of Rabobank Group ICT is a serious incident as it can hinder the implementation of
an increasing number of software releases for business development. The Service Desk Agent records the activities that
occur to find out the problems experienced in the form of an event log. Process mining can be used to generate process
model visualizations based on event logs to explicitly monitor the business. Fuzzy Miner and Heuristic Miner algorithms
can be used to handle complex event logs. In this study, an analysis of the Rabobank Group ICT incident was carried
out with process mining using the Fuzzy Miner and Heuristic Miner algorithms. Process mining is done by discovery,
conformance, and enhancement. Based on the results of the study, it is known that the division of the work area is
not good enough to cause a team to work on a lot of events while there are other teams that only work on one event.
Therefore, it is necessary to have a clear and balanced division of domains and workloads so that incidents do not recur.

Breast cancer is one of the most critical diseases suffered by many people around the world, making it the most common
medical risk they will face. This disease is considered the leading cause of death around the world, and early detection is
difficult. In the field of healthcare, where early diagnosis based on machine learning (ML) helps save patients’ lives
from the risks of diseases, better-performing diagnostic procedures are crucial. ML models have been used to improve
the effectiveness of early diagnosis. In this paper, we proposed a new feature selection method that combines two filter
methods, Pearson correlation and mutual information (PC-MI), to analyse the correlation amongst features and then
select important features before passing them to a classification model. Our method is capable of early breast cancer
prediction and depends on a soft voting classifier that combines a certain set of ML models (decision tree, logistic
regression and support vector machine) to produce one model that carries the strengths of the models that have been
combined, yielding the best prediction accuracy. Our work is evaluated by using the Wisconsin Diagnostic Breast Cancer
datasets. The proposed methodology outperforms previous work, achieving 99.3% accuracy, an F1 score of 0.9922, a
recall of 0.9846, a precision of 1 and an AUC of 0.9923. Furthermore, the accuracy of 10-fold cross-validation is 98.2%.

This paper focuses on the vibration suppression of a half-car model by using a modified PID controller. Mostly, car
vibrations could result from some road disturbances, such as bumps or potholes transmitted to a car body. The proposed
controller consists of three main components as in the case of the conventional PID controller which are (Proportional,
Integral, and Derivative) but the difference is in the positions of these components in the control loop system. Initially, a
linear half-car suspension system is modeled in two forms passive and active, the activation process occurred using a
controlled hydraulic actuator. Thereafter, the two systems have been simulated using MATLAB/Simulink software in
order to demonstrate the dynamic response. A comparison between conventional and modified PID controllers has been
carried out. The resulting dynamic response of the half-car model obtained from the simulation process was improved
when using a modified PID controller compared with the conventional PID controller. Moreover, the efficiency and
performance of the half-car model suspension have been significantly enhanced by using the proposed controller. Thus,
achieving high vehicle stability and ride comfort.

Self-driving cars are a fundamental research subject in recent years; the ultimate goal is to completely exchange the
human driver with automated systems. On the other hand, deep learning techniques have revealed performance and
effectiveness in several areas. The strength of self-driving cars has been deeply investigated in many areas including
object detection, localization as well, and activity recognition. This paper provides an approach to deep learning; which
combines the benefits of both convolutional neural network CNN together with Dense technique. This approach learns
based on features extracted from the feature extraction technique which is linear discriminant analysis LDA combined
with feature expansion techniques namely: standard deviation, min, max, mod, variance and mean. The presented
approach has proven its success in both testing and training data and achieving 100% accuracy in both terms.

In the world of modern technology and the huge spread of its use, it has been combined with healthcare systems and the
establishment of electronic health records (EHR) to follow up on patients. This merging of technology with healthcare
has allowed for more accurate EHRs that follow a patient to different healthcare facilities. Timely exchange of electronic
health information (EHR) between providers is critical for aiding medical research and providing fast patient treatment.
As a result, security issues and privacy problems are viewed as significant difficulties in the healthcare system. Several
remote user authentication methods have been suggested. In this research, we present a feasible patient EHR migration
solution for each patient. finally, each patient may securely delegate their current hospital’s information system to a
hospital certification authority in order to receive migration proof that can be used to transfer their EHR to a different
hospital. In addition, the proposed scheme is based on crypto-hash functions and asymmetric cryptosystems by using
homomorphic cryptography. The proposed scheme carried out two exhaustive formal security proofs for the work that
was provided. Using Scyther, a formal security tool, we present a secure user authentication technique in the proposed
healthcare scheme that ensures security and informal analysis.

The development of renewable resources and the deregulation of the market have made forecasting energy demand
more critical in recent years. Advanced intelligent models are created to ensure accurate power projections for several
time horizons to address new difficulties. Intelligent forecasting algorithms are a fundamental component of smart
grids and a powerful tool for reducing uncertainty in order to make more cost- and energy-efficient decisions about
generation scheduling, system reliability and power optimization, and profitable smart grid operations. However, since
many crucial tasks of power operators, such as load dispatch, rely on short-term forecasts, prediction accuracy in
forecasting algorithms is highly desired. This essay suggests a model for estimating Denmark’s power use that can
precisely forecast the month’s demand. In order to identify factors that may have an impact on the pattern of a number
of unique qualities in the city direct consumption of electricity. The current paper also demonstrates how to use an
ensemble deep learning technique and Random forest to dramatically increase prediction accuracy. In addition to their
ensemble, we showed how well the individual Random forest performed.

Path planning is an essential concern in robotic systems, and it refers to the process of determining a safe and optimal
path starting from the source state to the goal one within dynamic environments. We proposed an improved path
planning method in this article, which merges the Dijkstra algorithm for path planning with Potential Field (PF) collision
avoidance. In real-time, the method attempts to produce multiple paths as well as determine the suitable path that’s
both short and reliable (safe). The Dijkstra method is employed to produce multiple paths, whereas the Potential Field
is utilized to assess the safety of each route and choose the best one. The proposed method creates links between the
routes, enabling the robot to swap between them if it discovers a dynamic obstacle on its current route. Relating to path
length and safety, the simulation results illustrate that Dynamic Dijkstra-Potential Field (Dynamic D-PF) achieves better
performance than the Dijkstra and Potential Field each separately, and going to make it a promising solution for the
application of robotic automation within dynamic environments.

This paper discusses the design and performance of a frequency reconfigurable antenna for Internet of Things (IoT)
applications. The antenna is designed to operate on multiple frequency bands and be reconfigurable to adjust to different
communication standards and environmental conditions. The antenna design consists of monopole with one PIN diode
and 50Ω feed line. By changing the states of the diode, the antenna can be reconfigured to operate in a dual-band mode
and a wideband mode. The performance of the antenna was evaluated through simulation. The antenna demonstrated
good impedance matching, acceptable gain, and stable radiation patterns across the different frequency bands. The
antenna has compact dimensions of (26×19×1.6) mm3. It covers the frequency range 2.95 GHz -8.2 GHz, while the
coverage of the dual- band mode is (2.7-3.8) GHz and (4.57-7.4) GHz. The peak gain is 1.57 dBi for the wideband mode
with omnidirectional radiation pattern. On the other hand, the peak gain of the dual-band mode is 0.87 dBi at 3 GHz
and 0.47 dBi at 6 GHz with an omnidirectional radiation pattern too.

This article presents a power-efficient low noise amplifier (LNA) with high gain and low noise figure (NF) dedicated
to satellite communications at a frequency of 435 MHz. LNAs’ gain and NF play a significant role in the designs for
satellite ground terminals seeking high amplification and maintaining a high signal-to-noise ratio (SNR). The proposed
design utilized the transistor (BFP840ESD) to achieve a low NF of 0.459 dB and a high-power gain of 26.149 dB. The
study carries out the LNA design procedure, from biasing the transistor, testing its stability at the operation frequency,
and finally terminating the appropriate matching networks. In addition to the achieved high gain and low NF, the
proposed LNA consumes as low power as only 2 mW.

A wireless body area network (WBAN) connects separate sensors in many places of the human body, such as clothes,
under the skin. WBAN can be used in many domains such as health care, sports, and control system. In this paper,
a scheme focused on managing a patient’s health care is presented based on building a WBAN that consists of three
components, biometric sensors, mobile applications related to the patient, and a remote server. An excellent scheme is
proposed for the patient’s device, such as a mobile phone or a smartwatch, which can classify the signal coming from a
biometric sensor into two types, normal and abnormal. In an abnormal signal, the device can carry out appropriate
activities for the patient without requiring a doctor as a first case. The patient does not respond to the warning message
in a critical case sometimes, and the personal device sends an alert to the patient’s family, including his/her location. The
proposed scheme can preserve the privacy of the sensitive data of the patient in a protected way and can support several
security features such as mutual authentication, key management, anonymous password, and resistance to malicious
attacks. These features have been proven depending on the Automated Validation of Internet Security Protocols and
Applications. Moreover, the computation and communication costs are efficient compared with other related schemes.

Currently, an approach involving a coder with a combined structure for compressing images combining several different
coders, the system for connecting them to various bit planes and the control system for these connections has not been
studied. Thus, there is a need to develop a structure and study the effectiveness of a combined codec for compressing
images of various types without loss in the spatial domain based on arithmetic and (Run-Length Encoding) RLE-coding
algorithms. The essence of separate effective coding is to use independent coders of the same type or one coder connected
to the planes alternately in order to compress the higher and lower bit planes of the image or their combinations. In this
paper, the results of studying the effectiveness of using a combination of arithmetic and RLE-coding for several types of
images are presented. As a result of developing this structure, the effectiveness of combined coding for compressing
the differences in the channels of hyperspectral images (HSI) has been established, as hyperspectral images consist of
multi-spectral bands, instead of just the typical three bands (RGB) or (YCbCr) found in regular images. Where, each
pixel in a hyperspectral image represents the entire spectrum of light reflected by the object or scene at that particular
location.

The goal for collaborative robots has always driven advancements in robotic technology, especially in the manufacturing
sector. However, this is not the case in service sectors, especially in the health sector. Thus, this lack of focus has now
opened more room for the design and development of service robots that can be used in the health sector to help patients
with ailments, cognitive problems, and disabilities. There is currently a global effort towards the development of new
products and the use of robotic medical devices and computer-assisted systems. However, the major problem has been the
lack of a thorough and systematic review of robotic research into disease and epidemiology, especially from a technology
perspective. Also, medical robots are increasingly being used in healthcare to perform a variety of functions that improve
patient care. This scoping review is aimed at discovering the types of robots used in healthcare and where they are
deployed. Moreover, the current study is an overview of various forms of robotic technology and its uses the healthcare
industry. The considered technologies are the products of a partnership between the healthcare sector and academia.
They demonstrate the research and testing that are necessary for the service of robot development before they can be
employed in practical applications and service scenarios. The discussion also focused on the upcoming research areas in
robotic systems as well as some important technologies necessary for human-robot collaboration, such as wireless sensor
networks, big data, and artificial intelligence.

Advancements in internet accessibility and the affordability of digital picture sensors have led to the proliferation
of extensive image databases utilized across a multitude of applications. Addressing the semantic gap between lowlevel
attributes and human visual perception has become pivotal in refining Content Based Image Retrieval (CBIR)
methodologies, especially within this context. As this field is intensely researched, numerous efficient algorithms for
CBIR systems have surfaced, precipitating significant progress in the artificial intelligence field. In this study, we
propose employing a hard voting ensemble approach on features derived from three robust deep learning architectures:
Inception, Exception, and Mobilenet. This is aimed at bridging the divide between low-level image features and human
visual perception. The Euclidean method is adopted to determine the similarity metric between the query image and the
features database. The outcome was a noticeable improvement in image retrieval accuracy. We applied our approach
to a practical dataset named CBIR 50, which encompasses categories such as mobile phones, cars, cameras, and cats.
The effectiveness of our method was thereby validated. Our approach outshone existing CBIR algorithms with superior
accuracy (ACC), precision (PREC), recall (REC), and F1-score (F1-S), proving to be a noteworthy addition to the field
of CBIR. Our proposed methodology could be potentially extended to various other sectors, including medical imaging
and surveillance systems, where image retrieval accuracy is of paramount importance.

Wireless Multimedia Sensor Networks (WMSNs) are being extensively utilized in critical applications such as environmental
monitoring, surveillance, and healthcare, where the reliable transmission of packets is indispensable for
seamless network operation. To address this requirement, this work presents a pioneering Distributed Dynamic Cooperation
Protocol (DDCP) routing algorithm. The DDCP algorithm aims to enhance packet reliability in WMSNs by
prioritizing reliable packet delivery, improving packet delivery rates, minimizing end-to-end delay, and optimizing energy
consumption. To evaluate its performance, the proposed algorithm is compared against traditional routing protocols
like Ad hoc On-Demand Distance Vector (AODV) and Dynamic Source Routing (DSR), as well as proactive routing
protocols such as Optimized Link State Routing (OLSR). By dynamically adjusting the transmission range and selecting
optimal paths through cooperative interactions with neighboring nodes, the DDCP algorithm offers effective solutions.
Extensive simulations and experiments conducted on a wireless multimedia sensor node testbed demonstrate the superior
performance of the DDCP routing algorithm compared to AODV, DSR, and OLSR, in terms of packet delivery rate,
end-to-end delay, and energy efficiency. The comprehensive evaluation of the DDCP algorithm against multiple routing
protocols provides valuable insights into its effectiveness and efficiency in improving packet reliability within WMSNs.
Furthermore, the scalability and applicability of the proposed DDCP algorithm for large-scale wireless multimedia sensor
networks are confirmed. In summary, the DDCP algorithm exhibits significant potential to enhance the performance of
WMSNs, making it a suitable choice for a wide range of applications that demand robust and reliable data transmission.

In this work, the phase lock loop PLL-based controller has been adopted for tracking the resonant frequency to achieve
maximum power transfer between the power source and the resonant load. The soft switching approach has been
obtained to reduce switching losses and improve the overall efficiency of the induction heating system. The jury’s stability
test has been used to evaluate the system’s stability. In this article, a multilevel inverter has been used with a series
resonant load for an induction heating system to clarify the effectiveness of using it over the conventional full-bridge
inverter used for induction heating purposes. Reduced switches five-level inverter has been implemented to minimize
switching losses, the number of drive circuits, and the control circuit’s complexity. A comparison has been made between
the conventional induction heating system with full bridge inverter and the induction heating system with five level
inverter in terms of overall efficiency and total harmonic distortion THD. MATLAB/ SIMULINK has been used for
modeling and analysis. The mathematical analysis associated with simulation results shows that the proposed topology
and control system performs well.

In this study, we propose a compact, tri-band microstrip patch antenna for 5G applications, operating at 28 GHz, 38
GHz, and 60 GHz frequency bands. Starting with a basic rectangular microstrip patch, modifications were made to
achieve resonance in the target frequency bands and improve S11 performance, gain, and impedance bandwidth. An
inset feed was employed to enhance antenna matching, and a π–shaped slot was incorporated into the radiating patch
for better antenna characteristics. The design utilized a Rogers RT/Duroid-5880 substrate with a 0.508 mm thickness, a
2.2 dielectric constant, and a 0.0009 loss tangent. The final dimensions of the antenna are 8 x 8.5 x 0.508 mm3. The
maximum S11 values obtained at the resonant frequencies of 27.9 GHz, 38.4 GHz, and 56 GHz are -15.4 dB, -18 dB, and
-26.4 dB, respectively. The impedance bandwidths around these frequencies were 1.26 GHz (27.245 - 28.505), 1.08 GHz
(37.775 - 38.855), and 12.015 GHz (51.725 - 63.74), respectively. The antenna gains at the resonant frequencies are 7.96
dBi, 6.82 dBi, and 7.93 dBi, respectively. Radiation efficiencies of 88%, 84%, and 90% were achieved at the resonant
frequencies. However, it is observed that the radiation is maximum in the broadside direction at 28 GHz, although it
peaks at −41o/41o and −30o/30o at 38 GHz and 56 GHz, respectively. Furthermore, the antenna design, simulations,
and optimizations were carried out using HFSS, and the results were verified with CST. Both simulators showed a
reasonable degree of consistency, confirming the effectiveness and reliability of the proposed antenna design.