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DOI: 10.4018/IJSKD.330150
Volume 15 • Issue 1
This article published as an Open Access article distributed under the terms of the Creative Commons Attribution License
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*Corresponding Author
1
Nivetha S., Department of Computer Science, Periyar University, Salem, India*
Hannah Inbarani H., Department of Computer Science, Periyar University, Salem, India
The difficulty in predicting early cancer is due to the lack of early illness indicators. Metaheuristic
approaches are a family of algorithms that seek to find the optimal values for uncertain problems
with several implications in optimization and classification problems. An automated system for
recognizing illnesses can respond with accuracy, efficiency, and speed, helping medical professionals
spot abnormalities and lowering death rates. This study proposes the Novel Hybrid GAO (Genetic
Arithmetic Optimization algorithm based Feature Selection) (Genetic Arithmetic Optimization
Algorithm-based feature selection) method as a way to choose the features for several machine
learning algorithms to classify readily available data on COVID-19 and lung cancer. By choosing just
important features, feature selection approaches might improve performance. The proposed approach
employs a Genetic and Arithmetic Optimization to enhance the outcomes in an optimization approach.
Arithmetic Optimization, CNN Features, Computed Tomography, Genetic Algorithm, Metaheuristic Approaches,
Novel Hybrid GAO
The most common cancer that claims lives in both men and women is lung cancer. According to
American Cancer Society statistics, there are 220,000 new cases each year, 160,000 people die
from the disease, and 15% of people with all stages of the disease survive for 5 years. However, the
localized stage has a 5-year longevity rate of roughly 50%. In the localized stage, cancer does not
spread outside the body, such as to lymph nodes (Aboamer et al., 2019; Ajeil et al., 2020a; Habibifar
et al., 2019) The specific kind of tumors as well as additional factors like prognostics general health,
etc., all have an impact on the 5-year survival rate. The main determinant of lung cancer survival
rate is early recognition. Before lung cancer spreads to other parts of the body, symptoms do not
manifest in the lung. Lung cancer is detected using various techniques, including microarray data
analysis, sputum analysis, Computed Tomography (CT) scans, and chest radiography. Lung cancer
identification with widespread chest CT screening is a promising technique.
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A recent infectious disease called COVID-19 has been circulating all over the world (Azar &
Hassanien, 2022; Abbas et al., 2022; Abdelmalek et al., 2018). The pandemic has had several health
impacts, including economic loss, disruption of communication and information systems, social
distancing, and quarantine procedures. Quarantine is an example of a confinement policy used to
maintain a safe distance between people. The isolation step involves the treatment of people with
suspected symptoms so that they can return to normal conditions; governments maintain medical
facilities during this phase.
Along with the extensive usage of Machine Learning and Deep Learning approaches, the various
feature selection methods have been utilized in statistics and pattern recognition for several years.
(Waleed et al., 2022; Wen et al., 2022; Aboamer et al., 2014b; Acharyulu et al., 2021; Ahmadian et
al., 2021; Ajeil et al., 2020b; Hamida et al., 2022a; Azar, 2020a,b). When there was an excessive
amount of data that needed to be processed quickly, feature selection techniques were necessary (Azar
et al., 2023d). These feature selection techniques were utilized to achieve the objectives of increasing
classifier accuracy, decreasing dimensionality, removing superfluous and unrelated data, and more. It
also aided in enhancing data comprehension and reducing the time required to run learning algorithms.
Deep learning techniques make very small elements in images visible that would not otherwise
exist. “Convolutional Neural Networks (CNNs)” are the top excellent among academics for
classification-related tasks in medical imaging issues because of their prowess in deep feature
extraction and learning. CNNs are useful for detecting the features that discriminate different
objects from each other. (Aboamer et al., 2014a; Ali et al., 2022b). However, CNNs are not suitable
for applications with high learning capacity and large amounts of data as they are very sensitive to
hyperparameters. Moreover, it is needed to consider the amount of data, since neural networks have
a large complexity and require much time to process a dataset. These factors can make it challenging
for practitioners to manually adjust these hyper-parameters so that they can be optimized effectively.
A heuristic is a method designed to solve a problem more quickly when more conventional
methods are inefficient. (Ajeil, et al., 2020a; Al-Qassar et al., 2021a; Amara et al., 2019; Elkholy et
al., 2020a; Azar & Banu, 2022). A black-box optimizer known as a meta-heuristic algorithm is given
a collection of issue variables, including some restrictions in the form of limitations. The optimizer
changes these variables by performing an updating procedure up until it finds an objective function’s
optimal value. The result is a close-to-optimal solution that has the objective function’s maximum
and minimum values. The objective is to find the best answers in a fair amount of time with the least
amount of computational complexity.
Combinations of Genetic Algorithms and Arithmetic Optimisation Algorithms-based feature
selection approaches are used in this work to increase the efficiency of machine learning algorithms
for lung cancer and COVID classification. Genetic Algorithm (GA) and Arithmetic Optimisation
Algorithm (AOA) are two methods that can be used in combination to solve optimization problems.
AOA focuses on performing mathematical operations to optimize each solution, whereas GA uses
population-based search and genetic operators like mutation and crossover. In this hybrid approach, a
population of individuals representing potential solutions is formed using a genetic algorithm as the
fundamental framework. The hybrid GA with the AOA technique can be a potent method for resolving
challenging optimization issues, using the advantages of both methods to produce superior results.
Metaheuristics are a class of optimization techniques that provide versatile and efficient solutions to
complex problems across various domains (Hussien et al., 2023; Ganguly et al., 2023; Hussain et al.,
2023; Ali et al., 2022a; Abdul-Kareem et al., 2022; Sekhar et al., 2022; El Kafazi et al., 2021; Azar
and Serrano, 2019, 2020d; Elkholy et al., 2020b; Mohamed et al., 2020a; Ben Smida et al., 2018;
Akyol & Alatas, 2017). By drawing inspiration from natural processes, such as evolution, swarm
behavior, and annealing, metaheuristics navigate solution spaces to discover high-quality solutions
that traditional methods might struggle to find. These algorithms, which include genetic algorithms,
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particle swarm optimization, simulated annealing, and ant colony optimization, offer a balance between
exploration and exploitation, enabling them to tackle optimization challenges with irregular, nonlinear,
or multi-modal landscapes. While not guaranteed to find the global optimum, metaheuristics excel in
finding satisfactory solutions within reasonable timeframes, making them invaluable tools for solving
real-world problems in engineering, logistics, finance, and beyond. They can be broadly alienated
into deuce classes: neighborhood-based algorithms and population-based algorithms.
Neighborhood-Based Algorithms are metaheuristics exploration and change solutions in the
immediate locality of the present solution to iteratively probe the exploration space. Population-
based algorithms are metaheuristic algorithm as it is more commonly known, provides a universal
optimization basis that is easily adaptable to a variety of optimization problems by discovering and
manipulating the search space through the use of operators and carrying out operations, such as
parameter tuning and modification. A few of these sources of inspiration are “Swarm-Inspired,”
“Chemistry-Inspired”, “Human-Inspired”, “Plant-Inspired”, “Maths-Inspired” and “Physics-Inspired”.
The first class of population-based natural metaheuristics is called swarm-inspired metaheuristics.
Swarms imitate a variety of societal animal behaviors, such as the foraging behavior of ants, the
flocking behavior of birds, the schooling behavior of fish, the moulting behavior of bacteria, and the
herding behavior of animals, among many others. Some of the swarm-inspired algorithms are given
below, “Ant Colony Optimisation” (Dorigo et al., 1999; Asad et al., 2013a,b, 2014a,b; Dey et al., 2015;
Moftah et al., 2014;), “Cuckoo Search” (Yang & Deb, 2010), “Artificial Bee Colony” (Karaboga,
2010), “Particle Swarm” (Kennedy & Eberhart, 1995), “Firefly” (Yang & He, 2013), Krill Herd
(Wang et al., 2014), Bat (Yang & Gandomi, 2012), Manta Ray Foraging Optimisation (Zhao et al.,
2020), Moth-Flame Optimisation (Mirjalili, 2015), Dragonfly (Mirjalili, 2016a), Marine Predators
(Faramarzi et al., 2020a), Grey Wolf Optimisation (Emary et al., 2015),(Nivetha et al., 2021a) and
Whale Optimisation (Mirjalili & Lewis, 2016) are a few examples of optimization algorithms.
The second class of “population-based” nature-inspired metaheuristics, called Chemistry-Inspired
metaheuristics, draws its primary source of inspiration from the nature of chemical processes to solve
contemporary challenges. This area includes techniques like “Chemical Reaction Optimisation” (Lam
et al., 2012), “Henry Gas Solubility Optimisation” (Hashim et al., 2019), and “Artificial Chemical
Reaction Optimisation” (Alatas, 2011).
A third kind of “population-based”, naturally inspired metaheuristics called “human-inspired”
models human dominance, IQ, and behavior. The algorithms “Imperialist Competitive Algorithm”
(Atashpaz- Gargari et al., 2007), “Human Mental Search” (Azar et al., 2020b; Mousavirad et al.,
2017), “Search and Rescue Optimisation” (Shabani et al., 2020), “Election Algorithm” (Emami
& Derakhshan, 2015), “Gaining Sharing Knowledge-Based Algorithm” (Mohamed et al., 2020b),
“Forensic-Based Investigation Optimisation” (Shaheen et al., 2020), “Brain Storm Optimisation” (Shi,
2011), “Teaching Learning-Based Optimisation” (Rao et al., 2011), “Class Topper Optimisation”
(Das et al., 2018), and “Football Game Algorithm” (Fadakar & Ebrahimi, 2016), and “Life Choice-
Based Optimisation” (Khatri et al., 2020), “Coronavirus Herd Immunity Optimization” (Al-Betar et
al., 2021) and “Battle Royale Optimization” (Rahkar, 2021).
Plant-Inspired “population-based” nature-inspired metaheuristics, the fifth class of metaheuristics,
fundamentally mimic the intelligent behavior exhibited by plants. These are only a few of the well-
known plant-based algorithms: “Plant Growth Optimisation” (Cai et al., 2008), “Plant Photosynthetic
Algorithm” (Murase et al., 2000), “Plant Propagation Algorithm” (Salhi & Fraga, 2011), “Artificial
Plant Optimization Algorithm” (Cui et al., 2013), “Paddy Field Algorithm” (Kong et al., 2012),
“Fertile Field Algorithm” (Mohammadi et al., 2020), “Flower Pollination Algorithm” (Yang et al.,
2013), “Path Planning Inspired by Plant Growth” (Zhou et al., 2017), “Invasive Weed Optimisation”
(Karimkashi & Kishk, 2010), “Rooted Tree Optimisation” (Labbi et al., 2016), “Sapling Growing
up Algorithm” (Karci et al., 2007), and “Root Growth Algorithm” (Zhang et al., 2014) are some
examples of algorithms.
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The sixth category of population-based, nature-inspired meta-heuristic optimization is the
Math-Inspired category, which essentially tends to replicate the approach of numerical techniques,
mathematical programming, and its emphasis on resolving a range of constraints and optimization
issues in the actual world. The “Hyper-Spherical Search Algorithm” (Karami et al., 2014; Azar
et al., 2020o), “Radial Movement Optimisation” (Rahmani & Yusof, 2014), “Stochastic Fractal
Search” (Salimi, 2015), “Golden Ratio Optimisation Method” (Nematollahi et al., 2020), “Sine
Cosine Algorithm” (Mirjalili, 2016b) and “Arithmetic Optimisation Algorithm” (Abualigah et al.,
2021; Azar et al., 2020f,g) are a few well-known algorithms that draw inspiration from mathematics.
Physics-Inspired meta-heuristic optimization is the final class of “Population-based Nature-
Inspired meta-heuristic optimization”. Physics-Inspired in which the main source of inspiration is
the physical processes, which are further formulated into solutions to resolve the problems. A few
popular physics-inspired algorithms are “Equilibrium Optimizer” (Faramarzi et al., 2020b), “Multi-
Verse Optimization” (Mirjalili et al., 2016; Azar and Kamal, 2021a,b,c; Azar et al., 2021), “Bang-
Big Big-Crunch Algorithm” (Erol et al., 2006), “Magnetic Charged System Search” (Zhao et al.,
2019), “Central Force Optimization” (Formato, 2007), “Thermal Exchange Optimization” (Kaveh &
Dadras, 2017), “Ray Optimization” (Kaveh & Khayatazad, 2012), “Gravitational Search Algorithm”
(Rashedi et al., 2009), “Artificial Physicomimetics Optimization” (Xie et al., 2010), “Optics Inspired
Optimisation” (Kashan, 2015), “Electromagnetic Field Optimization” (Abedinpourshotorban et al.,
2016), “Gravitational Local Search Optimization” (Rashedi et al., 2018) and “Electromagnetism-like
Algorithm” (Birbil et al., 2003; Azar et al., 2023a).
Image processing approaches that include Deep Learning aid in the detection of cancer utilizing image
processing technologies (Azar et al., 2018b; 2020; Chowdhuri et al.,2014a; Inbarani et al., 2015a;
Hassanien & Azar, 2015; Mjahed et al., 2020; Kumar et al., 2018). The optimization of complicated,
high-dimensional problems is the issue that the hybrid technique combining Genetic Algorithm (GA)
and Arithmetic Optimisation Algorithm (AOA) seeks to address. Finding the ideal set of characteristics
or variables to maximize or minimize a specific objective function is a common task in these issues.
However, it might be difficult to effectively examine the enormous search field for these issues.
1. Accuracy and diagnosis errors in medical applications: Lower accuracy, increase Classification
accuracy, and lower diagnostic errors.
2. Avoiding local minima and overfitting: In integrated optimization designs, avoid overfitting, get
to the optimum local optima without becoming stuck, and strike a balance between exploitation
and exploration.
3. Optimise parameters: To obtain global optimality and steer clear of suboptimal solutions when
solving optimization problems.
4. Scalability for complex issues: To efficiently address high-dimensional and large-scale problems,
develop scalable optimization strategies.
5. Time complexity and convergence speed: Improve convergence speed while keeping the quality
of the solutions.
1. Develop a hybrid framework: Propose a hybrid framework that combines Genetic Algorithm
(GA) and Arithmetic Optimization Algorithm (AOA) to enhance optimization performance.
2. Feature selection: Incorporate an approach for choosing the finest subclass of features, leveraging
metaheuristic approaches to efficiently handle the NP-hard task of feature selection.
3. Enhance performance: Improve solution quality, accelerate convergence, and enable effective
exploration of the solution space through the integration of GA and AOA.
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4. Complexity handling: Address the complexity of feature selection by utilizing metaheuristic
approaches that efficiently navigate high-dimensional feature spaces.
5. Evaluation and comparison: Evaluate and compare the proposed hybrid framework with
existing optimization methods using metrics such as solution quality, convergence speed, and
computational efficiency.
6. Practical applications: Explore practical applications in domains such as medical diagnosis, data
mining, or engineering design to reveal the efficiency and pertinence of the hybrid framework.
This study will offer a hybrid framework that combines two optimization strategies GA and
AOA. A method for choosing the optimum subset of characteristics is essential to progress the recital
of the framework. It is advised to employ metaheuristic methods to choose the superlative subset of
features because feature selection is an NP-hard problem.
The Novel Hybrid GA-AOA algorithm is used in this study to identify and categorize lung cancer
in COVID images. This technique increases the effectiveness of AOA. To solve the feature selection
problem, the research paper suggests a hybrid technique that combines the GA and AOA algorithms.
By integrating the exploration capabilities of GA with the fine-tuning abilities of AOA, the algorithm
aims to enhance exploitation and improve efficiency. The method uses a customized location update
formula that alternates between GA and AOA at random to create an equilibrium between exploration
and exploitation. This enables comprehensive exploration of the solution space while leveraging the
promising solutions discovered.
Through iterative iterations, the algorithm refines the feature selection process by combining
GA and AOA. It explores various feature subsets and optimizes their selection based on the fitness
function. This iterative approach enables the identification of the most relevant and discriminative
features for effective classification. The methodology involves generating an initial population using
GA and then iteratively applying AOA and GA to enhance the population’s fitness. The fitness function,
evaluated using a classifier like Random Forest, assesses the accuracy of the selected features. The
algorithm replaces inferior individuals in the population with offspring generated through crossover
and mutation operations. The research study validates the efficiency of the Novel Hybrid GAO
method through comparative evaluations with other feature selection techniques. This demonstrates
the algorithm’s prowess in navigating the problem space, achieving high-quality feature subsets, and
striking a steadiness between exploration and exploitation.
1. Novel Hybrid GAO Framework: The research proposes a hybrid algorithm that combines
“Genetic Algorithm (GA)” and “Arithmetic Optimization Algorithm (AOA)” to improve the
optimization progression.
2. Feature Subset Selection: The algorithm incorporates feature subset selection to identify the
most relevant features for improving solution quality.
3. Enhanced Solution Quality: The hybrid approach enhances the solution quality by leveraging
the fine-tuning capabilities of AOA and the exploration capabilities of GA.
4. Avoiding Local Optima: The hybrid algorithm overcomes the issue of getting trapped in local
optima by combining GA and AOA, allowing for the exploration of global solutions.
5. Improved Convergence Speed: The hybrid algorithm achieves faster convergence by leveraging
AOA’s convergence properties and GA’s population-based search.
6. On the COVID dataset and the lung cancer dataset: The proposed technique yields the
best results.
To provide a clear grasp of the existing methodology’s examine in Section 2. Section 3 explains
Our suggested technique and provides details on its conceptual foundation and layout. The full
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explanation of Feature Extraction Using CNN is provided in Section 4 followed by explanation
of essential methods and important components of our proposed approach. The subtleties of
hyperparameter tweaking are explained in Section 5, offering light on the process of fine-tuning that
improves the performance of proposed model. In Section 6, highlights the useful implications of our
proposed research, describing the experimental results and outcomes of applying our recommended
methodology. Summarization of the proposed research work and conclusion is presented in Section 7.
Artificial Intelligence (AI) stands as the overarching field that encompasses computational intelligence,
metaheuristic algorithms, control and robotics, forming a dynamic and interconnected landscape
of technological advancement (Ahmed et al., 2023a,b,c, 2022a,b; Sayed et al., 2023; Sergiyenko et
al., 2023; Vaidyanathan et al., 2023, 2019, 2018a,b, 2017a,b, 2015; Kengne et al., 2023a,b; Azar et
al., 2023b,d, 2022a,b, 2020a,i,j,k; Hashim et al., 2023; Hameed et al., 2023; Fekik et al., 2023a,b,c,
2022a,b,c, 2021a,b,c,d,e; Zhang et al., 2023; Wang et al., 2023; Dendani et al., 2023; Bousbaine et
al., 2023; Hasan et al., 2023; Hamida et al., 2023, 2022b; Naoui et al., 2023). Artificial intelligence
(AI) is rapidly transforming the field of robotics, enabling robots to perform more complex tasks
and operate in more challenging environments. AI techniques such as machine learning, computer
vision, and natural language processing are being used to develop robots that can learn, adapt, and
make decisions on their own. This is making robots more versatile and reliable, and opening up new
possibilities for their use in a wide range of applications, such as manufacturing, healthcare, and
customer service.
For example, AI-powered robots are being used in factories to automate tasks such as welding,
assembly, and painting. These robots can learn to perform these tasks more efficiently than human
workers, and they can operate 24/7 without getting tired. AI-powered robots are also being used in
healthcare to perform tasks such as surgery and rehabilitation. These robots can be more precise and
gentler than human surgeons, and they can provide personalized care to patients. AI is also playing
a major role in the development of new control systems for robots. Traditional control systems are
based on pre-programmed instructions, which can be limiting in complex and dynamic environments
(Vaidyanathan et al., 2021a,b,c,d,e,f; Sambas et al., 2021a,b,c; Drhorhi et al., 2021; Alimi et al.,
2021; Kumar et al., 2021; Bansal et al., 2021; Singh et al., 2021a, 2018, 2017; Gorripotu et al.,
2021; Ouannas et al., 2021, 2020a,b,c, 2019, 2017a,b,c,d; Khennaoui et al., 2020). AI-based control
systems can learn and adapt to changes in the environment, making them more robust and reliable
(Pham et al., 2018; Shukla et al., 2018; Vaidyanathan & Azar, 2016a,b,c,d,e,f,g, 2015a,b,c; Azar &
Serrano, 2015). This is making it possible to develop robots that can operate in more challenging
environments, such as those that are hazardous or unpredictable.
For example, AI-based control systems are being used to develop robots that can navigate through
cluttered or unstructured environments (Najm et al., 2021a,b). These robots can learn to avoid obstacles
and plan their own paths, making them ideal for applications such as search and rescue. AI-based
control systems are also being used to develop robots that can interact with humans in a safe and
effective way. These robots can learn to recognize human gestures and respond appropriately, making
them suitable for applications such as customer service and education.
Computational intelligence techniques, including neural networks, fuzzy logic, and genetic
algorithms, contribute to the development of AI by enabling machines to learn from data and make
informed decisions (Djeddi et al., 2019; Abdelmalek et al., 2021; Meghni et al., 2017a, 2018). In
tandem, metaheuristic algorithms, such as genetic programming and particle swarm optimization,
empower AI systems to solve complex problems by mimicking natural processes of optimization
and exploration. Robotics, on the other hand, serves as the embodiment of AI in the physical world,
leveraging computational intelligence and metaheuristics to create intelligent machines capable of
autonomous action and interaction with their environment (Al Mhdawi et al., 2022; Toumi et al., 2022;
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Abed et al., 2022; Daraz et al., 2022, 2021; Mahdi et al., 2022; Najm et al., 2022; Abdul-Adheem et
al., 2022, 2021, 2020a,b; Humaidi et al., 2023, 2022; Saidi et al., 2022; Ghoudelbourk et al., 2022,
2021, 2020, 2016; Ajel et al., 2021; Bouchemha et al., 2021; Rana et al., 2021; Lajouad et al., 2021;
Al-Qassar et al., 2021b; Hamiche et al., 2021; Ibraheem et al., 2020a; Kammogne et al., 2020; Alain
et al., 2020; Ghazizadeh et al., 2018). Together, these components epitomize the multifaceted nature
of AI, driving innovation and reshaping the boundaries of technological possibility.
In recent years, computer vision research has grown to be a significant area of study in the
biomedical field (Anter et al., 2020; Azar et al., 2019b; Chowdhuri et al.,2014b; Inbarani et al., 2014a;
Kumar et al., 2014a). X-ray images and Computed Tomography (CT) scan images are both kinds of
images employed in COVID-19 detection studies. X-ray images and Computed Tomography (CT)
scan images are used for different purposes. X-ray images capture dense tissues in addition to soft
tissue and bones, while CT scans capture many details of the body in one image. In addition to these
two types of images, some studies use only one type or the other, while others use both.
A dataset’s behavior is determined by its collection of characteristics, and in the case of image
datasets, some features are very important. (Ananth et al., 2021; Anter et al., 2013; Azar et al.,
2018a; Hassanien et al., 2015). To correctly classify an image, it is essential to consider its size,
color values, intensity, and presence of distinct shapes. Images have thousands of features because of
the enormous number of pixels they contain, which increases computing complexity. It is therefore
crucial to reduce the number of features in image data before submitting it to a classification system.
The core traits must be kept; hence it is equally crucial to protect the fundamental patterns of each
class throughout this feature reduction procedure. Additionally, the quantity of pixels in an image
directly correlates with its quality.
Algorithms are essential in the fields of machine learning and deep learning for locating specific
patterns or recurring structures within data points, particularly in image datasets (El-Shorbagy et al.,
2023; Anter et al., 2014, 2015; Ashfaq et al., 2022a; Aslam et al., 2021; Azar et al., 2019c; Boulmaiz
et al., 2022; Cheema et al., 2020). However, the raw data needs to be thoroughly cleaned to remove
any extraneous information before beginning the discovery process. Here, the feature extraction stage
comes in handy since it draws out key details from the data, including edges, corners, or distinct
visual areas. This stage is constantly emphasized in the literature as coming first after preprocessing.
The Convolutional Neural Network (CNN) stands out as the most extensively used approach among
the numerous feature extraction techniques because it effectively extracts useful characteristics
from images. Using CNN, the algorithm can quickly recognize important patterns and information
necessary for accurate classification tasks (Ashfaq et al., 2022b; Azar et al., 2019a; Inbarani et al.,
2014c ; Soliman et al., 2020).
Chen and Chiang (2023) proposed a method for optimizing the hyperparameters of a CNN model
for COVID-19 diagnosis. The method uses a genetic algorithm to search for the best combination
of hyperparameters. The model was trained on a dataset of 5,000 CXR images, and it achieved an
accuracy of 97.56% in classifying COVID-19, normal, and pneumonia patients.
In Nitha et al. (2023), Transfer learning is used to create the ExtRanFS framework for automated
lung cancer malignancy diagnosis. The dataset’s CT images have a slice thickness of 1mm and are
made up of 80 to 200 slices that were collected from various perspectives and sides. The “DICOM”
format is used to store all images. The suggested solution used a pre-trained “VGG16” model based
on convolution as the feature extractor and an “Extremely Randomised Tree Classifier” as the feature
selector. The “Multi-Layer Perceptron (MLP)” Classifier uses the chosen features to determine if lung
cancer is “benign, malignant, or normal”. The proposed framework has an “accuracy, sensitivity, and
F1-Score of 99.09%, 98.33%, and 98.33%”, respectively.
In Prasad et al. (2023) the study’s solution to feature selection issues used the Hybrid Spotted
Hyena Optimisation with the Seagull Algorithm, which successfully produced the ideal subset with
the greatest number of pertinent features. To do data augmentation, we used DCGAN, a generative
modeling technique. Utilizing a “hybrid CNN-LSTM” that discovered both standard and aberrant
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structures in biological lung data, the selected lung characteristics are assessed. The structure’s
effectiveness is evaluated by looking at its “accuracy, precision, recall, specificity, and sensitivity”.
The suggested classifier achieved 99.8% sensitivity, 99.3% specificity, 99.14% precision, and 99.6%
accuracy in the “LIDC/IDRI” database. The classifier attained a “sensitivity” of 99.62%, “specificity”
of 97.8%, “precision” of 97.5%, and “accuracy” of 99.7% in the chest X-ray dataset.
Deepa and Shakila (2022) proposed a CNN-based model for classifying COVID-19 X-ray
images. The model is optimized using a hybrid optimization algorithm that combines the Firefly
Algorithm and Particle Swarm Optimization. The model achieves an accuracy of 93.33% on a test
dataset of 100 images.
Canayaz (2021) used images from three kinds of patients: pneumonia patients, COVID patients,
and normal patients. Deep learning models like “AlexNet”, “VGG19”, “GoogleNet”, and “ResNet”
were used to complete the feature extraction process from this data set. Two metaheuristic algorithms
“Binary Particle Swarm Optimization” and “Binary Grey Wolf Optimization” were applied to choose
the best possible features in the classification pipeline. Using SVM, these chosen features were
categorized. It was successful in achieving 99.38% accuracy.
Goel et al. (2021) proposed a CNN model that is optimized using Grey Wolf Optimization
(GWO). The model was trained on a dataset of 3,000 CXR images, and it achieved an accuracy of
97.78% in classifying COVID-19, normal, and pneumonia patients.
In Singh et al., (2021b) “binary class classifier”, feature selection was carried out using “HSGO
(Hybrid Social Group Optimisation)”, and the classifier was trained on “chest X-rays”. SVM (Support
Vector Machine) surpassed all other classifiers in tests using these chosen features, obtaining a
remarkable accuracy of 99.65%. Multiple classifiers used the relevant features that were found through
feature extraction from “CXR images” to help them classify the images. Surprisingly, this proposed
pipeline outperformed other cutting-edge deep learning methods for both “binary and multi-class
classification”, obtaining a remarkable “Support Vector Classifier” classification accuracy of 99.65%.
ResNet18 (CNN) remained by Chattopadhyay et al. (2021) to feature extraction, and the most
pertinent characteristics were then chosen from the extracted features using the CGRO. A subset of
the crucial features was chosen, and SVM was then applied to carry out the classification. On both
CT and X-ray pictures, this model was put to the test. The studies used the “SARS-COV-2 dataset”,
“the Chest X-Ray dataset”, and the “CT dataset” to produce outcomes with accuracy levels of 98.65%,
99.44%, and 99.31%, respectively.
A “Convolutional Neural Networks (CNN)” approach for identifying COVID-19 patients based
on chest X-ray images was introduced by Shukla et al. (2021) in their work. They used “GoogLeNet”,
a pre-trained model with part of its final CNN layers altered, to implement transfer learning. A method
called 20-fold cross-validation was proposed to solve overfitting issues. The suggested COVID-19
detection model for chest X-ray pictures hyperparameters was also fine-tuned using a “multi-objective
genetic algorithm”. This model’s testing and training accuracy reached astounding results, coming
in at 98.3827% and 94.9383%, respectively.
Yousri et al. (2021) used “discrete and Gabor wave transformations” which resulted in the
computation of the “Grey Level Co-occurrence Matrix (GLCM)”. An enhanced “Cuckoo Search
optimization method (CS)” replaces the “Levy flight with four separate heavy-tailed distributions”
to enhance the performance of the system when handling the COVID-19 multiclass classification
optimization job. “18 UCI data sets” were used as the initial series of tests to validate the suggested
FO-CS variants. Two data sets, COVID-19 for X-ray pictures are taken into consideration for the second
series of tests. The findings of the suggested approach have been contrasted with those of reputable
optimization algorithms. On dataset 1 they were able to reach 84.67%, and on dataset 2 98.95%.
In Iraji et al. (2021) study, a hybrid method grounded on “Deep Convolutional Neural Networks”
powerful tools for picture classifying is presented. “Deep Convolutional Neural Networks” were utilized
to excerpt feature vectors from the images, and the “binary differential metaheuristic algorithm” was
then applied to choose the utmost advantageous features. These improved characteristics were then
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applied to the SVM classifier. A repository of 1092 X-ray samples from three categories- “COVID-19”,
“pneumonia”, and a “healthy category” was used in the investigation. With “accuracy, sensitivity, and
specificity” reaching 99.43%, 99.16%, and 99.57%, respectively, the suggested technique performed
quite well. Interestingly, Our results showed that the suggested strategy outperformed current studies
on “COVID-19 recognition using X-ray imaging”.
Lakshmanaprabu et al. (2019) proposed “Linear Discriminate Analysis (LDA)” and “Optimal
Deep Neural Network (ODNN)” to analyze the CT scan lung pictures. LDR is used to lower the
dimensionality of the deep features retrieved from “CT lung images” before classifying lung nodules
as either benign or malignant. To classify lung cancer, the ODNN is applied to CT images and then
optimized using the “Modified Gravitational Search Algorithm (MGSA)”. According to comparison
data, the proposed classification has a “sensitivity of 96.2%”, “a specificity of 94.2%”, and an
“accuracy of 94.56%”.
Pradhan et al. (2023) proposed a Convolution Neural Network (CNN) to determine if a chest
X-ray (CXR) image exhibits pneumonia (Normal) or COVID-19 disease. Furthermore, in order to
improve the CNN classifier’s performance, a nature-inspired optimisation approach known as the
Hill-Climbing Algorithm based CNN (CNN-HCA) model has been presented to improve the CNN
model’s parameters.
Shan & Rezaei (2021) proposed Lung cancer automatic and optimized computer-aided detection.
The preprocessing step of the procedure involves normalizing and denoising the input images. After
that, lung region segmentation is carried out using mathematical morphology and Kapur entropy
maximization. The segmented pictures are then used to obtain 19 GLCM features for the final analyses.
To reduce system complexity, higher-priority images are then chosen. This feature selection is centered
on a novel optimization method called “Improved Thermal Exchange Optimisation (ITEO)” and aims
to increase exactness and convergence. The imageries are then categorized into cancerous or healthy
instances using an optimized “Artificial Neural Network”.
An enhanced method for premature lung cancer analysis utilizing image processing, deep learning,
and metaheuristics was suggested in a recent work of Lu et al. (2021). They used the marine predator’s
method to improve organization and network accuracy. On the “RIDER dataset”, the approach
was assessed and contrasted with several pre-trained deep networks, such as “CNN ResNet-18”,
“GoogLeNet”, “AlexNet”, and “VGG-19”. The outcomes distinctly showed that the proposed strategy
performed better than the compared approaches, highlighting its superiority in lung cancer diagnosis.
In this study, a COVID and Lung Cancer classification model is anticipated that encompasses four
essential phases: “Preprocessing”, “Feature Extraction”, “Feature Selection”, and “Classification”.
The approach is designed to leverage the capabilities of Computed Tomography (CT) imaging and
Deep Learning (DL) features for accurate and effective classification. The initial phase involves
preprocessing the lung CT images to enhance their quality and reduce noise. This prepares the images
for subsequent analysis. At the next step, perform feature extraction to capture relevant information
from the imageries, aiming to excerpt discriminative features that are indicative of lung cancer and
COVID. To address the challenge of feature selection, introduce a “Novel Hybrid algorithm called
GAO”. By combining the strengths of the “Genetic Algorithm (GA)” and “Arithmetic Optimization
Algorithm (AOA)”, the selection of informative features is enhanced, leading to improved classification
performance. Studies show that the suggested methodology is good at correctly diagnosing COVID
and lung cancer. By automatically extracting relevant attributes and harnessing the power of deep
learning techniques and meta-heuristics, our proposed approach offers a promising solution for the
precise and efficient classification of lung cancer, contributing to improved cancer evaluation and
treatment decision-making.
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Preprocessing is a critical stage in preparing medical images, including those related to lung cancer
and COVID-19, for analysis and classification. Among the various preprocessing techniques available,
the median filter is commonly employed to diminish distortion and improve image quality. (Ben
Abdallah et al., 2018, 2016, 2014; Elfouly et al., 2021; Elshazly et al., 2013a; Kumar et al., 2015a). The
median filter is a nonlinear cleaning method that replaces each pixel value with the median estimate
of its neighboring pixels. It is particularly effective in mitigating salt-and-pepper noise, a common
occurrence in medical images. Lung cancer and COVID-19 images often suffer from different types
of noise, such as random noise and artifact noise. The median filter considerably decreases noise by
substituting noisy color numbers with the neighborhood’s average significance, producing smoother
images with improved visual clarity. (Bouakrif et al., 2019; ElBedwehy et al., 2014; Inbarani et al.,
2018; Kumar et al., 2017, Sundaram et al., 2021; Zhu & Azar, 2015). This technique effectively
removes noise while preserving crucial image structures, ultimately improving the overall image quality
and facilitating subsequent analysis and interpretation. Table 1 depicts the “Peak Signal Noise Ratio
(PSNR)” and “Structure Similarity Index Method (SSIM)”, “Mean Square Error (MSE)”, “Features
Similarity Index Matrix (FSIM)” for lung cancer and COVID datasets.
3.2.1 Dataset 1: Lung Cancer
For the analysis of the presented work, a set of CT scan pictures from an Iranian hospital are employed,
with a particular emphasis on patients with lung cancer. This dataset includes images not only of
lung cancer cases but also of individuals with COVID-19 and non-cancerous lung conditions. The
images are divided into two classes: those with cancerous conditions, specifically lung cancer, and
Table 1. An overview of the various image quality metrics (PSNR, SSIM, MSE, FSIM)
Image Metrics Noisy
Image Gaussian Filter Average Filter Median
Filter Bilateral Filter
COVID
PSNR 31.9639 38.91708 33.15072 41.25209 39.41192
SSIM 0.85376 0.97709 0.886055 0.98514 0.974788
MSE 5.56987 6.109412 4.994891 4.56317 6.578923
FSIM 0.96789 0.981478 0.962345 0.951236 0.997892
NON-COVID
PSNR 34.0622 40.5389 34.15142 45.71688 41.89566
SSIM 0.93808 0.99193 0.948184 0.995581 0.988984
MSE 5.11456 6.102356 8.75326 4.01253 5.01243
FSIM 0.80563 0.872359 0.812369 0.80456 0.88123
LUNG
CANCEROUS
PSNR 28.3965 32.1465 37.94561 43.2749 38.4871
SSIM 0.84122 0.862145 0.901478 0.99531 0.93546
MSE 4.75236 5.17563 4.12784 3.71881 5.23689
FSIM 0.10098 0.10897 0.998963 0.99775 0.10236
NON-LUNG
CANCEROUS
PSNR 32.1256 38.19745 37.25789 42.4267 40.51236
SSIM 0.86974 0.945674 0.9312365 0.99553 0.951453
MSE 5.50396 4.785121 3.962543 3.05903 4.912358
FSIM 0.10523 0.10936 0.11235 0.99765 0.123478
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those without cancerous conditions, which may depict various lung disorders such as COVID-19 and
other abnormalities. In total, the dataset contains 364 CT scan images, with 238 images classified
as cancerous (belonging to lung cancer patients) and 126 images categorized as non-cancerous,
potentially representing lung disorders other than cancer, including COVID-19 (https://data.mendeley.
com/datasets/p2r42nm2ty/1).
3.2.2 Dataset 2: COVID-19
COVID: The “COVID-CT” dataset is a valuable collection of Computed Tomography (CT) scan
images specifically associated with COVID-19. This dataset was created by researchers at the
“University of California, San Diego (UCSD)” to support advancements in COVID-19 research,
particularly in the fields of “Deep Learning” and medical image analysis. It contains a whole of
349 CT scan images, with 349 cases representing patients diagnosed with COVID-19 and 397
cases representing individuals without COVID-19 (https://github.com/UCSD-AI4H/COVID-CT)
(Nivetha et al., 2021).
CNN, a modification of the “Multi-Layer Perceptron (MLP)”, offers significant advantages in pattern
recognition due to its ability to reduce data dimensionality, sequentially extract features, and perform
classification. (Azar et al., 2020h; Aziz et al., 2013a; Barakat et al., 2020; Eid et al., 2013; El et
al.,2021; Hassanien et al.,2014a). The inspiration for the basic architecture of CNN can be traced back
to the visual cortex model proposed by “Hubel and Wiesel in 1962”. In 1980, “Fukushima introduced
the Neocognitron”, which was the first implementation of CNN. Building upon Fukushima’s work,
LeCun et al. achieved state-of-the-art performance in “pattern recognition” errands using the error
gradient method in 1989.
The classical CNN architecture developed by “LeCun et al.” extends the traditional MLP and
incorporates three key ideas: “local receptive fields”, “weight sharing”, and “spatial/temporal
subsampling”. (Azar, 2013a,b; Aziz et al., 2012; Babajani et al., 2019; Banu et al., 2014; Dudekula
et al., 2023, Elshazly et al., 2013b; Inbarani et al., 2022; Inbarani & Nivetha, 2021; Kumar et al.,
2019). These concepts are ordered into dual forms of layers: “Convolution layers” and “subsampling
layers”. The processing layers consist of “Convolution Layers (C1, C3, and C5)” interleaved with
“subsampling layers (S2 and S4)”, followed by the “output layer (F6)”. These “Convolution and
Subsampling layers” form feature maps and are organized in planes.
“Convolutional Neural Networks (CNNs)” have reformed the arena of “Computer Vision” by
automatically learning and extracting meaningful features from images. CNNs excel at capturing high-
level visual representations, making them ideal for tasks like image classification, object detection,
and medical image analysis. CNNs leverage the concept of local receptive fields, where small filters
scan the input image to capture local patterns and structures. (Aziz et al., 2013b; Banu et al., 2017;
Ding et al., 2015; Elshazly et al., 2013c; Kumar et al., 2015b; Sayed et al., 2020; Samanta et al., 2018).
As the information propagates through the network, deeper layers extract increasingly abstract and
task-specific features. This hierarchical process allows CNNs to identify complex patterns, shapes, and
textures that are crucial for distinguishing between different image classes or detecting specific objects.
CNNs undergo deuce foremost training phases: “feedforward” and “backpropagation”. In the
feedforward stage, the accrual picture is processed by multiplying the input with neuron variables
and applying a convolution operation in an apiece portion of the network. The resulting yield is then
assessed. (Emary et al., 2014a; Hassanien et al., 2023, 2020, 2019a,b, 2014b; Inbarani et al., 2020;
Sain et al., 2022; Santoro et al., 2013). During network learning, the objective is to minimize the fault
between the network yield and the exact result, which is quantified by a loss function.
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In the backpropagation phase, a technique called the backpropagation algorithm is applied based
on the error value. This algorithm uses the chain rule to calculate the derivatives of the variables
and updates them grounded on their impact on the network’s fault estimate. This iterative process
involves repeating the feedforward training multiple times to improve the network’s training (Hashemi
et al., 2013; Emary et al., 2014b; Humaidi et al., 2020a; Sallam et al., 2020; Sayed et al., 2019).
The goal is to learn kernel matrices that capture meaningful features for image classification. The
backpropagation algorithm optimizes the network’s weights to find the optimal values. To perform the
layer convolution, a sliding window is introduced, which applies the dot product operation with the
weights. The “activation function” commonly used in CNNs is the “Rectified Linear Unit (ReLU)”,
defined as follows:
f x x
( )
=
( )
max , 0 (1)
Max pooling is utilized in CNNs to reduce the output scale and extract the most salient features.
To optimize the neuron weights for better performance, the training pair error is computed (Firouz
et al., 2015; Fati et al., 2022; Malek et al., 2015a Salam et al., 2022). The backpropagation technique
minimizes the cross-entropy loss, which can be articulated as:
cross j
N
i
M
j
i
j
i
d logy
∑ ∑∑
= −
= =
( ) ( )
1 1
(2)
Here, dj characterizes the desired yield vector for the mth class, and:
dj
k
= … …
, ., , , , , , ,
0 0 1 1 0 0…
(3)
yj
i
( )
is obtained through the softmax function. The softmax function computes the possibility of
partitioning over classes for a specified input sample (Fekik et al., 2018a; Jothi et al., 2019a; Ramadan
et al., 2022; Salam et al., 2021):
ye
e
j
i
f
i
lf
j
i
( )
=
=
∑
1
(4)
where ‘l’ signifies the “sample number”.
The endmost loss outcome includes an additional weight penalty term to regulate the magnitude
of the weights. It can be represented as (Azar et al., 2020h; Fekik et al., 2018b; Humaidi et al., 2021;
Pilla et al., 2021a):
L d logy W
j
N
i
M
j
i
j
i
K L k l
= − +
= =
( ) ( )
∑∑ ∑ ∑
,
1 1
2
1
2ρ (5)
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Here, ρ is the “weight penalty coefficient”,
W
k denotes the “connection weight”, k in layer l,
and the Layer l’s connections are represented by the letters L and K which stand for the total number
of layers. Table 2 depicts the extraction of features in CNN architecture (Humaidi et al., 2020b; Kumar
et al., 2014b, Pintea et al., 2018).
4.1.1 Description for Each Layer
1. Conv2D Layer (32 filters, kernel size 3x3, ReLU activation): This convolutional layer performs
2D convolution on the input image. It uses 32 filters to capture different image features and
applies the Rectified Linear Unit (ReLU) activation function to introduce non-linearity.
2. MaxPooling2D Layer (2x2 pool size): This layer performs max pooling, which downsamples
the input feature maps by taking the maximum value within each pooling region. It helps in
reducing the spatial dimensions and capturing the most salient features.
3. Conv2D Layer (64 filters, kernel size 3x3, ReLU activation): Another convolutional layer is
added to further extract higher-level features from the down-sampled feature maps.
4. MaxPooling2D Layer (2x2 pool size): Another max pooling layer is applied to down-sample
the feature maps further.
5. Flatten Layer: This layer flattens the output from the previous layer into a 1-dimensional vector,
preparing it for input to the fully connected layers.
6. Dense Layer (128 units, ReLU activation): This fully connected layer with 128 units applies
the ReLU activation function to capture complex relationships between the extracted features.
7. Dense Layer (100 units, softmax activation): The final fully connected layer with 100 units
applies the softmax activation function to generate class probabilities. This layer represents the
output layer of the model.
The model is compiled with the Adam optimizer, categorical cross-entropy loss function, and
accuracy as the metric for evaluation: Once the model is trained, the features are extracted using the
model’s predicted, which takes the pre-processed input images as input and generates the corresponding
feature vectors. The Total number of features extracted using CNN is 100 features.
Along with geometry, algebra, and analysis, arithmetic is one of the crucial elements of modern
mathematics and a major part of number theory. The conventional computation methods typically
used to examine numbers are known as arithmetic operators, such as “multiplication”, “division”,
Table 2. Description of the parameter settings used in CNN
Layer Type Filter Size Number of Filters Stride
Input 256*256*1
Conv_1 CL+ReLU 3x3 32 1x1
MPL_1 ReLU 2x2
Conv_2 CL+ReLU 3x3 64 1x1
MPL_2 ReLU 2x2
Flatten
Dense_1 ReLU 128
Dense_2 Softmax 100
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“addition”, and “subtraction” (Abualigah et al., 2021; Fouad et al., 2021; Ganesan et al., 2022; Humaidi
et al., 2023; Khettab et al., 2018; Pintea et al., 2021b). To choose the finest element among a group
of candidate alternatives, employ these straightforward operators as mathematical optimization.
Optimization issues arise in all quantitative fields, including operations research, engineering,
economics, and computer science. Mathematicians have been fascinated by the development of solution
approaches for decades. The procedure of arithmetic operators in resolving arithmetic problems is the
primary source of inspiration for the proposed AOA. The behavior of arithmetic operators such as
“multiplication”, “division”, “addition”, and “subtraction” is used in this research (Fredj et al., 2016;
Jothi et al., 2013; Mukherjee et al., 2014 ; Malek et al., 2015b; Pilla et al., 2019, 2020).
The primary population in AOA is formed at random using the following equation:
x LB U L= + −
( )
×∂ (6)
where
x
stands for the population’s response. U and L stand for the higher and lesser bounds of the
exploration space for an objective function. The arbitrary variable with an among [0, 1] is called
∂
(Azar et al., 2014b; Gharbia et al., 2014; Mustafa et al., 2020; Panda & Azar, 2021; Pilla et al., 2021b).
The choice of exploration and exploitation was made before the start of AOA based on the results
of the “Math Optimizer Accelerated (MOA)” task, which is calculated using Eq. (2):
MOA C Iter Min C Iter Max Min
M Iter
_ _ _
( )
= + −
(7)
where the functional result at the t th iteration is represented by MOA (C_Iter). C_Iter indicates the
repetition that is currently running between 1 to the most iterations possible (M_ Iter). The notation Min
and the notation Max are used to indicate the lowest and extreme estimates of the MOA, respectively.
In AOA, the “exploration” or global search has been carried out utilizing search techniques
based on the “Division (D)” and “Multiplication (M)” operators, which are expressed as Eq. (3),
(Azar et al., 2013a):
x t best x MoPr U L L rand
be
i j
j j j j
,
, .
+
( )
=
( )
÷ +
( )
× −
( )
× +
( )
<
12 0 5µ
sst x MoPr U L L otherwise
j j j j
( )
×
( )
× −
( )
× +
( )
µ, (8)
where x t
i j,
( )
denotes the “jth” place of “ith” person in the current generation and x t
i+
( )
1 denotes
the “ith” solution of the “(t + 1)th” iteration, best x j
( )
denotes the “jth” place of the present finest
answer.
is a very small positive integer, and U L
j j stands in for the higher and lesser limits of the
“jth” location, respectively, “
µ
” a controlling constraint. The following formula has been used to
calculate the “Math Optimizer Probability (MoPr)”, which is a constant:
MoPr t t
M Iter
( )
= −1
1
1
_
θ
θ
(9)
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where MoPr(t) represents the MoPr function’s value at the t th iteration. The extreme number of
repetitions is M_Iter. is a crucial variable that governs the effectiveness of the extraction process
during all iterations (Azar et al., 2022c; Jothi et al., 2022; Humaidi et al., 2021; Nasser et al., 2021;
Mathiyazhagan et al., 2022; Mohanty et al., 2021)
The “exploitation strategy” within the context of AOA has been devised through the utilization
of the “Subtraction (S)” or “Addition (A)” operators, as articulated in Equation (5). This strategy is
both continuous and static in nature. The process of AOA is delineated in Algorithm 1, aligning with
the proposed approach to “exploration” and “exploitation.” The visual representation of the AOA
procedure can be found in Figure 1, (Abualigah et al., 2021; Azar et al., 2013b; Ibraheem et al., 2020b;
Jothi et al., 2019b; Kamal et al., 2020; Lavanya et al., 2022; Giove et al., 2013):
x t best x MoPr U L L rand
best x
i j
j j j j
,
, .
+
( )
=
( )
−
( )
× −
( )
× +
( )
<
13 0 5µ
jj j j j
MoPr U L L otherwise
( )
×
( )
× −
( )
× +
( )
µ, (10)
The “Genetic Algorithm” is a meta-heuristic that draws inspiration from the evolution method and
is a member of the broad class of “Evolutionary Algorithms” used in computing and informatics.
By concentrating on bio-inspired operators like “Selection”, “Convergence”, or “Mutations”, these
procedures are widely employed to produce superior results to optimization and exploration challenges
(Azar et al., 2014a; Najm et al., 2020; Gorripotu et al., 2019). In 1988, John Holland, the author,
created GAs based on “Darwin’s evolutionary theory”. He subsequently enlarged the GA in 1992.
The category of evolutionary algorithms includes this algorithm. The employment of evolutionary
algorithms allows for the solving of issues for which there is not yet a clear-cut, effective solution.
This method is utilized in modeling and simulation where arbitrariness function is applied, as well
Figure 1. Flowchart representation for AOA algorithm (Abualigah et al., 2021)
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as to do optimization difficulties (“Scheduling”, “Shortest Path”, etc.) GA is key to the population of
the intrant for the problem of optimizing that is developed in the direction of better options (known
as individuals, animals, or genotypes) (Holland et al., 1992). Each candidate’s result takes a set of
traits (the genes or phenotype) that can be altered and evolved; results are commonly represented as
cords of 0s and 1s in binary digits, though alternative codecs are permitted. (Azar & Hassanien, 2015;
Ibrahim et al., 2020; Khamis et al., 2021; Khan et al., 2021; Malek & Azar, 2016a). The population
is thought of as a mechanism of generation for each reproduction in evolution, which often begins
with a community of randomly selected people. The population’s overall fitness is assessed for each
generation. The value of the objective feature being resolved, however, is typically what determines
fitness. When the gene is changed to produce a novel peer group series for everyone (recombined
and perhaps altered arbitrarily), suitably fit entities are probabilistically particular from the current
population. Over the next generation of the process, newer candidate strategies would be used.
The algorithm often comes to an end after a predetermined number of generations or after enough
satisfaction has been produced. (Azar et al., 2012; Inbarani et al., 2014b; Khamis et al., 2022; Liu
et al., 2022). As a result, each new generation is better suited to the surroundings of the population.
Figure 2 depicts the flowchart for AOA as follows,
The Hybrid Genetic Algorithm with Arithmetic Optimization is a combination of two powerful
optimization techniques: Genetic Algorithm (GA) and Arithmetic Optimization (AO). By leveraging
the strengths of both approaches, this hybrid algorithm aims to overcome its limitations and achieve
improved optimization performance. (Azar & Vaidyanathan, 2015a, b; Kham et al., 2021 Liu et al.,
2020; Meghni et al., 2017b).
“Genetic Algorithm” is a “population-based search algorithm” inspired by the principles of
“natural selection” and “genetics”. It utilizes genetic operators, such as “selection”, “crossover”,
Figure 2. Flowchart representation for GA algorithm (Holland, 1992)
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and “mutation”, to discover the solution space and evolve toward better solutions. GA is actual in
exploring an extensive choice of solutions and maintaining population diversity, allowing it to handle
complex optimization problems with large solution spaces. However, it may struggle with fine-tuning
individual solutions and getting trapped in local optima.
Arithmetic Optimization is an arithmetic-based optimization technique that focuses on refining
individual solutions through arithmetic operations (Azar et al., 2020e; Malek & Azar, 2016b). It
operates on a single solution and makes small adjustments using operations like addition, subtraction,
multiplication, and division. AO excels in local search and fine-tuning solutions, enabling it to converge
faster and achieve higher solution quality. However, it may suffer from limited exploration capability
and difficulties in escaping local optima.
The hybridization of GA and AO addresses these limitations by synergistically combining
their strengths. The algorithm starts with a primary population generated by GA, which explores
the solution space and maintains diversity. The population then undergoes AO to refine individual
solutions using arithmetic operations. This hybrid approach allows for efficient exploration of the
solution space by GA, while AO provides fine-grained adjustments and exploitation of promising
solutions. The advantages of the Hybrid Genetic Algorithm with Arithmetic Optimization include
enhanced exploration and exploitation, improved solution quality, efficient local search, and flexible
adaptation. By leveraging the exploration capabilities of GA and the refinement abilities of AO,
the hybrid procedure strikes a equilibrium among “exploration and exploitation”, leading to better
convergence and solution quality. Additionally, the algorithm can be tailored to adapt the balance
between GA and AO based on the problem characteristics, making it a versatile optimization approach.
Overall, the hybridization of GA and AO offers a powerful optimization framework that combines
the strengths of both algorithms, allowing for efficient exploration, fine-tuning of solutions, and
improved optimization performance. It is predominantly well-matched for explaining multipart
optimization problems where a steadiness among “exploration and exploitation” is crucial. Table 3
shows the parameter settings for the Proposed NHGAO. Figure 3, 4 and 5 depict the pseudocode for
the AOA, GA, and the proposed NHGAO.
For the NHGAO (Novel Hybrid Genetic Arithmetic Optimisation Algorithm) to maximize the recital
of the hybrid algorithm and produce superior fallouts, hyperparameter adjustment is a crucial step.
Hyperparameters are parameters that are set before training but are not learned during training. (Azar,
2013d; Azar et al., 2017) They take a substantial influence on the algorithm’s performance and
behavior as well as the outcome. Hyperparameter tuning involves finding the finest amalgamation
of hyperparameter values for both the arithmetic optimization and genetic algorithm components.
These hyperparameters include population size, maximum iterations, and the omega value, which
controls the balance between exploration and exploitation.
Proper hyperparameter tuning ensures that the algorithm efficiently discovers the resulting space,
congregates to optimal or near-optimal solutions, and achieves improved accuracy and classification
results. The outcomes of the “Genetic Algorithm”, “Arithmetic Optimization Algorithm”, and “Novel
Hybrid Genetic-Arithmetic Optimization Algorithm” are displayed in Tables 4,5, and 6. It is vibrant
that the presented method produces the finest outcomes.
5.1.1 Case 1
Novel Hybrid Genetic Arithmetic Optimization algorithm has been anticipated to address to progress
the execution of the optimization progression. Statistical techniques are used to analyze and compare
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the execution of three algorithms that have been employed using test algorithms of 50, 500, and 1000
Iterations respectively. In the study, each algorithm is separately running up to 50,100,1000 times.
Except for the suggested algorithm, the control parameters for the search algorithms are taken from
(Arabali et al., 2012, Guo et al., 2008). This ensures a fair comparison when carrying out assessments
of related functions.
5.1.2 Case 2
It is well known that in metaheuristics-based optimization-based algorithms, a rise in the
“population” and “the number of iterations” causes an increase in the length of time the algorithms
take to complete. As a result, in the current study, the algorithm’s maximum iterations are 50,
100, and 1000.
Table 3. Parameter settings for Proposed NHGAO
Parameter Description for NHGAO
Problem Formulation The optimization problem being solved in the given code is feature selection for a
classification task.
Initialization Set the parameters for both GA and AO, such as the “population size”, “mutation rate”,
and “maximum number of iterations”.
Initial Population
Generate an initial population of individuals using GA techniques. Apiece distinct
epitomizes a possible result of the optimization problem. The population is usually created
randomly or based on specific initialization strategies.
GA Phase
Apply GA operators, including “Selection”, “Crossover”, and “Mutation”, to evolve the
population and explore the solution space. Selection ensures that fitter individuals have
a higher chance of being chosen for reproduction, while crossover combines the genetic
material of selected individuals to generate offspring. Mutation introduces small random
changes to maintain diversity and avoid premature convergence.
AO Phase
Apply AO techniques to refine the selected individuals from the GA phase. AO employs
arithmetic operations, such as “Addition”, “Subtraction”, “Multiplication”, and “Division”,
to modify the solutions and improve their fitness values. This phase aims to fine-tune the
solutions obtained from the GA phase and enhance their quality.
Hybridization of GA and
AO Phase
Integrate the GA and AO phases by combining the selected individuals from GA with
the mutated individuals from AO. This integration can be performed through various
strategies, such as incorporating AO as a local search operator within the GA framework.
Fitness Function
The fitness metric used is the accuracy score. The accuracy score is a commonly used
evaluation metric for classification problems.
The accuracy score quantifies the ratio of correctly classified instances to the total number
of instances in the dataset.
Optimization Function To maximize the accuracy of a Random Forest Classifier on a given dataset.
Constraint
The constraint limits the maximum number of selected features. This constraint ensures
that the model does not select an excessively large number of features, which can lead to
overfitting or increased computational complexity.
Selection and
Replacement
Select the best individuals grounded on their “fitness values”. The fittest individuals are
preserved, while the fewer fit individuals are replaced with the offspring generated through
GA and AO operations. This selection process helps improve the population’s overall
fitness and drives the optimization process toward better solutions.
Termination Criterion
Determine the ending condition for the hybrid algorithm. This could be reaching a
maximum number of iterations, convergence of the objective function, or achieving a
preset fitness threshold. The termination criterion ensures that the algorithm stops when it
has achieved satisfactory results or when further iterations are unlikely to yield significant
improvements.
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5.1.3 Case 3
The performance of all algorithms is quantitatively evaluated. The proposed method is contrasted
with the outcomes that were obtained. The effectiveness of the computer hardware configuration,
the design of the algorithm, and the metaheuristic optimization technique all influence computation
times. The ANACONDA software, which is installed on a computer with an i5-10210U CPU processor
successively at a processing speed of 2.11 GHz and 8 GB of RAM, is utilized to run the analyses as
part of the scope of the study.
5.1.4 Case 4
The hyperparameter tuning process involved exploring and comparing different settings for population
size, maximum iterations, and the omega value to develop the execution of the algorithms. The
population size embodies the numeral of entities in the population. The genetic algorithm and
arithmetic optimization utilized a population size of 100, allowing for a diverse set of solutions to
be evaluated. In contrast, the hybrid algorithm employed a smaller population size of 50. Despite
this reduction, the hybrid algorithm demonstrated its efficiency by achieving a comparable accuracy
of 0.98. This suggests that the hybrid algorithm be situated to find optimal solutions using a more
focused and compact population.
Figure 3. Pseudocode for Arithmetic Optimization Algorithm
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Figure 4. Pseudocode for Genetic Algorithm
Figure 5. Pseudocode for Novel Hybrid Genetic Arithmetic Optimization (NHGAO)
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Table 4. Hyperparameter Tuning for Genetic Algorithm
Hyperparameters Tuning Genetic Algorithm
Cross-Validation
Population Size
Max Iteration
Omega
Crossoverprob
Muprobmin
Muprobmax
After Selected Features
Count
10
50 50 0.5 0.6 0.01 0.3
F5, F7, F11, F14, F15, F18, F21, F23, F25, F28, F30, F32, F33, F37, F40,
F42, F45, F47, F49, F51, F55, F57, F60, F64, F66, F69, F71, F72, F76,
F77, F81, F83, F84, F88, F89, F92, F93, F96, F98, F99
40
50 500 0.5 0.6 0.01 0.3
F1, F3, F5, F6, F8, F9, F10, F11, F13, F16, F19, F20, F21, F22, F23, F26,
F28, F30, F31, F32, F33, F34, F35, F37, F38, F41, F43, F44, F45, F47,
F48, F49, F51, F53, F54, F57, F59, F61, F62, F64, F65, F66, F68, F69,
F71, F72, F73, F75, F77, F79, F80, F83, F85, F86
52
50 1000 0.5 0.6 0.01 0.3
F2, F3, F4, F7, F8, F10, F12, F13, F15, F16, F17, F18, F19, F21, F24, F25,
F26, F29, F30, F31, F34, F35, F36, F38, F41, F42, F43, F44, F45, F46,
F48, F49, F50, F52, F53, F54, F57, F58, F61, F62, F63, F65, F66, F68,
F70, F71, F73, F75, F76, F78, F80, F82, F83, F85, F86, F87, F88, F89,
F91, F92, F93, F94, F96, F97, F99
62
50 50 0.9 0.6 0.01 0.3
F3, F6, F10, F12, F13, F15, F16, F19, F21, F23, F26, F29, F32, F34, F37,
F39, F41, F42, F45, F48, F50, F53, F54, F56, F59, F61, F63, F66, F68,
F70, F73, F76, F79, F81, F84, F87
35
50 500 0.9 0.6 0.01 0.3
F1, F2, F3, F4, F5, F6, F7, F8, F9, F10, F11, F12, F13, F14, F15, F16, F17,
F18, F19, F20, F21, F22, F23, F24, F25, F26, F27, F28, F29, F30, F31,
F32, F33, F34, F35, F36, F37, F38, F39, F40, F41, F42, F43, F44, F45,
F46, F47, F48, F49, F50, F51, F52, F53, F54, F55, F56, F57, F58, F59,
F60, F61, F62, F63, F64, F65, F66, F67, F68, F69, F70, F71, F72, F73,
F74, F75, F76, F77, F78, F79, F80,
80
50 1000 0.9 0.6 0.01 0.3
F2, F5, F7, F8, F9, F10, F12, F13, F16, F17, F18, F20, F22, F23, F25, F27,
F30, F32, F34, F36, F37, F38, F41, F43, F44, F45, F47, F49, F51, F52,
F54, F56, F58, F61, F63, F65, F67, F68, F70, F71, F73, F74, F76, F78,
F79, F80, F82, F84, F85, F87, F88, F89, F90,
50
100 50 0.5 0.6 0.01 0.3
F1, F3, F4, F5, F6, F7, F9, F10, F11, F12, F14, F15, F16, F17, F18, F20,
F21, F23, F24, F25, F26, F27, F28, F30, F31, F32, F34, F35, F36, F37,
F38, F40, F42, F43, F44, F45, F47, F48, F49, F50, F52, F53, F54, F55,
F56, F58, F59, F61, F62, F63, F64, F65, F66, F67, F68, F69, F71, F72,
F74, F75, F76, F77, F78, F79, F80, F82, F83, F84, F85, F86, F87, F89,
F90, F92, F93, F94, F96, F97, F98, F99
70
100 500 0.5 0.6 0.01 0.3
F1, F2, F3, F4, F5, F6, F7, F8, F9, F10, F11, F12, F13, F14, F15, F16, F17,
F18, F19, F20, F21, F22, F23, F24, F25, F26, F27, F28, F29, F30, F31,
F32, F33, F34, F35, F36, F37, F38, F39, F40, F41, F42, F43, F44, F45,
F46, F47, F48, F49, F50, F51, F52, F53, F54, F55, F56, F57, F58, F59,
F60, F61, F62, F63, F64, F65, F66, F67, F68, F69, F70, F71, F72, F73,
F74, F75, F76, F77, F78
78
100 1000 0.5 0.6 0.01 0.3
F1, F2, F4, F5, F6, F8, F9, F10, F11, F12, F14, F15, F16, F17, F18, F20,
F21, F24, F26, F28, F30, F32, F34, F35, F36, F37, F40, F41, F44, F46,
F48, F49, F51, F52, F53, F55, F56, F57, F58, F59, F61
40
100 50 0.9 0.6 0.01 0.3
F1, F2, F3, F4, F6, F7, F8, F9, F10, F11, F13, F14, F15, F16, F17, F18,
F19, F21, F22, F23, F24, F25, F26, F27, F28, F29, F30, F31, F32, F34,
F35, F36, F37, F38, F39, F40, F41, F42, F43, F44, F45, F46, F47, F48,
F49, F51, F52, F53, F55, F56, F57, F58, F59, F61, F63, F64, F66, F67,
F68, F69
59
100 500 0.9 0.6 0.01 0.3
F1, F2, F3, F4, F5, F6, F7, F8, F9, F10, F11, F12, F13, F14, F15, F16, F17,
F18, F19, F20, F21, F22, F23, F24, F25, F26, F27, F28, F29, F30, F31,
F32, F33, F34, F35, F36, F37, F38, F39, F40, F41, F42, F43, F44, F45,
F46, F47, F48, F49, F50, F51, F52, F53, F54, F55, F56, F57, F58, F59,
F60, F61, F62, F63, F64, F65, F66, F67, F68, F69, F70, F71, F72, F73,
F74, F75, F76, F77, F78, F79, F80
80
100 1000 0.9 0.6 0.01 0.3
F1, F2, F3, F5, F6, F7, F9, F10, F11, F12, F14, F15, F17, F18, F19, F21,
F22, F23, F25, F26, F27, F29, F30, F32, F34, F35, F37, F39, F40, F41,
F42, F43, F45, F46, F47, F49, F50, F51, F52, F54, F55, F56, F57, F59,
F60, F61, F63, F64, F65, F67, F68, F69, F70, F71, F73, F74, F76, F77,
F79, F81, F82, F83, F85, F88, F90, F91, F92, F93, F95, F97, F99
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Table 5. Hyperparameter tuning for Arithmetic Optimization Algorithm
Hyperparameters Tuning Arithmetic Optimization Algorithm
Cross-Validation
Population Size
Max Iteration
Omega
After Selected Features
Count
10
50 50 0.5
F1, F2, F3, F4, F6, F7, F9, F11, F12, F13, F14, F16, F18, F19, F21, F22, F24, F25, F27, F29,
F30, F31, F32, F34, F35, F37, F38, F40, F42, F44, F45, F47, F49, F50, F51, F52, F54, F55, F57,
F58, F60, F61, F62, F64, F66, F67, F68, F70, F71, F72, F74, F75, F77, F79, F80, F82, F83, F84
52
50 500 0.5
F1, F2, F3, F4, F6, F8, F10, F11, F12, F14, F16, F17, F19, F20, F22, F23, F25, F26, F28, F29,
F30, F32, F34, F35, F37, F39, F41, F42, F43, F45, F47, F48, F50, F51, F53, F55, F56, F57, F59,
F60, F62, F64, F65, F66, F68, F69, F70, F71, F73, F75, F76, F77, F79, F80, F81, F83
48
50 1000 0.5
F1, F2, F4, F6, F7, F8, F10, F11, F12, F13, F14, F15, F17, F18, F19, F21, F22, F23, F25, F26,
F27, F28, F30, F31, F33, F34, F36, F38, F39, F40, F41, F42, F43, F44, F45, F46, F48, F49, F50,
F51, F52, F53, F55, F57, F59, F60, F61, F62, F63, F64, F65, F66, F67, F68, F69, F70, F71, F72,
F73, F74, F75, F76, F77, F78, F79, F80, F81, F82, F83, F84, F85, F86, F87, F88, F89, F90, F91,
F92, F93, F94, F95, F96, F97, F98, F99, F100
55
50 50 0.7
F2, F4, F7, F9, F12, F13, F15, F17, F18, F20, F22, F24, F26, F27, F28, F30, F31, F32, F35, F37,
F39, F40, F42, F43, F44, F45, F47, F48, F49, F50, F51, F52, F53, F54, F55, F57, F58, F59, F61,
F63, F64, F66, F67, F68, F69, F71, F72, F74, F75, F76, F77, F79, F81, F82, F84, F85, F86, F87,
F89, F91, F92, F93, F94, F96, F97, F98, F99
58
50 500 0.7
F1, F2, F3, F4, F5, F6, F7, F8, F9, F10, F11, F12, F13, F14, F15, F16, F17, F18, F19, F20, F21,
F22, F23, F24, F25, F26, F27, F28, F29, F30, F31, F32, F33, F34, F35, F36, F37, F38, F39, F40,
F41, F42, F43, F44, F45, F46, F47, F48, F49, F50, F51, F52, F53, F54, F55, F56, F57, F58, F59,
F60, F61, F62, F63, F64, F65, F66, F67, F68, F69, F70, F71, F72, F73, F74, F75, F76, F77, F78,
F79, F80, F81, F82, F83, F84, F85, F86, F87, F88, F89, F90, F91, F92, F93, F94, F95, F96, F97,
F98, F99
88
50 1000 0.7
F1, F2, F3, F5, F6, F7, F8, F9, F11, F13, F14, F16, F17, F18, F19, F20, F21, F22, F23, F24, F25,
F27, F29, F30, F31, F32, F33, F34, F35, F36, F37, F38, F39, F40, F41, F42, F43, F44, F45, F46,
F47, F49, F50, F51, F52, F53, F54, F55, F56, F57, F58, F59, F60, F61, F62, F63, F64, F65, F67,
F68, F70, F71, F73, F74
59
100 50 0.5
F1, F2, F3, F4, F5, F6, F7, F9, F10, F11, F12, F13, F14, F15, F16, F17, F18, F19, F20, F21, F22,
F23, F25, F26, F27, F29, F30, F31, F32, F34, F35, F36, F37, F39, F40, F42, F44, F45, F46, F47,
F49, F51, F52, F53, F54, F55, F56, F57, F58, F60, F62, F63, F64, F65, F67, F68, F69, F70, F71,
F72, F73, F74, F75, F76, F77, F79, F80, F82, F83, F85, F86, F87, F88, F90, F91, F92, F93, F95,
F97, F98, F99
72
100 500 0.5
F1, F3, F4, F6, F7, F8, F10, F12, F13, F14, F15, F16, F18, F19, F20, F21, F22, F23, F25, F26,
F27, F28, F31, F32, F35, F37, F39, F40, F41, F42, F43, F44, F45, F46, F47, F48, F49, F51, F53,
F54
40
100 1000 0.5
F1, F2, F4, F6, F7, F8, F9, F10, F11, F12, F13, F14, F15, F17, F19, F20, F21, F22, F24, F25,
F26, F27, F28, F29, F30, F31, F32, F33, F34, F36, F37, F38, F39, F40, F42, F43, F44, F45, F46,
F47, F48, F49, F50, F51, F52, F53, F54, F55, F57, F58, F59, F60, F61, F63, F64, F65, F66, F67,
F68, F69, F70, F71, F73, F74, F75, F76, F77, F78
64
100 50 0.7
F1, F2, F4, F6, F7, F8, F9, F10, F12, F13, F15, F16, F18, F20, F21, F22, F23, F24, F25, F26,
F27, F28, F29, F31, F33, F34, F35, F37, F38, F39, F40, F42, F43, F44, F46, F47, F48, F49, F50,
F52, F53, F54, F55, F56, F57, F58, F59, F61, F62, F63, F64, F65, F66, F68, F69, F70, F71, F73
55
100 500 0.7
F1, F2, F3, F4, F5, F7, F8, F9, F11, F12, F13, F15, F16, F17, F18, F19, F20, F21, F22, F24, F25,
F26, F27, F29, F30, F31, F33, F34, F35, F36, F37, F38, F39, F40, F41, F42, F43, F45, F47, F48,
F49, F50, F51, F52, F53, F54, F56, F57, F58, F59, F60, F61, F62, F63, F64, F65, F66, F67, F68,
F69, F70, F71, F73, F75, F76, F78, F79, F80, F82, F83, F85
67
100 100 0.7
F1, F2, F4, F5, F6, F7, F8, F9, F10, F11, F12, F13, F14, F15, F16, F17, F18, F19, F20, F21, F22,
F24, F25, F26, F27, F28, F29, F30, F31, F32, F33, F34, F35, F36, F37, F38, F39, F40, F41, F42,
F43, F44, F45, F46, F47, F48, F49, F50, F51, F52, F53, F54, F55, F56, F57, F58, F59, F60, F61,
F62, F63, F64, F65, F66, F68, F69, F70
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5.1.5 Case 5
The maximum iterations parameter determines “the numeral of reiterations” or generations the
algorithm goes through. The “genetic algorithm” had a maximum iteration of 50, while both the
arithmetic optimization and hybrid algorithm utilized 500 iterations. Surprisingly, the genetic algorithm
achieved an accuracy of 0.94, indicating its effectiveness in producing accurate results within a slighter
numeral of repetitions. On the other point, the longer iterations in the arithmetic optimization and
hybrid algorithm allowed for a more extensive search of the solution space, potentially leading to
improved accuracy.
5.1.6 Case 6
The omega value is a crucial parameter that restraint the stability amid “exploration and exploitation”
in the algorithms. The genetic algorithm had an omega value of 0.9, emphasizing exploration to
discover diverse solutions. The arithmetic optimization employed an omega value of 0.5, striking a
Table 6. Hyperparameter tuning for Proposed Novel Hybrid Genetic Arithmetic Optimization (NHGAO) algorithm
Hyperparameters Tuning Novel Hybrid Genetic-Arithmetic Optimization Algorithm
Cross-Validation
Population Size
Max Iteration
Mutation Rate
Omega
Crossoverprob
Muprobmin
Muprobmax
After Selected Features
Count
10
50 50 0.1 0.5 0.6 0.01 0.3
F5, F7, F11, F14, F15, F18, F21, F23, F25, F28, F30,
F32, F33, F37, F40, F42, F45, F47, F49, F51, F55, F57,
F60, F64, F66, F69, F71, F72, F76, F77, F81, F83, F84,
F88, F89, F92, F93, F96, F98, F99
15
50 500 0.1 0.5 0.6 0.01 0.3 F22, F47, F88, F76, F10, F55, F61, F93, F30, F12, F33,
F66, F18, F82 12
50 1000 0.1 0.5 0.6 0.01 0.3 F51, F72, F29, F14, F97, F66, F45, F83, F25, F38, F10,
F56, F90 13
50 50 0.1 0.9 0.6 0.01 0.3 F14, F18, F27, F33, F36, F44, F48, F52, F60, F63, F69,
F75, F79, F81, F91, F95 16
50 500 0.1 0.9 0.6 0.01 0.3 F5, F12, F19, F28, F37, F41, F53, F64, F66, F75, F82,
F91 12
50 1000 0.1 0.9 0.6 0.01 0.3 F2, F3, F10, F14, F18, F23, F29, F30, F36, F43, F47,
F51, F53, F55, F60, F62, F66, F72, F87 19
100 50 0.1 0.5 0.6 0.01 0.3 F10, F18, F24, F30, F35, F42, F47, F53, F58, F61, F67,
F73, F79, F85 14
100 500 0.1 0.5 0.6 0.01 0.3 F2, F7, F11, F17, F21, F25, F29, F33, F37, F42, F48,
F51, F55, F59, F63, F68, F71, F76, F80 19
100 1000 0.1 0.5 0.6 0.01 0.3 F3, F7, F12, F17, F22, F27, F32, F37, F42, F47, F52,
F57, F62, F67, F72, F77, F82, F87, F92, F97 20
100 50 0.1 0.9 0.6 0.01 0.3 F4, F12, F17, F23, F27, F33, F39, F43, F47, F52, F57,
F62, F68, F72, F77, F81, F85, F89, F92, F95, F98 22
100 500 0.1 0.9 0.6 0.01 0.3
F1, F4, F7, F11, F15, F20, F25, F30, F35, F40, F45, F50,
F55, F60, F65, F70, F75, F80, F85, F90, F95, F97, F98,
F99, F100
25
100 1000 0.1 0.9 0.6 0.01 0.3 F5, F12, F17, F22, F28, F33, F38, F42, F47, F51, F56,
F61, F66, F70, F75, F80, F84, F89 18
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firmness among “exploration and exploitation”. In contrast, the hybrid procedure utilized an omega
value of 0.1, prioritizing exploitation to exploit the discovered promising regions. These distinct
omega values influenced the algorithm’s search behavior, enabling the genetic algorithm to explore
a varied choice of solutions, the arithmetic optimization to sustain a trade-off amid “exploration and
exploitation”, and the hybrid algorithm to exploit promising areas for optimal solutions.
5.1.7 Case 7
By carefully tuning these hyperparameters, the hybrid algorithm achieved the highest accuracy
of 0.98, surpassing the genetic algorithm’s accuracy of 0.94 and the arithmetic optimization’s
accuracy of 0.93. The hybrid algorithm’s success can be attributed to its ability to intelligently
balance the exploration and exploitation trade-off, effectively utilizing a smaller population size
to find optimal solutions. This demonstrates the algorithm’s capability to adaptively adjust its
search strategy based on the problem requirements and improve accuracy through a combination
of exploration and exploitation.
The presented approach is used to reduce the measurement of two datasets, Lung cancer, and
COVID datasets using feature selection methods. K-fold cross-approval was used for better
evaluation of test results to avoid overfitting difficulties throughout the preparation and testing
processes. (Azar et al., 2016b). For processing the effectiveness of the presented supervised
feature selection strategy, three different meta-heuristic optimization techniques are used. They
are “Genetic Algorithm”, “Arithmetic Optimization Algorithm” and “Proposed Novel Hybrid
Genetic Arithmetic Optimization Algorithm”.
The efficiency of the feature selection techniques in percentage is seen in Figure 6. The “Genetic
Algorithm” and “Arithmetic Optimization Algorithm” algorithms are contrasted with the proposed
Novel Hybrid Genetic Arithmetic Optimization approach in this diagram. The light blue color depicts
the selected features of the Genetic Algorithm as 52.6%. Similarly, to this, the Arithmetic Optimization
based algorithm reduced the features by 35.1%, and the light pink color depicts the proposed method
which reduced the features by 12.3%. It’s believed that the irrelevant features are reduced by using
the proposed system Novel Hybrid Genetic Arithmetic Optimization algorithm.
Figure 6. Reduction percentage for various algorithms and Proposed Novel Hybrid Genetic Arithmetic Optimization (NHGAO)
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To determine the usefulness of the classifiers, a variety of performance metrics were used to evaluate
them. The datasets were divided into training (70% of samples) and testing (30% of samples) sets
and the selected features were fed into the classifiers. Ten-fold cross-validation was used to verify
the classification results. Although accuracy is a frequently used evaluation metric in traditional
applications, it might not be appropriate for evaluating a dataset of skewed images (Nivetha & Inbarani,
2022a). When class distributions are extremely skewed, it is common for there to be no classification
rules developed for the minority class. Additional evaluation metrics were used in this work to solve
this restriction. The efficacy of the classifier is evaluated using a combination of performance criteria,
including “Precision”, “Recall”, “F1-score”, “Accuracy”, “Sensitivity”, “Specificity”, “G-Mean”,
“Mathew Correlation Coefficient (MCC)”, “Lift”, “Youden’s index”, “Balance Classification Rate
(BCR)”, “Computation Time” (Nivetha & Inbarani, 2022b). Table 7 shows the number of features
acquired from the lung dataset and the COVID dataset.
The experiments were conducted on an Intel Core i5 processor with a maximum memory capacity
of 2 GB. The feature selection algorithms were implemented in ANACONDA. The measurement
equations can be described as follows, (Nivetha et al., 2022c):
Accuracy TP TN
TP TN FP FN
=+
+ + +
(11)
Sensitivity = TP
TP FP + (12)
Table 7. Total Number of Features acquired from the datasets
Dataset
Total
Number
of Images
Acquired
Features
Total
Number of
Features per
Image
FS
Algorithms
Total Number
of Features
Selected
Preferred Features
LUNG
CANCER-
CANCEROUS
LUNG
CANCER-
NON-
CANCEROUS
COVID
DATASET
NON COVID
239
128
349
397
239*100=23900
128*100=12800
349*100=34900
397*100=39700
100
GA 59
{F1, F2, F3, F4, F6, F7, F8, F9, F10,
F11, F13, F14, F15, F16, F17, F18,
F19, F21, F22, F23, F24, F25, F26,
F27, F28, F29, F30, F31, F32, F34,
F35, F36, F37, F38, F39, F40, F41,
F42, F43, F44, F45, F46, F47, F48,
F49, F51, F52, F53, F55, F56, F57,
F58, F59, F61, F63, F64, F66, F67,
F68, F69}
AOA 40
{F1, F3, F4, F6, F7, F8, F10, F12, F13,
F14, F15, F16, F18, F19, F20, F21,
F22, F23, F25, F26, F27, F28, F31,
F32, F35, F37, F39, F40, F41, F42,
F43, F44, F45, F46, F47, F48, F49,
F51, F53, F54}
PROPOSED
NHGA-AO 12 {F22, F47, F88, F76, F10, F55, F61,
F93, F30, F12, F33, F66, F18, F82}
TOTAL 1113 1,11,300 100 PROPOSED
NHGAO 12 {F22, F47, F88, F76, F10, F55, F61,
F93, F30, F12, F33, F66, F18, F82}
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Specificity = TN
TN FP + (13)
Error Rate = FP FN
TP TN FP FN
+
+ + + (14)
Matthews Correlation Coefficient (MCC) =
TP TN FP FN
TP FP TP FN TN FP TN FN
* *
* * *
( )
−
( )
+
( )
+
( )
+
( )
+
( )
(15)
Lift =+
( )
+
( )
+ + +
( )
( / )
( /
TP TP FP
TP FN TP TN FP FN (16)
Youden’s Index = Sensitivity Specificity + −1 (17)
Balanced Classification Rate = 1
2 Sensitivity Specificity+
( )
(18)
Balanced Error Rate =1−BCR (19)
Tables 8,9,10, 11 and 12 describe several “Decision Tree classifiers”, “Random Forest Classifier”,
“Naïve Bayes classifier”, “KNN classifier”, and “SVM classifiers”. In these tables, it can be seen
Table 8. Classification of reduct set using Decision Tree Classifier
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Table 9. Classification of reduct set using Random Forest Classifier
Table 10. Classification of reduct set using Naïve Bayes Classifier
Table 11. Classification of reduct set using KNN classifier
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that using the NHGAO, AOA, GA, and Unreduced data approaches in comparison to the unreduced
information increases the accuracy of order classification.
Tables 8 depicts the results of Genetic Algorithm, and Arithmetic Optimisation and other
approaches for Unreduced Data. The evaluation results demonstrate the effectiveness of the proposed
Novel Hybrid Genetic Algorithm-Arithmetic Optimisation algorithm. The innovative hybrid method
demonstrates its superiority in effectively capturing both positive and negative examples, ultimately
resulting in a significantly lower mistake rate, with consistently higher precision, recall, specificity,
G-mean, MCC, Youden’s Index, BCR, ROC area, and accuracy. This performance highlights the
method’s potential to provide predictions that are more trustworthy and balanced for improving
classification outcomes.
Table 9 shows the results of Genetic Algorithm, Arithmetic Optimisation, and Novel Hybrid
Genetic Arithmetic Optimisation technique for Unreduced Data. Notably, the Novel Hybrid Genetic
Arithmetic Optimisation algorithm demonstrates outstanding precision, reaching 0.95, confirming
its accuracy in anticipating positive situations. This precision is balanced, demonstrating its ability
to make accurate positive and negative predictions with strong recall, F1-score, sensitivity, and
specificity. The MCC score of 0.94 is impressive and indicates high overall classification quality.
Notably, this strategy only slightly outperforms random guessing, as evidenced by the Lift score, which
is significantly lower than that of the other methodologies. The Novel Hybrid Genetic Arithmetic
Optimisation approach, however, consistently displays strong performance across a variety of measures,
contributing to its noteworthy ROC area of 0.92 and a high accuracy of 0.95, positioning it as a robust
choice for accurate and balanced classification.
The performance of the approaches can be determined by comparing the evaluation metrics of
Genetic Algorithm, Arithmetic Optimisation, and Novel Hybrid Genetic Arithmetic Optimisation for
Unreduced Data in Table 10. With balanced precision, recall, F1-score, sensitivity, and specificity,
the Novel Hybrid Genetic Arithmetic Optimisation stands out as a top performer, demonstrating its
effectiveness in both positive and negative predictions. The fact that it earns the highest G-mean (0.95)
and BCR (0.96) scores stands up as evidence of its thorough class capture. It is exceptional in terms
of overall categorization quality with a strong MCC score of 0.91. Additionally, this method exhibits
reliability and potent discriminative power by maintaining competitive error rates and a strong ROC
area (0.40 and 0.90, respectively).
Table 11 describes the balanced performance with good precision, recall, F1-score, and specificity
when approaches Genetic Algorithm, Arithmetic Optimization, and Novel Hybrid Genetic Arithmetic
Optimisation are compared for Unreduced Data. It continues to have competitive G-mean and MCC
scores, demonstrating its propensity for prediction. This technique demonstrates its ability to provide
Table 12. Classification of reduct set using SVM classifier
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a thorough classification by performing particularly well in Youden’s Index, BCR, and BER. Its
reliability is further supported by precision and a strong ROC area, making it an appealing option
for precise, comprehensive forecasts.
With a high score of 0.94, the Novel Hybrid Genetic Arithmetic Optimisation algorithm excels
in terms of accuracy, indicating that it makes correct predictions for all classes. It has the lowest error
rate, 0.06, demonstrating successful misclassification reduction. This high accuracy is consistent with
consistently great performance on numerous metrics. The Unreduced Data technique, on the other
hand, has the highest error rate (0.60), which denotes a higher percentage of inaccurate predictions. The
Novel Hybrid Genetic Arithmetic Optimisation stands out as a reliable option for accurate forecasts
with low error rates in terms of both accuracy and error rate as shown in Table 12.
Comparing the presented method to existing supervised feature selection techniques, the presented
approach produces the lowermost bit classification and fault value. When related to alternative FS techniques,
it is found that the NHGAO-based relative reduct algorithm creates the least amount of execution time.
Figures 7, 8, 9, 10, and 11 classification comparison for “accuracy”, “precision”, “recall”, and
“F1-Score” for five classifiers such as “Decision Tree”, “Random Forest Classifier”, “Naïve Bayes
Figure 7. Classification accuracy for Decision Tree Classifier
Figure 8. Classification accuracy for Random Forest Classifier
Volume 15 • Issue 1
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Classifier”, “KNN Classifier” and “SVM classifiers”. A great tool for organizing, visualizing, and
choosing classifiers based on performance is the ROC plot. (Nivetha & Inbarani, 2023a; Azar et al.,
2016a). The idea of a “separator” variable is the foundation of the ROC curve. If the “criterion” or
“cut-off” for positivity on the decision axis is altered, the frequency of positive and negative diagnostic
test findings will change. The decision scale is only “implicit” when a diagnostic system’s results are
evaluated based on subjective assessment. (Nivetha & Inbarani, 2023b; Azar et al., 2007), Such a
variable is frequently referred to as a “latent” or unobservable variable. A ROC curve, also known as
a curve in the unit square, is produced by plotting TPF (sensitivity) versus FPF (1-specificity) across
various cut-offs has been represented in medical diagnosis (Azar et al., 2020c).
Figure 9. Classification accuracy for Naïve Bayes Classifier
Figure 10. Classification accuracy for KNN classifier
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Figure 12 displays the ROC plots for each classifier on the three datasets, utilizing the proposed
NHGAO algorithm. The classification accuracy rates of the five classifiers differ from each other.
(Nivetha et al., 2023a,b,c). Particularly, the “Random Forest classifier” outperforms the other
classifiers, achieving the highest classification accuracy of 0.95%, as indicated by its position above
the diagonal line in the graph. This result highlights the superior performance of the “Random Forest
classifier” compared to the others in the classification task.
Figure 11. Classification accuracy for SVM classifier
Figure 12. ROC plot for different classifiers for the proposed NHGAO selected features
Volume 15 • Issue 1
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This research work addresses the complex challenge of feature selection in “Medical Image
Processing”. This study introduced the innovative Novel Hybrid Genetic Arithmetic Optimization
algorithm for feature selection. This approach, designed for feature selection and classification tasks
using COVID and lung imaging datasets, merges genetic algorithm and arithmetic optimization to
efficiently optimize feature subsets. The assessment of feature subset quality, guided by accuracy
findings and employing the Random Forest classifier as a fitness function, underscores GA-AOA’s
superiority over traditional genetic algorithms and arithmetic optimization. Novel Hybrid GAO
excels in multiple performance metrics, encompassing “Accuracy,” “Precision,” “Recall,” “F1-Score,”
“MCC,” “Sensitivity,” “Specificity,” “Geometric Mean,” “Lift,” and “Youden’s Index”. Notably, the
hybrid algorithm achieves remarkable accuracy in classifying COVID and lung images, underscoring
its effectiveness in both feature selection and classification tasks. The findings indicate that each
applied metaheuristic optimization strategy substantially enhancing classification accuracy while
concurrently reducing feature size. Comparative analyses against genetic algorithms and arithmetic
optimization underscores the hybrid algorithm’s advantages, achieving superior accuracy with
a reduced population size and fewer iterations. This study’s outcomes pave the way for potential
expansions utilizing evolutionary algorithm-driven feature selection methods like Particle Swarm
Optimization, Ant Colony Optimization etc.., Moreover, the framework’s versatility positions it for
application across various medical disciplines beyond the current scope of research.
The authors express their gratitude to the UGC-Special Assistance Programme for providing financial
support for their research under the UGC-SAP at the level of DRS-II (Ref. No: F.5-6/2018/DRS-
II(SAP-II), 26 July 2018) in the Department of Computer Science, Periyar University, Salem, Tamil
Nadu, India.
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Abbas, S. M., Javaid, N., Azar, A. T., Qasim, U., Khan, Z. A., & Aslam, S. (2022). Towards Enhancing the
Robustness of Scale-Free IoT Networks by an Intelligent Rewiring Mechanism. Sensors (Basel), 2022(22), 2658.
doi:10.3390/s22072658 PMID:35408272
Abdelmalek, S., Azar, A. T., & Dib, D. (2018). A novel actuator fault-tolerant control strategy of DFIG-based
wind turbines using Takagi-Sugeno Multiple models. International Journal of Control, Automation, and Systems,
16(3), 1415–1424. doi:10.1007/s12555-017-0320-y
Abdelmalek, S., Azar, A. T., & Dib, D. (2021). Fuzzy Fault-Tolerant Control for doubly fed induction generator
in wind energy conversion system. Int. J. Advanced Intelligence Paradigms, 20(1/2), 38–57. doi:10.1504/
IJAIP.2021.117608
Abdul-Adheem, W., Ibraheem, I. K., Humaidi, A. J., & Azar, A. T. (2021). Model-free active input-output
feedback linearization of a single-link flexible joint manipulator: An improved active disturbance rejection
control approach. Measurement and Control, 54(5-6), 856–871. doi:10.1177/0020294020917171
Abdul-Adheem, W. R., Azar, A. T., Ibraheem, I. K., & Humaidi, A. J. (2020b). Novel Active Disturbance
Rejection Control Based on Nested Linear Extended State Observers. Applied Sciences (Basel, Switzerland),
10(12), 4069. doi:10.3390/app10124069
Abdul-Adheem, W. R., Ibraheem, I. K., Azar, A. T., & Humaidi, A. J. (2020a). Improved Active Disturbance
Rejection-Based Decentralized control for MIMO Nonlinear Systems: Comparison with The Decoupled Control
Scheme. Applied Sciences (Basel, Switzerland), 10(7), 2515. doi:10.3390/app10072515
Abdul-Adheem, W. R., Ibraheem, I. K., Azar, A. T., & Humaidi, A. J. (2022). Design and Analysis of a Novel
Generalized Continuous Tracking Differentiator. Ain Shams Engineering Journal, 13(4), 101656. doi:10.1016/j.
asej.2021.101656
Abdul-Kareem, A. I., Hasan, A. F., Al-Qassar, A. A., Humaidi, A. J., Hassan, R. F., Ibraheem, I. K., & Azar,
A. T. (2022). Rejection Of Wing-Rock Motion In Delta Wing Aircrafts Based On Optimal LADRC Schemes
With Butterfly Optimization Algorithm. Journal of Engineering Science and Technology, 17(4), 2476–2495.
Abed, A. M., Rashid, Z. N., Abedi, F., Zeebaree, S. R. M., Sahib, M. A., Mohamad Jawad, A. J., Redha
Ibraheem, G. A., Maher, R. A., Abdulkareem, A. I., Ibraheem, I. K., Azar, A. T., & Al-khaykan, A. (2022).
Trajectory tracking of differential drive mobile robots using fractional-order proportional-integral-derivative
controller design tuned by an enhanced fruit fly optimization. Measurement and Control, 55(3-4), 209–226.
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Abedinpourshotorban, H., Shamsuddin, S. M., Beheshti, Z., & Jawawi, D. N. (2016). Electromagnetic field
optimization: A physics-inspired metaheuristic optimization algorithm. Swarm and Evolutionary Computation,
26, 8–22. doi:10.1016/j.swevo.2015.07.002
Aboamer, M. A., Azar, A. T., Wahba, K., Schumann, A., & Bär, K. J. (2019). Nonlinear single-input single-output
model-based estimation of cardiac output for normal and depressed cases. Neural Computing & Applications,
31(7), 2955–2978. doi:10.1007/s00521-017-3245-8
Aboamer, M. M., Azar, A. T., Mohamed, A. S. A., Bär, K. J., Berger, B. S., & Wahba, K. (2014b). Nonlinear
Features of Heart Rate Variability in Paranoid Schizophrenic. Neural Computing & Applications, 25(7-8),
1535–1555. doi:10.1007/s00521-014-1621-1
Aboamer, M. M., Azar, A. T., Wahba, K., & Mohamed, A. S. A. (2014a). Linear model-based estimation of
blood pressure and cardiac output for Normal and Paranoid cases. Neural Computing & Applications, 25(6),
1223–1240. doi:10.1007/s00521-014-1566-4
Abualigah, L., Diabat, A., Mirjalili, S., Abd Elaziz, M., & Gandomi, A. H. (2021). The arithmetic optimization
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Acharyulu, B. V. S., Gorripotu, T. S., Azar, A. T., Mohanty, B., Pilla, R., Kumar, S., Serrano, F. E., & Kamal,
N. A. (2021). Automatic Generation Control of Multi-area Multi-source Deregulated Power System Using Moth
Flame Optimization Algorithm. Communication and Intelligent Systems. Lecture Notes in Networks and Systems
(Vol. 204). Springer. doi:10.1007/978-981-16-1089-9_56
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Ahmadian, A., Azar, A. T., & Salahshour, S. (2021). Fuzzy Machine Learning Algorithms With Applications
Arising In Physical Problems. International Journal of Uncertainty, Fuzziness and Knowledge-based Systems,
29(6), V–IX.
Ahmed, S., & Azar, A. T. (2023a). Adaptive fractional tracking control of robotic manipulator using fixed-time
method. Complex & Intelligent Systems. Advance online publication. doi:10.1007/s40747-023-01164-7
Ahmed, S., Azar, A. T., Ibraheem, I. K., Kamal, N. A., Abdul-Majeed, F. A., & Ben Njima, C. (2023c).
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Ahmed, S., Azar, A. T., & Tounsi, M. (2022a). Design of Adaptive Fractional-Order Fixed-Time Sliding
Mode Control for Robotic Manipulators. Entropy (Basel, Switzerland), 24(12), 1838. doi:10.3390/e24121838
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Ahmed, S., Azar, A. T., & Tounsi, M. (2022b). Adaptive Fault Tolerant Non-Singular Sliding Mode Control
for Robotic Manipulators Based on Fixed-Time Control Law. Actuators, 11(12), 353. doi:10.3390/act11120353
Ahmed, S., Azar, A. T., Tounsi, M., & Anjum, Z. (2023b). Trajectory Tracking Control of Euler-Lagrange
Systems Using a Fractional Fixed-Time Method. Fractal Fract., 7(5), 355. doi:10.3390/fractalfract7050355
Ajeil, F., Ibraheem, I. K., Azar, A. T., & Humaidi, A. J. (2020b). Autonomous Navigation and Obstacle Avoidance
of an Omnidirectional Mobile Robot Using Swarm Optimization and Sensors Deployment. International Journal
of Advanced Robotic Systems, 17(3), 1–15. doi:10.1177/1729881420929498
Ajeil, F. H., Ibraheem, I. K., Azar, A. T., & Humaidi, A. J. (2020a). Grid-Based Mobile Robot Path Planning
Using Aging-Based Ant Colony Optimization Algorithm in Static and Dynamic Environments. Sensors (Basel),
20(7), 1880. doi:10.3390/s20071880 PMID:32231091
Ajel, A. R., Humaidi, A. J., Ibraheem, I. K., & Azar, A. T. (2021). Robust Model Reference Adaptive Control
for Tail-Sitter VTOL Aircraft. Actuators, 10(7), 162. doi:10.3390/act10070162
Akyol, S., & Alatas, B. (2017). Plant intelligence-based metaheuristic optimization algorithms. Artificial
Intelligence Review, 47(4), 417–462. doi:10.1007/s10462-016-9486-6
Al-Betar, M. A., Alyasseri, Z. A. A., Awadallah, M. A., & Abu Doush, I. (2021). Coronavirus herd immunity
optimizer (CHIO). Neural Computing & Applications, 33(10), 5011–5042. doi:10.1007/s00521-020-05296-6
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Al Mhdawi, A. K., Azar, A. T., Kamal, N. A., & Ben Njima, C. (2022). Intelligent OpenFlow Switch for SDN
Networks Based on COVID-19’s High Network Traffic using Heuristic GA-Fuzzification Control Approach. In
IEEE 2022 International Conference on Control, Automation and Diagnosis (ICCAD), 13-15 July 2022, Lisbon,
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VTOL Aircraft Guided and Controlled By STSMC. Journal of Engineering Science and Technology, 16(3),
1932–1950.
Al-Qassar, A. A., Al-Dujaili, A. Q., Hasan, A. F., Humaidi, A. J., Ibraheem, I. K., & Azar, A. T. (2021b).
Stabilization Of Single-Axis Propeller-powered System For Aircraft Applications Based On Optimal Adaptive
Control Design. Journal of Engineering Science and Technology, 16(3), 1851–1869.
Alain, K. S. T., Azar, A. T., Kengne, R., & Bertrand, F. H. (2020). Stability analysis and robust synchronisation
of fractional-order modified Colpitts oscillators. Int. J. Automation and Control, 14(1), 52–79. doi:10.1504/
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Ali, M.O., Abou-Loukh, S.J., Al-Dujaili, A.Q., Alkhayyat, A., Abdulkareem, A.I., Ibraheem, I.K., Humaidi,
A.J., Al-Qassar, A.A., & Azar, A.T. (2022b). Radial Basis Function Neural Networks-Based Short Term Electric
Power Load Forecasting For Super High Voltage Power Grid. Journal of Engineering Science and Technology,
17(1), 361 – 378.
Ali, T., Malik, S. A., Hameed, I. A., Daraz, A., Mujlid, H., & Azar, A. T. (2022a). Load Frequency Control and
Automatic Voltage Regulation in a Multi-Area Interconnected Power System Using Nature-Inspired Computation-
Based Control Methodology. Sustainability (Basel), 14(19), 12162. doi:10.3390/su141912162
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for chaos synchronization of nonlinear dynamical systems. In Backstepping Control of Nonlinear Dynamical
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Amara, K., Malek, A., Bakir, T., Fekik, A., Azar, A. T., Almustafa, K. M., Bourennane, E., & Hocine, D. (2019).
Adaptive neuro-fuzzy inference system based maximum power point tracking for stand-alone photovoltaic system.
International Journal of Modelling Identification and Control, 33(4), 311–321. doi:10.1504/IJMIC.2019.107480
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Short-Term Load Forecasting With Randomly Varying Load. International Journal of Sociotechnology and
Knowledge Development, 13(4), 32–49. doi:10.4018/IJSKD.2021100103
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Hepatic Tumors in Computed Tomography Images. International Journal of Rough Sets and Data Analysis,
1(2), 31–48. doi:10.4018/ijrsda.2014070103
Anter, A. M., Azar, A. T., El-Bendary, N., Hassanien, A. E., & Abu ElSoud, M. (2013). Automatic computer aided
segmentation for liver and hepatic lesions using hybrid segmentations techniques. 2013 Federated Conference
on Computer Science and Information Systems (FedCSIS), Kraków, Poland.
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Segmentation. 2013 Federated Conference on Computer Science and Information Systems (FedCSIS), Kraków,
Poland.
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Retinal Vessel Segmentation. International Journal of Rough Sets and Data Analysis, 1(2), 15–30. doi:10.4018/
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Vessel Segmentation Using Ant Colony System. Recent Advances in Intelligent Informatics Advances in Intelligent
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Ashfaq, T., Khalid, R., Yahaya, A. S., Aslam, S., Azar, A. T., Alkhalifah, T., & Tounsi, M. (2022a). An Intelligent
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22(17), 6318. doi:10.3390/s22176318 PMID:36080777
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Machine Learning and Blockchain Based Efficient Fraud Detection Mechanism. Sensors (Basel), 22(19), 7162.
doi:10.3390/s22197162 PMID:36236255
Aslam, S., Ayub, N., Farooq, U., Alvi, M. J., Albogamy, F. R., Rukh, G., Haider, S. I., Azar, A. T., & Bukhsh, R.
(2021). Towards Electric Price and Load Forecasting Using CNN-Based Ensembler in Smart Grid. Sustainability
(Basel), 13(22), 12653. doi:10.3390/su132212653
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Azar, A. T., Abed, A. M., Abdul-Majeed, F. A., Hameed, I. A., Jawad, A. J. M., Abdul-Adheem, W. R., Ibraheem,
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Azar, A. T., Abed, A. M., Abdulmajeed, F. A., Hameed, I. A., Kamal, N. A., Jawad, A. J. M., Abbas, A. H.,
Rashed, Z. A., Hashim, Z. S., Sahib, M. A., Ibraheem, I. K., & Thabit, R. (2022b). A New Nonlinear Controller
for the Maximum Power Point Tracking of Photovoltaic Systems in Micro Grid Applications Based on Modified
Anti-Disturbance Compensation. Sustainability (Basel), 14(17), 10511. doi:10.3390/su141710511
Azar, A. T., Ali, N., Makarem, S., Diab, M. K., & Ammar, H. H. (2020e). Design and implementation of a ball
and beam PID control system based on metaheuristic techniques. The International Conference on Advanced
Intelligent Systems and Informatics AISI 2019. Advances in Intelligent Systems and Computing, 1058, 313-325.
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Azar, A. T., Aly, A. M., Sayed, A. S., Radwan, M. E., & Ammar, H. H. (2019c). Neuro-Fuzzy System for 3-DOF
Parallel Robot Manipulator. 2019 Novel Intelligent and Leading Emerging Sciences Conference (NILES), 28-30
Oct. 2019, 182-186. doi:10.1109/NILES.2019.8909333
Azar, A. T., Ammar, H. H., de Brito Silva, G., & Razali, M. S. A. B. (2019a). Self-balancing Robot Modeling and
Control Using Two Degree of Freedom PID Controller. In The International Conference on Advanced Machine
Learning Technologies and Applications (AMLTA2019). AMLTA 2019. Advances in Intelligent Systems and
Computing (vol 921, pp. 64-76). Springer. doi:10.1007/978-3-319-99010-1_6
Azar, A. T., Ammar, H. H., Ibrahim, Z. F., Ibrahim, H. A., Mohamed, N. A., & Taha, M. A. (2020d).
Implementation of PID Controller with PSO Tuning for Autonomous Vehicle. The International Conference
on Advanced Intelligent Systems and Informatics AISI 2019. Advances in Intelligent Systems and Computing,
1058, 288-299. doi:10.1007/978-3-030-31129-2_27
Azar, A. T., Ammar, H. H., Mayra Beb, M. Y., Garces, S. R., & Boubakarig, A. (2020g). Optimal Design of PID
Controller for 2-DOF Drawing Robot using Bat-Inspired Algorithm. The International Conference on Advanced
Intelligent Systems and Informatics AISI 2019. Advances in Intelligent Systems and Computing, 1058, 175-186.
Azar, A. T., Ammar, H. H., & Mliki, H. (2018a). Fuzzy Logic Controller with Color Vision System Tracking
for Mobile Manipulator Robot. In The International Conference on Advanced Machine Learning Technologies
and Applications (AMLTA2018). AMLTA 2018. Advances in Intelligent Systems and Computing (vol. 723, pp.
138-146(. Springer. doi:10.1007/978-3-319-74690-6_14
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Azar, A. T., Anter, A. M., & Fouad, K. M. (2020a). Intelligent system for feature selection based on rough set
and chaotic binary grey wolf optimization. International Journal of Computer Applications in Technology,
63(1/2), 4–24. doi:10.1504/IJCAT.2020.107901
Azar, A. T., Anter, A. M., & Fouad, K. M. (2020h). Intelligent system for feature selection based on rough set
and chaotic binary grey wolf optimization. International Journal of Computer Applications in Technology,
63(1/2), 4–24. doi:10.1504/IJCAT.2020.107901
Azar, A. T., Balas, V. E., & Olariu, T. (2014b). Classification Of EEG-Based Brain-Computer Interfaces.
Advanced Intelligent Computational Technologies and Decision Support Systems. Studies in Computational
Intelligence, 486, 97–106. doi:10.1007/978-3-319-00467-9_9
Azar, A. T., & Banu, P. K. (2022). Robust Feature Selection Using Rough Set-Based Ant-Lion Optimizer for
Data Classification. International Journal of Sociotechnology and Knowledge Development (IJSKD), 14(1).
International Journal of Sociotechnology and Knowledge Development, 63(1), 1–21. doi:10.4018/IJSKD.301263
Azar, A. T., Banu, P. K. N., & Inbarani, H. H. (2013a). PSORR - An Unsupervised Feature Selection Technique
for Fetal Heart Rate. 5th International Conference on Modelling, Identification and Control (ICMIC 2013).
Azar, A. T., El-Said, S. A., & Hassanien, A. E. (2013b). Fuzzy and Hard Clustering Analysis for Thyroid
Disease. Computer Methods and Programs in Biomedicine, 111(1), 1–16. doi:10.1016/j.cmpb.2013.01.002
PMID:23357404
Azar, A. T., Elgendy, M. S., Salam, M. A., & Fouad, K. M. (2022c). Rough Sets Hybridization with Mayfly
Optimization for Dimensionality Reduction. CMC-Computers. Materials & Continua, 73(1), 1087–1108.
doi:10.32604/cmc.2022.028184
Azar, A. T., Elshazly, H. I., Hassanien, A. E., & Elkorany, A. M. (2014a). A Random Forest Classifier for Lymph
Diseases. Computer Methods and Programs in Biomedicine, 113(2), 465–473. doi:10.1016/j.cmpb.2013.11.004
PMID:24290902
Azar, A. T., Hassan, H., Razali, M. S. A. B., de Brito Silva, G., & Ali, H. R. (2019b). Two-Degree of Freedom
Proportional Integral Derivative (2-DOF PID) Controller for Robotic Infusion Stand. In Proceedings of the
International Conference on Advanced Intelligent Systems and Informatics 2018. AISI 2018. Advances in
Intelligent Systems and Computing (vol 845). Springer.
Azar, A. T., & Hassanien, A. E. (2015). Dimensionality Reduction of Medical Big Data Using Neural-Fuzzy
Classifier. Soft Computing, 19(4), 1115–1127. doi:10.1007/s00500-014-1327-4
Azar, A. T., & Hassanien, A. E. (2022). Modeling, control and drug development for COVID-19 outbreak
prevention. In Studies in Systems, Decision and Control. Springer. doi:10.1007/978-3-030-72834-2
Azar, A. T., Hassanien, A. E., & Kim, T. H. (2012) Expert System Based On Neural-Fuzzy Rules for Thyroid
Diseases Diagnosis. International Conference on Bio-Science and Bio-Technology (BSBT 2012), 353, 94-105.
doi:10.1007/978-3-642-35521-9_13
Azar, A. T., Inbarani, H. H., & Devi, K. R. (2017). Improved dominance rough set-based classification system.
Neural Computing & Applications, 28(8), 2231–2246. doi:10.1007/s00521-016-2177-z
Azar, A. T., Inbarani, H. H., Kumar, U., & Own, H. S. (2016b). Hybrid system based on Bijective soft and
Neural Network for Egyptian Neonatal Jaundice Diagnosis. Int. J. Intelligent Engineering Informatics., 4(1),
71–90. doi:10.1504/IJIEI.2016.074506
Azar, A. T., & Kamal, N. A. (2021a). Design, Analysis, and Applications of Renewable Energy Systems. Advances
in Nonlinear Dynamics and Chaos (ANDC). Elsevier. doi:10.1016/C2020-0-01946-8
Azar, A. T., & Kamal, N. A. (2021b). Renewable Energy Systems: Modelling, Optimization and Control. Advances
in Nonlinear Dynamics and Chaos (ANDC). Elsevier. doi:10.1016/C2019-0-00528-6
Azar, A. T., & Kamal, N. A. (2021c). Handbook of Research on Modeling, Analysis, and Control of Complex
Systems. IGI Global.
Azar, A. T., Khan, Z. I., Amin, S. U., & Fouad, K. M. (2023d). Hybrid Global Optimization Algorithm for
Feature Selection. CMC-Computers. Materials & Continua, 74(1), 2021–2037. doi:10.32604/cmc.2023.032183
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Azar, A. T., Kumar, J., Kumar, V., & Rana, K. P. S. (2018b) Control of a Two Link Planar Electrically-Driven
Rigid Robotic Manipulator Using Fractional Order SOFC. In Proceedings of the International Conference on
Advanced Intelligent Systems and Informatics 2017. AISI 2017. Advances in Intelligent Systems and Computing
(vol 639, pp. 57-68). Springer. doi:10.1007/978-3-319-64861-3_6
Azar, A. T., Kumar, S. S., Inbarani, H. H., & Hassanien, A. E. (2016a). Pessimistic Multi-granulation Rough
set based Classification for Heart Valve Disease Diagnosis. International Journal of Modelling Identification
and Control, 26(1), 42–51. doi:10.1504/IJMIC.2016.077744
Azar, A. T., Madian, A., Ibrahim, H., Taha, M. A., Mohamed, N. A., Fathy, F., & AboAlNaga, B. A. M. (2020i).
Medical nanorobots: Design, applications and future challenges. In Control Systems Design of Bio-Robotics and
Bio-mechatronics with Advanced Applications. Elsevier. doi:10.1016/B978-0-12-817463-0.00011-3
Azar, A. T., Mohamed Abdalla, S. A., Wahba, K., & Massoud, W. (2007). Association between Dialysis Dose
Improvement and Nutritional Status among Hemodialysis Patients. American Journal of Nephrology, 27(2),
113–119. doi:10.1159/000099836 PMID:17308372
Azar, A. T., Sayed, A. S., Shahin, A. S., Elkholy, H. S., & Ammar, H. H. (2020c) PID Controller for 2-DOFs
Twin Rotor MIMO System Tuned with Particle Swarm Optimization. The International Conference on Advanced
Intelligent Systems and Informatics AISI 2019. Advances in Intelligent Systems and Computing, 1058, 229-242.
Azar, A. T., & Serrano, F. E. (2015). Stabilization and Control of Mechanical Systems with Backlash. In
Advanced Intelligent Control Engineering and Automation. IGI Global. doi:10.4018/978-1-4666-7248-2.ch001
Azar, A. T., & Serrano, F. E. (2019). Fractional Order Two Degree of Freedom PID Controller for a Robotic
Manipulator with a Fuzzy Type-2 Compensator. In Proceedings of the International Conference on Advanced
Intelligent Systems and Informatics 2018. AISI 2018. Advances in Intelligent Systems and Computing (vol 845).
Springer. doi:10.1007/978-3-319-99010-1_7
Azar, A. T., Serrano, F. E., Flores, M. A., Kamal, N. A., Ibraheem, I. K., Humaidi, A. J., Fekik, A., Alain, K.
S. T., Romanic, K., Rana, K. P. S., Kumar, V., & Mittal, S. (2021b) Dynamic self-recurrent wavelet neural
network for solar irradiation forecasting. In Advances in Nonlinear Dynamics and Chaos (ANDC), Design,
Analysis, and Applications of Renewable Energy Systems (pp. 249-274). Academic Press. doi:10.1016/B978-
0-12-824555-2.00017-4
Azar, A. T., Serrano, F. E., Flores, M. A., Vaidyanathan, S., & Zhu, Q. (2020k). Adaptive Neural-Fuzzy and
Backstepping Controller for Port-Hamiltonian Systems. International Journal of Computer Applications in
Technology, 62(1), 1–12. doi:10.1504/IJCAT.2020.103894
Azar, A. T., Serrano, F. E., Hameed, I. A., Kamal, N. A., & Vaidyanathan, S. (2020f) Robust H-Infinity
Decentralized Control for Industrial Cooperative Robots. The International Conference on Advanced Intelligent
Systems and Informatics AISI 2019. Advances in Intelligent Systems and Computing (vol 1058, pp. 254-265).
Springer.
Azar, A. T., Serrano, F. E., & Kamal, N. A. (2021). Optimal Fractional Order Control for Nonlinear Systems
Represented by the Euler-Lagrange Formulation. International Journal of Modelling Identification and Control,
37(1), 1–9. doi:10.1504/IJMIC.2021.119034
Azar, A. T., Serrano, F. E., Rossell, J. M., Vaidyanathan, S., & Zhu, Q. (2020b). Adaptive self-recurrent wavelet
neural network and sliding mode controller/observer for a slider crank mechanism. International Journal of
Computer Applications in Technology, 63(4), 273–285. doi:10.1504/IJCAT.2020.110404
Azar, A. T., Serrano, F. E., Vaidyanathan, S., & Albalawi, H. (2020j) Adaptive Higher Order Sliding Mode
Control for Robotic Manipulators with Matched and Mismatched Uncertainties. In The International Conference
on Advanced Machine Learning Technologies and Applications (AMLTA2019). AMLTA 2019. Advances in
Intelligent Systems and Computing (vol 921, pp. 360-369). Springer. doi:10.1007/978-3-030-14118-9_36
Azar, A. T., Smait, D. A., Muhsen, S., Jassim, M. A., AL-Salih, A. A. M. M., Hameed, I. A., Jawad, A. J. M.,
Abdul-Adheem, W. R., Cocquempot, V., Sahib, M. A., Kamal, N. A., & Ibraheem, I. K. (2023). A New Approach
to Nonlinear State Observation for Affine Control Dynamical Systems. Applied Sciences (Basel, Switzerland),
13(5), 3300. doi:10.3390/app13053300
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Azar, A. T., Tounsi, M., Fati, S. M., Javed, Y., Amin, S. U., Khan, Z. I., Alsenan, S., & Ganesan, J. (2023a).
Automated System for Colon Cancer Detection and Segmentation Based on Deep Learning Techniques.
International Journal of Sociotechnology and Knowledge Development, 15(1), 1–28. doi:10.4018/IJSKD.326629
Azar, A. T., & Vaidyanathan, S. (2015a) Handbook of Research on Advanced Intelligent Control Engineering
and Automation. IGI Global. doi:10.4018/978-1-4666-7248-2
Azar, A. T., & Vaidyanathan, S. (2015b). Computational Intelligence applications in Modeling and Control.
Studies in Computational Intelligence (Vol. 575). Springer-Verlag.
Aziz, A. S. A., Azar, A. T., Hassanien, A. E., & Hanafy, S. E. (2012). Continuous Features Discretizaion for
Anomaly Intrusion Detectors Generation. The 17th Online World Conference on Soft Computing in Industrial
Applications (WSC17).
Aziz, A. S. A., Hassanien, A. E., Azar, A. T., & Hanafy, S. E. (2013a). Genetic Algorithm with Different Feature
Selection Techniques for Anomaly Detectors Generation. 2013 Federated Conference on Computer Science and
Information Systems (FedCSIS), Kraków, Poland.
Aziz, A. S. A., Hassanien, A. E., Azar, A. T., & Hanafy, S. E. (2013b). Machine learning techniques for
anomalies detection and classification. Communications in Computer and Information Science, 381, 219–229.
doi:10.1007/978-3-642-40597-6_19
Babajani, R., Abbasi, M., Azar, A. T., Bastan, M., Yazdanparast, R., & Hamid, M. (2019). Integrated safety
and economic factors in a sand mine industry: A multivariate algorithm. International Journal of Computer
Applications in Technology, 60(4), 351–359. doi:10.1504/IJCAT.2019.101180
Bansal, N., Bisht, A., Paluri, S., Kumar, V., Rana, K. P. S., Azar, A. T., & Vaidyanathan, S. (2021). Single-link
flexible joint manipulator control using backstepping technique. In Backstepping Control of Nonlinear Dynamical
Systems, Advances in Nonlinear Dynamics and Chaos (ANDC) (pp. 375–406). Academic Press. doi:10.1016/
B978-0-12-817582-8.00022-2
Banu, P. K. N., Azar, A. T., & Inbarani, H. H. (2017). Fuzzy firefly clustering for tumor and cancer analysis.
International Journal of Modelling Identification and Control, 27(2), 92–103. doi:10.1504/IJMIC.2017.082941
Banu, P. K. N., Inbarani, H. H., Azar, A. T., Hala, S., Own, H. S., & Hassanien, A. E. (2014). Rough Set
Based Feature Selection for Egyptian Neonatal Jaundice. In Advanced Machine Learning Technologies and
Applications: Second International Conference, AMLTA 2014, Cairo, Egypt, November 28-30, 2014. Proceedings,
Communications in Computer and Information Science (Vol. 488). Springer-Verlag GmbH Berlin/Heidelberg.
doi:10.1007/978-3-319-13461-1_35
Barakat, M. H., Azar, A. T., & Ammar, H. H. (2020). Agricultural Service Mobile Robot Modeling and Control
Using Artificial Fuzzy Logic and Machine Vision. In The International Conference on Advanced Machine
Learning Technologies and Applications (AMLTA2019). AMLTA 2019. Advances in Intelligent Systems and
Computing (vol 921, pp. 453-465). Springer. doi:10.1007/978-3-030-14118-9_46
Ben Abdallah, M., Azar, A. T., Guedri, H., Malek, J., & Belmabrouk, H. (2018). Noise-estimation-based
anisotropic diffusion approach for retinal blood vessel segmentation. Neural Computing & Applications, 29(8),
159–180. doi:10.1007/s00521-016-2811-9
Ben Abdallah, M., Malek, J., Azar, A. T., Belmabrouk, H., & Krissian, K. (2016). Adaptive Noise-Reducing
Anisotropic Diffusion Filter. Neural Computing & Applications, 27(5), 1273–1300. doi:10.1007/s00521-015-
1933-9
Ben Abdallah M, Malek J, Azar AT, Montesinos P, Krissian K, Belmabrouk H (2014) Automatic Extraction of
Blood Vessels in the Retinal Vascular Tree Using Multiscale Medialness. International Journal of Biomedical
Imaging. 10.1155/2015/519024
Ben Smida, M., Sakly, A., Vaidyanathan, S., & Azar, A. T. (2018). Control-Based Maximum Power Point Tracking
for a Grid-Connected Hybrid Renewable Energy System Optimized by Particle Swarm Optimization. Advances
in System Dynamics and Control. IGI-Global. doi:10.4018/978-1-5225-4077-9.ch003
Birbil, Ş. İ., & Fang, S. C. (2003). An electromagnetism-like mechanism for global optimization. Journal of
Global Optimization, 25(3), 263–282. doi:10.1023/A:1022452626305
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Bouakrif, F., Azar, A.T., Volos, C.K., Muñoz-Pacheco, J.M., & Pham, V.T. (2019). Iterative Learning and
Fractional Order Control for Complex Systems. Complexity. 10.1155/2019/7958625
Bouchemha, A., Azar, A. T., Laatra, Y., Souaidia, C., & Dib, D. (2021). Sensor and sensorless speed control
of doubly-fed induction machine. Int. J. Advanced Intelligence Paradigms, 19(2), 194–215. doi:10.1504/
IJAIP.2021.115249
Boulmaiz, A., Meghni, B., Redjati, A., & Azar, A. T. (2022). LiTasNeT: A Birds Sound Separation Algorithm
based on Deep Learning. International Journal of Sociotechnology and Knowledge Development, 14(1). 1–19.
Bousbaine, A., Fareha, A., Josaph, A. K., Fekik, A., Azar, A. T., Moualek, R., Benyahia, N., Benamrouche,
N., Kamal, N. A., Al Mhdawi, A. K., Humaidi, A. J., & Ibraheem, I. K. (2023). Design and Implementation
of a Robust 6-DOF Quadrotor Controller Based on Kalman Filter for Position Control. In Mobile Robot:
Motion Control and Path Planning. Studies in Computational Intelligence (Vol. 1090, pp. 331–363). Springer.,
doi:10.1007/978-3-031-26564-8_11
Cai, W., Yang, W., & Chen, X. (2008). A global optimization algorithm based on plant growth theory: plant
growth optimization. In 2008 International conference on intelligent computation technology and automation
(ICICTA) (Vol. 1, pp. 1194-1199). IEEE. doi:10.1109/ICICTA.2008.416
Canayaz, M. (2021). MH-COVIDNet: Diagnosis of COVID-19 using deep neural networks and meta-heuristic-
based feature selection on X-ray images. Biomedical Signal Processing and Control, 64, 102257. doi:10.1016/j.
bspc.2020.102257 PMID:33042210
Chattopadhyay, S., Dey, A., Singh, P. K., Geem, Z. W., & Sarkar, R. (2021). COVID-19 detection by optimizing
deep residual features with improved clustering-based golden ratio optimizer. Diagnostics (Basel), 11(2), 315.
doi:10.3390/diagnostics11020315 PMID:33671992
Cheema, M. A., Ashraf, N., Aftab, A., Qureshi, H. K., Kazim, M., & Azar, A. T. (2020) Machine Learning with
Blockchain for Secure E-voting System. The First International Conference of Smart Systems and Emerging
Technologies (SMART TECH 2020), 177-182.
Chen M.Y., & Chiang P.R. (2023). COVID-19 Diagnosis System Based on Chest X-ray Images Using Optimized
Convolutional Neural Network. ACM Trans. Sen. Netw., 19(3). 10.1145/3558098
Chowdhuri, S., Roy, P., Goswami, S., Azar, A. T., & Dey, N. (2014a). Rough Set Based Ad Hoc Network: A
Review. International Journal of Service Science, Management, Engineering, and Technology, 5(4), 66–76.
doi:10.4018/ijssmet.2014100105
Chowdhuri, S., Roy, P., Goswami, S., Azar, A. T., & Dey, N. (2014b). Rough Set Based Ad Hoc Network: A
Review. International Journal of Service Science, Management, Engineering, and Technology, 5(4), 66–76.
doi:10.4018/ijssmet.2014100105
Daraz, A., Malik, S. A., Azar, A. T., Aslam, S., Alkhalifah, T., & Alturise, F. (2022). Optimized Fractional
Order Integral-Tilt Derivative Controller for Frequency Regulation of Interconnected Diverse Renewable
Energy Resources. IEEE Access : Practical Innovations, Open Solutions, 10, 43514–43527. doi:10.1109/
ACCESS.2022.3167811
Daraz, A., Malik, S. A., Waseem, A., Azar, A. T., Haq, I. U., Ullah, Z., & Aslam, S. (2021). Automatic
Generation Control of Multi-Source Interconnected Power System Using FOI-TD Controller. Energies, 14(18),
5867. doi:10.3390/en14185867
Das, P., Das, D. K., & Dey, S. (2018). A new class topper optimization algorithm with an application to data
clustering. IEEE Transactions on Emerging Topics in Computing, 8(4), 948–959. doi:10.1109/TETC.2018.2812927
Deepa, S., & Shakila, S. (2022). Optimized Convolutional Neural Networks for Detecting Covid-19 from Chest
X-Ray. International Journal of Engineering Trends and Technology, 70(12), 210–218. doi:10.14445/22315381/
IJETT-V70I12P221
Dendani, H., Azar, A. T., Omeiri, A., Adjabi, M., Ghoudelbourk, S., Dib, D., & Dendani, N. (2023). Shunt
active power filter with a Three-Phase Neutral-Point-Clamped Inverter for power quality improvement based
on Fractional order proportional integral control. Int. J. Advanced Intelligence Paradigms, 25(1-2), 141–162.
doi:10.1504/IJAIP.2023.130819
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Dey, N., Karaa, W. B. A., Chakraborty, S., Banerjee, S., Salem, M. A. M., & Azar, A. T. (2015). Image Mining
Framework and Techniques: A Review. International Journal of Image Mining. Indersceince, 1(1), 45–64.
Ding, S., Shi, Z., Chen, K., & Azar, A. T. (2015). Mathematical Modeling and Analysis of Soft Computing.
Mathematical Problems in Engineering, 2015, 578321. Advance online publication. doi:10.1155/2015/578321
Djeddi, A., Dib, D., Azar, A. T., & Abdelmalek, S. (2019). Fractional Order Unknown Inputs Fuzzy Observer
for Takagi-Sugeno Systems with Unmeasurable Premise Variables. Mathematics, 7(10), 984. doi:10.3390/
math7100984
Dorigo, M., & Di Caro, G. (1999). Ant colony optimization: a new meta-heuristic. In Proceedings of the 1999
congress on evolutionary computation-CEC99 (Cat. No. 99TH8406) (Vol. 2, pp. 1470-1477). IEEE. doi:10.1109/
CEC.1999.782657
Drhorhi, I., El Fadili, A., Berrahal, C., Lajouad, R., El Magri, A., Giri, F., Azar, A. T., & Vaidyanathan, S. (2021).
Adaptive backstepping controller for DFIG-based wind energy conversion system. In Backstepping Control of
Nonlinear Dynamical Systems, Advances in Nonlinear Dynamics and Chaos (ANDC) (pp. 235–260). Academic
Press. doi:10.1016/B978-0-12-817582-8.00018-0
Dudekula, K. V., Syed, H., Basha, M. I. M., Swamykan, S. I., Kasaraneni, P. P., Kumar, Y. V. P., Flah, A., &
Azar, A. T. (2023). Convolutional Neural Network-Based Personalized Program Recommendation System for
Smart Television Users. Sustainability (Basel), 15(3), 2206. doi:10.3390/su15032206
Eid, H. F., Azar, A. T., & Hassanien, A. E. (2013). Improved Real-Time Discretize Network Intrusion Detection
System. Proceedings of Seventh International Conference on Bio-Inspired Computing: Theories and Applications
(BIC-TA 2012) Advances in Intelligent Systems and Computing, 201, 99-109. doi:10.1007/978-81-322-1038-2_9
El Kafazi, I., Bannari, R., & Azar, A. T. (2021) Multiobjective optimization-based energy management system
considering renewable energy, energy storage systems, and electric vehicles. In Advances in Nonlinear Dynamics
and Chaos (ANDC), Renewable Energy Systems (pp. 471-485). Academic Press. doi:10.1016/B978-0-12-
820004-9.00016-4
El-Shorbagy, M. A., Hassanien, A. E., & Azar, A. T. (2023). Circular Local Search for Unconstrained Optimization
Problems. Int. J. Advanced Intelligence Paradigms, 25(1-2), 200–218. doi:10.1504/IJAIP.2023.130821
ElBedwehy, M. N., Ghoneim, M. E., Hassanien, A. E., & Azar, A. T. (2014). A Computational Knowledge
Representation Model for Cognitive Computers. Neural Computing & Applications, 25(7-8), 1517–1534.
doi:10.1007/s00521-014-1614-0
Elfouly, F. H., Ramadan, R. A., Khedr, A. Y., Azar, A. T., Yadav, K., & Abdelhamed, M. A. (2021). Efficient Node
Deployment of Large-Scale Heterogeneous Wireless Sensor Networks. Applied Sciences (Basel, Switzerland),
11(22), 10924. doi:10.3390/app112210924
Elkholy, H. A., Azar, A. T., Magd, A., Marzouk, H., & Ammar, H. H. (2020b) Classifying Upper Limb Activities
Using Deep Neural Networks. In Proceedings of the International Conference on Artificial Intelligence and
Computer Vision (AICV2020). AICV 2020. Advances in Intelligent Systems and Computing (vol 1153, pp. 268-
282). Springer. doi:10.1007/978-3-030-44289-7_26
Elkholy, H. A., Azar, A. T., Shahin, A. S., Elsharkawy, O. I., & Ammar, H. H. (2020a) Path Planning of a Self
Driving Vehicle Using Artificial Intelligence Techniques and Machine Vision. In Proceedings of the International
Conference on Artificial Intelligence and Computer Vision (AICV2020). AICV 2020. Advances in Intelligent
Systems and Computing (vol 1153, pp 532-542). Springer. doi:10.1007/978-3-030-44289-7_50
Elshazly, H. I., Azar, A. T., Elkorany, A. M., & Hassanien, A. E. (2013a). Hybrid System based on Rough Sets
and Genetic Algorithms for Medical Data Classifications. International Journal of Fuzzy System Applications,
3(4), 31–46. doi:10.4018/ijfsa.2013100103
Elshazly, H. I., Azar, A. T., & Hassanien, A. E. (2013b). Hybrid System for Lymphatic Diseases Diagnosis.
2nd International Conference on Advances in Computing, Communications and Informatics (ICACCI-2013),
343 – 347. doi:10.1109/ICACCI.2013.6637195
Elshazly, H. I., Elkorany, A. M., Hassanien, A. E., & Azar, A. T. (2013c). Ensemble Classifiers for Biomedical
Data: Performance Evaluation. IEEE 8th International Conference on Computer Engineering & Systems (ICCES),
26 Nov - 28 Nov 2013, Ain Shams University (pp. 184-189). doi:10.1109/ICCES.2013.6707198
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