0045-7906/© 2023 Elsevier Ltd. All rights reserved.
Improved metaheuristics with deep learning based object detector for intelligent control in autonomous vehicles
☆Naif Alasmari
a, Manal Abdullah Alohali
b, Majdi Khalid
c, Nabil Almalki
d, Abdelwahed Motwakel
e,*, Mohamed Ibrahim Alsaid
f, Azza Elneil Osman
f, Amani A Alneil
faDepartment of Information Systems, College of Science & Art at Mahayil, King Khalid University, Saudi Arabia
bDepartment of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
cDepartment of Computer Science, College of Computers and Information Systems, Umm Al-Qura University, Makkah 21955, Saudi Arabia
dDepartment of Special Education, College of Education, King Saud University, Riyadh 12372, Saudi Arabia
eDepartment of Information Systems, College of business administration in Hawtat bani Tamim, Prince Sattam bin Abdulaziz University, Saudi Arabia.
fDepartment of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, AlKharj, Saudi Arabia
A R T I C L E I N F O Editor: Gupta Deepak Keywords:
Intelligent control Autonomous systems Artificial intelligence Hyperparameter tuning Object detection Metaheuristics
A B S T R A C T
In recent times, the autonomous systems have gained considerable interest due to their improved performance and lesser requirements for manual support. The autonomous systems have been extensively implemented in various domains such as logistics, industries, health care, finance, and so on. Intelligence control is the incorporation of autonomous and Artificial Intelligence (AI) systems that assist in critical decision-making steps. Autonomous driving is a new field in intel- ligent transportation systems that requires automated classification, detection, and the ranging of on-road difficulties. Therefore, the current study develops an Improved Metaheuristics technique with Deep Learning-based Object Detectors for Intelligent Control in Autonomous Vehicles (IMDLOD-ICAV). The presented technique mainly detects the objects to assist, in driving the autonomous vehicles. In the current research work, the RetinaNet model is applied as an object detector whereas the hyperparameter tuning process is executed with the help of the Nadam optimizer. Besides, the Elman Neural Network (ENN) model is also exploited to recognize the objects with a high accuracy. The parameter tuning process is performed with the help of the Improved Dragonfly Algorithm (IDFA). The authors conducted a comprehensive set of experi- ments to establish the superior performance of the proposed IMDLOD-ICAV technique. The outcomes confirmed the enhanced performance of the IMDLOD-ICAV technique with a maximum accuracy of 99.38%.
1. Introduction
Autonomous car functions on the basis of commands without any human intervention. Based on this working mechanism, this type
☆ This paper was recommended for publication by Associate Editor Gupta Deepak
* Corresponding author.
E-mail address: [email protected] (A. Motwakel).
Contents lists available at ScienceDirect
Computers and Electrical Engineering
journal homepage: www.elsevier.com/locate/compeleceng
https://doi.org/10.1016/j.compeleceng.2023.108718
Received 23 January 2023; Received in revised form 30 March 2023; Accepted 3 April 2023
of car is called as unmanned vehicle, driverless car, self-driving car or robot [1]. Autonomous vehicles can perceive their surrounding areas (both track and the obstacles) and commute to their destination with the help of radars, sensors, and cameras. The advanced control systems can interpret the data, captured by the sensors, to identify the obstacles and select the best navigation route for the vehicles [2]. Numerous investigations have been conducted earlier to introduce the concept of unmanned vehicles in day-to-day lives of human beings. Nowadays, such vehicles have become a reality. Even though it is yet to be commercially available on a large scale, the functioning of the autonomous cars is being thoroughly tested under various conditions on roads[3]. In self-driving vehicles, the main characteristics revolve around the accurate and correct detection of the track of the vehicles and the surrounding obstacles. Car or a vehicle must be well capable of detecting the obstacles in a precise manner so that it may prevent itself from collision, by staying away from the objects, at a safer distance [4]. Further, track detection is a major characteristic, since the vehicle should be capable of keeping itself within the limits of the track, follow the road lane rules and correctly remain on the track [5].
The latest developments in Machine Learning (ML), Deep Learning (DL) and Artificial Intelligence (AI) techniques led to their implementation in a variety of prominent applications. Autonomous car is one of the applications that has been predicted to have a revolutionary and a profound impact upon the society and how humans commute from one place to another [6]. Even though the domestication and the acceptance of techniques might confront the prolonged or initial reluctance, the autonomous car marks the first far-reaching incorporation of the personal robots into the human society. In recent times, increased attention has been paid upon the application of AI in driving the cars [7]. With a tremendous growth in AI-related techniques, the cars are ultimately poised to advance into self-driving robots and are trusted with individual’s lives which in turn cause socioeconomic impacts [8]. But, for the real function, the autonomous cars must be equipped with cognition and perception to constantly take the safest and appropriate actions, address the high-pressure real-time scenarios and make an appropriate decision. Visual Recognition System (VRS) is embedded in the AI of the autonomous vehicles including object detection, image classification, localization, and segmentation [9]. Object recognition has emerged as a subfield of Computer Vision (CV) that benefits from the DL techniques, particularly the Convolution Neural Networks (CNN). The current study discussed about the vision system of the autonomous cars and the role of DL to actuate the kinematic manoeuvres, interpret the complex vision and enhance the perception in autonomous cars [10].
The current study introduces an Improved Metaheuristics technique with Deep Learning-based Object Detectors for Intelligent Control in Autonomous Vehicles (IMDLOD-ICAV). In the presented IMDLOD-ICAV technique, the RetinaNet model is applied as an object detector whereas the hyperparameter tuning process is performed with the help of the Nadam optimizer. Besides, the Elman Neural Network (ENN) model is also exploited to recognize the objects with a high accuracy. The parameter tuning process is per- formed with the help of the Improved Dragonfly Algorithm (IDFA). A comprehensive set of experiments was carried out on the benchmark dataset to establish the improved efficiency of the proposed IMDLOD-ICAV technique.
The rest of the paper is organized as follows. Section 2 provides the related works and Section 3 discusses about the proposed model. Then, Section 4 provides the analytical results and finally, the Section 5 concludes the paper.
2. Related works
Li et al. [11] established a Light Enhancement net (LE-net) model dependent upon CNN, primarily to transform the daytime images to low-light images. In this study, the authors presented a generation connection and then utilized it for the construction of the image pairs. The presented LE-net was then trained and validated using the created low-light image. At last, the authors observed the efficacy of the LE-net model under real-night conditions at several low-light levels. Weon et al. [12] related 3D LiDAR data with 2D image datasets through the fusion of hybrid-level multi-sensors. At first, the 3D LiDAR data signified every object in the sensor recognition range in the form of dots. Then, every redundant data, comprising the ground data, was filtered out.
The authors, in the literature [13], established an effectual target-tracking method to move the objects. This technique, proposed to track the moving objects, was dependent on the development of easy online and real-time tracking method. It was established by combining a DL-related metric system with an easy online and a real-time tracking technique named (Deep SORT). This technique utilized a proposal tracking approach with Kalman filter and DL-based association metric. Cai et al. [14] presented a one-stage object recognition infrastructure to enhance the recognition accuracy, with the help of a true real-time function, YOLOv4. The final resultant layer in the CSPDarknet53 was exchanged with a deformable convolutional layer to enhance the recognition accuracy. To execute the feature fusion process, a novel feature fusion component PAN++was planned and a 5-scale detection layer was utilized to improve the recognition accuracy of the slight objects.
Tseng et al. [15] presented a one-phase multi-task NN for sample segmentation that meets the necessities of real-time processing with appropriate accuracy and can be applied in self-drive applications. Then, the model accomplished both object recognition and segmentation objectives, simultaneously. This work led to further more investigations using two public databases. The authors, in the study conducted earlier [16], examined an effectual multi-modal MOT infrastructure with online joint recognition and tracking methods along with a robust data connection for autonomous drive applications. Li et al. [17] introduced an enhanced 3D object recognition approach based on a 2-stage detector named ‘Improved Point-Voxel Region CNN (IPV-RCNN)’. The presented approach involves online training for K-means clustering, up-sampling convolutional layer, and data augmentation for the bounding boxes to achieve 3D recognition in rare point clouds.
3. The proposed model
In the current study, the authors have developed an intelligent object detection model for autonomous vehicles, named as the IMDLOD-ICAV technique. The presented model aims at recognizing the objects to assist an individual in driving the autonomous
vehicles. The presented IMDLOD-ICAV technique follows two major processes such as object detection and object classification. Fig. 1 defines the overall flow of the proposed IMDLOD-ICAV scheme.
3.1. Object detection module
In the current research work, the RetinaNet model is implemented as an object detector. The RetinaNet model primarily encom- passes three sub-networks such as two FCNs, ResNet, and the Feature Pyramid Network (FPN) [18]. The ResNet model uses different network layers. The network layer includes 152_layers, 50_layers, and 101_layers. It selects the 101_layer with the optimal training efficacy. Further, it also removes the structure of echocardiography using the ResNet model and puts it away for the next subnetworks.
The FPN technique is used to efficiently remove all the dimensional features from the image with the help of the conventional CNN technique.
Initially, a 1D image is exploited as an input for the ResNet model. Then, the features of every layer are selected by FPN based on the convolution network and are later incorporated in the construction of the latter feature output combination. Then, the images are detected and the co-ordinates are recorded.
Focal loss: Focal loss is an enhanced form of binary CE expression in which the Cross-Entropy (CE) loss is calculated as follows.
CE(p, y) ={
−log(p), if y=1,
−log(1−p), otherwise, (1)
Here, y ∈[ ±, 1] indicates the ground truth type and p ∈[0, 1] represents the forecast probability of the model to type, y =1.
pt=
{ p, if y=1,
1−p, otherwise, (2)
The abovementioned formula is written as follows.
CE(p, y) =CE(pt) = −log(pt). (3)
The subsequent method is used to resolve the data imbalance problem between negative and the positive samples.
CE(pt) = −αt log(pt), (4)
As per the equation, αt=
{α, ify=1
l−α, otherwise (5)
Here, α ∈[0, 1] denotes the weight factor. In order to resolve the problem of complicated instances, the C concentrating variable is established to attain the final process of the focal loss.
FL(pt) = −αt(1−pt)γ log(pt). (6)
The hyperparameters of the RetinaNet model are fine-tuned with the help of the Nadam optimizer. The presented method in- tegrates the estimated Nesterov-accelerated adaptive moment with that of the Adam optimizer [19]. These techniques bring a huge
Fig. 1. Overall flow of the proposed IMDLOD-ICAV approach.
benefit i.e., the employed adaptive moment estimation assists in accomplishing a higher precision step in gradient direction, by updating a model parameter with the momentum, before the gradient computation and it can be determined as given below.
wt=wt−1−α× mt
̅̅̅̅̂vt
√ +ε, (7)
Here, mt=(
1−β1,t)
̂gt+ β1,t+1m̂t, m̂t= mt
1−∏t+1
i=1β1i, (8)
̂gt= gt
1−∏t+1 i=1β1i.
3.2. Object classification module
At this stage, the ENN model is applied for the purpose of object classification. ENN is a type of FFNN mechanism that functions on the basis of BPNN’s Hidden Layer (HL) and undertakes layer (UL) that are applied as the delay operators to accomplish the aim of memory. Here, the network system has a strong global stability and can be adapted to time-varying dynamic features [20]. In general, the ENN is split into output, input, HL, and the UL layers. The UL layer remembers the earlier moment output values of the HL, which can be observed as a one-step delay operator. Therefore, this internal feedback architecture increases the capability of the ENN model to manage the dynamic datasets. By remembering the internal state data, to provide the function of dynamic mapping, it is adapted based on the time-varying characteristics. Fig. 2 represents the infrastructure of ENN model.
Assume an ENN model with m output andn input; the number of neurons of the UL and HL are denoted by r, and the connection weight of the input layer to HL is represented by w1; the connection weight of the UL to HL is indicated by w2; the connection weight of the HL to output layer is indicated by w3; u(k −1) shows the input of the ENN; x(k) denotes the result of HL; xc(k) indicates the output of UL; and y(k) signifies the resultant of NNs. Therefore,
x(k) =f(w2xc(k) +w1(u(k−1))) (9)
Here,
xc(k) =x(k−1) (10)
Fig. 2.Architecture of the ENN model.
where f shows the transfer function of the HL that is widely applied in the S-type function as follows.
f(x) = (1+e−x)−1 (11)
and the output of NN is as follows.
y(k) =g(w3x(k)) (12)
In Eq. (12), g indicates a transfer function of the resultant layer that is frequently a linear function. The amended weight is utilized by the BP model as given herewith.
E=∑m
k=1(tk−yk)2 (13)
Where tk indicates the output vector of the object.
To fine tune the parameters involved in the ENN model, the IDFA is utilized in this study. DFA is inspired by the swarm behavior of dragonflies (DFs) [21]. The objective of swarming is to migrate or hunt in large numbers (dynamic or static swarming). In a dynamic swarm, hundreds of dragonflies build an individual group and move in a direction for a long distance. In terms of static swarming, small groups of DFs pass through a small area to hunt other insects. These kinds of behaviors include both local movement as well as abrupt changes. The aforementioned behaviors are considered to be the main motive of the DFA. In order to direct the artificial dragonflies to various paths, separation weight (s), alignment weight (a), cohesion weight (c), enemy factor (e), food factor (f)and inertia weight (w) are applied. Higher alignment and lower cohesion weights are used to explore the searching space whereas the lower alignment and higher-cohesion weights are used to exploit it. Then, the swarm weights (s, a, c, f, e, andw) are adaptively tuned in the optimization technique to balance both exploration and exploitation phases.
Si= −∑N
j=1
X−Xj. (14)
In Eq. (14), X demonstrates the position of the present individual, Xj denotes the position of the jth individual nearby the DFs, N refers to the number of individual neighbors of the DFs and S illustrates the separation movement for the ith individual.
Ai=
∑N j=1Vj
N , (15)
In Eq. (15), V refers to the velocity of the jth neighboring DF andAi signifies the alignment movement for the ith individual.
Ci=
∑N j=1Xj
N −X, (16)
In this expression, N denotes the neighborhood size, Ci indicates the cohesion for the ith individual, X characterizes the existing individual DF and Xj shows the position of the jth individual, nearby the DFs.
Fi= X+−X, (17)
In Eq. (17), X+indicates the position of food source, Fi represents the attraction of food for the ith DF and X denotes the position of the present individual DF.
Ei= X−+X, (18)
In Eq. (18), X denotes the position of the present individual DF, Ei denotes the enemy distraction movement for the ith individual andX−indicates the position of the enemy. The artificial DFs exploit the X location vector and ΔX step vector to update their position in the searching space. The step vector corresponds to the velocity vector in the PSO algorithm.
ΔXt+1= (sSi+aAi+cCi+fFi+eEi) +wΔXt, (19)
Now, w denotes the inertia weight; Ci represents the cohesion for the ith DF; Ai represents the alignment for the ith DF; Ei shows the location of the enemy for the ith DF; Fi shows the food source for the ith individual; Si denotes the separation for the ith DF; and t represents the number of iterations. The assessment for the position vector begins, when the step vector assessment is completed:
Table 1
Details of the dataset.
Class No. of Images
Car 200
Truck 200
Pedestrian 200
Cyclist 200
Total Number of Images 800
Xt+1=Xt+ΔXt+1, (20) In this expression, t represents the present iteration. The IDFA is derived based on the Oppositional-Based Learning (OBL) model.
Initially, the description of the opposite point and opposite number are given [22]. Consider that x ∈[a, b] is a real number, which can be calculated as follows.
Fig. 3.Sample Images.
̃x=a+b−x. (21) Assume x(x1,x2,…, xD) as a point from D dimension space, whereas xi ∈[ai,bi], i =1, 2, …, D is as follows
̃xi=ai+bi−xi. (22)
Now, it initially partitions the population PN into two parts in which the 1st half of the population PN1 is generated by following an arbitrary distribution. Next, the residual half population PN2 is initialized based on the OBL as given below.
PN2=ai+bi−PN1. (23)
Finally, the set {PN1∪PN2}is rearranged as the primary population, P∗N.The IDFA model uses the OBL method to calculate the opposite point that shares the same concept in literature. To conquer a better performance of the classification, the IDFA method derives a fitness function. It describes a positive integer to distinguish the enhanced performance of the candidate solution. The fitness function corresponds to the reduced classification error rate as given below.
Fig. 4. Confusion matrices of the proposed IMDLOD-ICAV system (a-b) TRS/TSS of 60:40 and (c-d) TRS/TSS of 70:30.
fitness(xi) =ClassifierErrorRate(xi) =no.of misclassified instances
Total no.of instances ∗100 (24)
4. Performance validation
The proposed model was simulated in Python tool. In the current section, the object detection results achieved by the proposed IMDLOD-ICAV approach upon KITTI database [23] are discussed. The KITTI database contains 800 images under four classes, as defined in Table 1. Fig. 3 illustrates some of the sample images.
The confusion matrices generated by the proposed IMDLOD-ICAV approach on object detection are shown in Fig. 4 under varying sizes of Training (TRS) and Testing (TSS) datasets. The figure infers that the proposed IMDLOD-ICAV algorithm proficiently catego- rized all the four types of vehicles such as the car, truck, pedestrian, and the cyclist.
Table 2 reports the overall object detection outcomes achieved by the proposed IMDLOD-ICAV model on 60% of TRS and 40% of TSS. Fig. 5 showcases the average detection performance accomplished by the IMDLOD-ICAV methodology on 60% of the TRS database.
The experimental values depict that the proposed IMDLOD-ICAV model categorized all the four kinds of vehicles appropriately. It is also noticed that the IMDLOD-ICAV model achieved an average accuy of 98.54%, PPV of 97.07%, TPR of 97.09%, TNR of 99.03%, Fscore Table 2
Object detection outcomes of the proposed IMDLOD-ICAV system under 60:40 of TRS and TSS.
Class Accuracy PPV TPR TNR F-Score AUC Score
Training Phase (60%)
Car 99.17 97.56 99.17 99.16 98.36 99.17
Truck 98.12 98.32 94.35 99.44 96.30 96.90
Pedestrian 98.75 96.75 98.35 98.89 97.54 98.62
Cyclist 98.12 95.65 96.49 98.63 96.07 97.56
Average 98.54 97.07 97.09 99.03 97.07 98.06
Testing Phase (40%)
Car 97.19 90.70 98.73 96.68 94.55 97.71
Truck 97.50 95.95 93.42 98.77 94.67 96.10
Pedestrian 98.75 96.30 98.73 98.76 97.50 98.74
Cyclist 97.19 98.73 90.70 99.57 94.55 95.14
Average 97.66 95.42 95.40 98.44 95.31 96.92
Fig. 5. Average outcomes of the IMDLOD-ICAV method on 60% of TRS.
of 97.07%, and an AUCscore of 98.06%.
Fig. 6 demonstrates the average detection performance achieved by the proposed IMDLOD-ICAV methodology on 40% of the TSS database. The experimental values denote that the IMDLOD-ICAV technique appropriately classified all the four kinds of vehicles. It is to be noted that the IMDLOD-ICAV system achieved an average accuy of 97.66%, PPV of 95.42%, TPR of 95.40%, TNR of 98.44%, Fscore of 95.31%, and an AUCscore of 96.92%.
Table 3 showcases the overall object detection outcomes yielded by the proposed IMDLOD-ICAV methodology on 70% of TRS and 30% of TSS. Fig. 7 portrays the average detection performance of the IMDLOD-ICAV system on 70% of TRS. The experimental values infer that the IMDLOD-ICAV approach appropriately classified all the four kinds of vehicles. It is to be noted that the proposed IMDLOD-ICAV method yielded an average accuy of 99.11%, PPV of 98.26%, TPR of 98.21%, TNR of 99.40%, Fscore of 98.23%, and an AUCscore of 98.81%.
Fig. 8 illustrates the average detection performance of the IMDLOD-ICAV methodology on 30% of TSS. The experimental values show that the IMDLOD-ICAV technique properly classified all the four kinds of vehicles. Further, it is also noticed that the IMDLOD- ICAV system achieved an average accuy of 99.38%, PPV of 98.72%, TPR of 98.74%, TNR of 99.59%, Fscore of 98.73%, and an AUCscore of 99.17%.
The TACY and VACY values, achieved by the IMDLOD-ICAV method in terms of object detection, are shown in Fig. 9. The figure infers that the proposed IMDLOD-ICAV technique accomplished a superior performance with maximum TACY and VACY values. The IMDLOD-ICAV system obtained the maximal TACY outcomes.
Fig. 6. Average outcomes of the IMDLOD-ICAV methodology under 40% of TSS.
Table 3
Object detection outcomes of the IMDLOD-ICAV system on 70:30 of TRS and TSS.
Class Accuracy PPV TPR TNR F-Score AUC Score
Training Phase (70%)
Car 98.93 99.26 96.43 99.76 97.83 98.10
Truck 99.64 99.29 99.29 99.76 99.29 99.52
Pedestrian 98.57 95.97 98.62 98.55 97.28 98.59
Cyclist 99.29 98.52 98.52 99.53 98.52 99.02
Average 99.11 98.26 98.21 99.40 98.23 98.81
Testing Phase (30%)
Car 99.58 100.00 98.33 100.00 99.16 99.17
Truck 100.00 100.00 100.00 100.00 100.00 100.00
Pedestrian 98.75 96.43 98.18 98.92 97.30 98.55
Cyclist 99.17 98.46 98.46 99.43 98.46 98.95
Average 99.38 98.72 98.74 99.59 98.73 99.17
The TLOS and VLOS values, produced by the IMDLOD-ICAV method on object detection, are portrayed in Fig. 10. The figure shows that the IMDLOD-ICAV technique demonstrated an improved performance with minimum TLOS and VLOS values. The IMDLOD-ICAV system produced the least VLOS outcomes.
Fig. 7. Average outcomes of the proposed IMDLOD-ICAV system under 70% of TRS.
Fig. 8. Average analytical outcomes of the IMDLOD-ICAV system under 30% of TSS.
An evident precision-recall assessment was conducted upon the IMDLOD-ICAV methodology using the test database and the results are presented in Fig. 11. The figure shows that the proposed IMDLOD-ICAV technique improved the precision-recall values under different classes.
Fig. 9.TACY and VACY outcomes of the IMDLOD-ICAV approach.
Fig. 10.TLOS and VLOS outcomes of the IMDLOD-ICAV approach.
Finally, a brief comparative study was conducted between the proposed IMDLOD-ICAV model and other existing models and the results are shown in Table 4 and Fig. 12 [24]. The experimental values indicate that the ResNet approach achieved the least classi- fication results. In addition, the ZFNet and SVM models too portrayed closer performance. Although the VGG and LSTM models gained near-optimal results, the proposed IMDLOD-ICAV model accomplished a superior performance with an accuy of 99.38%, PPV of 98.72%, TPR of 98.74%, and an Fscore of 98.73%. These outcomes assured the enhanced performance of the IMDLOD-ICAV approach on object detection in autonomous systems.
5. Conclusion
In the current study, the authors have developed an intelligent object detection model for autonomous vehicles, named the IMDLOD-ICAV technique. The IMDLOD-ICAV technique follows two major processes such as the object detection and object classi- fication. In the presented IMDLOD-ICAV technique, the RetinaNet model is applied as an object detector whereas the hyperparameter tuning process is executed with the help of the Nadam optimizer. Next, the IDFA with ENN model is applied for the purpose of object classification. A comprehensive set of experiments was performed to establish the superior performance of the IMDLOD-ICAV method.
The outcomes highlighted the enhancements of the IMDLOD-ICAV technique on other approaches with a maximum accuracy of 99.38%. In the future, the performance of the IMDLOD-ICAV algorithm will be extended to the design of multimodal fusion approaches.
Declaration of Competing Interest
The authors declare that they have no known competing financial interestsor personal relationships that could have appeared to Fig. 11. Precision-recall outcomes of the IMDLOD-ICAV system.
Table 4
Comparative analysis outcomes of the IMDLOD-ICAV system and other existing algorithms.
Methods Accuracy PPV TPR F-Score
IMDLOD-ICAV 99.38 98.72 98.74 98.73
ResNet 96.52 96.37 78.68 80.19
VGG 98.76 98.56 85.67 97.55
ZFNet 97.34 63.54 60.58 71.22
SVM 97.52 97.23 96.92 98.25
LSTM 98.10 97.58 98.33 96.86
influence the work reported in this paper.
Data availability
Data sharing does not apply to this article as no datasets were generated during the current study.
Acknowledgement
The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through large group Research Project under grant number (RGP2/206/44). Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2023R330), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia. Research Supporting Project number (RSPD2023R521), King Saud University, Riyadh, Saudi Arabia. This study is supported via funding from Prince Sattam bin Abdulaziz University project number (PSAU/2023/R/1444).
Data Availability Statement
Data sharing does not apply to this article as no datasets were generated during the current study.
Ethics approval
This article does not contain any studies with human participants performed by any of the authors.
Consent to Participate Not applicable.
Informed Consent Not applicable.
Fig. 12. Comparative analysis outcomes of the IMDLOD-ICAV system and other existing algorithms.
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Naif Alasmari is an assistant professor with the Information Systems Department, King Khalid University, KSA. He has many peer-reviewed published paper. His- research interests include AI, Deep learning, IoT, Smart Cities, Machine Learning, and Enterprise Systems
Manal Abdullah Alohali is an Assistant Professor at the Information Systems Department, Princess Nourah bint Abdulrahman University (PNU), Saudi Arabia.
Currently, she is the Department’s Head. She received a PhD degree in Computer Science from University of Plymouth, UK. Her research interests reside in the areas of Information Systems, Machine Learning and Cyber Security. She received PNU’s Research Excellence Award.
Majdi Khalid is an assistant professor of Computer Science at the Umm Al-Qura University, Makkah, Saudi Arabia. He has many published peer-review articles. His- current research interests include Natural Language Processing, Machine Learning, Deep Learning, Computer Networks, Wireless Sensor Networks and Network Security.
Nabil Almalki is an assistant professor with Department of Special Education, College of Education, King Saud University, Riyadh 12372, Saudi Arabia. He has many published peer-review articles. His-current research interests include AI, deep learning and Intelligent Control.
Abdelwahed Motwakel is an Assistant Professor in the Department of Information Systems, College of business administration in Hawtat bani Tamim, Prince Sattam bin Abdulaziz University, Saudi Arabia. His-research interest is Fuzzy Logic, Neural Network, Machine Learning, Artificial Intelligence
Mohamed Ibrahim Alsaid is an Assistant Professor in the Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, AlKharj, Saudi Arabia. His-research interest is Fuzzy Logic, Neural Network, Machine Learning, Artificial Intelligence
Azza Elneil Osman is a lecturer in the Department of Computer and Self Development at Prince Sattam bin Abdulaziz University. She has many published papers. Her research interest in AI, IoT, Machine Learning.
Amani A Alneil is a lecturer in the Department of Computer and Self Development at Prince Sattam bin Abdulaziz University. She has many published papers. Her research interest in AI, Data mining, Text mining, Machine Learning.