Original/Review Paper
H.3.9. Problem Solving, Control Methods, and Search
Zahra Jahan; Abbas Dideban; Farzaneh Tatari
Abstract
This paper introduces an adaptive optimal distributed algorithm based on event-triggered control to solve multi-agent discrete-time zero-sum graphical games for unknown nonlinear constrained-input systems with external disturbances. Based on the value iteration heuristic dynamic programming, the proposed ...
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This paper introduces an adaptive optimal distributed algorithm based on event-triggered control to solve multi-agent discrete-time zero-sum graphical games for unknown nonlinear constrained-input systems with external disturbances. Based on the value iteration heuristic dynamic programming, the proposed algorithm solves the event-triggered coupled Hamilton-Jacobi-Isaacs equations assuming unknown dynamics to develop distributed optimal controllers and satisfy leader-follower consensus for agents interacting on a communication graph. The algorithm is implemented using the actor-critic neural network, and unknown system dynamics are approximated using the identifier network. Introducing and solving nonlinear zero-sum discrete-time graphical games in the presence of unknown dynamics, control input constraints and external disturbances, differentiate this paper from the previously published works. Also, the control input, external disturbance, and the neural network's weights are updated aperiodic and only at the triggering instants to simplify the computational process. The closed-loop system stability and convergence to the Nash equilibrium are proven. Finally, simulation results are presented to confirm theoretical findings.
Original/Review Paper
H.3. Artificial Intelligence
Ali Rebwar Shabrandi; Ali Rajabzadeh Ghatari; Mohammad Dehghan nayeri; Nader Tavakoli; Sahar Mirzaei
Abstract
This study proposes a high-level design and configuration for an intelligent dual (hybrid and private) blockchain-based system. The configuration includes the type of network, level of decentralization, nodes, and roles, block structure information, authority control, and smart contracts and intended ...
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This study proposes a high-level design and configuration for an intelligent dual (hybrid and private) blockchain-based system. The configuration includes the type of network, level of decentralization, nodes, and roles, block structure information, authority control, and smart contracts and intended to address the two main categories of challenges–operation management and data management–through three intelligent modules across the pandemic stages. In the pre-hospital stage, an intelligent infection prediction system is proposed that utilizes in-house data to address the lack of a simple, efficient, agile, and low-cost screening method for identifying potentially infected individuals promptly and preventing the overload of patients entering hospitals. In the in-hospital stage, an intelligent prediction system is proposed to predict infection severity and hospital Length of Stay (LoS) to identify high-risk patients, prioritize them for receiving care services, and facilitate better resource allocation. In the post-hospital stage, an intelligent prediction system is proposed to predict the reinfection and readmission rates, to help reduce the burden on the healthcare system and provide personalized care and follow-up for higher-risk patients. In addition, the distribution of limited Personal protective equipment (PPE) is made fair using private blockchain (BC) and smart contracts. These modules were developed using Python and utilized to evaluate the performance of state-of-the-art machine learning (ML) techniques through 10-fold cross-validation at each stage. The most critical features were plotted and analyzed using SHapely Adaptive exPlanations (SHAP). Finally, we explored the implications of our system for both research and practice and provided recommendations for future enhancements.
Original/Review Paper
H.3. Artificial Intelligence
Seyed Alireza Bashiri Mosavi; Mohsen Javaherian; Omid Khalaf Beigi
Abstract
One way of analyzing COVID-19 is to exploit X-ray and computed tomography (CT) images of the patients' chests. Employing data mining techniques on chest images can provide in significant improvements in the diagnosis of COVID-19. However, in feature space learning of chest images, there exists a large ...
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One way of analyzing COVID-19 is to exploit X-ray and computed tomography (CT) images of the patients' chests. Employing data mining techniques on chest images can provide in significant improvements in the diagnosis of COVID-19. However, in feature space learning of chest images, there exists a large number of features that affect COVID-19 identification performance negatively. In this work, we aim to design the dual hybrid partial-oriented feature selection scheme (DHPFSS) for selecting optimal features to achieve high-performance COVID-19 prediction. First, by applying the Zernike function to the data, moments of healthy chest images and infected ones were extracted. After Zernike moments (ZMs) segmentation, subsets of ZMs (SZMs1:n) are entered into the DHPFSS to select SZMs1:n-specific optimal ZMs (OZMs1:n). The DHPFSS consists of the filter phase and dual incremental wrapper mechanisms (IWMs), namely incremental wrapper subset selection (IWSS) and IWSS with replacement (IWSSr). Each IWM is fed by ZMs sorted by filter mechanism. The dual IWMs of DHPFSS are accompanied with the support vector machine (SVM) and twin SVM (TWSVM) classifiers equipped with radial basis function kernel as SVMIWSSTWSVM and SVMIWSSrTWSVM blocks. After selecting OZMs1:n, the efficacy of the union of OZMs1:n is evaluated based on the cross-validation technique. The obtained results manifested that the proposed framework has accuracies of 98.66%, 94.33%, and 94.82% for COVID-19 prediction on COVID-19 image data (CID) including 1CID, 2CID, and 3CID respectively, which can improve accurate diagnosis of illness in an emergency or the absence of a specialist.
Applied Article
H.5.7. Segmentation
Ehsan Ehsaeyan
Abstract
This paper presents a novel approach to image segmentation through multilevel thresholding, leveraging the speed and precision of the technique. The proposed algorithm, based on the Grey Wolf Optimizer (GWO), integrates Darwinian principles to address the common stagnation issue in metaheuristic algorithms, ...
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This paper presents a novel approach to image segmentation through multilevel thresholding, leveraging the speed and precision of the technique. The proposed algorithm, based on the Grey Wolf Optimizer (GWO), integrates Darwinian principles to address the common stagnation issue in metaheuristic algorithms, which often results in local optima and premature convergence. The search agents are efficiently steered across the search space by a dual mechanism of encouragement and punishment employed by our strategy, thereby curtailing computational time. This is implemented by segmenting the population into distinct groups, each tasked with discovering superior solutions. To validate the algorithm’s efficacy, 9 test images from the Pascal VOC dataset were selected, and the renowned energy curve method was employed for verification. Additionally, Kapur entropy was utilized to gauge the algorithm’s performance. The method was benchmarked against four disparate search algorithms, and its dominance was underscored by achieving the best outcomes in 20 out of 27 cases for image segmentation. The experimental findings collectively affirm that the Darwinian Grey Wolf Optimizer (DGWO) stands as a formidable instrument for multilevel thresholding.
Original/Review Paper
H.3.9. Problem Solving, Control Methods, and Search
Heydar Toossian Shandiz; Mohsen Erfan Hajipour; Amir Ali Bagheri
Abstract
The aim of this paper is to create an efficient controller that can precisely track the position of autonomous surface vessels by utilizing the dynamic inversion control technique. One of the key objectives of this controller is to mitigate or eliminate the effects of environmental disturbances like ...
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The aim of this paper is to create an efficient controller that can precisely track the position of autonomous surface vessels by utilizing the dynamic inversion control technique. One of the key objectives of this controller is to mitigate or eliminate the effects of environmental disturbances like wind, waves, and water flow. On the other hand, intelligent methods are used to remove disturbances and fixing modeling errors. These methods include the use of fuzzy methods to adjust the control parameters in the linear controller used in the dynamic inversion controller and the use of perceptron neural network along with the dynamic inversion controller. The effectiveness of the proposed methods is evaluated not only based on the step response but also on their ability to track a complex path. Finally, the proposed methods have been compared with one of the classic methods, namely the PID control. This evaluation provides insights into how the proposed methods fare in terms of both step response and trajectory tracking when compared to the traditional PID control approach.
Original/Review Paper
H.3.2.2. Computer vision
Mobina Talebian; Kourosh Kiani; Razieh Rastgoo
Abstract
Fingerprint verification has emerged as a cornerstone of personal identity authentication. This research introduces a deep learning-based framework for enhancing the accuracy of this critical process. By integrating a pre-trained Inception model with a custom-designed architecture, we propose a model ...
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Fingerprint verification has emerged as a cornerstone of personal identity authentication. This research introduces a deep learning-based framework for enhancing the accuracy of this critical process. By integrating a pre-trained Inception model with a custom-designed architecture, we propose a model that effectively extracts discriminative features from fingerprint images. To this end, the input fingerprint image is aligned to a base fingerprint through minutiae vector comparison. The aligned input fingerprint is then subtracted from the base fingerprint to generate a residual image. This residual image, along with the aligned input fingerprint and the base fingerprint, constitutes the three input channels for a pre-trained Inception model. Our main contribution lies in the alignment of fingerprint minutiae, followed by the construction of a color fingerprint representation. Moreover, we collected a dataset, including 200 fingerprint images corresponding to 20 persons, for fingerprint verification. The proposed method is evaluated on two distinct datasets, demonstrating its superiority over existing state-of-the-art techniques. With a verification accuracy of 99.40% on the public Hong Kong Dataset, our approach establishes a new benchmark in fingerprint verification. This research holds the potential for applications in various domains, including law enforcement, border control, and secure access systems.
Original/Review Paper
H.3.2.2. Computer vision
Zobeir Raisi; Valimohammad Nazarzehi; Rasoul Damani; Esmaeil Sarani
Abstract
This paper explores the performance of various object detection techniques for autonomous vehicle perception by analyzing classical machine learning and recent deep learning models. We evaluate three classical methods, including PCA, HOG, and HOG alongside different versions of the SVM classifier, and ...
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This paper explores the performance of various object detection techniques for autonomous vehicle perception by analyzing classical machine learning and recent deep learning models. We evaluate three classical methods, including PCA, HOG, and HOG alongside different versions of the SVM classifier, and five deep-learning models, including Faster-RCNN, SSD, YOLOv3, YOLOv5, and YOLOv9 models using the benchmark INRIA dataset. The experimental results show that although classical methods such as HOG + Gaussian SVM outperform other classical approaches, they are outperformed by deep learning techniques. Furthermore, Classical methods have limitations in detecting partially occluded, distant objects and complex clothing challenges, while recent deep-learning models are more efficient and provide better performance (YOLOv9) on these challenges.
Applied Article
H.5. Image Processing and Computer Vision
Jalaluddin Zarei; Mohammad Hossein Khosravi
Abstract
Agricultural experts try to detect leaf diseases in the shortest possible time. However, limitations such as lack of manpower, poor eyesight, lack of sufficient knowledge, and quarantine restrictions in the transfer of diseases to the laboratory can be acceptable reasons to use digital technology to ...
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Agricultural experts try to detect leaf diseases in the shortest possible time. However, limitations such as lack of manpower, poor eyesight, lack of sufficient knowledge, and quarantine restrictions in the transfer of diseases to the laboratory can be acceptable reasons to use digital technology to detect pests and diseases and finally dispose of them. One of the available solutions in this field is using convolutional neural networks. On the other hand, the performance of CNNs depends on the large amount of data. While there is no suitable dataset for the native trees of South Khorasan province, this motivates us to create a suitable dataset with a large amount of data. In this article, we introduce a new dataset in 9 classes of images of Healthy Barberry leaves, Barberry Rust disease, Barberry Pandemis ribeana Tortricidae pest, Healthy Jujube leaves, Jujube Ziziphus Tingid disease, Jujube Parenchyma-Eating Butterfly pest, Healthy Pomegranate leaves, Pomegranate Aphis punicae pest, and Pomegranate Leaf-Cutting Bees pest and also check the performance of several well-known convolutional neural networks using all gradient descent optimizer algorithms on this dataset. Our most important achievement is the creation of a dataset with a high data volume of pests and diseases in different classes. In addition, our experiments show that common CNN architectures, along with gradient descent optimizers, have an acceptable performance on the proposed dataset. We call the proposed dataset ”Birjand Native Plant Leaves (BNPL) Dataset”. It is available at the address https://kaggle.com/datasets/ec17162ca01825fb362419503cbc84c73d162bffe936952253ed522705228e06.
Original/Review Paper
D. Data
Zahra Ghorbani; Ali Ghorbanian
Abstract
Increasing the accuracy of time-series clustering while reducing execution time is a primary challenge in the field of time-series clustering. Researchers have recently applied approaches, such as the development of distance metrics and dimensionality reduction, to address this challenge. However, using ...
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Increasing the accuracy of time-series clustering while reducing execution time is a primary challenge in the field of time-series clustering. Researchers have recently applied approaches, such as the development of distance metrics and dimensionality reduction, to address this challenge. However, using segmentation and ensemble clustering to solve this issue is a key aspect that has received less attention in previous research. In this study, an algorithm based on the selection and combination of the best segments created from a time-series dataset was developed. In the first step, the dataset was divided into segments of equal lengths. In the second step, each segment is clustered using a hierarchical clustering algorithm. In the third step, a genetic algorithm selects different segments and combines them using combinatorial clustering. The resulting clustering of the selected segments was selected as the final dataset clustering. At this stage, an internal clustering criterion evaluates and sorts the produced solutions. The proposed algorithm was executed on 82 different datasets in 10 repetitions. The results of the algorithm indicated an increase in the clustering efficiency of 3.07%, reaching a value of 67.40. The obtained results were evaluated based on the length of the time series and the type of dataset. In addition, the results were assessed using statistical tests with the six algorithms existing in the literature.
Original/Review Paper
H.5. Image Processing and Computer Vision
Khosro Rezaee
Abstract
Traditional Down syndrome identification often relies on professionals visually recognizing facial features, a method that can be subjective and inconsistent. This study introduces a hybrid deep learning (DL) model for automatically identifying Down syndrome in children's facial images, utilizing facial ...
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Traditional Down syndrome identification often relies on professionals visually recognizing facial features, a method that can be subjective and inconsistent. This study introduces a hybrid deep learning (DL) model for automatically identifying Down syndrome in children's facial images, utilizing facial analysis techniques to enhance diagnostic accuracy and enable real-time detection. The model employs the MobileNetV2 architecture to address dataset bias and diversity issues while ensuring efficient feature extraction. The framework also integrates the structure with optimized Bidirectional Long Short-Term Memory (BiLSTM) to enhance feature classification. Trained and validated on facial images from children with Down syndrome and healthy controls from the Kaggle dataset, the model achieved 97.60% accuracy and 97.50% recall. The approach also integrates cloud and edge processing for efficient real-time analysis, offering adaptability to new images and conditions.
Technical Paper
H.5. Image Processing and Computer Vision
Sekine Asadi Amiri; Fatemeh Mohammady
Abstract
Fungal infections, capable of establishing in various tissues and organs, are responsible for many human diseases that can lead to serious complications. The initial step in diagnosing fungal infections typically involves the examination of microscopic images. Direct microscopic examination using potassium ...
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Fungal infections, capable of establishing in various tissues and organs, are responsible for many human diseases that can lead to serious complications. The initial step in diagnosing fungal infections typically involves the examination of microscopic images. Direct microscopic examination using potassium hydroxide is commonly employed as a screening method for diagnosing superficial fungal infections. Although this type of examination is quicker than other diagnostic methods, the evaluation of a complete sample can be time-consuming. Moreover, the diagnostic accuracy of these methods may vary depending on the skill of the practitioner and does not guarantee full reliability. This paper introduces a novel approach for diagnosing fungal infections using a modified VGG19 deep learning architecture. The method incorporates two significant changes: replacing the Flatten layer with Global Average Pooling (GAP) to reduce feature count and model complexity, thereby enhancing the extraction of significant features from images. Additionally, a Dense layer with 1024 neurons is added post-GAP, enabling the model to better learn and integrate these features. The Defungi microscopic dataset was used for training and evaluating the model. The proposed method can identify fungal diseases with an accuracy of 97%, significantly outperforming the best existing method, which achieved an accuracy of 92.49%. This method not only significantly outperforms existing methods, but also, given its high accuracy, is valuable in the field of diagnosing fungal infections. This work demonstrates that the use of deep learning in diagnosing fungal diseases can lead to a substantial improvement in the quality of health services.
Other
H.6.2. Models
Simon Kawuma; Elias Kumbakumba; Vicent Mabirizi; Deborah Nanjebe; Kenneth Mworozi; Adolf Oyesigye Mukama; Lydia Kyasimire
Abstract
Tuberculosis (TB) is an underestimated cause of death in children, with only 45% of cases correctly diagnosed and reported. It is estimated that 1.12 million TB cases occurred among newborns, children, and adolescents aged less or equal 14 years. In Uganda, TB prevalence is 8.5% in children and 16.7% ...
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Tuberculosis (TB) is an underestimated cause of death in children, with only 45% of cases correctly diagnosed and reported. It is estimated that 1.12 million TB cases occurred among newborns, children, and adolescents aged less or equal 14 years. In Uganda, TB prevalence is 8.5% in children and 16.7% in adolescents. Treatment and diagnosing TB is difficulty and its high mortality rate is due to many gaps in the diagnosis of this illness especially among children. As a strategy to curb TB mortality rate in children, there exist a need to improve and expedite the screening for TB among children. Chest X-ray (CXR) are commonly used in TB burden countries like Uganda to diagnose TB patients but interpretation of the patients’ radiograph needs skilled radiologists who are few. To this end, this research aims to close the TB mortality gap in children by applying AI, primarily deep learning techniques, to detect TB in children. The study created five models, one from scratch and four transfer learning and were trained and verified using digital CXR radiograph images of children who visit the TB clinic at Mbarara Regional Referral Hospital. The model classifies clinical images of patients into normal or Tuberculosis. Transfer learning models; VGG16, VGG19, Inception V3, and ResNet50 outperformed scratch model with validation accuracy of 79.91%, 69.21%, 53.0%, 51.09% and 50.01% respectively. We hope that once the deep learning models are implemented and adopted by the radiologist, it will reduce the time spent by radiologist while analyzing CXR images.
