H. Rahmani; H. Kamali; H. Shah-Hosseini
Abstract
Nowadays, a significant amount of studies are devoted to discovering important nodes in graph data. Social networks as graph data have attracted a lot of attention. There are various purposes for discovering the important nodes in social networks such as finding the leaders in them, i.e. the users who ...
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Nowadays, a significant amount of studies are devoted to discovering important nodes in graph data. Social networks as graph data have attracted a lot of attention. There are various purposes for discovering the important nodes in social networks such as finding the leaders in them, i.e. the users who play an important role in promoting advertising, etc. Different criteria have been proposed in discovering important nodes in graph data. Measuring a node’s importance by a single criterion may be inefficient due to the variety of graph structures. Recently, a combination of criteria has been used in the discovery of important nodes. In this paper, we propose a system for the Discovery of Important Nodes in social networks using Genetic Algorithms (DINGA). In our proposed system, important nodes in social networks are discovered by employing a combination of eight informative criteria and their intelligent weighting. We compare our results with a manually weighted method, that uses random weightings for each criterion, in four real networks. Our method shows an average of 22% improvement in the accuracy of important nodes discovery.
H.5. Image Processing and Computer Vision
Mohammad Mahdi Nakhaie; Sasan Karamizadeh; Mohammad Ebrahim Shiri; Kambiz Badie
Abstract
Lung cancer is a highly serious illness, and detecting cancer cells early significantly enhances patients' chances of recovery. Doctors regularly examine a large number of CT scan images, which can lead to fatigue and errors. Therefore, there is a need to create a tool that can automatically detect and ...
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Lung cancer is a highly serious illness, and detecting cancer cells early significantly enhances patients' chances of recovery. Doctors regularly examine a large number of CT scan images, which can lead to fatigue and errors. Therefore, there is a need to create a tool that can automatically detect and classify lung nodules in their early stages. Computer-aided diagnosis systems, often employing image processing and machine learning techniques, assist radiologists in identifying and categorizing these nodules. Previous studies have often used complex models or pre-trained networks that demand significant computational power and a long time to execute. Our goal is to achieve accurate diagnosis without the need for extensive computational resources. We introduce a simple convolutional neural network with only two convolution layers, capable of accurately classifying nodules without requiring advanced computing capabilities. We conducted training and validation on two datasets, LIDC-IDRI and LUNA16, achieving impressive accuracies of 99.7% and 97.52%, respectively. These results demonstrate the superior accuracy of our proposed model compared to state-of-the-art research papers.
A. Rahati; K. Rahbar
Abstract
Doing sports movements correctly is very important in ensuring body health. In this article, an attempt has been made to achieve the movements correction through the usage of a different approach based on the 2D position of the joints from the image in 3D space. A person performing in front of the camera ...
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Doing sports movements correctly is very important in ensuring body health. In this article, an attempt has been made to achieve the movements correction through the usage of a different approach based on the 2D position of the joints from the image in 3D space. A person performing in front of the camera with landmarks on his/her joints is the subject of the input image. The coordinates of the joints are then measured in 2D space which is adapted to the extracted 2D skeletons from the reference skeletal sparse model modified movements. The accuracy and precision of this approach is accomplished on the standard Adidas dataset. Its efficiency has also been studied under the influence of cumulative Gaussian and impulse noises. Meanwhile, the average error of the model in detecting the wrong exercise in the set of sports movements is reported to be 5.69 pixels.
M. Shokohi nia; A. Dideban; F. Yaghmaee
Abstract
Despite success of ontology in knowledge representation, its reasoning is still challenging. The most important challenge in reasoning of ontology-based methods is improving realization in the reasoning process. The time complexity of the realization problem-solving process is equal to that of NEXP Time. ...
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Despite success of ontology in knowledge representation, its reasoning is still challenging. The most important challenge in reasoning of ontology-based methods is improving realization in the reasoning process. The time complexity of the realization problem-solving process is equal to that of NEXP Time. This can be done by solving the subsumption and satisfiability problems. On the other hand, uncertainty and ambiguity in these characteristics are unavoidable. Considering these requirements, use of the Fuzzy theory is necessary. This study proposed a method for overcoming this problem, which offers a new solution with a suitable time position. The purpose of this study is to model and improve reasoning and realization in an ontology using Fuzzy-Colored Petri Nets (FCPNs).To this end, an algorithm is presented for improve the realization problem. Then, unified modelling language (UML) class diagram is used for standard description and representing efficiency characteristics; RDFS representation is converted to UML diagram. Then fuzzy concepts in Fuzzy-colored Petri nets are further introduced. In the next step, an algorithm is presented to convert ontology description based on UML class diagram to an executive model based on FCPNs. Using this approach, a simple method is developed to from an executive model and reasoning based on FCPNs which can be employed to obtain the results of interest by applying different queries. Finally, the efficiency of the proposed method is evaluated with the results indicating the improve the performance of the proposed method from different aspects.
M. Babazadeh Shareh; H.R. Navidi; H. Haj Seyed Javadi; M. HosseinZadeh
Abstract
In cooperative P2P networks, there are two kinds of illegal users, namely free riders and Sybils. Free riders are those who try to receive services without any sort of cost. Sybil users are rational peers which have multiple fake identities. There are some techniques to detect free riders and Sybil users ...
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In cooperative P2P networks, there are two kinds of illegal users, namely free riders and Sybils. Free riders are those who try to receive services without any sort of cost. Sybil users are rational peers which have multiple fake identities. There are some techniques to detect free riders and Sybil users which have previously been proposed by a number of researchers such as the Tit-for-tat and Sybil guard techniques. Although such previously proposed techniques were quite successful in detecting free riders and Sybils individually, there is no technique capable of detecting both these riders simultaneously. Therefore, the main objective of this research is to propose a single mechanism to detect both kinds of these illegal users based on Game theory. Obtaining new centrality and bandwidth contribution formulas with an incentive mechanism approach is the basic idea of the present research’s proposed solution. The result of this paper shows that as the life of the network passes, free riders are identified, and through detecting Sybil nodes, the number of services offered to them will be decreased.
H.5. Image Processing and Computer Vision
S. Asadi Amiri; Z. Mohammadpoory; M. Nasrolahzadeh
Abstract
Content based image retrieval (CBIR) systems compare a query image with images in a dataset to find similar images to a query image. In this paper a novel and efficient CBIR system is proposed using color and texture features. The color features are represented by color moments and color histograms of ...
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Content based image retrieval (CBIR) systems compare a query image with images in a dataset to find similar images to a query image. In this paper a novel and efficient CBIR system is proposed using color and texture features. The color features are represented by color moments and color histograms of RGB and HSV color spaces and texture features are represented by localized Discrete Cosine Transform (DCT) and localized Gray level co-occurrence matrix and local binary patterns (LBP). The DCT coefficients and Gray level co-occurrence matrix of the blocks are examined for assessing the block details. Also, LBP is used for rotation invariant texture information of the image. After feature extraction, Shannon entropy criterion is used to reduce inefficient features. Finally, an improved version of Canberra distance is employed to compare similarity of feature vectors. Experimental analysis is carried out using precision and recall on Corel-5K and Corel-10K datasets. Results demonstrate that the proposed method can efficiently improve the precision and recall and outperforms the most existing methods.s the most existing methods.
H.3. Artificial Intelligence
Hamid Ghaffari; Hemmatollah Pirdashti; Mohammad Reza Kangavari; Sjoerd Boersma
Abstract
An intelligent growth chamber was designed in 2021 to model and optimize rice seedlings' growth. According to this, an experiment was implemented at Sari University of Agricultural Sciences and Natural Resources, Iran, in March, April, and May 2021. The model inputs included radiation, temperature, carbon ...
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An intelligent growth chamber was designed in 2021 to model and optimize rice seedlings' growth. According to this, an experiment was implemented at Sari University of Agricultural Sciences and Natural Resources, Iran, in March, April, and May 2021. The model inputs included radiation, temperature, carbon dioxide, and soil acidity. These growth factors were studied at ambient and incremental levels. The model outputs were seedlings' height, root length, chlorophyll content, CGR, RGR, the leaves number, and the shoot's dry weight. Rice seedlings' growth was modeled using LSTM neural networks and optimized by the Bayesian method. It concluded that the best parameter setting was at epoch=100, learning rate=0.001, and iteration number=500. The best performance during training was obtained when the validation RMSE=0.2884.
M. Azimi hemat; F. Shamsezat Ezat; M. Kuchaki Rafsanjani
Abstract
In content-based image retrieval (CBIR), the visual features of the database images are extracted, and the visual features database is assessed to find the images closest to the query image. Increasing the efficiency and decreasing both the time and storage space of indexed images is the priority in ...
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In content-based image retrieval (CBIR), the visual features of the database images are extracted, and the visual features database is assessed to find the images closest to the query image. Increasing the efficiency and decreasing both the time and storage space of indexed images is the priority in developing image retrieval systems. In this research, an efficient system is proposed for image retrieval by applying fuzzy techniques, which are advantageous in increasing the efficiency and decreasing the length of the feature vector and storage space. The effect of increasing the considered content features' count is assessed to enhance image retrieval efficiency. The fuzzy features consist of color, statistical information related to the spatial dependency of the pixels on each other, and the position of image edges. These features are indexed in fuzzy vector format 16, 3, and 16 lengths. The extracted vectors are compared through the fuzzy similarity measures, where the most similar images are retrieved. To evaluate the proposed systems' performance, this system and three other non-fuzzy systems where fewer features are of concern were implemented. These four systems are tested on a database containing 1000 images, and the results indicate improvement in the retrieval precision and storage space.
A. Nozaripour; H. Soltanizadeh
Abstract
Sparse representation due to advantages such as noise-resistant and, having a strong mathematical theory, has been noticed as a powerful tool in recent decades. In this paper, using the sparse representation, kernel trick, and a different technique of the Region of Interest (ROI) extraction which we ...
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Sparse representation due to advantages such as noise-resistant and, having a strong mathematical theory, has been noticed as a powerful tool in recent decades. In this paper, using the sparse representation, kernel trick, and a different technique of the Region of Interest (ROI) extraction which we had presented in our previous work, a new and robust method against rotation is introduced for dorsal hand vein recognition. In this method, to select the ROI, by changing the length and angle of the sides, undesirable effects of hand rotation during taking images have largely been neutralized. So, depending on the amount of hand rotation, ROI in each image will be different in size and shape. On the other hand, because of the same direction distribution on the dorsal hand vein patterns, we have used the kernel trick on sparse representation to classification. As a result, most samples with different classes but the same direction distribution will be classified properly. Using these two techniques, lead to introduce an effective method against hand rotation, for dorsal hand vein recognition. Increases of 2.26% in the recognition rate is observed for the proposed method when compared to the three conventional SRC-based algorithms and three classification methods based sparse coding that used dictionary learning.
S. Javadi; R. Safa; M. Azizi; Seyed A. Mirroshandel
Abstract
Online scientific communities are bases that publish books, journals, and scientific papers, and help promote knowledge. The researchers use search engines to find the given information including scientific papers, an expert to collaborate with, and the publication venue, but in many cases due to search ...
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Online scientific communities are bases that publish books, journals, and scientific papers, and help promote knowledge. The researchers use search engines to find the given information including scientific papers, an expert to collaborate with, and the publication venue, but in many cases due to search by keywords and lack of attention to the content, they do not achieve the desired results at the early stages. Online scientific communities can increase the system efficiency to respond to their users utilizing a customized search. In this paper, using a dataset including bibliographic information of user’s publication, the publication venues, and other published papers provided as a way to find an expert in a particular context where experts are recommended to a user according to his records and preferences. In this way, a user request to find an expert is presented with keywords that represent a certain expertise and the system output will be a certain number of ranked suggestions for a specific user. Each suggestion is the name of an expert who has been identified appropriate to collaborate with the user. In evaluation using IEEE database, the proposed method reached an accuracy of 71.50 percent that seems to be an acceptable result.
H.3. Artificial Intelligence
Ali Rebwar Shabrandi; Ali Rajabzadeh Ghatari; Nader Tavakoli; Mohammad Dehghan Nayeri; Sahar Mirzaei
Abstract
To mitigate COVID-19’s overwhelming burden, a rapid and efficient early screening scheme for COVID-19 in the first-line is required. Much research has utilized laboratory tests, CT scans, and X-ray data, which are obstacles to agile and real-time screening. In this study, we propose a user-friendly ...
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To mitigate COVID-19’s overwhelming burden, a rapid and efficient early screening scheme for COVID-19 in the first-line is required. Much research has utilized laboratory tests, CT scans, and X-ray data, which are obstacles to agile and real-time screening. In this study, we propose a user-friendly and low-cost COVID-19 detection model based on self-reportable data at home. The most exhausted input features were identified and included in the demographic, symptoms, semi-clinical, and past/present disease data categories. We employed Grid search to identify the optimal combination of hyperparameter settings that yields the most accurate prediction. Next, we apply the proposed model with tuned hyperparameters to 11 classic state-of-the-art classifiers. The results show that the XGBoost classifier provides the highest accuracy of 73.3%, but statistical analysis shows that there is no significant difference between the accuracy performance of XGBoost and AdaBoost, although it proved the superiority of these two methods over other methods. Furthermore, the most important features obtained using SHapely Adaptive explanations were analyzed. “Contact with infected people,” “cough,” “muscle pain,” “fever,” “age,” “Cardiovascular commodities,” “PO2,” and “respiratory distress” are the most important variables. Among these variables, the first three have a relatively large positive impact on the target variable. Whereas, “age,” “PO2”, and “respiratory distress” are highly negatively correlated with the target variable. Finally, we built a clinically operable, visible, and easy-to-interpret decision tree model to predict COVID-19 infection.
Mojtaba Nasehi; Mohsen Ashourian; Hosein Emami
Abstract
Vehicle type recognition has been widely used in practical applications such as traffic control, unmanned vehicle control, road taxation, smuggling detection, and so on. In this paper, various techniques such as data augmentation and space filtering have been used to improve and enhance the data. Then, ...
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Vehicle type recognition has been widely used in practical applications such as traffic control, unmanned vehicle control, road taxation, smuggling detection, and so on. In this paper, various techniques such as data augmentation and space filtering have been used to improve and enhance the data. Then, a developed algorithm that integrates VGG neural network and YOLO algorithm has been used to detect and identify vehicles, Then the implementation on the Raspberry hardware board and practically through a scenario is mentioned. Real including image data sets are analyzed. The results show the good performance of the implemented algorithm in terms of detection performance (98%), processing speed, and environmental conditions, which indicates its capability in practical applications with low cost.
Z. Hassani; M. Alambardar Meybodi
Abstract
A major pitfall in the standard version of Particle Swarm Optimization (PSO) is that it might get stuck in the local optima. To escape this issue, a novel hybrid model based on the combination of PSO and AntLion Optimization (ALO) is proposed in this study. The proposed method, called H-PSO-ALO, uses ...
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A major pitfall in the standard version of Particle Swarm Optimization (PSO) is that it might get stuck in the local optima. To escape this issue, a novel hybrid model based on the combination of PSO and AntLion Optimization (ALO) is proposed in this study. The proposed method, called H-PSO-ALO, uses a local search strategy by employing the Ant-Lion algorithm to select the less correlated and salient feature subset. The objective is to improve the prediction accuracy and adaptability of the model in various datasets by balancing the exploration and exploitation processes. The performance of our method has been evaluated on 30 benchmark classification problems, CEC 2017 benchmark problems, and some well-known datasets. To verify the performance, four algorithms, including FDR-PSO, CLPSO, HFPSO, MPSO, are elected to be compared with the efficiency of H-PSO-ALO. Considering the experimental results, the proposed method outperforms the others in many cases, so it seems it is a desirable candidate for optimization problems on real-world datasets.
H.3. Artificial Intelligence
Mahdi Rasouli; Vahid Kiani
Abstract
The identification of emotions in short texts of low-resource languages poses a significant challenge, requiring specialized frameworks and computational intelligence techniques. This paper presents a comprehensive exploration of shallow and deep learning methods for emotion detection in short Persian ...
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The identification of emotions in short texts of low-resource languages poses a significant challenge, requiring specialized frameworks and computational intelligence techniques. This paper presents a comprehensive exploration of shallow and deep learning methods for emotion detection in short Persian texts. Shallow learning methods employ feature extraction and dimension reduction to enhance classification accuracy. On the other hand, deep learning methods utilize transfer learning and word embedding, particularly BERT, to achieve high classification accuracy. A Persian dataset called "ShortPersianEmo" is introduced to evaluate the proposed methods, comprising 5472 diverse short Persian texts labeled in five main emotion classes. The evaluation results demonstrate that transfer learning and BERT-based text embedding perform better in accurately classifying short Persian texts than alternative approaches. The dataset of this study ShortPersianEmo will be publicly available online at https://github.com/vkiani/ShortPersianEmo.
G.3.7. Database Machines
Abdul Aziz Danaa Abukari; Mohammed Daabo Ibrahim; Alhassan Abdul-Barik
Abstract
Hidden Markov Models (HMMs) are machine learning models that has been applied to a range of real-life applications including intrusion detection, pattern recognition, thermodynamics, statistical mechanics among others. A multi-layered HMMs for real-time fraud detection and prevention whilst reducing ...
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Hidden Markov Models (HMMs) are machine learning models that has been applied to a range of real-life applications including intrusion detection, pattern recognition, thermodynamics, statistical mechanics among others. A multi-layered HMMs for real-time fraud detection and prevention whilst reducing drastically the number of false positives and negatives is proposed and implemented in this study. The study also focused on reducing the parameter optimization and detection times of the proposed models using a hybrid algorithm comprising the Baum-Welch, Genetic and Particle-Swarm Optimization algorithms. Simulation results revealed that, in terms of Precision, Recall and F1-scores, our proposed model performed better when compared to other approaches proposed in literature.
B.3. Communication/Networking and Information Technology
S. Mojtaba Matinkhah; Roya Morshedi; Akbar Mostafavi
Abstract
The Internet of Things (IoT) has emerged as a rapidly growing technology that enables seamless connectivity between a wide variety of devices. However, with this increased connectivity comes an increased risk of cyber-attacks. In recent years, the development of intrusion detection systems (IDS) has ...
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The Internet of Things (IoT) has emerged as a rapidly growing technology that enables seamless connectivity between a wide variety of devices. However, with this increased connectivity comes an increased risk of cyber-attacks. In recent years, the development of intrusion detection systems (IDS) has become critical for ensuring the security and privacy of IoT networks. This article presents a study that evaluates the accuracy of an intrusion detection system (IDS) for detecting network attacks in the Internet of Things (IoT) network. The proposed IDS uses the Decision Tree Classifier and is tested on four benchmark datasets: NSL-KDD, BOT-IoT, CICIDS2017, and MQTT-IoT. The impact of noise on the training and test datasets on classification accuracy is analyzed. The results indicate that clean data has the highest accuracy, while noisy datasets significantly reduce accuracy. Furthermore, the study finds that when both training and test datasets are noisy, the accuracy of classification decreases further. The findings of this study demonstrate the importance of using clean data for training and testing an IDS in IoT networks to achieve accurate classification. This research provides valuable insights for the development of a robust and accurate IDS for IoT networks.
H.3. Artificial Intelligence
Seyed Alireza Bashiri Mosavi; Omid Khalaf Beigi
Abstract
A speedy and accurate transient stability assessment (TSA) is gained by employing efficient machine learning- and statistics-based (MLST) algorithms on transient nonlinear time series space. In the MLST’s world, the feature selection process by forming compacted optimal transient feature space ...
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A speedy and accurate transient stability assessment (TSA) is gained by employing efficient machine learning- and statistics-based (MLST) algorithms on transient nonlinear time series space. In the MLST’s world, the feature selection process by forming compacted optimal transient feature space (COTFS) from raw high dimensional transient data can pave the way for high-performance TSA. Hence, designing a comprehensive feature selection scheme (FSS) that populates COTFS with the relevant-discriminative transient features (RDTFs) is an urgent need. This work aims to introduce twin hybrid FSS (THFSS) to select RDTFs from transient 28-variate time series data. Each fold of THFSS comprises filter-wrapper mechanisms. The conditional relevancy rate (CRR) is based on mutual information (MI) and entropy calculations are considered as the filter method, and incremental wrapper subset selection (IWSS) and IWSS with replacement (IWSSr) formed by kernelized support vector machine (SVM) and twin SVM (TWSVM) are used as wrapper ones. After exerting THFSS on transient univariates, RDTFs are entered into the cross-validation-based train-test procedure for evaluating their efficiency in TSA. The results manifested that THFSS-based RDTFs have a prediction accuracy of 98.87 % and a processing time of 102.653 milliseconds for TSA.
H.6. Pattern Recognition
Sadegh Rahmani Rahmani-Boldaji; Mehdi Bateni; Mahmood Mortazavi Dehkordi
Abstract
Efficient regular-frequent pattern mining from sensors-produced data has become a challenge. The large volume of data leads to prolonged runtime, thus delaying vital predictions and decision makings which need an immediate response. So, using big data platforms and parallel algorithms is an appropriate ...
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Efficient regular-frequent pattern mining from sensors-produced data has become a challenge. The large volume of data leads to prolonged runtime, thus delaying vital predictions and decision makings which need an immediate response. So, using big data platforms and parallel algorithms is an appropriate solution. Additionally, an incremental technique is more suitable to mine patterns from big data streams than static methods. This study presents an incremental parallel approach and compact tree structure for extracting regular-frequent patterns from the data of wireless sensor networks. Furthermore, fewer database scans have been performed in an effort to reduce the mining runtime. This study was performed on Intel 5-day and 10-day datasets with 6, 4, and 2 nodes clusters. The findings show the runtime was improved in all 3 cluster modes by 14, 18, and 34% for the 5-day dataset and by 22, 55, and 85% for the 10-day dataset, respectively.
R. Azizi; A. M. Latif
Abstract
In this work, we show that an image reconstruction from a burst of individually demosaicked RAW captures propagates demosaicking artifacts throughout the image processing pipeline. Hence, we propose a joint regularization scheme for burst denoising and demosaicking. We model the burst alignment functions ...
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In this work, we show that an image reconstruction from a burst of individually demosaicked RAW captures propagates demosaicking artifacts throughout the image processing pipeline. Hence, we propose a joint regularization scheme for burst denoising and demosaicking. We model the burst alignment functions and the color filter array sampling functions into one linear operator. Then, we formulate the individual burst reconstruction and the demosaicking problems into a three-color-channel optimization problem. We introduce a crosschannel prior to the solution of this optimization problem and develop a numerical solver via alternating direction method of multipliers. Moreover, our proposed method avoids the complexity of alignment estimation as a preprocessing step for burst reconstruction. It relies on a phase correlation approach in the Fourier’s domain to efficiently find the relative translation, rotation, and scale among the burst captures and to perform warping accordingly. As a result of these steps, the proposed joint burst denoising and demosaicking solution improves the quality of reconstructed images by a considerable margin compared to existing image model-based methods.
H.3. Artificial Intelligence
Farid Ariai; Maryam Tayefeh Mahmoudi; Ali Moeini
Abstract
In the era of pervasive internet use and the dominance of social networks, researchers face significant challenges in Persian text mining, including the scarcity of adequate datasets in Persian and the inefficiency of existing language models. This paper specifically tackles these challenges, aiming ...
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In the era of pervasive internet use and the dominance of social networks, researchers face significant challenges in Persian text mining, including the scarcity of adequate datasets in Persian and the inefficiency of existing language models. This paper specifically tackles these challenges, aiming to amplify the efficiency of language models tailored to the Persian language. Focusing on enhancing the effectiveness of sentiment analysis, our approach employs an aspect-based methodology utilizing the ParsBERT model, augmented with a relevant lexicon. The study centers on sentiment analysis of user opinions extracted from the Persian website 'Digikala.' The experimental results not only highlight the proposed method's superior semantic capabilities but also showcase its efficiency gains with an accuracy of 88.2% and an F1 score of 61.7. The importance of enhancing language models in this context lies in their pivotal role in extracting nuanced sentiments from user-generated content, ultimately advancing the field of sentiment analysis in Persian text mining by increasing efficiency and accuracy.
F.2.11. Applications
Ali Sedehi; Alireza Alfi; Mohammadreza Mirjafari
Abstract
This paper addresses a key challenge in designing a suitable controller for DC-DC converters to regulate the output voltage effectively within a limited time frame. In addition to non-minimum phase behavior of such type of converter, a significant issue, namely parametric uncertainty, can further complicate ...
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This paper addresses a key challenge in designing a suitable controller for DC-DC converters to regulate the output voltage effectively within a limited time frame. In addition to non-minimum phase behavior of such type of converter, a significant issue, namely parametric uncertainty, can further complicate this task. Robust control theory is an efficient approach to deal with this problem. However, its implementation often requires high-order controllers, which may not be practical due to hardware and computational constraints. Here, we propose a low-order robust controller satisfying the robust stability and performance criteria of conventional high-order controllers. To tackle this issue, a constraint optimization problem is formulated, and the evolutionary algorithms are adopted to achieve the optimal parameter values of the controller. Both simulation and experimental outcomes have been documented, and a comparative analysis with an optimal Proportional-Integral (PI) controller has been conducted to substantiate efficiency to the proposed methodology.
H.5. Image Processing and Computer Vision
Farima Fakouri; Mohsen Nikpour; Abbas Soleymani Amiri
Abstract
Due to the increased mortality caused by brain tumors, accurate and fast diagnosis of brain tumors is necessary to implement the treatment of this disease. In this research, brain tumor classification performed using a network based on ResNet architecture in MRI images. MRI images that available in the ...
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Due to the increased mortality caused by brain tumors, accurate and fast diagnosis of brain tumors is necessary to implement the treatment of this disease. In this research, brain tumor classification performed using a network based on ResNet architecture in MRI images. MRI images that available in the cancer image archive database included 159 patients. First, two filters called median and Gaussian filters were used to improve the quality of the images. An edge detection operator is also used to identify the edges of the image. Second, the proposed network was first trained with the original images of the database, then with Gaussian filtered and Median filtered images. Finally, accuracy, specificity and sensitivity criteria have been used to evaluate the results. Proposed method in this study was lead to 87.21%, 90.35% and 93.86% accuracy for original, Gaussian filtered and Median filtered images. Also, the sensitivity and specificity was calculated 82.3% and 84.3% for the original images, respectively. Sensitivity for Gaussian and Median filtered images was calculated 90.8% and 91.57%, respectively and specificity was calculated 93.01% and 93.36%, respectively. As a conclusion, image processing approaches in preprocessing stage should be investigated to improve the performance of deep learning networks.
B.3. Communication/Networking and Information Technology
Roya Morshedi; S. Mojtaba Matinkhah; Mohammad Taghi Sadeghi
Abstract
IoT devices has witnessed a substantial increase due to the growing demand for smart devices. Intrusion Detection Systems (IDS) are critical components for safeguarding IoT networks against cyber threats. This study presents an advanced approach to IoT network intrusion detection, leveraging deep learning ...
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IoT devices has witnessed a substantial increase due to the growing demand for smart devices. Intrusion Detection Systems (IDS) are critical components for safeguarding IoT networks against cyber threats. This study presents an advanced approach to IoT network intrusion detection, leveraging deep learning techniques and pristine data. We utilize the publicly available CICIDS2017 dataset, which enables comprehensive training and testing of intrusion detection models across various attack scenarios, such as Distributed Denial of Service (DDoS) attacks, port scans, botnet activity, and more. Our goal is to provide a more effective method than the previous methods. Our proposed deep learning model incorporates dense transition layers and LSTM architecture, designed to capture both spatial and temporal dependencies within the data. We employed rigorous evaluation metrics, including sparse categorical cross-entropy loss and accuracy, to assess model performance. The results of our approach show outstanding accuracy, reaching a peak of 0.997 on the test data. Our model demonstrates stability in loss and accuracy metrics, ensuring reliable intrusion detection capabilities. Comparative analysis with other machine learning models confirms the effectiveness of our approach. Moreover, our study assesses the model's resilience to Gaussian noise, revealing its capacity to maintain accuracy in challenging conditions. We provide detailed performance metrics for various attack types, offering insights into the model's effectiveness across diverse threat scenarios.
H.3. Artificial Intelligence
Sajjad Alizadeh Fard; Hossein Rahmani
Abstract
Fraud in financial data is a significant concern for both businesses and individuals. Credit card transactions involve numerous features, some of which may lack relevance for classifiers and could lead to overfitting. A pivotal step in the fraud detection process is feature selection, which profoundly ...
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Fraud in financial data is a significant concern for both businesses and individuals. Credit card transactions involve numerous features, some of which may lack relevance for classifiers and could lead to overfitting. A pivotal step in the fraud detection process is feature selection, which profoundly impacts model accuracy and execution time. In this paper, we introduce an ensemble-based, explainable feature selection framework founded on SHAP and LIME algorithms, called "X-SHAoLIM". We applied our framework to diverse combinations of the best models from previous studies, conducting both quantitative and qualitative comparisons with other feature selection methods. The quantitative evaluation of the "X-SHAoLIM" framework across various model combinations revealed consistent accuracy improvements on average, including increases in Precision (+5.6), Recall (+1.5), F1-Score (+3.5), and AUC-PR (+6.75). Beyond enhanced accuracy, our proposed framework, leveraging explainable algorithms like SHAP and LIME, provides a deeper understanding of features' importance in model predictions, delivering effective explanations to system users.
