Artificial Intelligence
S. H. Zahiri; R. Iranpoor; N. Mehrshad
Abstract
Background and Objectives: Person re-identification is an important application in computer vision, enabling the recognition of individuals across non-overlapping camera views. However, the large number of pedestrians with varying appearances, poses, and environmental conditions makes this task particularly ...
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Background and Objectives: Person re-identification is an important application in computer vision, enabling the recognition of individuals across non-overlapping camera views. However, the large number of pedestrians with varying appearances, poses, and environmental conditions makes this task particularly challenging. To address these challenges, various learning approaches have been employed. Achieving a balance between speed and accuracy is a key focus of this research. Recently introduced transformer-based models have made significant strides in machine vision, though they have limitations in terms of time and input data. This research aims to balance these models by reducing the input information, focusing attention solely on features extracted from a convolutional neural network model. Methods: This research integrates convolutional neural network (CNN) and Transformer architectures. A CNN extracts important features of a person in an image, and these features are then processed by the attention mechanism in a Transformer model. The primary objective of this work is to enhance computational speed and accuracy in Transformer architectures. Results: The results obtained demonstrate an improvement in the performance of the architectures under consistent conditions. In summary, for the Market-1501 dataset, the mAP metric increased from approximately 30% in the downsized Transformer model to around 74% after applying the desired modifications. Similarly, the Rank-1 metric improved from 48% to approximately 89%.Conclusion: Indeed, although it still has limitations compared to larger Transformer models, the downsized Transformer architecture has proven to be much more computationally efficient. Applying similar modifications to larger models could also yield positive effects. Balancing computational costs while improving detection accuracy remains a relative goal, dependent on specific domains and priorities. Choosing the appropriate method may emphasize one aspect over another.
Artificial Intelligence
S. Kabiri Rad; V. Afshin; S. H. Zahiri
Abstract
Background and Objectives: When dealing with high-volume and high-dimensional datasets, the distribution of samples becomes sparse, and issues such as feature redundancy or irrelevance arise. Dimensionality reduction techniques aim to incorporate correlation between features and map the original features ...
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Background and Objectives: When dealing with high-volume and high-dimensional datasets, the distribution of samples becomes sparse, and issues such as feature redundancy or irrelevance arise. Dimensionality reduction techniques aim to incorporate correlation between features and map the original features into a lower dimensional space. This usually reduces the computational burden and increases performance. In this paper, we study the problem of predicting heart disease in a situation where the dataset is large and (or) the proportion of instances belonging to one class compared to others is significantly low.Methods: We investigated three of the prominent dimensionality reduction techniques, including Principal Component Analysis (PCA), Information Bottleneck (IB) and Variational Autoencoder (VAE) on popular classification algorithms. To have adequate samples in all classes to properly feed the classifier, an efficient data balancing technique is used to compensate for fewer positives than negatives. Among all data balancing methods, a SMOTE-based method is selected, which generates new samples at the boundary of the samples distribution and avoids the synthesis of noise and redundant data. Results: The experimental results show that VAE-based method outperforms other dimensionality reduction algorithms in the performance measures. The proposed hybrid method improves accuracy to 97.1% and sensitivity to 99.2%.Conclusion: Finally, it can be concluded that the combination of VAE with oversampling algorithms can significantly enhance system performance as well as computational time.
Artificial Intelligence
L. Hafezi; S. Zarifzadeh; M. R. Pajoohan
Abstract
Background and Objectives: Detecting multiple entities within financial texts and accurately analyzing the sentiment associated with each is a challenging yet critical task. Traditional models often struggle to capture the nuanced relationships between multiple entities, especially when sentiments are ...
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Background and Objectives: Detecting multiple entities within financial texts and accurately analyzing the sentiment associated with each is a challenging yet critical task. Traditional models often struggle to capture the nuanced relationships between multiple entities, especially when sentiments are context-dependent and spread across different levels of a document. Addressing these complexities requires advanced models that can not only identify multiple entities but also distinguish their individual sentiments within a broader context. This study aims to introduce and evaluate two novel methods, ENT-HAN and SNT-HAN, built upon the Hierarchical Attention Networks, specifically designed to enhance the accuracy of both entity extraction and sentiment analysis in complex financial documents.Methods: In this study, we design ENT-HAN and SNT-HAN methods to address the tasks of multi-entity detection and sentiment analysis within financial texts. The first method focuses on entity extraction, where capture hierarchical relationships between words and sentences. By utilizing word-level attention, the model identifies the most relevant tokens for recognizing entities, while sentence-level attention helps refine the context in which these entities appear, allowing the model to detect multiple entities with precision. The second method is applied for sentiment analysis, aiming to classify sentiments into positive, negative, or neutral categories. The sentiment analysis model employs hierarchical attention to identify the most important words and sentences that convey sentiment about each entity. This approach ensures that the model not only focuses on the overall sentiment of the text but also accounts for context-specific variations in sentiment across different entities. Both methods were evaluated on FinEntity dataset, and the results demonstrate their effectiveness, with significantly improving the accuracy of both entity extraction and sentiment classification tasks.Results: The ENT-HAN and SNT-HAN demonstrated strong performance in both entity extraction and sentiment analysis, outperforming the methods they were compared against. For entity extraction, ENT-HAN was evaluated against RNN and BERT models, showing superior accuracy in identifying multiple entities within complex texts. In sentiment analysis, SNT-HAN was compared to the best-performing method previously applied to FinEntity dataset. Despite the good performance of the existing methods, SNT-HAN demonstrated superior results, achieving a better accuracy.Conclusion: The outcome of this research highlights the potential of the ENT-HAN and SNT-HAN for improving entity extraction and sentiment analysis accuracy in financial documents. Their ability to model attention at multiple levels allows for a more nuanced understanding of text, establishing them as a valuable resource for complex tasks in financial text analysis.
Artificial Intelligence
S. S. Musavian; A. Taghizade; F. Z. Ahmadi; S. Norouzi
Abstract
Background and Objectives: The purpose of this study is to propose a solution for using large fuzzy sets in assessment tasks with a significant number of items, focusing on the assessment of media and educational tools. Ensuring fairness is crucial in evaluation tasks, especially when different evaluators ...
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Background and Objectives: The purpose of this study is to propose a solution for using large fuzzy sets in assessment tasks with a significant number of items, focusing on the assessment of media and educational tools. Ensuring fairness is crucial in evaluation tasks, especially when different evaluators assign different ratings to the same process or their ratings may even vary in different situations. Also, previous non-fuzzy assessment methods show that the mean value of assessors scores is not a good representation when the variance of scores is significant. Fuzzy evaluation methods can solve this problem by addressing the uncertainty in evaluation tasks. Although some studies have been conducted on fuzzy assessment, but their main focus is fuzzy calculations and no solution has been proposed for the problem arising when fuzzy rule set is considerably huge. Methods: Fuzzy rules are the main key for fuzzy inference. This part of a fuzzy system often is generated by experts. In this study,15 experts were asked to create the set of fuzzy rules. Fuzzy rules relate inputs to outputs by descriptive linguistic expressions. Making these expressions is so more convenient than if we determine an exact relationship between inputs and outputs. The number of fussy rules has an exponential relationship with the number of inputs. Therefore, for a task with more than say 6 inputs, we should deal with a huge set of fuzzy rules. This paper presents a solution that enables the use of large fuzzy sets in fuzzy systems using a multi-stage hierarchical approach.Results: Justice is always the most important issue in an assessment process. Due to its nature, a fuzzy calculation-based assessment provides an assessment in a just manner. Since many assessment tasks are often involved more than 10 items to be assessed, generating a fuzzy rule set is impossible. Results show the final score is very sensitive to slight differences in score of an item given by assessors. Besides that, assessors often are not able to consider all items simultaneously to assign a coefficient for the effect of each item on final score. This will be seriously a problem when the final score depends on many input items. In this study, we proposed a fuzzy analysis method to ensure equitable evaluation of educational media and instructional tools within the teaching process. Results of none-fuzzy scoring system show that final score has intense variations when assessment is down in different times and by different assessors. It is because of the manner that importance coefficients are calculated for each item of assessment. In fuzzy assessment no importance coefficient is used for each item.Conclusion: In this study, a novel method was proposed to determine the score of an activity, a task, or a tool that is designed for learning purposes based on Fuzzy sets and their respective calculations. Because of the nature of fuzzy systems, approximate descriptive expressions are used to relate input items to final score instead of an exact function that is impossible to be estimated. Fuzzy method is a robust system that ensure us a fair assessment.
Artificial Intelligence
B. Mahdipour; S. H. Zahiri; I. Behravan
Abstract
Background and Objectives: Path planning is one of the most important topics related to the navigation of all kinds of moving vehicles such as airplanes, surface and subsurface vessels, cars, etc. Undoubtedly, in the process of making these tools more intelligent, detecting and crossing obstacles without ...
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Background and Objectives: Path planning is one of the most important topics related to the navigation of all kinds of moving vehicles such as airplanes, surface and subsurface vessels, cars, etc. Undoubtedly, in the process of making these tools more intelligent, detecting and crossing obstacles without encountering them by taking the shortest path is one of the most important goals of researchers. Significant success in this field can lead to significant progress in the use of these tools in a variety of applications such as industrial, military, transportation, commercial, etc. In this paper, a metaheuristic-based approach with the introduction of new fitness functions is presented for the problem of path planning for various types of surface and subsurface moving vehicles.Methods: The proposed approach for path planning in this research is based on the metaheuristic methods, which makes use of a novel fitness function. Particle Swarm Optimization (PSO) is the metaheuristic method leveraged in this research but other types of metaheuristic methods can also be used in the proposed architecture for path planning.Results: The efficiency of the proposed method, is tested on two synthetic environments for finding the best path between the predefined origin and destination for both surface and subsurface unmanned intelligent vessels. In both cases, the proposed method was able to find the best path or the closest answer to it.Conclusion: In this paper, an efficient method for the path planning problem is presented. The proposed method is designed using Particle Swarm Optimization (PSO). In the proposed method, several effective fitness function have been defined so that the best path or one of the closest answers can be obtained by utilized metaheuristic algorithm. The results of implementing the proposed method on real and simulated geographic data show its good performance. Also, the obtained quantitative results (time elapsed, success rate, path cost, standard deviation) have been compared with other similar methods. In all of these measurements, the proposed algorithm outperforms other methods or is comparable to them.
Artificial Intelligence
K. Moeenfar; V. Kiani; A. Soltani; R. Ravanifard
Abstract
Background and Objectives: In this paper, a novel and efficient unsupervised machine learning algorithm named EiForestASD is proposed for distinguishing anomalies from normal data in data streams. The proposed algorithm leverages a forest of isolation trees to detect anomaly data instances. Methods: ...
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Background and Objectives: In this paper, a novel and efficient unsupervised machine learning algorithm named EiForestASD is proposed for distinguishing anomalies from normal data in data streams. The proposed algorithm leverages a forest of isolation trees to detect anomaly data instances. Methods: The proposed method EiForestASD incorporates an isolation forest as an adaptable detector model that adjusts to new data over time. To handle concept drifts in the data stream, a window-based concept drift detection is employed that discards only those isolation trees that are incompatible with the new concept. The proposed method is implemented using the Python programming language and the Scikit-Multiflow library.Results: Experimental evaluations were conducted on six real-world and two synthetic data streams. Results reveal that the proposed method EiForestASD reduces computation time by 19% and enhances anomaly detection rate by 9% compared to the baseline method iForestASD. These results highlight the efficacy and efficiency of the EiForestASD in the context of anomaly detection in data streams.Conclusion: The EiForestASD method handles concept change using an intelligent strategy where only those trees from the detector model incompatible with the new concept are removed and reconstructed. This modification of the concept drift handling mechanism in the EiForestASD significantly reduces computation time and improves anomaly detection accuracy.
Artificial Intelligence
M. Soluki; Z. Askarinejadamiri; N. Zanjani
Abstract
Background and Objectives: This article explores a method for generating Persian texts using the GPT-2 language model and the Hazm library. Researchers and writers often require tools that can assist them in the writing process and even think on their behalf in various domains. By leveraging the GPT-2 ...
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Background and Objectives: This article explores a method for generating Persian texts using the GPT-2 language model and the Hazm library. Researchers and writers often require tools that can assist them in the writing process and even think on their behalf in various domains. By leveraging the GPT-2 model, it becomes possible to generate acceptable and creative texts, which increases writing speed and efficiency, thus mitigating the high costs associated with article writing.Methods: In this research, the GPT-2 model is employed to generate and predict Persian texts. The Hazm library is utilized for natural language processing and automated text generation. The results of this study are evaluated using different datasets and output representations, demonstrating that employing the Hazm library with input data exceeding 1000 yields superior outcomes compared to other text generation methodsResults: Through extensive experimentation and analysis, the study demonstrates the effectiveness of this combination in generating coherent and contextually appropriate text in the Persian language. The results highlight the potential of leveraging advanced language models and linguistic processing tools for enhancing natural language generation tasks in Persian. The findings of this research contribute to the growing field of Persian language processing and provide valuable insights for researchers and practitioners working on text generation applications in similar languages.Conclusion: Overall, this study showcases the promising capabilities of the GPT-2 model and Hazm library in Persian text generation, underscoring their potential for future advancements in the field This research serves as a valuable guide and tool for generating Persian texts in the field of research and scientific writing, contributing to cost and time reduction in article writing
Artificial Intelligence
M. Amoozegar; S. Golestani
Abstract
Background and Objectives: In recent years, various metaheuristic algorithms have become increasingly popular due to their effectiveness in solving complex optimization problems across diverse domains. These algorithms are now being utilized for an ever-expanding number of real-world applications across ...
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Background and Objectives: In recent years, various metaheuristic algorithms have become increasingly popular due to their effectiveness in solving complex optimization problems across diverse domains. These algorithms are now being utilized for an ever-expanding number of real-world applications across many fields. However, there are two critical factors that can significantly impact the performance and optimization capability of metaheuristic algorithms. First, comprehensively understanding the intrinsic behavior of the algorithms can provide key insights to improve their efficiency. Second, proper calibration and tuning of an algorithm's parameters can dramatically enhance its optimization effectiveness. Methods: In this study, we propose a novel response surface methodology-based approach to thoroughly analyze and elucidate the behavioral dynamics of optimization algorithms. This technique constructs an informative empirical model to determine the relative importance and interaction effects of an algorithm's parameters. Although applied to investigate the Gravitational Search Algorithm, this systematic methodology can serve as a generally applicable strategy to gain quantitative and visual insights into the functionality of any metaheuristic algorithm.Results: Extensive evaluation using 23 complex benchmark test functions exhibited that the proposed technique can successfully identify ideal parameter values and their comparative significance and interdependencies, enabling superior comprehension of an algorithm's mechanics.Conclusion: The presented modeling and analysis framework leverages multifaceted statistical and visualization tools to uncover the inner workings of algorithm behavior for more targeted calibration, thereby enhancing the optimization performance. It provides an impactful approach to elucidate how parameter settings shape algorithm searche so they can be calibrated for optimal efficiency.
Artificial Intelligence
S. Nemati
Abstract
Background and Objectives: Community question-answering (CQA) websites have become increasingly popular as platforms for individuals to seek and share knowledge. Identifying users with a special shape of expertise on CQA websites is a beneficial task for both companies and individuals. Specifically, ...
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Background and Objectives: Community question-answering (CQA) websites have become increasingly popular as platforms for individuals to seek and share knowledge. Identifying users with a special shape of expertise on CQA websites is a beneficial task for both companies and individuals. Specifically, finding those who have a general understanding of certain areas but lack expertise in other fields is crucial for companies who are planning internship programs. These users, called dash-shaped users, are willing to work for low wages and have the potential to quickly develop into skilled professionals, thus minimizing the risk of unsuccessful recruitment. Due to the vast number of users on CQA websites, they provide valuable resources for finding individuals with various levels of expertise. This study is the first of its kind to directly classify CQA users based solely on the textual content of their posts. Methods: To achieve this objective, we propose an ensemble of advanced deep learning algorithms and traditional machine learning methods for the binary classification of CQA users into two categories: those with dash-shaped expertise and those without. In the proposed method, we used the stack generalization to fuse the results of the dep and machine learning methods. To evaluate the effectiveness of our approach, we conducted an extensive experiment on three large datasets focused on Android, C#, and Java topics extracted from the Stack Overflow website. Results: The results on four datasets of the Stack Overflow, demonstrate that our ensemble method not only outperforms baseline methods including seven traditional machine learning and six deep models, but it achieves higher performance than state-of-the-art deep models by an average of 10% accuracy and F1-measure. Conclusion: The proposed model showed promising results in confirming that by using only their textual content of questions, we can classify the users in CQA websites. Specifically, the results showed that using the contextual content of the questions, the proposed model can be used for detecting the dash-shaped users precisely. Moreover, the proposed model is not limited to detecting dash-shaped users. It can also classify other shapes of expertise, such as T- and C-shaped users, which are valuable for forming agile software teams. Additionally, our model can be used as a filter method for downstream applications, like intern recommendations.
Artificial Intelligence
N. Ghanbari; S. H. Zahiri; H. Shahraki
Abstract
Background and Objectives: In this paper, a new version of the particle swarm optimization (PSO) algorithm using a linear ranking function is proposed for clustering uncertain data. In the proposed Uncertain Particle Swarm Clustering method, called UPSC method, triangular fuzzy numbers (TFNs) are used ...
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Background and Objectives: In this paper, a new version of the particle swarm optimization (PSO) algorithm using a linear ranking function is proposed for clustering uncertain data. In the proposed Uncertain Particle Swarm Clustering method, called UPSC method, triangular fuzzy numbers (TFNs) are used to represent uncertain data. Triangular fuzzy numbers are a good type of fuzzy numbers and have many applications in the real world.Methods: In the UPSC method input data are fuzzy numbers. Therefore, to upgrade the standard version of PSO, calculating the distance between the fuzzy numbers is necessary. For this purpose, a linear ranking function is applied in the fitness function of the PSO algorithm to describe the distance between fuzzy vectors. Results: The performance of the UPSC is tested on six artificial and nine benchmark datasets. The features of these datasets are represented by TFNs.Conclusion: The experimental results on fuzzy artificial datasets show that the proposed clustering method (UPSC) can cluster fuzzy datasets like or superior to other standard uncertain data clustering methods such as Uncertain K-Means Clustering (UK-means) and Uncertain K-Medoids Clustering (UK-medoids) algorithms. Also, the experimental results on fuzzy benchmark datasets demonstrate that in all datasets except Libras, the UPSC method provides better results in accuracy when compared to other methods. For example, in iris data, the clustering accuracy has increased by 2.67% compared to the UK-means method. In the case of wine data, the accuracy increased with the UPSC method is 1.69%. As another example, it can be said that the increase in accuracy for abalone data was 4%. Comparing the results with the rand index (RI) also shows the superiority of the proposed clustering method.
Artificial Intelligence
S. Nemati
Abstract
Background and Objectives: Twitter is a microblogging platform for expressing assessments, opinions, and sentiments on different topics and events. While there have been several studies around sentiment analysis of tweets and their popularity in the form of the number of retweets, predicting the sentiment ...
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Background and Objectives: Twitter is a microblogging platform for expressing assessments, opinions, and sentiments on different topics and events. While there have been several studies around sentiment analysis of tweets and their popularity in the form of the number of retweets, predicting the sentiment of first-order replies remained a neglected challenge. Predicting the sentiment of tweet replies is helpful for both users and enterprises. In this study, we define a novel problem; given just a tweet's text, the goal is to predict the overall sentiment polarity of its upcoming replies.Methods: To address this problem, we proposed a graph convolutional neural network model that exploits the text's dependencies. The proposed model contains two parallel branches. The first branch extracts the contextual representation of the input tweets. The second branch extracts the structural and semantic information from tweets. Specifically, a Bi-LSTM network and a self-attention layer are used in the first layer for extracting syntactical relations, and an affective knowledge-enhanced dependency tree is used in the second branch for extracting semantic relations. Moreover, a graph convolutional network is used on the top of these branches to learn the joint feature representation. Finally, a retrieval-based attention mechanism is used on the output of the graph convolutional network for learning essential features from the final affective picture of tweets.Results: In the experiments, we only used the original tweets of the RETWEET dataset for training the models and ignored the replies of the tweets in the training process. The results on three versions of the RETWEET dataset showed that the proposed model outperforms the LSTM-based models and similar state-of-the-art graph convolutional network models. Conclusion: The proposed model showed promising results in confirming that by using only the content of a tweet, we can predict the overall sentiment of its replies. Moreover, the results showed that the proposed model achieves similar or comparable results with simpler deep models when trained on a public tweet dataset such as ACL 2014 dataset while outperforming both simple deep models and state-of-the-art graph convolutional deep models when trained on the RETWEET dataset. This shows the proposed model's effectiveness in extracting structural and semantic relations in the tweets.
Artificial Intelligence
M. Yousefzadeh; A. Golmakani; G. Sarbishaei
Abstract
Background and Objectives: To design an efficient tracker in a crowded environment based on artificial intelligence and image processing, there are several challenges such as the occlusion, fast motion, in-plane rotation, variations in target illumination and Other challenges of online tracking are the ...
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Background and Objectives: To design an efficient tracker in a crowded environment based on artificial intelligence and image processing, there are several challenges such as the occlusion, fast motion, in-plane rotation, variations in target illumination and Other challenges of online tracking are the time complexity of the algorithm, increasing memory space, and tracker dependence on the target model. In this paper, for the first time, sketch matrix theory in ridge regression for video sequences has been proposed.Methods: A new tracking object method based on the element-wise matrix with an online training method is proposed including the kernel correlation Filter (KCF), circular, and sketch matrix. The proposed algorithm is not only the free model but also increases the robustness of the tracker related to the scale variation, occlusion, fast motion, and reduces KCF drift.Results: The simulation results demonstrate that the proposed sketch kernel correlation filter (SHKCF) can increase the computational speed of the algorithm and reduces both the time complexity and the memory space. Finally, the proposed tracker is implemented and experimentally evaluated based on video sequences of OTB50, OTB100 and VOT2016 benchmarks.Conclusion: The experimental results show that the SHKCF method obtains not only OPE partial evaluation of Out of view, Occlusion and Motion Blur in object accuracy but also achieved the partial evaluation of Illumination Variation, Out of Plane Rotation, Scale Variation, Out of View, Occlusion, In of Plane Rotation, Background Clutter, Fast Motion and Deformation in object overlap which are the first rank compared to the state-the-art works. The result of accuracy, robustness and time complexity are obtained 0.929, 0.93 and 35.4, respectively.
Artificial Intelligence
Z. Ghasemi-Naraghi; A. Nickabadi; R. Safabakhsh
Abstract
Background and Obejctives: Multi-task learning is a widespread mechanism to improve the learning of multiple objectives with a shared representation in one deep neural network. In multi-task learning, it is critical to determine how to combine the tasks loss functions. The straightforward way is to optimize ...
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Background and Obejctives: Multi-task learning is a widespread mechanism to improve the learning of multiple objectives with a shared representation in one deep neural network. In multi-task learning, it is critical to determine how to combine the tasks loss functions. The straightforward way is to optimize the weighted linear sum of multiple objectives with equal weights. Despite some studies that have attempted to solve the realtime multi-person pose estimation problem from a 2D image, major challenges still remain unresolved. Methods: The prevailing solutions are two-stream, learning two tasks simultaneously. They intrinsically use a multi-task learning approach for predicting the confidence maps of body parts and the part affinity fields to associate the parts to each other. They optimize the average of the two tasks loss functions, while the two tasks have different levels of difficulty and uncertainty. In this work, we overcome this problem by applying a multi-task objective that captures task-based uncertainties without any additional parameters. Since the estimated poses can be more certain, the proposed method is called “CertainPose”. Results: Experiments are carried out on the COCO keypoints data sets. The results show that capturing the task-dependent uncertainty makes the training procedure faster and causes some improvements in human pose estimation. Conclusion: The highlight advantage of our method is improving the realtime multi-person pose estimation without increasing computational complexity.
Artificial Intelligence
H. Karim Tabbahfar; F. Tabib Mahmoudi
Abstract
Background and Objectives: Considering the drought and global warming, it is very important to monitor changes in water bodies for surface water management and preserve water resources in the natural ecosystem. For this purpose, using the appropriate spectral indices has high capabilities to distinguish ...
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Background and Objectives: Considering the drought and global warming, it is very important to monitor changes in water bodies for surface water management and preserve water resources in the natural ecosystem. For this purpose, using the appropriate spectral indices has high capabilities to distinguish surface water bodies from other land covers. This research has a special consideration to the effect of different types of land covers around water bodies. For this reason, two different water bodies, lake and wetland, have been used to evaluate the implementation results.Methods: The main objective of this research is to evaluate the capabilities of the genetic algorithm in optimum selection of the spectral indices extracted from Sentinel-2 satellite image due to distinguish surface water bodies in two case studies: 1) the pure water behind the Karkheh dam and 2) the Shadegan wetland having water mixed with vegetation. In this regard, the set of optimal indices is obtained with the genetic algorithm followed by the support vector machine (SVM) classifier. Results: The evaluation of the classification results based on the optimum selected spectral indices showed that the overall accuracy and Kappa coefficient of the recognized surface water bodies are 98.18 and 0.9827 in the Karkheh dam and 98.04 and 0.93 in Shadegan wetland, respectively. Evaluation of each of the spectral indices measured in both study areas was carried out using quantitative decision tree (DT) classifier. The best obtained DT classification results show the improvements in overall accuracy by 1.42% in the Karkheh Dam area and 1.56% in the Shadegan Wetland area based on the optimum selected indices by genetic algorithm followed by SVM classifier. Moreover, the obtained classification results are superior compared with Random Forest classifier using the optimized set of spectral features.Conclusion: Applying the genetic algorithm on the spectral indices was able to obtain two optimal sets of effective indices that have the highest amount of accuracy in classifying water bodies from other land cover objects in the study areas. Considering the collective performance, genetic algorithm selects an optimal set of indices that can detect water bodies more accurately than any single index.
Artificial Intelligence
S. Kalantary; J. Akbari Torkestani; A. Shahidinejad
Abstract
Background and Objectives: With the great growth of applications sensitive to latency, and efforts to reduce latency and cost and to improve the quality of service on the Internet of Things ecosystem, cloud computing and communication between things and the cloud are costly and inefficient; Therefore, ...
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Background and Objectives: With the great growth of applications sensitive to latency, and efforts to reduce latency and cost and to improve the quality of service on the Internet of Things ecosystem, cloud computing and communication between things and the cloud are costly and inefficient; Therefore, fog computing has been proposed to prevent sending large volumes of data generated by things to cloud centers and, if possible, to process some requests. Today's advances in 5G networks and the Internet of Things show the benefits of fog computing more than ever before, so that services can be delivered with very little delay as resources and features of fog nodes approach the end user.Methods: Since the cloud-fog paradigm is a layered architecture, to reduce the overall delay, the fog layer is divided into two sub-layers in this paper, including super nodes and ordinary nodes in order to use the coverage of super peer networks to use the connections between fog nodes in addition to taking advantage of the features of that network and improving the performance of large-scale systems. It causes fog nodes to interact with each other in processing requests and fewer data will be sent to the cloud, resulting in a reduction in overall latency. To reduce the cost of bandwidth used among fog nodes, we have organized a sub-layer of super nodes in the form of a Perfect Difference Graph (PDG). The new platform proposed for aggregation of fog computing and Internet of Things (FOT) is called the P2P-based Fog supported Platform (PFP).Results: We evaluate the utility of our proposed method by applying ifogsim simulator and the results achieved are as follows: (1) power consumption parameter in our proposed method 24% and 38% have improved compared to the structure three-layer fog computing architecture and without fog layer respectively; (2) network usage parameter in our proposed method 26% and 32% have improved compared to the structure three-layer fog computing architecture and without fog layer respectively; (3) average response time parameter in our proposed method 17% and 58% have improved compared to the structure three-layer fog computing architecture and without fog layer respectively; and (4) delay parameter in our proposed method 1% and 0.4% have improved compared to the structure three-layer fog computing architecture and without fog layer respectively.Conclusion: Numerical results obtained from the simulation show that the delay and cost parameters are significantly improved compared to the structure without fog layer and three-layer fog computing architecture. Also, the results show that increasing number of things has the same effect in all cases.
Artificial Intelligence
M. Khazaei
Abstract
Background and Objectives: IP multimedia subsystems (IMS) have been introduced as the Next Generation Network (NGN) platform while considering Session Initiation Protocol (SIP) as the signaling protocol. SIP lacks a proper overload mechanism. Hence, this challenge causes decline in the multimedia QoS. ...
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Background and Objectives: IP multimedia subsystems (IMS) have been introduced as the Next Generation Network (NGN) platform while considering Session Initiation Protocol (SIP) as the signaling protocol. SIP lacks a proper overload mechanism. Hence, this challenge causes decline in the multimedia QoS. The main propose of overload control mechanism is to keep the network throughput at the same network capacity with overload.Methods: NGN distributed with IMS is a complex innovative network consisting of interacting subsystems. Hence, multi-agent systems (MAS) receiving further attention for solving complex problems can solve the problem of overload in these networks. To this end, each IMS server is considered as an intelligent agent that can learn and negotiate with other agents while maintaining autonomy, thus eliminating the overload by communication and knowledge transfer between the agents. In the present research, using MAS and their properties, the intelligent hop by hop method is provided based on Q-learning and negotiation capability for the first time.Results: In the proposed method, parameters of overload controller are obtained by reinforcement learning. In order to check the validity of controller performance, a comparison is made with the similar method in which the optimal parameters are achieved based on trial and error. The result of the comparison confirms the validity of the proposed method. In order to evaluate the efficiency of the learner method, it is compared with similar and standard methods, for which the results are compared to show performance. The results show, the proposed method has approximately improved the throughput by 13%, the delay by 49% and the number of rejected sessions by 17% compare with methods, passing control messages through the network such as CPU occupancy methods. While compare with external controller methods like holonic, throughput is improved by 1% and the number of rejected requests is decreased by 10%, but delay is increased by 6% due to the convergence time of the learning and negotiation process.Conclusion: To overcome overload, complex IMS servers are considered as learner and negotiator agents. This is a new method to achieve the required parameters without relying on expert knowledge or person as well as, heterogeneous IMS entities can be inserted into the problem to complete study in future.
Artificial Intelligence
S.M. Notghimoghadam; H. Farsi; S. Mohamadzadeh
Abstract
Background and Objectives: Object detection has been a fundamental issue in computer vision. Research findings indicate that object detection aided by convolutional neural networks (CNNs) is still in its infancy despite -having outpaced other methods. Methods: This study proposes a straightforward, ...
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Background and Objectives: Object detection has been a fundamental issue in computer vision. Research findings indicate that object detection aided by convolutional neural networks (CNNs) is still in its infancy despite -having outpaced other methods. Methods: This study proposes a straightforward, easily implementable, and high-precision object detection method that can detect objects with minimum least error. Object detectors generally fall into one-stage and two-stage detectors. Unlike one-stage detectors, two-stage detectors are often more precise, despite performing at a lower speed. In this study, a one-stage detector is proposed, and the results indicated its sufficient precision. The proposed method uses a feature pyramid network (FPN) to detect objects on multiple scales. This network is combined with the ResNet 50 deep neural network. Results: The proposed method is trained and tested on Pascal VOC 2007 and COCO datasets. It yields a mean average precision (mAP) of 41.91 in Pascal Voc2007 and 60.07% in MS COCO. The proposed method is tested under additive noise. The test images of the datasets are combined with the salt and pepper noise to obtain the value of mAP for different noise levels up to 50% for Pascal VOC and MS COCO datasets. The investigations show that the proposed method provides acceptable results. Conclusion: It can be concluded that using deep learning algorithms and CNNs and combining them with a feature network can significantly enhance object detection precision.
Artificial Intelligence
S.V. Moravvej; M.J. Maleki Kahaki; M. Salimi Sartakhti; M. Joodaki
Abstract
Background and Objectives: Text summarization plays an essential role in reducing time and cost in many domains such as medicine, engineering, etc. On the other hand, manual summarization requires much time. So, we need an automated system for summarizing. How to select sentences is critical in summarizing. ...
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Background and Objectives: Text summarization plays an essential role in reducing time and cost in many domains such as medicine, engineering, etc. On the other hand, manual summarization requires much time. So, we need an automated system for summarizing. How to select sentences is critical in summarizing. Summarization techniques that have been introduced in recent years are usually greedy in the choice of sentences, which leads to a decrease in the quality of the summary. In this paper, a non-greedily method for selecting essential sentences from a text is presented.Methods: The present paper presents a method based on a generative adversarial network and attention mechanism called GAN-AM for extractive summarization. Generative adversarial networks have two generator and discriminator networks whose parameters are independent of each other. First, the features of the sentences are extracted by two traditional and embedded methods. We extract 12 traditional features. Some of these features are extracted from sentence words and others from the sentence. In addition, we use the well-known Skip-Gram model for embedding. Then, the features are entered into the generator as a condition, and the generator calculates the probability of each sentence in summary. A discriminator is used to check the generated summary of the generator and to strengthen its performance. We introduce a new loss function for discriminator training that includes generator output, real and fake summaries of each document. During training and testing, each document enters the generator with different noises. It allows the generator to see many combinations of sentences that are suitable for quality summaries.Results: We evaluate our results on CNN/Daily Mail and Medical datasets. Summaries produced by the generator show that our model performs better than other methods compared based on the ROUGE metric. We apply different sizes of noise to the generator to check the effect of noise on our model. The results indicate that the noise-free model has poor results.Conclusion: Unlike recent works, in our method, the generator selects sentences non-greedily. Experimental results show that the generator with noise can produce summaries that are related to the main subject.
Artificial Intelligence
M. Abdollahi; Z. Boujarnezhad
Abstract
Background and Objectives: As cities are developing and the population increases significantly, one of the most important challenges for city managers is the urban transportation system. An Intelligent Transportation System (ITS) uses information, communication, and control techniques to assist the transportation ...
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Background and Objectives: As cities are developing and the population increases significantly, one of the most important challenges for city managers is the urban transportation system. An Intelligent Transportation System (ITS) uses information, communication, and control techniques to assist the transportation system. The ITS includes a large number of traffic sensors that collect high volumes of data to provide information to support and improve traffic management operations. Due to the high traffic volume, the classic methods of traffic control are unable to satisfy the requirements of the variable, and the dynamic nature of traffic. Accordingly, Artificial Intelligence and the Internet of Things meet this demand as a decentralized solution.Methods: This paper presents an optimal method to find the best route and compare it with the previous methods. The proposed method has three phases. First, the area should be clustered under servicing and, second, the requests will be predicted using the time series neural network. then, the Whale Optimization Algorithm (WOA) will be run to select the best route.Results: To evaluate the parameters, different scenarios were designed and implemented. The simulation results show that the service time parameter of the proposed method is improved by about 18% and 40% in comparison with the Grey Wolf Optimizer (GWO) and Random Movement methods. Also, the difference between this parameter in the two methods of Harris Hawks Optimizer (HHO) and WOA is about 5% and the HHO has performed better.Conclusion: The interaction of AI and IoT can lead to solutions to improve ITS and to increase client satisfaction. We use WOA to improve time servicing and throughput. The Simulation results show that this method can be increase satisfaction for clients.
Artificial Intelligence
I. Zabbah; K. Layeghi; Reza Ebrahimpour
Abstract
Background and Objectives: COVID-19 disease still has a devastating effect on society health. The use of X-ray images is one of the most important methods of diagnosing the disease. One of the challenges specialists are faced is no diagnosing in time. Using Deep learning can reduce the diagnostic error ...
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Background and Objectives: COVID-19 disease still has a devastating effect on society health. The use of X-ray images is one of the most important methods of diagnosing the disease. One of the challenges specialists are faced is no diagnosing in time. Using Deep learning can reduce the diagnostic error of COVID-19 and help specialists in this field. Methods: The aim of this study is to provide a method based on a combination of deep learning(s) in parallel so that it can lead to more accurate results in COVID-19 disease by gathering opinions. In this research, 4 pre-trained (fine-tuned) deep model have been used. The dataset of this study is X-ray images from Github containing 1125 samples in 3 classes include normal, COVID-19 and pneumonia contaminated.Results: In all networks, 70% of the samples were used for training and 30% for testing. To ensure accuracy, the K-fold method was used in the training process. After modeling and comparing the generated models and recording the results, the accuracy of diagnosis of COVID-19 disease showed 84.3% and 87.2% when learners were not combined and experts were combined respectively. Conclusion: The use of machine learning techniques can lead to the early diagnosis of COVID-19 and help physicians to accelerate the healing process. This study shows that a combination of deep experts leads to improved diagnosis accuracy.
Artificial Intelligence
I. Behravan; S.M. Razavi
Abstract
Background and Objectives: Stock markets have a key role in the economic situation of the countries. Thus one of the major methods of flourishing the economy can be getting people to invest their money in the stock market. For this purpose, reducing the risk of investment can persuade people to trust ...
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Background and Objectives: Stock markets have a key role in the economic situation of the countries. Thus one of the major methods of flourishing the economy can be getting people to invest their money in the stock market. For this purpose, reducing the risk of investment can persuade people to trust the market and invest. Hence, Productive tools for predicting the future of the stock market have an undeniable effect on investors and traders’ profit.Methods: In this research, a two-stage method has been introduced to predict the next week's index value of the market, and the Tehran Stock Exchange Market has been selected as a case study. In the first stage of the proposed method, a novel clustering method has been used to divide the data points of the training dataset into different groups and in the second phase for each cluster’s data, a hybrid regression method (HHO-SVR) has been trained to detect the patterns hidden in each group. For unknown samples, after determining their cluster, the corresponding trained regression model estimates the target value. In the hybrid regression method, HHO is hired to select the best feature subset and also to tune the parameters of SVR.Results: The experimental results show the high accuracy of the proposed method in predicting the market index value of the next week. Also, the comparisons made with other metaheuristics indicate the superiority of HHO over other metaheuristics in solving such a hard and complex optimization problem. Using the historical information of the last 20 days, our method has achieved 99% accuracy in predicting the market index of the next 7 days while PSO, MVO, GSA, IPO, linear regression and fine-tuned SVR has achieved 67%, 98%, 38%, 4%, 5.6% and 98 % accuracy respectively.Conclusion: in this research we have tried to forecast the market index of the next m (from 1 to 7) days using the historical data of the past n (from 10 to 100) days. The experiments showed that increasing the number of days (n), used to create the dataset, will not necessarily improve the performance of the method.
Artificial Intelligence
H. Nosrati Nahook; S. Tabatabaei
Abstract
Background and Objectives: The Ant-Miner algorithm works based on Ant Colony Optimization as a tool for data analysis , and is used to explore classified laws from a set of data. In the current study, two new methods have been proposed for the purpose of optimizing this algorithm. The first method adopted ...
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Background and Objectives: The Ant-Miner algorithm works based on Ant Colony Optimization as a tool for data analysis , and is used to explore classified laws from a set of data. In the current study, two new methods have been proposed for the purpose of optimizing this algorithm. The first method adopted logical negation operation on the records of the produced laws, while the second employed a new Pheromone Update strategy called “Generalized exacerbation of quality conflict”. The two proposed methods were executed in Visual studio C#.Net , and 8 public datasets were applied in the test. Each one of these datasets was executed 10 times both in an independent way and combined with others, and the average results were recorded.Methods: In this study, we have proposed two approaches for the earlier method. Using the first method in the construction of rule records, idioms that include the rules can be made in the form of . Compared to the idioms of the early algorithm, these idioms are more compatible while constructing rules with high coverage. The advantage of this generalization is the reduction of the produced rules, which results in greater understandability of the output. During the process of pheromone update in the ordinary ACO algorithms, the amount of the sprayed pheromone is a function of the quality of rules. The objective of the second method is to strengthen the conflict between not-found, weak, good, and superior solutions. This method is a new strategy of pheromone update where ants with high-quality solutions are motivated through increasing the amount of pheromone sprayed on the trail that they have found; conversely, the ants that find weaker solutions are punished through eliminating pheromone from their trails. Results: The optimization of the initial algorithm using the two proposed methods produces a smaller number of rules, but increases the number of construction diagrams and prevents the production of low-quality rules.Conclusion: The results of tests performed on the dataset indicated the enhancement of algorithm efficiency in idioms of fewer tests, increased prediction accuracy of laws, and improved comprehensibility of the produced laws using the proposed methods.
Artificial Intelligence
K. Ali Mohsin Alhameedawi; R. Asgarnezhad
Abstract
Background and Objectives: Autism is the most well-known disease that occurs in any age people. There is an increasing concern in appealing machine learning techniques to diagnose these incurable conditions. But, the poor quality of most datasets contains the production of efficient models for the forecast ...
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Background and Objectives: Autism is the most well-known disease that occurs in any age people. There is an increasing concern in appealing machine learning techniques to diagnose these incurable conditions. But, the poor quality of most datasets contains the production of efficient models for the forecast of autism. The lack of suitable pre-processing methods outlines inaccurate and unstable results. For diagnosing the disease, the techniques handled to improve the classification performance yielded better results, and other computerized technologies were applied.Methods: An effective and high performance model was introduced to address pre-processing problems such as missing values and outliers. Several based classifiers applied on a well-known autism data set in the classification stage. Among many alternatives, we remarked that combine replacement with the mean and improvement selection with Random Forest and Decision Tree technologies provide our obtained highest results.Results: The best-obtained accuracy, precision, recall, and F-Measure values of the MVO-Autism suggested model were the same, and equal 100% outperforms their counterparts. Conclusion: The obtained results reveal that the suggested model can increase classification performance in terms of evaluation metrics. The results are evidence that the MVO-Autism model outperforms its counterparts. The reason is that this model overcomes both problems.
Artificial Intelligence
R. Mohammadi Farsani; E. Pazouki
Abstract
Background and Objectives: Many real-world problems are time series forecasting (TSF) problem. Therefore, providing more accurate and flexible forecasting methods have always been a matter of interest to researchers. An important issue in forecasting the time series is the predicated time interval.Methods: ...
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Background and Objectives: Many real-world problems are time series forecasting (TSF) problem. Therefore, providing more accurate and flexible forecasting methods have always been a matter of interest to researchers. An important issue in forecasting the time series is the predicated time interval.Methods: In this paper, a new method is proposed for time series forecasting that can make more accurate predictions at larger intervals than other existing methods. Neural networks are an effective tool for estimating time series due to their nonlinearity and their ability to be used for different time series without specific information of those. A variety of neural networks have been introduced so far, some of which have been used in forecasting time series. Encoder decoder Networks are an example of networks that can be used in time series forcasting. an encoder network encodes the input data based on a particular pattern and then a decoder network decodes the output based on the encoded input to produce the desired output. Since these networks have a better understanding of the context, they provide a better performance. An example of this type of network is transformer. A transformer neural network based on the self-attention is presented that has special capability in forecasting time series problems.Results: The proposed model has been evaluated through experimental results on two benchmark real-world TSF datasets from different domain. The experimental results states that, in terms of long-term estimation Up to eight times more resistant and in terms of estimation accuracy about 20 percent improvement, compare to other well-known methods, is obtained. Computational complexity has also been significantly reduced.Conclusion: The proposed tool could perform better or compete with other introduced methods with less computational complexity and longer estimation intervals. It was also found that with better configuration of the network and better adjustment of attention, it is possible to obtain more desirable results in any specific problem.
Artificial Intelligence
S. Tabatabaei; H. Nosrati Nahook
Abstract
Background and Objectives: With the recent progressions in wireless communication technology, powerful and costless wireless receivers are used in a variety of mobile applications. Mobile networks are a self-arranged network, which is including of mobile nodes that communicate with each other without ...
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Background and Objectives: With the recent progressions in wireless communication technology, powerful and costless wireless receivers are used in a variety of mobile applications. Mobile networks are a self-arranged network, which is including of mobile nodes that communicate with each other without a central control Mobile networks gained considerable attention due to the adaptability, scalability, and costs reduction. Routing and power consumption is a major problem in mobile networks because the network topology changes frequently. Mobile wireless networks suffer from high error rates, power constraints, and limited bandwidth. Due to the high importance of routing protocols in dynamic multi-hop networks, many researchers have paid attention to the routing problem in Mobile Ad hoc Networks (MANET). This paper proposes a new routing algorithm in MANETs which is based upon the Cuckoo optimization algorithm (COA).Methods: COA is inspired by the lifestyle of a family of birds called cuckoo. These birds’ lifestyle, egg-laying features, and breeding are the basis of the development of this optimization algorithm. COA is started by an initial population. There are two types of population of cuckoos in different societies: mature cuckoos and eggs. This algorithm tries to find more stable links for routing.Results: Simulation results prove the high performance of proposed work in terms of throughput, delay, hop count, and discovery time.Conclusion: The cuckoo search convergence is based on the establishment of the Markov chain model to prove that it satisfies the two conditions of the global convergence in a random search algorithm. Also, the cuckoo search that suitable for solving continuous problems and multi-objective problems. We have done a lot of experiments to verify the performance of the Cuckoo algorithm for routing in MANETs. The result of experiments shows the superiority of the proposed method against a well-known AODV algorithm.
