H.3. Artificial Intelligence
Naeimeh Mohammad Karimi; Mehdi Rezaeian
Abstract
In the era of massive data, analyzing bioinformatics fields and discovering its functions are very important. The rate of sequence generation using sequence generation techniques is increasing rapidly, and researchers are faced with many unknown functions. One of the essential operations in bioinformatics ...
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In the era of massive data, analyzing bioinformatics fields and discovering its functions are very important. The rate of sequence generation using sequence generation techniques is increasing rapidly, and researchers are faced with many unknown functions. One of the essential operations in bioinformatics is the classification of sequences to discover unknown proteins. There are two methods to classify sequences: the traditional method and the modern method. The conventional methods use sequence alignment, which has a high computational cost. In the contemporary method, feature extraction is used to classify proteins. In this regard, methods such as DeepFam have been presented. This research is an improvement of the DeepFam model, and the special focus is on extracting the appropriate features to differentiate the sequences of different categories. As the model improved, the features tended to be more generic. The grad-CAM method has been used to analyze the extracted features and interpret improved network layers. Then, we used the fitting vector from the transformer model to check the performance of Grad-CAM. The COG database, a massive database of protein sequences, was used to check the accuracy of the presented method. We have shown that by extracting more efficient features, the conserved regions in the sequences can be discovered more accurately, which helps to classify the proteins better. One of the critical advantages of the presented method is that by increasing the number of categories, the necessary flexibility is maintained, and the classification accuracy in three tests is higher than that of other methods.
H.3. Artificial Intelligence
Ali Nasr-Esfahani; Mehdi Bekrani; Roozbeh Rajabi
Abstract
Artificial intelligence (AI) has significantly advanced speech recognition applications. However, many existing neural network-based methods struggle with noise, reducing accuracy in real-world environments. This study addresses isolated spoken Persian digit recognition (zero to nine) under noisy conditions, ...
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Artificial intelligence (AI) has significantly advanced speech recognition applications. However, many existing neural network-based methods struggle with noise, reducing accuracy in real-world environments. This study addresses isolated spoken Persian digit recognition (zero to nine) under noisy conditions, particularly for phonetically similar numbers. A hybrid model combining residual convolutional neural networks and bidirectional gated recurrent units (BiGRU) is proposed, utilizing word units instead of phoneme units for speaker-independent recognition. The FARSDIGIT1 dataset, augmented with various approaches, is processed using Mel-Frequency Cepstral Coefficients (MFCC) for feature extraction. Experimental results demonstrate the model’s effectiveness, achieving 98.53%, 96.10%, and 95.92% accuracy on training, validation, and test sets, respectively. In noisy conditions, the proposed approach improves recognition by 26.88% over phoneme unit-based LSTM models and surpasses the Mel-scale Two Dimension Root Cepstrum Coefficients (MTDRCC) feature extraction technique along with MLP model (MTDRCC+MLP) by 7.61%.
H.3. Artificial Intelligence
Vahideh Monemizadeh; Kourosh Kiani
Abstract
Anomaly detection is becoming increasingly crucial across various fields, including cybersecurity, financial risk management, and health monitoring. However, it faces significant challenges when dealing with large-scale, high-dimensional, and unlabeled datasets. This study focuses on decision tree-based ...
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Anomaly detection is becoming increasingly crucial across various fields, including cybersecurity, financial risk management, and health monitoring. However, it faces significant challenges when dealing with large-scale, high-dimensional, and unlabeled datasets. This study focuses on decision tree-based methods for anomaly detection due to their scalability, interpretability, and effectiveness in managing high-dimensional data. Although Isolation Forest (iForest) and its extended variant, Extended Isolation Forest (EIF), are widely used, they exhibit limitations in identifying anomalies, particularly in handling normal data distributions and preventing the formation of ghost clusters. The Rotated Isolation Forest (RIF) was developed to address these challenges, enhancing the model's ability to discern true anomalies from normal variations by employing randomized rotations in feature space. Building on this approach, we proposed the Discrete Rotated Isolation Forest (DRIF) model, which integrates an Autoencoder for dimensionality reduction. Using a discrete probability distribution and an Autoencoder enhance computational efficiency. Experimental evaluations on synthetic and real-world datasets demonstrate that proposed model outperforms iForest, EIF, and RIF. And also achieving higher Receiver Operating Characteristic-Area Under the Curve (ROC-AUC) scores and significantly faster execution times. These findings establish the proposed model as a robust, scalable, and efficient approach for unsupervised anomaly detection in high-dimensional datasets.
H.3. Artificial Intelligence
Rasoul Hosseinzadeh; Mahdi Sadeghzadeh
Abstract
The attention mechanisms have significantly advanced the field of machine learning and deep learning across various domains, including natural language processing, computer vision, and multimodal systems. This paper presents a comprehensive survey of attention mechanisms in Transformer architectures, ...
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The attention mechanisms have significantly advanced the field of machine learning and deep learning across various domains, including natural language processing, computer vision, and multimodal systems. This paper presents a comprehensive survey of attention mechanisms in Transformer architectures, emphasizing their evolution, design variants, and domain-specific applications in NLP, computer vision, and multimodal learning. We categorize attention types by their goals like efficiency, scalability, and interpretability, and provide a comparative analysis of their strengths, limitations, and suitable use cases. This survey also addresses the lack of visual intuitions, offering a clearer taxonomy and discussion of hybrid approaches, such as sparse-hierarchical combinations. In addition to foundational mechanisms, we highlight hybrid approaches, theoretical underpinnings, and practical trade-offs. The paper identifies current challenges in computation, robustness, and transparency, offering a structured classification and proposing future directions. By comparing state-of-the-art techniques, this survey aims to guide researchers in selecting and designing attention mechanisms best suited for specific AI applications, ultimately fostering the development of more efficient, interpretable, and adaptable Transformer-based models.
H.3. Artificial Intelligence
Habib Khodadadi; Vali Derhami
Abstract
The exploration-exploitation trade-off poses a significant challenge in reinforcement learning. For this reason, action selection methods such as ε-greedy and Soft-Max approaches are used instead of the greedy method. These methods use random numbers to select an action that balances exploration ...
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The exploration-exploitation trade-off poses a significant challenge in reinforcement learning. For this reason, action selection methods such as ε-greedy and Soft-Max approaches are used instead of the greedy method. These methods use random numbers to select an action that balances exploration and exploitation. Chaos is commonly utilized across various scientific disciplines because of its features, including non-periodicity, unpredictability, ergodicity and pseudorandom behavior. In this paper, we employ numbers generated by different chaotic systems to select action and identify better maps in diverse states and quantities of actions. Based on our experiments on various environments such as the Multi-Armed Bandit (MAB), taxi-domain, and cliff-walking, we found that many of the chaotic methods increase the speed of learning and achieve higher rewards.
H.3. Artificial Intelligence
Thomas Njoroge Kinyanjui; Kelvin Mugoye; Rachael Kibuku
Abstract
This paper presents a Multi-Head Self-Attention Fusion Network (MHSA-FN) for real-time crop disease classification, addressing key limitations in existing models, including suboptimal feature extraction, inefficient feature recalibration, and weak multi-scale fusion. Unlike prior works that rely solely ...
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This paper presents a Multi-Head Self-Attention Fusion Network (MHSA-FN) for real-time crop disease classification, addressing key limitations in existing models, including suboptimal feature extraction, inefficient feature recalibration, and weak multi-scale fusion. Unlike prior works that rely solely on CNNs or transformers, MHSA-FN integrates MobileNetV2, EfficientNetV2, and Vision Transformers (ViTs) with a structured multi-level attention framework for enhanced feature learning. A gated fusion mechanism and a Multiscale Fusion Module (MSFM) optimize local texture details and global spatial relationships. The model was trained on a combined dataset of PlantVillage and locally collected images, improving adaptability to real-world conditions. It achieved 98.66% training accuracy and 99.0% test accuracy across 76 disease classes, with 99.34% precision, 99.01% recall, and 99.04% F1 score. McNemar’s test (p = 0.125) and Bayesian superiority probability (0.851) validated its robustness. Confidence variance analysis (0.000010) outperformed existing models, demonstrating MHSA-FN as a scalable, high-performance AI solution for precision agriculture in resource-constrained environments.
H.3. Artificial Intelligence
Monireh Azimi Hemat; Ezat Valipour; Laya Ali Ahmadipoor
Abstract
Visual features extracted from images in content-based image retrieval systems are inherently ambiguous. Consequently, applying fuzzy sets for image indexing in image retrieval systems has improved efficiency. In this article, the intuitionistic fuzzy sets are used to enhance the performance of the Fuzzy ...
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Visual features extracted from images in content-based image retrieval systems are inherently ambiguous. Consequently, applying fuzzy sets for image indexing in image retrieval systems has improved efficiency. In this article, the intuitionistic fuzzy sets are used to enhance the performance of the Fuzzy Content-Based Image Retrieval (F-CBIR) system. To this aim, an Intuitionistic Fuzzy Content-Based Image Retrieval (IF-CBIR) is proposed by applying intuitionistic fuzzy generators on fuzzy sets. Due to the diversity of the intuitionistic fuzzy distance measure, several are assessed in IF-CBIR; in these assessments, the measure with higher performance is identified. Finally, the proposed IF-CBIR and the existing crisp CBIR and F-CBIR simulate on Corel 5K and Corel 10K databases. The results show that our proposed method has higher (10-15%) precision compared to the mentioned methods.
H.3. Artificial Intelligence
Mohamad Mahdi Yadegar; Hossein Rahmani
Abstract
In recent years, new technologies have brought new innovations into the financial and commercial world, giving fraudsters many ways to commit fraud and cost companies big time. We can build systems that detect fraudulent patterns and prevent future incidents using advanced technologies. Machine learning ...
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In recent years, new technologies have brought new innovations into the financial and commercial world, giving fraudsters many ways to commit fraud and cost companies big time. We can build systems that detect fraudulent patterns and prevent future incidents using advanced technologies. Machine learning algorithms are being used more for fraud detection in financial data. But the common challenge is the imbalance of the dataset which hinders traditional machine learning methods. Finding the best approach towards these imbalance datasets is the problem many of the researchers are facing when trying to use machine learning methods. In this paper, we propose the method called FinFD-GCN that use Graph Convolutional Networks (GCNs) for fraud detection in credit card transaction datasets. FinFD-GCN represents transactions as graph in which each node represents a transaction and each edge represents similarity between transactions. By using this graph representation FinFD-GCN can capture complex relationships and anomalies that may have been overlooked by traditional methods or were even impossible to detect with conventional approaches, thus enhancing the accuracy and robustness of fraud detection in financial data. We use common evaluation metrics and confusion matrices to evaluate the proposed method. FinFD-GCN achieves significant improvements in recall and AUC compared to traditional methods such as logistic regression, support vector machines, and random forests, making it a robust solution for credit card fraud detection. By using the GCN model for fraud detection in this credit card dataset we outperformed base models 5% and 10%, with respect to F1 and AUC, respectively.
H.3. Artificial Intelligence
Pouria Rabiei; Nosratali Ashrafi-Payaman
Abstract
Today, the amount of data with graph structure has increased dramatically. Detecting structural anomalies in the graph, such as nodes and edges whose behavior deviates from the expected behavior of the network, is important in real-world applications. Thus, in our research work, we extract the structural ...
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Today, the amount of data with graph structure has increased dramatically. Detecting structural anomalies in the graph, such as nodes and edges whose behavior deviates from the expected behavior of the network, is important in real-world applications. Thus, in our research work, we extract the structural characteristics of the dynamic graph by using graph convolutional neural networks, then by using temporal neural network Like GRU, we extract the short-term temporalcharacteristics of the dynamic graph and by using the attention mechanism integrated with GRU, long-term temporal dependencies are considered. Finally, by using the neural network classifier, the abnormal edge is detected in each timestamp. Conducted experiments on the two datasets, UC Irvine messages and Digg with three baselines, including Goutlier, Netwalk and CMSketch illustrate our model outperform existing methods in a dynamic graph by 10 and 15% onaverage on the UCI and Digg datasets respectively. We also measured the model with AUC and confusion matrix for 1, 5, and 10 percent anomaly injection.
H.3. Artificial Intelligence
Fariba Taghinezhad; Mohammad Ghasemzadeh
Abstract
Artificial neural networks are among the most significant models in machine learning that use numeric inputs. This study presents a new single-layer perceptron model based on categorical inputs. In the proposed model, every quality value in the training dataset receives a trainable weight. Input data ...
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Artificial neural networks are among the most significant models in machine learning that use numeric inputs. This study presents a new single-layer perceptron model based on categorical inputs. In the proposed model, every quality value in the training dataset receives a trainable weight. Input data is classified by determining the weight vector that corresponds to the categorical values in it. To evaluate the performance of the proposed algorithm, we have used 10 datasets. We have compared the performance of the proposed method to that of other machine learning models, including neural networks, support vector machines, naïve Bayes classifiers, and random forests. According to the results, the proposed model resulted in a 36% reduction in memory usage when compared to baseline models across all datasets. Moreover, it demonstrated a training speed enhancement of 54.5% for datasets that contained more than 1000 samples. The accuracy of the proposed model is also comparable to other machine learning models.
H.3. Artificial Intelligence
Seyed Alireza Bashiri Mosavi; Omid Khalaf Beigi; Arash Mahjoubifard
Abstract
Using intelligent approaches in diagnosing the COVID-19 disease based on machine learning algorithms (MLAs), as a joint work, has attracted the attention of pattern recognition and medicine experts. Before applying MLAs to the data extracted from infectious diseases, techniques such as RAT and RT-qPCR ...
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Using intelligent approaches in diagnosing the COVID-19 disease based on machine learning algorithms (MLAs), as a joint work, has attracted the attention of pattern recognition and medicine experts. Before applying MLAs to the data extracted from infectious diseases, techniques such as RAT and RT-qPCR were used by data mining engineers to diagnose the contagious disease, whose weaknesses include the lack of test kits, the placement of the specialist and the patient pointed at a place and low accuracy. This study introduces a three-stage learning framework including a feature extractor by visual geometry group 16 (VGG16) model to solve the problems caused by the lack of samples, a three-channel convolution layer, and a classifier based on a three-layer neural network. The results showed that the Covid VGG16 (CoVGG16) has an accuracy of 96.37% and 100%, precision of 96.52% and 100%, and recall of 96.30% and 100% for COVID-19 prediction on the test sets of the two datasets (one type of CT-scan-based images and one type of X-ray-oriented ones gathered from Kaggle repositories).
H.3. Artificial Intelligence
Ali Rebwar Shabrandi; Ali Rajabzadeh Ghatari; Mohammad Dehghan nayeri; Nader Tavakoli; Sahar Mirzaei
Abstract
This study proposes a high-level design and configuration for an intelligent dual (hybrid and private) blockchain-based system. The configuration includes the type of network, level of decentralization, nodes, and roles, block structure information, authority control, and smart contracts and intended ...
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This study proposes a high-level design and configuration for an intelligent dual (hybrid and private) blockchain-based system. The configuration includes the type of network, level of decentralization, nodes, and roles, block structure information, authority control, and smart contracts and intended to address the two main categories of challenges–operation management and data management–through three intelligent modules across the pandemic stages. In the pre-hospital stage, an intelligent infection prediction system is proposed that utilizes in-house data to address the lack of a simple, efficient, agile, and low-cost screening method for identifying potentially infected individuals promptly and preventing the overload of patients entering hospitals. In the in-hospital stage, an intelligent prediction system is proposed to predict infection severity and hospital Length of Stay (LoS) to identify high-risk patients, prioritize them for receiving care services, and facilitate better resource allocation. In the post-hospital stage, an intelligent prediction system is proposed to predict the reinfection and readmission rates, to help reduce the burden on the healthcare system and provide personalized care and follow-up for higher-risk patients. In addition, the distribution of limited Personal protective equipment (PPE) is made fair using private blockchain (BC) and smart contracts. These modules were developed using Python and utilized to evaluate the performance of state-of-the-art machine learning (ML) techniques through 10-fold cross-validation at each stage. The most critical features were plotted and analyzed using SHapely Adaptive exPlanations (SHAP). Finally, we explored the implications of our system for both research and practice and provided recommendations for future enhancements.
H.3. Artificial Intelligence
Seyed Alireza Bashiri Mosavi; Mohsen Javaherian; Omid Khalaf Beigi
Abstract
One way of analyzing COVID-19 is to exploit X-ray and computed tomography (CT) images of the patients' chests. Employing data mining techniques on chest images can provide in significant improvements in the diagnosis of COVID-19. However, in feature space learning of chest images, there exists a large ...
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One way of analyzing COVID-19 is to exploit X-ray and computed tomography (CT) images of the patients' chests. Employing data mining techniques on chest images can provide in significant improvements in the diagnosis of COVID-19. However, in feature space learning of chest images, there exists a large number of features that affect COVID-19 identification performance negatively. In this work, we aim to design the dual hybrid partial-oriented feature selection scheme (DHPFSS) for selecting optimal features to achieve high-performance COVID-19 prediction. First, by applying the Zernike function to the data, moments of healthy chest images and infected ones were extracted. After Zernike moments (ZMs) segmentation, subsets of ZMs (SZMs1:n) are entered into the DHPFSS to select SZMs1:n-specific optimal ZMs (OZMs1:n). The DHPFSS consists of the filter phase and dual incremental wrapper mechanisms (IWMs), namely incremental wrapper subset selection (IWSS) and IWSS with replacement (IWSSr). Each IWM is fed by ZMs sorted by filter mechanism. The dual IWMs of DHPFSS are accompanied with the support vector machine (SVM) and twin SVM (TWSVM) classifiers equipped with radial basis function kernel as SVMIWSSTWSVM and SVMIWSSrTWSVM blocks. After selecting OZMs1:n, the efficacy of the union of OZMs1:n is evaluated based on the cross-validation technique. The obtained results manifested that the proposed framework has accuracies of 98.66%, 94.33%, and 94.82% for COVID-19 prediction on COVID-19 image data (CID) including 1CID, 2CID, and 3CID respectively, which can improve accurate diagnosis of illness in an emergency or the absence of a specialist.
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.
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.
H.3. Artificial Intelligence
Damianus Kofi Owusu; Christiana Cynthia Nyarko; Joseph Acquah; Joel Yarney
Abstract
Head and neck cancer (HNC) recurrence is ever increasing among Ghanaian men and women. Because not all machine learning classifiers are equally created, even if multiple of them suite very well for a given task, it may be very difficult to find one which performs optimally given different distributions. ...
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Head and neck cancer (HNC) recurrence is ever increasing among Ghanaian men and women. Because not all machine learning classifiers are equally created, even if multiple of them suite very well for a given task, it may be very difficult to find one which performs optimally given different distributions. The stacking learns how to best combine weak classifier models to form a strong model. As a prognostic model for classifying HNSCC recurrence patterns, this study tried to identify the best stacked ensemble classifier model when the same ML classifiers for feature selection and stacked ensemble learning are used. Four stacked ensemble models; in which first one used two base classifiers: gradient boosting machine (GBM) and distributed random forest (DRF); second one used three base classifiers: GBM, DRF, and deep neural network (DNN); third one used four base classifiers: GBM, DRF, DNN, and generalized linear model (GLM); and fourth one used five base classifiers: GBM, DRF, DNN, GLM, and Naïve bayes (NB) were developed, using GBM meta-classifier in each case. The results showed that implementing stacked ensemble technique consisting of five base classifiers on gradient boosted features achieved better performance than achieved on other feature subsets, and implementing this stacked ensemble technique on gradient boosted features achieved better performance compared to other stacked ensemble techniques implemented on gradient boosted features and other feature subsets used. Learning stacked ensemble technique having five base classifiers on GBM features is clinically appropriate as a prognostic model for classifying and predicting HNSCC patients’ recurrence data.
H.3. Artificial Intelligence
Afrooz Moradbeiky; Farzin Yaghmaee
Abstract
Knowledge graphs are widely used tools in the field of reasoning, where reasoning is facilitated through link prediction within the knowledge graph. However, traditional methods have limitations, such as high complexity or an inability to effectively capture the structural features of the graph. The ...
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Knowledge graphs are widely used tools in the field of reasoning, where reasoning is facilitated through link prediction within the knowledge graph. However, traditional methods have limitations, such as high complexity or an inability to effectively capture the structural features of the graph. The main challenge lies in simultaneously handling both the structural and similarity features of the graph. In this study, we employ a constraint satisfaction approach, where each proposed link must satisfy both structural and similarity constraints. For this purpose, each constraint is considered from a specific perspective, referred to as a view. Each view computes a probability score using a GRU-RNN, which satisfies its own predefined constraint. In the first constraint, the proposed node must have a probability of over 0.5 with frontier nodes. The second constraint computes the Bayesian graph, and the proposed node must have a link in the Bayesian graph. The last constraint requires that a proposed node must fall within an acceptable fault. This allows for N-N relationships to be accurately determined, while also addressing the limitations of embedding. The results of the experiments showed that the proposed method improved performance on two standard datasets.
H.3. Artificial Intelligence
Ali Zahmatkesh Zakariaee; Hossein Sadr; Mohamad Reza Yamaghani
Abstract
Machine learning (ML) is a popular tool in healthcare while it can help to analyze large amounts of patient data, such as medical records, predict diseases, and identify early signs of cancer. Gastric cancer starts in the cells lining the stomach and is known as the 5th most common cancer worldwide. ...
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Machine learning (ML) is a popular tool in healthcare while it can help to analyze large amounts of patient data, such as medical records, predict diseases, and identify early signs of cancer. Gastric cancer starts in the cells lining the stomach and is known as the 5th most common cancer worldwide. Therefore, predicting the survival of patients, checking their health status, and detecting their risk of gastric cancer in the early stages can be very beneficial. Surprisingly, with the help of machine learning methods, this can be possible without the need for any invasive methods which can be useful for both patients and physicians in making informed decisions. Accordingly, a new hybrid machine learning-based method for detecting the risk of gastric cancer is proposed in this paper. The proposed model is compared with traditional methods and based on the empirical results, not only the proposed method outperform existing methods with an accuracy of 98% but also gastric cancer can be one of the most important consequences of H. pylori infection. Additionally, it can be concluded that lifestyle and dietary factors can heighten the risk of gastric cancer, especially among individuals who frequently consume fried foods and suffer from chronic atrophic gastritis and stomach ulcers. This risk is further exacerbated in individuals with limited fruit and vegetable intake and high salt consumption.
H.3. Artificial Intelligence
Amir Mehrabinezhad; Mohammad Teshnelab; Arash Sharifi
Abstract
Due to the growing number of data-driven approaches, especially in artificial intelligence and machine learning, extracting appropriate information from the gathered data with the best performance is a remarkable challenge. The other important aspect of this issue is storage costs. The principal component ...
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Due to the growing number of data-driven approaches, especially in artificial intelligence and machine learning, extracting appropriate information from the gathered data with the best performance is a remarkable challenge. The other important aspect of this issue is storage costs. The principal component analysis (PCA) and autoencoders (AEs) are samples of the typical feature extraction methods in data science and machine learning that are widely used in various approaches. The current work integrates the advantages of AEs and PCA for presenting an online supervised feature extraction selection method. Accordingly, the desired labels for the final model are involved in the feature extraction procedure and embedded in the PCA method as well. Also, stacking the nonlinear autoencoder layers with the PCA algorithm eliminated the kernel selection of the traditional kernel PCA methods. Besides the performance improvement proved by the experimental results, the main advantage of the proposed method is that, in contrast with the traditional PCA approaches, the model has no requirement for all samples to feature extraction. As regards the previous works, the proposed method can outperform the other state-of-the-art ones in terms of accuracy and authenticity for feature extraction.
H.3. Artificial Intelligence
Zeinab Poshtiban; Elham Ghanbari; Mohammadreza Jahangir
Abstract
Analyzing the influence of people and nodes in social networks has attracted a lot of attention. Social networks gain meaning, despite the groups, associations, and people interested in a specific issue or topic, and people demonstrate their theoretical and practical tendencies in such places. Influential ...
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Analyzing the influence of people and nodes in social networks has attracted a lot of attention. Social networks gain meaning, despite the groups, associations, and people interested in a specific issue or topic, and people demonstrate their theoretical and practical tendencies in such places. Influential nodes are often identified based on the information related to the social network structure and less attention is paid to the information spread by the social network user. The present study aims to assess the structural information in the network to identify influential users in addition to using their information in the social network. To this aim, the user’s feelings were extracted. Then, an emotional or affective score was assigned to each user based on an emotional dictionary and his/her weight in the network was determined utilizing centrality criteria. Here, the Twitter network was applied. Thus, the structure of the social network was defined and its graph was drawn after collecting and processing the data. Then, the analysis capability of the network and existing data was extracted and identified based on the algorithm proposed by users and influential nodes. Based on the results, the nodes identified by the proposed algorithm are considered high-quality and the speed of information simulated is higher than other existing algorithms.
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.
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.
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.
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.3. Artificial Intelligence
Saheb Ghanbari Motlagh; Fateme Razi Astaraei; Mojtaba Hajihosseini; Saeed Madani
Abstract
This study explores the potential use of Machine Learning (ML) techniques to enhance three types of nano-based solar cells. Perovskites of methylammonium-free formamidinium (FA) and mixed cation-based cells exhibit a boosted efficiency when employing ML techniques. Moreover, ML methods are utilized to ...
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This study explores the potential use of Machine Learning (ML) techniques to enhance three types of nano-based solar cells. Perovskites of methylammonium-free formamidinium (FA) and mixed cation-based cells exhibit a boosted efficiency when employing ML techniques. Moreover, ML methods are utilized to identify optimal donor complexes, high blind temperature materials, and to advance the thermodynamic stability of perovskites. Another significant application of ML in dye-sensitized solar cells (DSSCs) is the detection of novel dyes, solvents, and molecules for improving the efficiency and performance of solar cells. Some of these materials have increased cell efficiency, short-circuit current, and light absorption by more than 20%. ML algorithms to fine-tune network and plasmonic field bandwidths improve the efficiency and light absorption of surface plasmonic resonance (SPR) solar cells. This study outlines the potential of ML techniques to optimize and improve the development of nano-based solar cells, leading to promising results for the field of solar energy generation and supporting the demand for sustainable and dependable energy.
