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
monireh azimi hemat; Ezat Valipour; laya aliahmadipoor
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
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.
H.3. Artificial Intelligence
Amirhossein Khabbaz; Mansoor Fateh; Ali Pouyan; Mohsen Rezvani
Abstract
Autism spectrum disorder (ASD) is a collection of inconstant characteristics. Anomalies in reciprocal social communications and disabilities in perceiving communication patterns characterize These features. Also, exclusive repeated interests and actions identify ASD. Computer games have affirmative effects ...
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Autism spectrum disorder (ASD) is a collection of inconstant characteristics. Anomalies in reciprocal social communications and disabilities in perceiving communication patterns characterize These features. Also, exclusive repeated interests and actions identify ASD. Computer games have affirmative effects on autistic children. Serious games have been widely used to elevate the ability to communicate with other individuals in these children. In this paper, we propose an adaptive serious game to rate the social skills of autistic children. The proposed serious game employs a reinforcement learning mechanism to learn such ratings adaptively for the players. It uses fuzzy logic to estimate the communication skills of autistic children. The game adapts itself to the level of the child with autism. For that matter, it uses an intelligent agent to tune the challenges through playtime. To dynamically evaluate the communication skills of these children, the game challenges may grow harder based on the development of a child's skills through playtime. We also employ fuzzy logic to estimate the playing abilities of the player periodically. Fifteen autistic children participated in experiments to evaluate the presented serious game. The experimental results show that the proposed method is effective in the communication skill of autistic children.
H.3. Artificial Intelligence
Hassan Haji Mohammadi; Alireza Talebpour; Ahamd Mahmoudi Aznaveh; Samaneh Yazdani
Abstract
Coreference resolution is one of the essential tasks of natural languageprocessing. This task identifies all in-text expressions that refer to thesame entity in the real world. Coreference resolution is used in otherfields of natural language processing, such as information extraction,machine translation, ...
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Coreference resolution is one of the essential tasks of natural languageprocessing. This task identifies all in-text expressions that refer to thesame entity in the real world. Coreference resolution is used in otherfields of natural language processing, such as information extraction,machine translation, and question-answering.This article presents a new coreference resolution corpus in Persiannamed Mehr corpus. The article's primary goal is to develop a Persiancoreference corpus that resolves some of the previous Persian corpus'sshortcomings while maintaining a high inter-annotator agreement. Thiscorpus annotates coreference relations for noun phrases, namedentities, pronouns, and nested named entities. Two baseline pronounresolution systems are developed, and the results are reported. Thecorpus size includes 400 documents and about 170k tokens. Corpusannotation is done by WebAnno preprocessing tool.
H.3. Artificial Intelligence
Saiful Bukhori; Muhammad Almas Bariiqy; Windi Eka Y. R; Januar Adi Putra
Abstract
Breast cancer is a disease of abnormal cell proliferation in the breast tissue organs. One method for diagnosing and screening breast cancer is mammography. However, the results of this mammography image have limitations because it has low contrast and high noise and contrast as non-coherence. This research ...
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Breast cancer is a disease of abnormal cell proliferation in the breast tissue organs. One method for diagnosing and screening breast cancer is mammography. However, the results of this mammography image have limitations because it has low contrast and high noise and contrast as non-coherence. This research segmented breast cancer images derived from Ultrasonography (USG) photo using a Convolutional Neural Network (CNN) using the U-Net architecture. Testing on the CNN model with the U-Net architecture results the highest Mean Intersection over Union (Mean IoU) value in the data scenario with a ratio of 70:30, 100 epochs, and a learning rate of 5x10-5, which is 77%, while the lowest Mean IoU in the data scenario with a ratio 90:10, 50 epochs, and a learning rate of 1x10-4 learning rate, which is 64.4%.
H.3. Artificial Intelligence
Akram Pasandideh; Mohsen Jahanshahi
Abstract
Link prediction (LP) has become a hot topic in the data mining, machine learning, and deep learning community. This study aims to implement bibliometric analysis to find the current status of the LP studies and investigate it from different perspectives. The present study provides a Scopus-based bibliometric ...
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Link prediction (LP) has become a hot topic in the data mining, machine learning, and deep learning community. This study aims to implement bibliometric analysis to find the current status of the LP studies and investigate it from different perspectives. The present study provides a Scopus-based bibliometric overview of the LP studies landscape since 1987 when LP studies were published for the first time. Various kinds of analysis, including document, subject, and country distribution are applied. Moreover, author productivity, citation analysis, and keyword analysis is used, and Bradford’s law is applied to discover the main journals in this field. Most documents were published by conferences in the field. The majority of LP documents have been published in the computer science and mathematics fields. So far, China has been at the forefront of publishing countries. In addition, the most active sources of LP publications are lecture notes in Computer Science, including subseries lecture notes in Artificial Intelligence (AI) and lecture notes in Bioinformatics, and IEEE Access. The keyword analysis demonstrates that while social networks had attracted attention in the early period, knowledge graphs have attracted more attention, recently. Since the LP problem has been approached recently using machine learning (ML), the current study may inform researchers to concentrate on ML techniques. This is the first bibliometric study of “link prediction” literature and provides a broad landscape of the field.
H.3. Artificial Intelligence
Mohammad Hossein Shayesteh; Behrooz Shahrokhzadeh; Behrooz Masoumi
Abstract
This paper provides a comprehensive review of the potential of game theory as a solution for sensor-based human activity recognition (HAR) challenges. Game theory is a mathematical framework that models interactions between multiple entities in various fields, including economics, political science, ...
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This paper provides a comprehensive review of the potential of game theory as a solution for sensor-based human activity recognition (HAR) challenges. Game theory is a mathematical framework that models interactions between multiple entities in various fields, including economics, political science, and computer science. In recent years, game theory has been increasingly applied to machine learning challenges, including HAR, as a potential solution to improve recognition performance and efficiency of recognition algorithms. The review covers the shared challenges between HAR and machine learning, compares previous work on traditional approaches to HAR, and discusses the potential advantages of using game theory. It discusses different game theory approaches, including non-cooperative and cooperative games, and provides insights into how they can improve the HAR systems. The authors propose new game theory-based approaches and evaluate their effectiveness compared to traditional approaches. Overall, this review paper contributes to expanding the scope of research in HAR by introducing game-theoretic concepts and solutions to the field and provides valuable insights for researchers interested in applying game-theoretic approaches to HAR.
H.3. Artificial Intelligence
M. Taghian; A. Asadi; R. Safabakhsh
Abstract
The quality of the extracted features from a long-term sequence of raw prices of the instruments greatly affects the performance of the trading rules learned by machine learning models. Employing a neural encoder-decoder structure to extract informative features from complex input time-series has proved ...
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The quality of the extracted features from a long-term sequence of raw prices of the instruments greatly affects the performance of the trading rules learned by machine learning models. Employing a neural encoder-decoder structure to extract informative features from complex input time-series has proved very effective in other popular tasks like neural machine translation and video captioning. In this paper, a novel end-to-end model based on the neural encoder-decoder framework combined with deep reinforcement learning is proposed to learn single instrument trading strategies from a long sequence of raw prices of the instrument. In addition, the effects of different structures for the encoder and various forms of the input sequences on the performance of the learned strategies are investigated. Experimental results showed that the proposed model outperforms other state-of-the-art models in highly dynamic environments.
