Text mining, which derives information from written sources such as websites, books, e-mails, articles, and online news, processes and structures data using advanced approaches. The vast majority of SMS (Short Message Service) messages are unwanted short text documents. Effectively classifying these documents will aid in the detection of spam. The study attempted to identify the most effective techniques on SMS data at each stage of text mining. Four of the most well-known feature selection approaches were used, each of which is one of these parameters. As a result, the strategy that yielded the best results was chosen. In addition, another parameter that produces the best results with this approach, the classifier, was determined. The DFS feature selection approach produced the best results with the SVM classifier, according to the experimental results. This study establishes a general framework for future research in this area that will employ text mining techniques.
{"title":"Classification of Unwanted SMS Data (Spam) with Text Mining Techniques","authors":"Rasim Çekik","doi":"10.55195/jscai.1210559","DOIUrl":"https://doi.org/10.55195/jscai.1210559","url":null,"abstract":"Text mining, which derives information from written sources such as websites, books, e-mails, articles, and online news, processes and structures data using advanced approaches. The vast majority of SMS (Short Message Service) messages are unwanted short text documents. Effectively classifying these documents will aid in the detection of spam. The study attempted to identify the most effective techniques on SMS data at each stage of text mining. Four of the most well-known feature selection approaches were used, each of which is one of these parameters. As a result, the strategy that yielded the best results was chosen. In addition, another parameter that produces the best results with this approach, the classifier, was determined. The DFS feature selection approach produced the best results with the SVM classifier, according to the experimental results. This study establishes a general framework for future research in this area that will employ text mining techniques.","PeriodicalId":48494,"journal":{"name":"Journal of Artificial Intelligence and Soft Computing Research","volume":"12 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2022-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85606725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-28DOI: 10.2478/jaiscr-2023-0004
Dinesh Kumar, Dharmendra Sharma
Abstract Introducing variation in the training dataset through data augmentation has been a popular technique to make Convolutional Neural Networks (CNNs) spatially invariant but leads to increased dataset volume and computation cost. Instead of data augmentation, augmentation of feature maps is proposed to introduce variations in the features extracted by a CNN. To achieve this, a rotation transformer layer called Rotation Invariance Transformer (RiT) is developed, which applies rotation transformation to augment CNN features. The RiT layer can be used to augment output features from any convolution layer within a CNN. However, its maximum effectiveness is shown when placed at the output end of final convolution layer. We test RiT in the application of scale-invariance where we attempt to classify scaled images from benchmark datasets. Our results show promising improvements in the networks ability to be scale invariant whilst keeping the model computation cost low.
{"title":"Feature Map Augmentation to Improve Scale Invariance in Convolutional Neural Networks","authors":"Dinesh Kumar, Dharmendra Sharma","doi":"10.2478/jaiscr-2023-0004","DOIUrl":"https://doi.org/10.2478/jaiscr-2023-0004","url":null,"abstract":"Abstract Introducing variation in the training dataset through data augmentation has been a popular technique to make Convolutional Neural Networks (CNNs) spatially invariant but leads to increased dataset volume and computation cost. Instead of data augmentation, augmentation of feature maps is proposed to introduce variations in the features extracted by a CNN. To achieve this, a rotation transformer layer called Rotation Invariance Transformer (RiT) is developed, which applies rotation transformation to augment CNN features. The RiT layer can be used to augment output features from any convolution layer within a CNN. However, its maximum effectiveness is shown when placed at the output end of final convolution layer. We test RiT in the application of scale-invariance where we attempt to classify scaled images from benchmark datasets. Our results show promising improvements in the networks ability to be scale invariant whilst keeping the model computation cost low.","PeriodicalId":48494,"journal":{"name":"Journal of Artificial Intelligence and Soft Computing Research","volume":"13 1","pages":"51 - 74"},"PeriodicalIF":2.8,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45000719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-28DOI: 10.2478/jaiscr-2023-0003
Mehdi Hosseinzadeh Aghdam
Abstract Nowadays, textual information grows exponentially on the Internet. Text summarization (TS) plays a crucial role in the massive amount of textual content. Manual TS is time-consuming and impractical in some applications with a huge amount of textual information. Automatic text summarization (ATS) is an essential technology to overcome mentioned challenges. Non-negative matrix factorization (NMF) is a useful tool for extracting semantic contents from textual data. Existing NMF approaches only focus on how factorized matrices should be modeled, and neglect the relationships among sentences. These relationships provide better factorization for TS. This paper suggests a novel non-negative matrix factorization for text summarization (NMFTS). The proposed ATS model puts regularizes on pairwise sentences vectors. A new cost function based on the Frobenius norm is designed, and an algorithm is developed to minimize this function by proposing iterative updating rules. The proposed NMFTS extracts semantic content by reducing the size of documents and mapping the same sentences closely together in the latent topic space. Compared with the basic NMF, the convergence time of the proposed method does not grow. The convergence proof of the NMFTS and empirical results on the benchmark data sets show that the suggested updating rules converge fast and achieve superior results compared to other methods.
{"title":"Automatic Extractive and Generic Document Summarization Based on NMF","authors":"Mehdi Hosseinzadeh Aghdam","doi":"10.2478/jaiscr-2023-0003","DOIUrl":"https://doi.org/10.2478/jaiscr-2023-0003","url":null,"abstract":"Abstract Nowadays, textual information grows exponentially on the Internet. Text summarization (TS) plays a crucial role in the massive amount of textual content. Manual TS is time-consuming and impractical in some applications with a huge amount of textual information. Automatic text summarization (ATS) is an essential technology to overcome mentioned challenges. Non-negative matrix factorization (NMF) is a useful tool for extracting semantic contents from textual data. Existing NMF approaches only focus on how factorized matrices should be modeled, and neglect the relationships among sentences. These relationships provide better factorization for TS. This paper suggests a novel non-negative matrix factorization for text summarization (NMFTS). The proposed ATS model puts regularizes on pairwise sentences vectors. A new cost function based on the Frobenius norm is designed, and an algorithm is developed to minimize this function by proposing iterative updating rules. The proposed NMFTS extracts semantic content by reducing the size of documents and mapping the same sentences closely together in the latent topic space. Compared with the basic NMF, the convergence time of the proposed method does not grow. The convergence proof of the NMFTS and empirical results on the benchmark data sets show that the suggested updating rules converge fast and achieve superior results compared to other methods.","PeriodicalId":48494,"journal":{"name":"Journal of Artificial Intelligence and Soft Computing Research","volume":"13 1","pages":"37 - 49"},"PeriodicalIF":2.8,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48314411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-28DOI: 10.2478/jaiscr-2023-0001
C. Park
Abstract Outlier detection aims to find a data sample that is significantly different from other data samples. Various outlier detection methods have been proposed and have been shown to be able to detect anomalies in many practical problems. However, in high dimensional data, conventional outlier detection methods often behave unexpectedly due to a phenomenon called the curse of dimensionality. In this paper, we compare and analyze outlier detection performance in various experimental settings, focusing on text data with dimensions typically in the tens of thousands. Experimental setups were simulated to compare the performance of outlier detection methods in unsupervised versus semi-supervised mode and uni-modal versus multi-modal data distributions. The performance of outlier detection methods based on dimension reduction is compared, and a discussion on using k-NN distance in high dimensional data is also provided. Analysis through experimental comparison in various environments can provide insights into the application of outlier detection methods in high dimensional data.
{"title":"A Comparative Study for Outlier Detection Methods in High Dimensional Text Data","authors":"C. Park","doi":"10.2478/jaiscr-2023-0001","DOIUrl":"https://doi.org/10.2478/jaiscr-2023-0001","url":null,"abstract":"Abstract Outlier detection aims to find a data sample that is significantly different from other data samples. Various outlier detection methods have been proposed and have been shown to be able to detect anomalies in many practical problems. However, in high dimensional data, conventional outlier detection methods often behave unexpectedly due to a phenomenon called the curse of dimensionality. In this paper, we compare and analyze outlier detection performance in various experimental settings, focusing on text data with dimensions typically in the tens of thousands. Experimental setups were simulated to compare the performance of outlier detection methods in unsupervised versus semi-supervised mode and uni-modal versus multi-modal data distributions. The performance of outlier detection methods based on dimension reduction is compared, and a discussion on using k-NN distance in high dimensional data is also provided. Analysis through experimental comparison in various environments can provide insights into the application of outlier detection methods in high dimensional data.","PeriodicalId":48494,"journal":{"name":"Journal of Artificial Intelligence and Soft Computing Research","volume":"13 1","pages":"5 - 17"},"PeriodicalIF":2.8,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44468491","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-28DOI: 10.2478/jaiscr-2023-0002
I. Laktionov, O. Vovna, M. Kabanets
Abstract Nowadays, applied computer-oriented and information digitalization technologies are developing very dynamically and are widely used in various industries. One of the highest priority sectors of the economy of Ukraine and other countries around the world, the needs of which require intensive implementation of high-performance information technologies, is agriculture. The purpose of the article is to synthesise scientific and practical provisions to improve the information technology of the comprehensive monitoring and control of microclimate in industrial greenhouses. The object of research is non-stationary processes of aggregation and transformation of measurement data on soil and climatic conditions of the greenhouse microclimate. The subject of research is methods and models of computer-oriented analysis of measurement data on the soil and climatic state of the greenhouse microclimate. The main scientific and practical effect of the article is the development of the theory of intelligent information technologies for monitoring and control of greenhouse microclimate through the development of methods and models of distributed aggregation and intellectualised transformation of measurement data based on fuzzy logic.
{"title":"Information Technology for Comprehensive Monitoring and Control of the Microclimate in Industrial Greenhouses Based on Fuzzy Logic","authors":"I. Laktionov, O. Vovna, M. Kabanets","doi":"10.2478/jaiscr-2023-0002","DOIUrl":"https://doi.org/10.2478/jaiscr-2023-0002","url":null,"abstract":"Abstract Nowadays, applied computer-oriented and information digitalization technologies are developing very dynamically and are widely used in various industries. One of the highest priority sectors of the economy of Ukraine and other countries around the world, the needs of which require intensive implementation of high-performance information technologies, is agriculture. The purpose of the article is to synthesise scientific and practical provisions to improve the information technology of the comprehensive monitoring and control of microclimate in industrial greenhouses. The object of research is non-stationary processes of aggregation and transformation of measurement data on soil and climatic conditions of the greenhouse microclimate. The subject of research is methods and models of computer-oriented analysis of measurement data on the soil and climatic state of the greenhouse microclimate. The main scientific and practical effect of the article is the development of the theory of intelligent information technologies for monitoring and control of greenhouse microclimate through the development of methods and models of distributed aggregation and intellectualised transformation of measurement data based on fuzzy logic.","PeriodicalId":48494,"journal":{"name":"Journal of Artificial Intelligence and Soft Computing Research","volume":"13 1","pages":"19 - 35"},"PeriodicalIF":2.8,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49011467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-28DOI: 10.2478/jaiscr-2023-0005
D. Lewy, J. Mańdziuk
Abstract Real life applications of deep learning (DL) are often limited by the lack of expert labeled data required to effectively train DL models. Creation of such data usually requires substantial amount of time for manual categorization, which is costly and is considered to be one of the major impediments in development of DL methods in many areas. This work proposes a classification approach which completely removes the need for costly expert labeled data and utilizes noisy web data created by the users who are not subject matter experts. The experiments are performed with two well-known Convolutional Neural Network (CNN) architectures: VGG16 and ResNet50 trained on three randomly collected Instagram-based sets of images from three distinct domains: metropolitan cities, popular food and common objects - the last two sets were compiled by the authors and made freely available to the research community. The dataset containing common objects is a webly counterpart of PascalVOC2007 set. It is demonstrated that despite significant amount of label noise in the training data, application of proposed approach paired with standard training CNN protocol leads to high classification accuracy on representative data in all three above-mentioned domains. Additionally, two straightforward procedures of automatic cleaning of the data, before its use in the training process, are proposed. Apparently, data cleaning does not lead to improvement of results which suggests that the presence of noise in webly data is actually helpful in learning meaningful and robust class representations. Manual inspection of a subset of web-based test data shows that labels assigned to many images are ambiguous even for humans. It is our conclusion that for the datasets and CNN architectures used in this paper, in case of training with webly data, a major factor contributing to the final classification accuracy is representativeness of test data rather than application of data cleaning procedures.
{"title":"Training CNN Classifiers Solely on Webly Data","authors":"D. Lewy, J. Mańdziuk","doi":"10.2478/jaiscr-2023-0005","DOIUrl":"https://doi.org/10.2478/jaiscr-2023-0005","url":null,"abstract":"Abstract Real life applications of deep learning (DL) are often limited by the lack of expert labeled data required to effectively train DL models. Creation of such data usually requires substantial amount of time for manual categorization, which is costly and is considered to be one of the major impediments in development of DL methods in many areas. This work proposes a classification approach which completely removes the need for costly expert labeled data and utilizes noisy web data created by the users who are not subject matter experts. The experiments are performed with two well-known Convolutional Neural Network (CNN) architectures: VGG16 and ResNet50 trained on three randomly collected Instagram-based sets of images from three distinct domains: metropolitan cities, popular food and common objects - the last two sets were compiled by the authors and made freely available to the research community. The dataset containing common objects is a webly counterpart of PascalVOC2007 set. It is demonstrated that despite significant amount of label noise in the training data, application of proposed approach paired with standard training CNN protocol leads to high classification accuracy on representative data in all three above-mentioned domains. Additionally, two straightforward procedures of automatic cleaning of the data, before its use in the training process, are proposed. Apparently, data cleaning does not lead to improvement of results which suggests that the presence of noise in webly data is actually helpful in learning meaningful and robust class representations. Manual inspection of a subset of web-based test data shows that labels assigned to many images are ambiguous even for humans. It is our conclusion that for the datasets and CNN architectures used in this paper, in case of training with webly data, a major factor contributing to the final classification accuracy is representativeness of test data rather than application of data cleaning procedures.","PeriodicalId":48494,"journal":{"name":"Journal of Artificial Intelligence and Soft Computing Research","volume":"13 1","pages":"75 - 92"},"PeriodicalIF":2.8,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48816169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-01DOI: 10.2478/jaiscr-2022-0017
Marcin Gabryel, Dawid Lada, Z. Filutowicz, Zofia Patora-Wysocka, Marek Kisiel-Dorohinicki, Guangxing Chen
Abstract This paper presents a neural network model for identifying non-human traffic to a web-site, which is significantly different from visits made by regular users. Such visits are undesirable from the point of view of the website owner as they are not human activity, and therefore do not bring any value, and, what is more, most often involve costs incurred in connection with the handling of advertising. They are made most often by dishonest publishers using special software (bots) to generate profits. Bots are also used in scraping, which is automatic scanning and downloading of website content, which actually is not in the interest of website authors. The model proposed in this work is learnt by data extracted directly from the web browser during website visits. This data is acquired by using a specially prepared JavaScript that monitors the behavior of the user or bot. The appearance of a bot on a website generates parameter values that are significantly different from those collected during typical visits made by human website users. It is not possible to learn more about the software controlling the bots and to know all the data generated by them. Therefore, this paper proposes a variational autoencoder (VAE) neural network model with modifications to detect the occurrence of abnormal parameter values that deviate from data obtained from human users’ Internet traffic. The algorithm works on the basis of a popular autoencoder method for detecting anomalies, however, a number of original improvements have been implemented. In the study we used authentic data extracted from several large online stores.
{"title":"Detecting Anomalies in Advertising Web Traffic with the Use of the Variational Autoencoder","authors":"Marcin Gabryel, Dawid Lada, Z. Filutowicz, Zofia Patora-Wysocka, Marek Kisiel-Dorohinicki, Guangxing Chen","doi":"10.2478/jaiscr-2022-0017","DOIUrl":"https://doi.org/10.2478/jaiscr-2022-0017","url":null,"abstract":"Abstract This paper presents a neural network model for identifying non-human traffic to a web-site, which is significantly different from visits made by regular users. Such visits are undesirable from the point of view of the website owner as they are not human activity, and therefore do not bring any value, and, what is more, most often involve costs incurred in connection with the handling of advertising. They are made most often by dishonest publishers using special software (bots) to generate profits. Bots are also used in scraping, which is automatic scanning and downloading of website content, which actually is not in the interest of website authors. The model proposed in this work is learnt by data extracted directly from the web browser during website visits. This data is acquired by using a specially prepared JavaScript that monitors the behavior of the user or bot. The appearance of a bot on a website generates parameter values that are significantly different from those collected during typical visits made by human website users. It is not possible to learn more about the software controlling the bots and to know all the data generated by them. Therefore, this paper proposes a variational autoencoder (VAE) neural network model with modifications to detect the occurrence of abnormal parameter values that deviate from data obtained from human users’ Internet traffic. The algorithm works on the basis of a popular autoencoder method for detecting anomalies, however, a number of original improvements have been implemented. In the study we used authentic data extracted from several large online stores.","PeriodicalId":48494,"journal":{"name":"Journal of Artificial Intelligence and Soft Computing Research","volume":"12 1","pages":"255 - 256"},"PeriodicalIF":2.8,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42517276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-01DOI: 10.2478/jaiscr-2022-0016
Krystian Łapa, K. Cpałka, Marek Kisiel-Dorohinicki, J. Paszkowski, Maciej Dębski, Van-Hung Le
Abstract Population Based Algorithms (PBAs) are excellent search tools that allow searching space of parameters defined by problems under consideration. They are especially useful when it is difficult to define a differentiable evaluation criterion. This applies, for example, to problems that are a combination of continuous and discrete (combinatorial) problems. In such problems, it is often necessary to select a certain structure of the solution (e.g. a neural network or other systems with a structure usually selected by the trial and error method) and to determine the parameters of such structure. As PBAs have great application possibilities, the aim is to develop more and more effective search formulas used in them. An interesting approach is to use multiple populations and process them with separate PBAs (in a different way). In this paper, we propose a new multi-population-based algorithm with: (a) subpopulation evaluation and (b) replacement of the associated PBAs subpopulation formulas used for their processing. In the simulations, we used a set of typical CEC2013 benchmark functions. The obtained results confirm the validity of the proposed concept.
{"title":"Multi-Population-Based Algorithm with an Exchange of Training Plans Based on Population Evaluation","authors":"Krystian Łapa, K. Cpałka, Marek Kisiel-Dorohinicki, J. Paszkowski, Maciej Dębski, Van-Hung Le","doi":"10.2478/jaiscr-2022-0016","DOIUrl":"https://doi.org/10.2478/jaiscr-2022-0016","url":null,"abstract":"Abstract Population Based Algorithms (PBAs) are excellent search tools that allow searching space of parameters defined by problems under consideration. They are especially useful when it is difficult to define a differentiable evaluation criterion. This applies, for example, to problems that are a combination of continuous and discrete (combinatorial) problems. In such problems, it is often necessary to select a certain structure of the solution (e.g. a neural network or other systems with a structure usually selected by the trial and error method) and to determine the parameters of such structure. As PBAs have great application possibilities, the aim is to develop more and more effective search formulas used in them. An interesting approach is to use multiple populations and process them with separate PBAs (in a different way). In this paper, we propose a new multi-population-based algorithm with: (a) subpopulation evaluation and (b) replacement of the associated PBAs subpopulation formulas used for their processing. In the simulations, we used a set of typical CEC2013 benchmark functions. The obtained results confirm the validity of the proposed concept.","PeriodicalId":48494,"journal":{"name":"Journal of Artificial Intelligence and Soft Computing Research","volume":"12 1","pages":"239 - 253"},"PeriodicalIF":2.8,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47137157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-01DOI: 10.2478/jaiscr-2022-0020
Rafał Grycuk, R. Scherer, A. Marchlewska, Christian Napoli
Abstract We propose a method for content-based retrieving solar magnetograms. We use the SDO Helioseismic and Magnetic Imager output collected with SunPy PyTorch libraries. We create a mathematical representation of the magnetic field regions of the Sun in the form of a vector. Thanks to this solution we can compare short vectors instead of comparing full-disk images. In order to decrease the retrieval time, we used a fully-connected autoencoder, which reduced the 256-element descriptor to a 32-element semantic hash. The performed experiments and comparisons proved the efficiency of the proposed approach. Our approach has the highest precision value in comparison with other state-of-the-art methods. The presented method can be used not only for solar image retrieval but also for classification tasks.
{"title":"Semantic Hashing for Fast Solar Magnetogram Retrieval","authors":"Rafał Grycuk, R. Scherer, A. Marchlewska, Christian Napoli","doi":"10.2478/jaiscr-2022-0020","DOIUrl":"https://doi.org/10.2478/jaiscr-2022-0020","url":null,"abstract":"Abstract We propose a method for content-based retrieving solar magnetograms. We use the SDO Helioseismic and Magnetic Imager output collected with SunPy PyTorch libraries. We create a mathematical representation of the magnetic field regions of the Sun in the form of a vector. Thanks to this solution we can compare short vectors instead of comparing full-disk images. In order to decrease the retrieval time, we used a fully-connected autoencoder, which reduced the 256-element descriptor to a 32-element semantic hash. The performed experiments and comparisons proved the efficiency of the proposed approach. Our approach has the highest precision value in comparison with other state-of-the-art methods. The presented method can be used not only for solar image retrieval but also for classification tasks.","PeriodicalId":48494,"journal":{"name":"Journal of Artificial Intelligence and Soft Computing Research","volume":"12 1","pages":"299 - 306"},"PeriodicalIF":2.8,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41625672","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-01DOI: 10.2478/jaiscr-2022-0019
Hung-Cuong Nguyen, Thi-Hao Nguyen, Jakub Nowak, A. Byrski, A. Siwocha, Van-Hung Le
Abstract Two-dimensional human pose estimation has been widely applied in real-world applications such as sports analysis, medical fall detection, human-robot interaction, with many positive results obtained utilizing Convolutional Neural Networks (CNNs). Li et al. at CVPR 2020 proposed a study in which they achieved high accuracy in estimating 2D keypoints estimation/2D human pose estimation. However, the study performed estimation only on the cropped human image data. In this research, we propose a method for automatically detecting and estimating human poses in photos using a combination of YOLOv5 + CC (Contextual Constraints) and HRNet. Our approach inherits the speed of the YOLOv5 for detecting humans and the efficiency of the HRNet for estimating 2D keypoints/2D human pose on the images. We also performed human marking on the images by bounding boxes of the Human 3.6M dataset (Protocol #1) for human detection evaluation. Our approach obtained high detection results in the image and the processing time is 55 FPS on the Human 3.6M dataset (Protocol #1). The mean error distance is 5.14 pixels on the full size of the image (1000 × 1002). In particular, the average results of 2D human pose estimation/2D keypoints estimation are 94.8% of PCK and 99.2% of PDJ@0.4 (head joint). The results are available.
{"title":"Combined YOLOv5 and HRNet for High Accuracy 2D Keypoint and Human Pose Estimation","authors":"Hung-Cuong Nguyen, Thi-Hao Nguyen, Jakub Nowak, A. Byrski, A. Siwocha, Van-Hung Le","doi":"10.2478/jaiscr-2022-0019","DOIUrl":"https://doi.org/10.2478/jaiscr-2022-0019","url":null,"abstract":"Abstract Two-dimensional human pose estimation has been widely applied in real-world applications such as sports analysis, medical fall detection, human-robot interaction, with many positive results obtained utilizing Convolutional Neural Networks (CNNs). Li et al. at CVPR 2020 proposed a study in which they achieved high accuracy in estimating 2D keypoints estimation/2D human pose estimation. However, the study performed estimation only on the cropped human image data. In this research, we propose a method for automatically detecting and estimating human poses in photos using a combination of YOLOv5 + CC (Contextual Constraints) and HRNet. Our approach inherits the speed of the YOLOv5 for detecting humans and the efficiency of the HRNet for estimating 2D keypoints/2D human pose on the images. We also performed human marking on the images by bounding boxes of the Human 3.6M dataset (Protocol #1) for human detection evaluation. Our approach obtained high detection results in the image and the processing time is 55 FPS on the Human 3.6M dataset (Protocol #1). The mean error distance is 5.14 pixels on the full size of the image (1000 × 1002). In particular, the average results of 2D human pose estimation/2D keypoints estimation are 94.8% of PCK and 99.2% of PDJ@0.4 (head joint). The results are available.","PeriodicalId":48494,"journal":{"name":"Journal of Artificial Intelligence and Soft Computing Research","volume":"12 1","pages":"281 - 298"},"PeriodicalIF":2.8,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44147032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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