Pub Date : 2022-10-12DOI: 10.1109/AICT55583.2022.10013568
Adnan Yahya, Hala Salameh, Maram Belbeisi, Noor Shamasneh
Making information in electronic documents easily accessible has been a major concern over the past years. There has been increasing interest in gleaning information from unstructured text and presenting it as structured data using information extraction (IE). Since Arabic has seen major growth in web content, mainly unstructured text, the need for IE from Arabic documents has gained importance. The processing capacity needed for IE far exceeds human ability to extract knowledge manually. The medical field is one such area, where awareness of health issues makes the task of automating medical informatics crucial for better access to medical knowledge. Thus, work on extracting information from medical documents has increased rapidly. In this paper we address the issue of IE from Arabic drug leaflets. We use a combination of rule-based, machine learning and deep learning methods and employ a suit of tools that account for the particularities of Arabic to extract information from Arabic drug package inserts to make this information available in structured form and thus better accessible to regular users and health care providers. A prototype system that utilizes the IE results was developed with useful functionality such as alerting to possible Adverse Drug Reactions (ADR) and finding drug alternatives.
{"title":"Information Extraction from Arabic Medications Leaflets","authors":"Adnan Yahya, Hala Salameh, Maram Belbeisi, Noor Shamasneh","doi":"10.1109/AICT55583.2022.10013568","DOIUrl":"https://doi.org/10.1109/AICT55583.2022.10013568","url":null,"abstract":"Making information in electronic documents easily accessible has been a major concern over the past years. There has been increasing interest in gleaning information from unstructured text and presenting it as structured data using information extraction (IE). Since Arabic has seen major growth in web content, mainly unstructured text, the need for IE from Arabic documents has gained importance. The processing capacity needed for IE far exceeds human ability to extract knowledge manually. The medical field is one such area, where awareness of health issues makes the task of automating medical informatics crucial for better access to medical knowledge. Thus, work on extracting information from medical documents has increased rapidly. In this paper we address the issue of IE from Arabic drug leaflets. We use a combination of rule-based, machine learning and deep learning methods and employ a suit of tools that account for the particularities of Arabic to extract information from Arabic drug package inserts to make this information available in structured form and thus better accessible to regular users and health care providers. A prototype system that utilizes the IE results was developed with useful functionality such as alerting to possible Adverse Drug Reactions (ADR) and finding drug alternatives.","PeriodicalId":441475,"journal":{"name":"2022 IEEE 16th International Conference on Application of Information and Communication Technologies (AICT)","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124599108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-12DOI: 10.1109/AICT55583.2022.10013579
S. Aliyeva, Ali Parsayan
This paper aims to provide a system ensuring turnstile access based on facial recognition and vaccine passport verification in order to enable touch-free entrance to buildings, universities, offices, etc. The algorithm of the proposed method is comprised of two essential parts: YOLO algorithm for face detection and CNN for face recognition. After successful user authentication, there are two important criteria that should be met for granting access to the person: Person should not be an active COVID-19 patient and Person should have a valid vaccine passport. The proposed method results 95.57% accuracy rate for face detection with YOLO algorithm and 70% for face recognition with CNN.
{"title":"Turnstile Access based on Facial Recognition and Vaccine Passport Verification","authors":"S. Aliyeva, Ali Parsayan","doi":"10.1109/AICT55583.2022.10013579","DOIUrl":"https://doi.org/10.1109/AICT55583.2022.10013579","url":null,"abstract":"This paper aims to provide a system ensuring turnstile access based on facial recognition and vaccine passport verification in order to enable touch-free entrance to buildings, universities, offices, etc. The algorithm of the proposed method is comprised of two essential parts: YOLO algorithm for face detection and CNN for face recognition. After successful user authentication, there are two important criteria that should be met for granting access to the person: Person should not be an active COVID-19 patient and Person should have a valid vaccine passport. The proposed method results 95.57% accuracy rate for face detection with YOLO algorithm and 70% for face recognition with CNN.","PeriodicalId":441475,"journal":{"name":"2022 IEEE 16th International Conference on Application of Information and Communication Technologies (AICT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129702287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-12DOI: 10.1109/AICT55583.2022.10013604
Lei Zhang, Zhuolin Yang, Kedar K. Aras, Igor R. Efimov, G. Adam
The Hilbert transform is widely used in biomedical signal processing and requires efficient implementation. We propose the implementation of the discrete Hilbert transform based on emerging memristor devices. It uses two matrix multiplication layers using weights programmed in the memristor array and a linear Hadamard product calculation layer mappable to CMOS. The functionality was tested on a dataset of optical cardiac signals from the human heart. The results show negligible <1% angle error between the proposed implementation and the MATLAB function. It also has robustness to non-idealities. This proposed solution can be applied to bio-signal processing at the edge.
{"title":"Discrete Hilbert Transform via Memristor Crossbars for Compact Biosignal Processing","authors":"Lei Zhang, Zhuolin Yang, Kedar K. Aras, Igor R. Efimov, G. Adam","doi":"10.1109/AICT55583.2022.10013604","DOIUrl":"https://doi.org/10.1109/AICT55583.2022.10013604","url":null,"abstract":"The Hilbert transform is widely used in biomedical signal processing and requires efficient implementation. We propose the implementation of the discrete Hilbert transform based on emerging memristor devices. It uses two matrix multiplication layers using weights programmed in the memristor array and a linear Hadamard product calculation layer mappable to CMOS. The functionality was tested on a dataset of optical cardiac signals from the human heart. The results show negligible <1% angle error between the proposed implementation and the MATLAB function. It also has robustness to non-idealities. This proposed solution can be applied to bio-signal processing at the edge.","PeriodicalId":441475,"journal":{"name":"2022 IEEE 16th International Conference on Application of Information and Communication Technologies (AICT)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130070229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-12DOI: 10.1109/AICT55583.2022.10013587
M. Rakhimov, Ravshanjon Akhmadjonov, Shahzod Javliev
Artificial intelligence (AI) systems in medicine are one of the most important modern trends in global healthcare. Artificial intelligence technologies are fundamentally changing the global healthcare system, making it possible to radically rebuild the system of medical diagnostics while reducing healthcare costs. AI is actively used in research to develop methods for diagnosing coronary heart disease (CHD). There are different types of CHD. Before treating a disease, it is necessary to determine which class of diseases it belongs to. Based on the feature space of the disease, it is possible to classify the type of CHD. Machine learning algorithms can solve this problem. The k-nearest neighbors (KNN) algorithm is a simple, easy-to-implement supervised machine learning algorithm that can be used to solve classification problems. The dataset is the more important part of the supervised machine learning algorithm for training. Gathering data is the most important step in solving any supervised machine learning problem. But choosing more important part from the collected data is one of the tasks to be solved. The main purpose of this study is to select more useful parametric attributes from the dataset to obtain a high F1-score of CHD classification.
{"title":"Artificial Intelligence in Medicine for Chronic Disease Classification Using Machine Learning","authors":"M. Rakhimov, Ravshanjon Akhmadjonov, Shahzod Javliev","doi":"10.1109/AICT55583.2022.10013587","DOIUrl":"https://doi.org/10.1109/AICT55583.2022.10013587","url":null,"abstract":"Artificial intelligence (AI) systems in medicine are one of the most important modern trends in global healthcare. Artificial intelligence technologies are fundamentally changing the global healthcare system, making it possible to radically rebuild the system of medical diagnostics while reducing healthcare costs. AI is actively used in research to develop methods for diagnosing coronary heart disease (CHD). There are different types of CHD. Before treating a disease, it is necessary to determine which class of diseases it belongs to. Based on the feature space of the disease, it is possible to classify the type of CHD. Machine learning algorithms can solve this problem. The k-nearest neighbors (KNN) algorithm is a simple, easy-to-implement supervised machine learning algorithm that can be used to solve classification problems. The dataset is the more important part of the supervised machine learning algorithm for training. Gathering data is the most important step in solving any supervised machine learning problem. But choosing more important part from the collected data is one of the tasks to be solved. The main purpose of this study is to select more useful parametric attributes from the dataset to obtain a high F1-score of CHD classification.","PeriodicalId":441475,"journal":{"name":"2022 IEEE 16th International Conference on Application of Information and Communication Technologies (AICT)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126785610","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-12DOI: 10.1109/AICT55583.2022.10013494
Aleksandr M. Bulavchuk, D. Semenova
The paper considers a fuzzy statement of the investment project scheduling problem. Project resources are limited and presented in cash. The fuzzy net present value constitutes the optimality criterion for the problem. The GASPIA algorithm proposed by the authors and modified for the fuzzy case has been used to solve the problem. The fitness function and the rules of crossover have undergone changes. During computational experiments, solutions have been found for various parameters of the L-transform of fuzzy numbers. The stability conditions for the obtained solution are determined.
{"title":"Genetic Algorithm for RCPSP With Fuzzy NPV","authors":"Aleksandr M. Bulavchuk, D. Semenova","doi":"10.1109/AICT55583.2022.10013494","DOIUrl":"https://doi.org/10.1109/AICT55583.2022.10013494","url":null,"abstract":"The paper considers a fuzzy statement of the investment project scheduling problem. Project resources are limited and presented in cash. The fuzzy net present value constitutes the optimality criterion for the problem. The GASPIA algorithm proposed by the authors and modified for the fuzzy case has been used to solve the problem. The fitness function and the rules of crossover have undergone changes. During computational experiments, solutions have been found for various parameters of the L-transform of fuzzy numbers. The stability conditions for the obtained solution are determined.","PeriodicalId":441475,"journal":{"name":"2022 IEEE 16th International Conference on Application of Information and Communication Technologies (AICT)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125271227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-12DOI: 10.1109/AICT55583.2022.10013593
R. Shikhaliyev
Computer networks are getting more complex these days. A computer network failure can result in the loss of important data, disruption of network services and applications, and economic loss and threaten national security. Therefore, it is crucial to detect failures on time and diagnose their root cause, which is possible with the help of proactive computer network monitoring. The paper proposes a conceptual model of a system for proactive computer network monitoring. Proactive monitoring is based on predicting the network behavior. To achieve high prediction accuracy, we propose to use a homogeneous ensemble, which consists of a single base learning algorithm. Base learning LSTM models for an ensemble of deep neural networks were created using the bagging algorithm. We use the CICIDS2017 intrusion detection evaluation dataset to evaluate the proposed approach. Experimental results show that our method is an effective approach to improving the accuracy of anomaly prediction in computer networks.
{"title":"Proactive Computer Network Monitoring based on Homogeneous LSTM Ensemble","authors":"R. Shikhaliyev","doi":"10.1109/AICT55583.2022.10013593","DOIUrl":"https://doi.org/10.1109/AICT55583.2022.10013593","url":null,"abstract":"Computer networks are getting more complex these days. A computer network failure can result in the loss of important data, disruption of network services and applications, and economic loss and threaten national security. Therefore, it is crucial to detect failures on time and diagnose their root cause, which is possible with the help of proactive computer network monitoring. The paper proposes a conceptual model of a system for proactive computer network monitoring. Proactive monitoring is based on predicting the network behavior. To achieve high prediction accuracy, we propose to use a homogeneous ensemble, which consists of a single base learning algorithm. Base learning LSTM models for an ensemble of deep neural networks were created using the bagging algorithm. We use the CICIDS2017 intrusion detection evaluation dataset to evaluate the proposed approach. Experimental results show that our method is an effective approach to improving the accuracy of anomaly prediction in computer networks.","PeriodicalId":441475,"journal":{"name":"2022 IEEE 16th International Conference on Application of Information and Communication Technologies (AICT)","volume":"141 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127554392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-12DOI: 10.1109/AICT55583.2022.10013549
Luigi Pugliese, Massimo Violante, Sara Groppo
One of the most important causes of death while driving is sleepiness. To solve this problem, different kinds of technologies are needed. A recent work presented an approach based on Photoplethysmogram (PPG) analysis to predict the sleep onset. As PPG is not always available, especially in the case of commercial of the shelf wearable devices that provide features such as heart beat and respiration rate, in the paper we present a novel approach to predict sleep onset, which leverages a virtual sensor able to provide an estimation of the PPG-related Heart Rate Variability (HRV) through Respiration Rate (RR) analysis. The experimental results show 100% sensitivity and specificity in the collected data.
{"title":"Real-time sleep prediction using a virtual sensor to estimate Heart Rate Variability through Respiratory Rate","authors":"Luigi Pugliese, Massimo Violante, Sara Groppo","doi":"10.1109/AICT55583.2022.10013549","DOIUrl":"https://doi.org/10.1109/AICT55583.2022.10013549","url":null,"abstract":"One of the most important causes of death while driving is sleepiness. To solve this problem, different kinds of technologies are needed. A recent work presented an approach based on Photoplethysmogram (PPG) analysis to predict the sleep onset. As PPG is not always available, especially in the case of commercial of the shelf wearable devices that provide features such as heart beat and respiration rate, in the paper we present a novel approach to predict sleep onset, which leverages a virtual sensor able to provide an estimation of the PPG-related Heart Rate Variability (HRV) through Respiration Rate (RR) analysis. The experimental results show 100% sensitivity and specificity in the collected data.","PeriodicalId":441475,"journal":{"name":"2022 IEEE 16th International Conference on Application of Information and Communication Technologies (AICT)","volume":"174 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122999311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-12DOI: 10.1109/AICT55583.2022.10013595
Z. Guliyev, Ali Parsayan
Reinforcement learning (RL) has been proven to be a feasible method for learning complicated actions autonomously from sensory observations. Even though many of the deep RL studies have been centered on modelled control and computer games, which has nothing to do with the limits of learning in actual surroundings, deep RL has also revealed its potential in allowing robots to acquire complicated abilities in the real-world situations. Real-world robotics, on the other hand, is an intriguing area for testing the algorithms of this kind, because it is directly related to the learning procedure of humans. Deep RL might enable developing movement abilities without a precise modelling of the robot dynamics and with minimum engineering. However, because of hyper-parameter sensitivity and low sampling capability, it is difficult to implement deep RL to robotic tasks involving real-world applications. It is comparable simple to tune hyper-parameters in simulations, while it can be a challenging task when it comes to physical world, for example, biped robots. Acquiring the ability to move and perceive in the actual world involves a variety of difficulties, some are simpler to handle than others that are frequently overlooked in RL studies which are limited to simulated environments. This paper provides approaches to deal with a variety of frequent difficulties in deep RL arising while training a biped robot to walk and follow a specific path.
{"title":"Reinforcement Learning Based Robot Control","authors":"Z. Guliyev, Ali Parsayan","doi":"10.1109/AICT55583.2022.10013595","DOIUrl":"https://doi.org/10.1109/AICT55583.2022.10013595","url":null,"abstract":"Reinforcement learning (RL) has been proven to be a feasible method for learning complicated actions autonomously from sensory observations. Even though many of the deep RL studies have been centered on modelled control and computer games, which has nothing to do with the limits of learning in actual surroundings, deep RL has also revealed its potential in allowing robots to acquire complicated abilities in the real-world situations. Real-world robotics, on the other hand, is an intriguing area for testing the algorithms of this kind, because it is directly related to the learning procedure of humans. Deep RL might enable developing movement abilities without a precise modelling of the robot dynamics and with minimum engineering. However, because of hyper-parameter sensitivity and low sampling capability, it is difficult to implement deep RL to robotic tasks involving real-world applications. It is comparable simple to tune hyper-parameters in simulations, while it can be a challenging task when it comes to physical world, for example, biped robots. Acquiring the ability to move and perceive in the actual world involves a variety of difficulties, some are simpler to handle than others that are frequently overlooked in RL studies which are limited to simulated environments. This paper provides approaches to deal with a variety of frequent difficulties in deep RL arising while training a biped robot to walk and follow a specific path.","PeriodicalId":441475,"journal":{"name":"2022 IEEE 16th International Conference on Application of Information and Communication Technologies (AICT)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121948303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-12DOI: 10.1109/AICT55583.2022.10013610
Timofei I. Tislenko, D. Semenova, Nataly A. Sergeeva, E. Goldenok, Nadezhda V. Kononova
The article examines a mathematical model of the selecting phases process of traffic light facilities of the road network section. A Markov decision process with a finite number of actions and states is used as a mathematical model, and the minimization problem is reduced to the Multiagent Reinforcement Learning for Integrated Network (MARLIN) problem. A Q-learning algorithm was implemented and a series of computational experiments were conducted in the Anylogic simulation system for a real section of the Krasnoyarsk road network to study the model.
{"title":"Multiagent Reinforcement Learning for Integrated Network: Applying to a Part of the Road Network of Krasnoyarsk City","authors":"Timofei I. Tislenko, D. Semenova, Nataly A. Sergeeva, E. Goldenok, Nadezhda V. Kononova","doi":"10.1109/AICT55583.2022.10013610","DOIUrl":"https://doi.org/10.1109/AICT55583.2022.10013610","url":null,"abstract":"The article examines a mathematical model of the selecting phases process of traffic light facilities of the road network section. A Markov decision process with a finite number of actions and states is used as a mathematical model, and the minimization problem is reduced to the Multiagent Reinforcement Learning for Integrated Network (MARLIN) problem. A Q-learning algorithm was implemented and a series of computational experiments were conducted in the Anylogic simulation system for a real section of the Krasnoyarsk road network to study the model.","PeriodicalId":441475,"journal":{"name":"2022 IEEE 16th International Conference on Application of Information and Communication Technologies (AICT)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131387996","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-12DOI: 10.1109/AICT55583.2022.10013645
A. Adamov, Gozel Khasanova
Thanks to Web, ubiquitous digital technologies and the increasing usage of digital environment by humans for work, entertainment, education and other activities, huge amounts of textual data is generated and available online. Text is the most informative and at the same time most sophisticated data type in terms of its comprehension by machines. The Text Analytics is a field that involves number of computer science disciplines to process textual data and transforms it into computer readable format suitable for another field of study Natural Language Processing to extract meaning.This research paper is an attempt to apply broad variety of statistical analysis methods to the corpora of several Turkic languages using Universal Declaration of Human Rights as a Corpus. Quantitative Text Analysis as a research area is focused on understanding the human language through statistics and numbers. As the language is the most effective tool to describe the social world, the Quantitative Text Analysis enables social exploration of the rial world at the scale.
{"title":"Quantitative and Semantic Analysis of Texts in Turkic Languages using Universal Declaration of Human Rights (UDHR) as a Corpus","authors":"A. Adamov, Gozel Khasanova","doi":"10.1109/AICT55583.2022.10013645","DOIUrl":"https://doi.org/10.1109/AICT55583.2022.10013645","url":null,"abstract":"Thanks to Web, ubiquitous digital technologies and the increasing usage of digital environment by humans for work, entertainment, education and other activities, huge amounts of textual data is generated and available online. Text is the most informative and at the same time most sophisticated data type in terms of its comprehension by machines. The Text Analytics is a field that involves number of computer science disciplines to process textual data and transforms it into computer readable format suitable for another field of study Natural Language Processing to extract meaning.This research paper is an attempt to apply broad variety of statistical analysis methods to the corpora of several Turkic languages using Universal Declaration of Human Rights as a Corpus. Quantitative Text Analysis as a research area is focused on understanding the human language through statistics and numbers. As the language is the most effective tool to describe the social world, the Quantitative Text Analysis enables social exploration of the rial world at the scale.","PeriodicalId":441475,"journal":{"name":"2022 IEEE 16th International Conference on Application of Information and Communication Technologies (AICT)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124297190","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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