Pub Date : 2022-10-12DOI: 10.1109/AICT55583.2022.10013528
Ankit Gupta, N. Basit
Although millions of patients have diabetes, it is often challenging to interpret symptoms that historically lead to the condition. To solve this disparity, we created an end-to-end platform that uses a Random Forest model that predicts early-stage diabetes with 95.6% accuracy, then visualizes patient data for those with similar symptoms. After users enter their data for the five most strongly-correlated diabetes symptoms, the model predicts whether the user has diabetes. As a result, this project transforms how patients communicate about their own data, thereby serving as a mechanism to start important conversations with their doctors or others around the world.
{"title":"Empowering Diabetes Patients by Providing Machine Learning-Driven Predictions and Personalized Visualization Results","authors":"Ankit Gupta, N. Basit","doi":"10.1109/AICT55583.2022.10013528","DOIUrl":"https://doi.org/10.1109/AICT55583.2022.10013528","url":null,"abstract":"Although millions of patients have diabetes, it is often challenging to interpret symptoms that historically lead to the condition. To solve this disparity, we created an end-to-end platform that uses a Random Forest model that predicts early-stage diabetes with 95.6% accuracy, then visualizes patient data for those with similar symptoms. After users enter their data for the five most strongly-correlated diabetes symptoms, the model predicts whether the user has diabetes. As a result, this project transforms how patients communicate about their own data, thereby serving as a mechanism to start important conversations with their doctors or others around the world.","PeriodicalId":441475,"journal":{"name":"2022 IEEE 16th International Conference on Application of Information and Communication Technologies (AICT)","volume":"115 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":"132308099","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/AICT55583.2022.10013602
Anastasya A. Gusarova, D. Semenova, G. N. Chernov, E. Goldenok, N. Lukyanova, Nataly V. Mishina
The heart rate variability analysis is carried out using mathematical methods in the time domain, frequency domain and nonlinear methods. The electrocardiographic records in normal and cardiac pathology from the open research resource PhysioNet were materials of the study. A database of the results of the various patient groups analysis was formed. A comparative analysis of the indicators revealed statistically significant differences in most variability indicators between normal rhythm patient groups. patient groups with class I CHF and patient groups with II, III CHF classes. The LASSO method revealed the main, most significant indicators can be used to fully characterize of the rhythm variability, as well as the possible detection its normal or pathology. Based on these indicators, patient clustering was carried out in order to distinguish two groups: the normal and the cardiac pathology, while the quality of the clustering was assessed by the external metric (the Rand index).
{"title":"Analysis of Normal and Pathological Heart Rate Variability Based on Electrocardiogram Data","authors":"Anastasya A. Gusarova, D. Semenova, G. N. Chernov, E. Goldenok, N. Lukyanova, Nataly V. Mishina","doi":"10.1109/AICT55583.2022.10013602","DOIUrl":"https://doi.org/10.1109/AICT55583.2022.10013602","url":null,"abstract":"The heart rate variability analysis is carried out using mathematical methods in the time domain, frequency domain and nonlinear methods. The electrocardiographic records in normal and cardiac pathology from the open research resource PhysioNet were materials of the study. A database of the results of the various patient groups analysis was formed. A comparative analysis of the indicators revealed statistically significant differences in most variability indicators between normal rhythm patient groups. patient groups with class I CHF and patient groups with II, III CHF classes. The LASSO method revealed the main, most significant indicators can be used to fully characterize of the rhythm variability, as well as the possible detection its normal or pathology. Based on these indicators, patient clustering was carried out in order to distinguish two groups: the normal and the cardiac pathology, while the quality of the clustering was assessed by the external metric (the Rand index).","PeriodicalId":441475,"journal":{"name":"2022 IEEE 16th International Conference on Application of Information and Communication Technologies (AICT)","volume":"22 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":"127198878","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/AICT55583.2022.10013544
G. Muradova, Mehran Hematyar, Jala Jamalova
According to diagnostic criteria, a patient can find clinics he needs depending on the symptoms of disease. The paper shows an effective solution that allows rapid access to information about clinic using Redis in memory database technology. In this paper using Redis help us to collect a wide array of geospatial capabilities finding the best way and build out this type of functionality. Redis has the capability to store by Redis intelligent optimized systems in their native format, and update and serve them with minimal computing infrastructure needed to implement these algorithms at scale. Also, our approach is to study the effect of the databases on system’s working speed, comparing Redis and MS SQL.
{"title":"Advantages of Redis in-memory database to efficiently search for healthcare medical supplies using geospatial data","authors":"G. Muradova, Mehran Hematyar, Jala Jamalova","doi":"10.1109/AICT55583.2022.10013544","DOIUrl":"https://doi.org/10.1109/AICT55583.2022.10013544","url":null,"abstract":"According to diagnostic criteria, a patient can find clinics he needs depending on the symptoms of disease. The paper shows an effective solution that allows rapid access to information about clinic using Redis in memory database technology. In this paper using Redis help us to collect a wide array of geospatial capabilities finding the best way and build out this type of functionality. Redis has the capability to store by Redis intelligent optimized systems in their native format, and update and serve them with minimal computing infrastructure needed to implement these algorithms at scale. Also, our approach is to study the effect of the databases on system’s working speed, comparing Redis and MS SQL.","PeriodicalId":441475,"journal":{"name":"2022 IEEE 16th International Conference on Application of Information and Communication Technologies (AICT)","volume":"2 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":"131814048","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/AICT55583.2022.10013522
G. Tatar, S. Bayar, I. Çiçek
Finite impulse response (FIR) filters are widely used in electronic design applications such as digital signal processing, image processing and digital communications. The demand for high performance is increasing particularly in modern real-time signal processing applications. Due to the trade-offs between the performance requirements and design constraints, it is required to develop new design approaches that not only improve the computational efficiency, but also support processors with application-specific hardware accelerators. In this study, design of a low-pass FIR filter operating at 10 MSps sampling rate with 2Mhz cutoff frequency and -40dB/decade attenuation rate is considered as a sample problem, and its performance and cost have been comparatively examined on various hardware platforms. We tested the performance of the designed filter by implementing it on a plain ARM-based processor, FPGA+ARM based System-on-Chip, and an Intel i7-based processor. As a result of the study, we observed that while the filter design implemented on the FPGA+ARM-based SoC works 8.86 times faster than the implemented on a solo ARM-based processor, 1.98 times slower than the implementation on the Intel i7-based processor. In addition, we have determined that the FIR filter design implemented on the FPGA+ARM based SoC exhibits the highest efficiency from the price/performance perspective.
有限脉冲响应(FIR)滤波器广泛应用于数字信号处理、图像处理和数字通信等电子设计领域。特别是在现代实时信号处理应用中，对高性能的要求越来越高。由于性能需求和设计约束之间的权衡，需要开发新的设计方法，不仅要提高计算效率，还要支持具有特定于应用程序的硬件加速器的处理器。本研究将设计一个采样率为10 MSps、截止频率为2Mhz、衰减率为-40dB/decade的低通FIR滤波器作为采样问题，并在各种硬件平台上对其性能和成本进行了比较检验。我们通过在基于ARM的普通处理器、基于FPGA+ARM的片上系统和基于Intel i7的处理器上实现所设计的滤波器来测试其性能。研究结果表明，在FPGA+ arm SoC上实现的滤波器设计比在单独的arm处理器上实现的滤波器设计快8.86倍，比在基于Intel i7的处理器上实现的滤波器设计慢1.98倍。此外，我们已经确定，从价格/性能的角度来看，在基于FPGA+ARM的SoC上实现的FIR滤波器设计具有最高的效率。
{"title":"Hardware Acceleration of FIR Filter Implementation on ZYNQ SoC","authors":"G. Tatar, S. Bayar, I. Çiçek","doi":"10.1109/AICT55583.2022.10013522","DOIUrl":"https://doi.org/10.1109/AICT55583.2022.10013522","url":null,"abstract":"Finite impulse response (FIR) filters are widely used in electronic design applications such as digital signal processing, image processing and digital communications. The demand for high performance is increasing particularly in modern real-time signal processing applications. Due to the trade-offs between the performance requirements and design constraints, it is required to develop new design approaches that not only improve the computational efficiency, but also support processors with application-specific hardware accelerators. In this study, design of a low-pass FIR filter operating at 10 MSps sampling rate with 2Mhz cutoff frequency and -40dB/decade attenuation rate is considered as a sample problem, and its performance and cost have been comparatively examined on various hardware platforms. We tested the performance of the designed filter by implementing it on a plain ARM-based processor, FPGA+ARM based System-on-Chip, and an Intel i7-based processor. As a result of the study, we observed that while the filter design implemented on the FPGA+ARM-based SoC works 8.86 times faster than the implemented on a solo ARM-based processor, 1.98 times slower than the implementation on the Intel i7-based processor. In addition, we have determined that the FIR filter design implemented on the FPGA+ARM based SoC exhibits the highest efficiency from the price/performance perspective.","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":"124632662","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/aict55583.2022.10013499
{"title":"AICT 2022 Panel Discussion","authors":"","doi":"10.1109/aict55583.2022.10013499","DOIUrl":"https://doi.org/10.1109/aict55583.2022.10013499","url":null,"abstract":"","PeriodicalId":441475,"journal":{"name":"2022 IEEE 16th International Conference on Application of Information and Communication Technologies (AICT)","volume":"345 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":"122321963","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/AICT55583.2022.10013506
Cheikh B A
We enter bigdata domain when we face data that are so much large that they cannot fit in one machine, or the processing cannot fit in one machine RAM, or would last. We focus our study on large NFAs. We describe, implement and compare two solutions of NFA determinization. These novel solutions are based on two different distributed and parallel programing paradigms, namely MapReduce and BSP/Pregel. Running examples are provided with details on execution. This contribution belongs to the first stages of our main target consisting of a language of a high level for big and distributed graph programming.
{"title":"A Comparative Study of Large Automata Distributed Processing","authors":"Cheikh B A","doi":"10.1109/AICT55583.2022.10013506","DOIUrl":"https://doi.org/10.1109/AICT55583.2022.10013506","url":null,"abstract":"We enter bigdata domain when we face data that are so much large that they cannot fit in one machine, or the processing cannot fit in one machine RAM, or would last. We focus our study on large NFAs. We describe, implement and compare two solutions of NFA determinization. These novel solutions are based on two different distributed and parallel programing paradigms, namely MapReduce and BSP/Pregel. Running examples are provided with details on execution. This contribution belongs to the first stages of our main target consisting of a language of a high level for big and distributed graph programming.","PeriodicalId":441475,"journal":{"name":"2022 IEEE 16th International Conference on Application of Information and Communication Technologies (AICT)","volume":"21 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":"121067556","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/AICT55583.2022.10013594
Lala Shahbandayeva, Ulviyya Mammadzada, Ilaha Manafova, Sevinj Jafarli, A. Adamov
Traditional intrusion detection systems may effectively detect known attacks and intrusions with predefined signatures. This requires training the systems to detect various versions of the same attack patterns and constantly keep updated databases of known attack signatures. However, as the skills of security researchers and practitioners expand, so do those of attackers. In order to detect attack types that are unknown, undefined, or designed to bypass the signature and pattern-based intrusion detection systems, the need for more intelligent systems arises. Machine learning is widely used in such systems for this purpose. While researchers and security professionals have designed approaches to this problem using various types of machine learning, our hybrid approach attempts to provide a novel way to effectively detect attacks. This is done by using a set of supervised learning algorithms to detect known attacks and unsupervised learning to detect unknown and zero-day attacks. By utilizing the CSE-CIC-IDS 2018 dataset, we have trained our classifiers to detect benign traffic and 14 known attacks with a selection of 23 features. The network traffic flows that are not classified with a specific level of certainty are sent to the clustering phase to be detected as benign or malicious traffic. Our results indicate that the three classification algorithms used, K-Nearest Neighbors, Random Forest, and Artificial Neural Networks, are able to successfully classify the known attacks with F1-scores between 0.93 and 0.969, and the clustering algorithm HDBSCAN is able to successfully cluster unclassified benign and malicious traffic with unknown labels with F1-scores between 0.85 and 0.957.
{"title":"Network Intrusion Detection using Supervised and Unsupervised Machine Learning","authors":"Lala Shahbandayeva, Ulviyya Mammadzada, Ilaha Manafova, Sevinj Jafarli, A. Adamov","doi":"10.1109/AICT55583.2022.10013594","DOIUrl":"https://doi.org/10.1109/AICT55583.2022.10013594","url":null,"abstract":"Traditional intrusion detection systems may effectively detect known attacks and intrusions with predefined signatures. This requires training the systems to detect various versions of the same attack patterns and constantly keep updated databases of known attack signatures. However, as the skills of security researchers and practitioners expand, so do those of attackers. In order to detect attack types that are unknown, undefined, or designed to bypass the signature and pattern-based intrusion detection systems, the need for more intelligent systems arises. Machine learning is widely used in such systems for this purpose. While researchers and security professionals have designed approaches to this problem using various types of machine learning, our hybrid approach attempts to provide a novel way to effectively detect attacks. This is done by using a set of supervised learning algorithms to detect known attacks and unsupervised learning to detect unknown and zero-day attacks. By utilizing the CSE-CIC-IDS 2018 dataset, we have trained our classifiers to detect benign traffic and 14 known attacks with a selection of 23 features. The network traffic flows that are not classified with a specific level of certainty are sent to the clustering phase to be detected as benign or malicious traffic. Our results indicate that the three classification algorithms used, K-Nearest Neighbors, Random Forest, and Artificial Neural Networks, are able to successfully classify the known attacks with F1-scores between 0.93 and 0.969, and the clustering algorithm HDBSCAN is able to successfully cluster unclassified benign and malicious traffic with unknown labels with F1-scores between 0.85 and 0.957.","PeriodicalId":441475,"journal":{"name":"2022 IEEE 16th International Conference on Application of Information and Communication Technologies (AICT)","volume":"16 4 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":"130634183","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/AICT55583.2022.10013589
M. A. Motin, Mufti Mahmud, David J. Brown
Parkinson’s disease (PD) is the second most common neurological disorder. It is characterised by stiffness, rigidity, tremor, freezing gait and postural instability. PD is monitored clinically by expert neurologists by visually inspecting upper and lower limb movements, speech, gait and facial expressions. This is time-consuming, error-prone and requires an expert neurologist to perform these manual inspections. The electroencephalogram (EEG) is a non-invasive method of monitoring brain activity. This work proposes an EEG-based automated PD monitoring technique. PD was identified using explainable machine learning classifiers based on 31 features extracted from EEG signals. To distinguish PD from healthy controls, the support vector machine classifier with a polynomial kernel achieves 87.10% accuracy, 93.33% sensitivity and 81.25% specificity.
{"title":"Detecting Parkinson’s Disease from Electroencephalogram Signals: An Explainable Machine Learning Approach","authors":"M. A. Motin, Mufti Mahmud, David J. Brown","doi":"10.1109/AICT55583.2022.10013589","DOIUrl":"https://doi.org/10.1109/AICT55583.2022.10013589","url":null,"abstract":"Parkinson’s disease (PD) is the second most common neurological disorder. It is characterised by stiffness, rigidity, tremor, freezing gait and postural instability. PD is monitored clinically by expert neurologists by visually inspecting upper and lower limb movements, speech, gait and facial expressions. This is time-consuming, error-prone and requires an expert neurologist to perform these manual inspections. The electroencephalogram (EEG) is a non-invasive method of monitoring brain activity. This work proposes an EEG-based automated PD monitoring technique. PD was identified using explainable machine learning classifiers based on 31 features extracted from EEG signals. To distinguish PD from healthy controls, the support vector machine classifier with a polynomial kernel achieves 87.10% accuracy, 93.33% sensitivity and 81.25% specificity.","PeriodicalId":441475,"journal":{"name":"2022 IEEE 16th International Conference on Application of Information and Communication Technologies (AICT)","volume":"43 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":"129081773","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/AICT55583.2022.10013586
P. Rafiee, Omur Ozel
We consider information updating problems where generation of the update takes non-negligible time before making update transmissions in a sequential fashion through a Gilbert-Elliot erasure channel with causal channel state information (CSI), strictly causal channel feedback or no CSI. The generation time for each update is independent with a general distribution. The transmission (Tx) queue has a single data buffer to save the latest generated update. This model is inspired by sequential operations in intermittent computing based energy harvesting nodes. Once energy recharges, the node decides whether to generate a new update or to (re)transmit the update. We investigate window based and probabilistic retransmission schemes and obtain closed form average peak age of information (PAoI) expressions. We then provide numerical results that compare average PAoI performances with and without CSI and channel feedback, particularly with respect to threshold-based policies that allow young packets to be transmitted in each scenario, which are candidates for optimal policies.
{"title":"Age of Information Analysis for Intermittent Updating Through a Gilbert Elliot Erasure Channel","authors":"P. Rafiee, Omur Ozel","doi":"10.1109/AICT55583.2022.10013586","DOIUrl":"https://doi.org/10.1109/AICT55583.2022.10013586","url":null,"abstract":"We consider information updating problems where generation of the update takes non-negligible time before making update transmissions in a sequential fashion through a Gilbert-Elliot erasure channel with causal channel state information (CSI), strictly causal channel feedback or no CSI. The generation time for each update is independent with a general distribution. The transmission (Tx) queue has a single data buffer to save the latest generated update. This model is inspired by sequential operations in intermittent computing based energy harvesting nodes. Once energy recharges, the node decides whether to generate a new update or to (re)transmit the update. We investigate window based and probabilistic retransmission schemes and obtain closed form average peak age of information (PAoI) expressions. We then provide numerical results that compare average PAoI performances with and without CSI and channel feedback, particularly with respect to threshold-based policies that allow young packets to be transmitted in each scenario, which are candidates for optimal policies.","PeriodicalId":441475,"journal":{"name":"2022 IEEE 16th International Conference on Application of Information and Communication Technologies (AICT)","volume":"2 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":"129715068","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/AICT55583.2022.10013615
Khlebalov Fedor, Svetlana Strinyuk, V. Lanin
This article aims at explaining the design decisions of a voice chatbot for learning Maritime English. Even though Maritime English is a rather conservative conventional system to a great extent, practicing real life English is a key factor for language fluency. Voice chatbots can provide more practice for mastering listening and comprehension skills. Through the critical analysis of existing systems for learning the English language approaches to design and developing voice chatbot for learning Maritime English are worked out. The practical significance lies in creating an effective tool for learning and mastering Maritime English. Major advantages of the developed system are familiar environment and user-friendly interface.
{"title":"Developing Voice Chatbot for Learning Maritime English","authors":"Khlebalov Fedor, Svetlana Strinyuk, V. Lanin","doi":"10.1109/AICT55583.2022.10013615","DOIUrl":"https://doi.org/10.1109/AICT55583.2022.10013615","url":null,"abstract":"This article aims at explaining the design decisions of a voice chatbot for learning Maritime English. Even though Maritime English is a rather conservative conventional system to a great extent, practicing real life English is a key factor for language fluency. Voice chatbots can provide more practice for mastering listening and comprehension skills. Through the critical analysis of existing systems for learning the English language approaches to design and developing voice chatbot for learning Maritime English are worked out. The practical significance lies in creating an effective tool for learning and mastering Maritime English. Major advantages of the developed system are familiar environment and user-friendly interface.","PeriodicalId":441475,"journal":{"name":"2022 IEEE 16th International Conference on Application of Information and Communication Technologies (AICT)","volume":"193 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":"114181812","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：