Pub Date : 2021-12-01DOI: 10.1109/uemcon53757.2021.9666659
Cesar Martinez Melgoza, Tyler Groom, Henry Lin, Ameya Govalkar, Kayla Lee, Acacia Codding, K. George
In the age of digital communications, radar receivers prove to be essential for applications involving classification such as air traffic control towers, defense systems, and navigation systems. Detecting Emitters within a Radar Environment presents hurdles to the System Designers such as accounting for interference and trying to classify multiple emitters when they are stacked. This paper presents a few-shot machine learning model that utilizes Siamese networks with classification. Given a relatively small dataset, the Siamese network's task is to find the difference between stacked pulses and normal pulse trains, as well as classify the pulse-descriptor words (PDWs), of the signals in the environment. The PDWs will characterize various aspects of the signal with help from a dynamic-thresholding deinterleaving algorithm. The data for this experiment are laboratory generated signals that are transmitted and received using MATLAB, the Zynq Ultrascale+ MPSoC ZCU104 FPGA board, and the AD-FMCOMMS2-EBZ RF module.
{"title":"Environment Classification and Deinterleaving using Siamese Networks and Few-Shot Learning","authors":"Cesar Martinez Melgoza, Tyler Groom, Henry Lin, Ameya Govalkar, Kayla Lee, Acacia Codding, K. George","doi":"10.1109/uemcon53757.2021.9666659","DOIUrl":"https://doi.org/10.1109/uemcon53757.2021.9666659","url":null,"abstract":"In the age of digital communications, radar receivers prove to be essential for applications involving classification such as air traffic control towers, defense systems, and navigation systems. Detecting Emitters within a Radar Environment presents hurdles to the System Designers such as accounting for interference and trying to classify multiple emitters when they are stacked. This paper presents a few-shot machine learning model that utilizes Siamese networks with classification. Given a relatively small dataset, the Siamese network's task is to find the difference between stacked pulses and normal pulse trains, as well as classify the pulse-descriptor words (PDWs), of the signals in the environment. The PDWs will characterize various aspects of the signal with help from a dynamic-thresholding deinterleaving algorithm. The data for this experiment are laboratory generated signals that are transmitted and received using MATLAB, the Zynq Ultrascale+ MPSoC ZCU104 FPGA board, and the AD-FMCOMMS2-EBZ RF module.","PeriodicalId":127072,"journal":{"name":"2021 IEEE 12th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133046803","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 : 2021-12-01DOI: 10.1109/UEMCON53757.2021.9666604
Deyby Huamanchahua, Jorge Sierra-Huertas, Dana Terrazas-Rodas, Alexander Janampa-Espinoza, Jorge Gonzáles, Sofia Huamán-Vizconde
Upper extremity exoskeletons offer an alternative way to support or rehabilitate patients with physical injury, stroke and spinal cord injury (SCI). This research article presents the kinematic analysis of Exo-First Exoskeleton, which is an 4 DoF upper limb exoskeleton, with the aim of assisting or rehabilitating the shoulder and elbow of the human body. This device covers the entire upper limb of a person, from the clavicle to before the wrist. It is capable of executing motions such as internal-external rotation, adduction-abduction or flexion-extension of the shoulder; and flexion-extension of the elbow. The Denavit-Hartenberg (D-H) method was used to obtain the mathematical model that describes the forward and inverse kinematics of the exoskeleton. Furthermore, the exoskeleton end effector trajectories were obtained using the MATLAB software. The results showed that the proposed design for patients with physical disabilities provides a safer Range of Motion (ROM).
{"title":"Kinematic Analysis of an 4 DOF Upper-Limb Exoskeleton","authors":"Deyby Huamanchahua, Jorge Sierra-Huertas, Dana Terrazas-Rodas, Alexander Janampa-Espinoza, Jorge Gonzáles, Sofia Huamán-Vizconde","doi":"10.1109/UEMCON53757.2021.9666604","DOIUrl":"https://doi.org/10.1109/UEMCON53757.2021.9666604","url":null,"abstract":"Upper extremity exoskeletons offer an alternative way to support or rehabilitate patients with physical injury, stroke and spinal cord injury (SCI). This research article presents the kinematic analysis of Exo-First Exoskeleton, which is an 4 DoF upper limb exoskeleton, with the aim of assisting or rehabilitating the shoulder and elbow of the human body. This device covers the entire upper limb of a person, from the clavicle to before the wrist. It is capable of executing motions such as internal-external rotation, adduction-abduction or flexion-extension of the shoulder; and flexion-extension of the elbow. The Denavit-Hartenberg (D-H) method was used to obtain the mathematical model that describes the forward and inverse kinematics of the exoskeleton. Furthermore, the exoskeleton end effector trajectories were obtained using the MATLAB software. The results showed that the proposed design for patients with physical disabilities provides a safer Range of Motion (ROM).","PeriodicalId":127072,"journal":{"name":"2021 IEEE 12th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133810442","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 : 2021-12-01DOI: 10.1109/uemcon53757.2021.9666647
Dakota Barrios, Tyler Groom, K. George
Teaching a language using tactile vocabulary objects is an effective method of teaching for those with who have communication disabilities such as being blind or deaf. The effectiveness of tactile language learning can be greatly complemented by a tactile communication device, which allows students to easily form sentences then quickly and accurately relay them to the teacher. This paper goes over the design and quantitative results of a tactile communication device specifically based around the inclusion of Radio Frequency Identification (RFID) modules.
{"title":"Design and Implementation of an RFID Based Tactile Communication Device","authors":"Dakota Barrios, Tyler Groom, K. George","doi":"10.1109/uemcon53757.2021.9666647","DOIUrl":"https://doi.org/10.1109/uemcon53757.2021.9666647","url":null,"abstract":"Teaching a language using tactile vocabulary objects is an effective method of teaching for those with who have communication disabilities such as being blind or deaf. The effectiveness of tactile language learning can be greatly complemented by a tactile communication device, which allows students to easily form sentences then quickly and accurately relay them to the teacher. This paper goes over the design and quantitative results of a tactile communication device specifically based around the inclusion of Radio Frequency Identification (RFID) modules.","PeriodicalId":127072,"journal":{"name":"2021 IEEE 12th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114360488","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 : 2021-12-01DOI: 10.1109/UEMCON53757.2021.9666484
Gavin Elliott, Kevin Meehan, Jennifer Hyndman
Domestic violence is a prevalent crime in our society, more so with the introduction of COVID19 restrictions. For the victim, it can be a traumatic experience, so much as to not report the crime. Consequently, the ‘Signal for Help’ hand gestures were recently introduced as a discrete method to enable the victim to confidently express their need for help. This research investigates the classification of these hand gestures using a deep learning approach, which has not previously been implemented in this context. A deep learning approach is chosen due to the favourable results obtained in different contexts on hand gesture classification. Due to the unavailability of a dataset containing images of these hand gestures, a ‘Signal for Help’ dataset containing 112 images is generated as part of this study. These images are pre-processed to be of size 50x50 dimensions. Furthermore, a synthetic version of this dataset is also generated from the pre-processed images containing 2,352 images. The aims of this research are to show that using a synthetic ‘Signal for Help’ dataset improves model performance, and using deep learning is effective in ‘Signal for Help’ hand gesture classification. The results in this research show that using a synthetic ‘Signal for Help’ dataset improves model performance and is effective for ‘Signal for Help’ hand gesture classification.
{"title":"Using CNN and Tensorflow to recognise ‘Signal for Help’ Hand Gestures","authors":"Gavin Elliott, Kevin Meehan, Jennifer Hyndman","doi":"10.1109/UEMCON53757.2021.9666484","DOIUrl":"https://doi.org/10.1109/UEMCON53757.2021.9666484","url":null,"abstract":"Domestic violence is a prevalent crime in our society, more so with the introduction of COVID19 restrictions. For the victim, it can be a traumatic experience, so much as to not report the crime. Consequently, the ‘Signal for Help’ hand gestures were recently introduced as a discrete method to enable the victim to confidently express their need for help. This research investigates the classification of these hand gestures using a deep learning approach, which has not previously been implemented in this context. A deep learning approach is chosen due to the favourable results obtained in different contexts on hand gesture classification. Due to the unavailability of a dataset containing images of these hand gestures, a ‘Signal for Help’ dataset containing 112 images is generated as part of this study. These images are pre-processed to be of size 50x50 dimensions. Furthermore, a synthetic version of this dataset is also generated from the pre-processed images containing 2,352 images. The aims of this research are to show that using a synthetic ‘Signal for Help’ dataset improves model performance, and using deep learning is effective in ‘Signal for Help’ hand gesture classification. The results in this research show that using a synthetic ‘Signal for Help’ dataset improves model performance and is effective for ‘Signal for Help’ hand gesture classification.","PeriodicalId":127072,"journal":{"name":"2021 IEEE 12th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","volume":"475 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124575430","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 : 2021-12-01DOI: 10.1109/UEMCON53757.2021.9666603
Jamal Alotaibi, Lubna K. Alazzawi
The advancement of the Internet-of-Vehicles (IoV) innovation aids the development of intelligent transportation systems (ITS). There are several interoperability challenges in today’s IoV networks, such as security and privacy issues, information irregularity, and so on. Because vehicle data is private and sensitive, it necessitates extra caution. Authentication of communicating devices is one such technique for securing data. The information sent via public channels is secured using authentication. Many protocols have been developed; however, traditional authentication models cannot be applied directly to circumstances needing low latency in particular. Furthermore, they are ineffective for two primary reasons: first, they are unable to adapt to the growing volume of data collected, and second, they are prone to cyber-attacks. As a result, in this paper, we attempt to propose a viable solution that is fully robust and overcomes the aforementioned problems. To protect IoV devices data during communication, we designed a lightweight and fog-based authentication scheme. Our approach ensures minimal communication cost and complies with high-security standards. Finally, we assess and compare our method’s performance in terms of network parameters such as throughput, end-to-end delay, and the rate of packet loss. Results indicate that our method scale well with the increasing number of vehicles while maintaining a minimal communication cost.
{"title":"A Lightweight and Fog-based Authentication Scheme for Internet-of-Vehicles","authors":"Jamal Alotaibi, Lubna K. Alazzawi","doi":"10.1109/UEMCON53757.2021.9666603","DOIUrl":"https://doi.org/10.1109/UEMCON53757.2021.9666603","url":null,"abstract":"The advancement of the Internet-of-Vehicles (IoV) innovation aids the development of intelligent transportation systems (ITS). There are several interoperability challenges in today’s IoV networks, such as security and privacy issues, information irregularity, and so on. Because vehicle data is private and sensitive, it necessitates extra caution. Authentication of communicating devices is one such technique for securing data. The information sent via public channels is secured using authentication. Many protocols have been developed; however, traditional authentication models cannot be applied directly to circumstances needing low latency in particular. Furthermore, they are ineffective for two primary reasons: first, they are unable to adapt to the growing volume of data collected, and second, they are prone to cyber-attacks. As a result, in this paper, we attempt to propose a viable solution that is fully robust and overcomes the aforementioned problems. To protect IoV devices data during communication, we designed a lightweight and fog-based authentication scheme. Our approach ensures minimal communication cost and complies with high-security standards. Finally, we assess and compare our method’s performance in terms of network parameters such as throughput, end-to-end delay, and the rate of packet loss. Results indicate that our method scale well with the increasing number of vehicles while maintaining a minimal communication cost.","PeriodicalId":127072,"journal":{"name":"2021 IEEE 12th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134099550","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 : 2021-12-01DOI: 10.1109/UEMCON53757.2021.9666733
D. Dissanayake, R. Rajapaksha, U. P. Prabhashawara, S. A. D. S. P. Solanga, J. Jayakody
Due to a lack of knowledge about the building structure and possible impediments, the majority of blind persons require assistance when traveling through unknown regions. To solve this issue, this paper provides "Guide-Me" as a strategy for indoor navigation with optimum accessibility, usability, and security, decreasing obstacles that the user may meet when traveling through indoor surroundings. Because the intended audience for this research is blind or visually impaired persons, "Guide-Me" makes use of the user’s voice-based inputs. This paper also includes Bluetooth beacon integration for localization, a Smart stick with sensors for obstacle detection, a machine learning model for voice authentication, and an algorithm protocol for a secure connection between server and application Integration driven architecture to assist vision impaired in navigating the known and unknown indoor environment.
{"title":"Guide-Me: Voice authenticated indoor user guidance system","authors":"D. Dissanayake, R. Rajapaksha, U. P. Prabhashawara, S. A. D. S. P. Solanga, J. Jayakody","doi":"10.1109/UEMCON53757.2021.9666733","DOIUrl":"https://doi.org/10.1109/UEMCON53757.2021.9666733","url":null,"abstract":"Due to a lack of knowledge about the building structure and possible impediments, the majority of blind persons require assistance when traveling through unknown regions. To solve this issue, this paper provides \"Guide-Me\" as a strategy for indoor navigation with optimum accessibility, usability, and security, decreasing obstacles that the user may meet when traveling through indoor surroundings. Because the intended audience for this research is blind or visually impaired persons, \"Guide-Me\" makes use of the user’s voice-based inputs. This paper also includes Bluetooth beacon integration for localization, a Smart stick with sensors for obstacle detection, a machine learning model for voice authentication, and an algorithm protocol for a secure connection between server and application Integration driven architecture to assist vision impaired in navigating the known and unknown indoor environment.","PeriodicalId":127072,"journal":{"name":"2021 IEEE 12th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132445055","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 : 2021-12-01DOI: 10.1109/uemcon53757.2021.9666633
Masoud Malekzadeh, P. Hajibabaee, Maryam Heidari, Samira Zad, Özlem Uzuner, James H. Jones
Text classification is one of the fundamental problems in Natural Language Processing (NLP). Several research studies have used deep learning approaches such as Convolution Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) for text classification. Over the past decade, graph-based approaches have been used to solve various NLP tasks including text classification. This paper reviews the most recent state-of-the-art graph-based text classification, datasets, and performance evaluations versus baseline models.
{"title":"Review of Graph Neural Network in Text Classification","authors":"Masoud Malekzadeh, P. Hajibabaee, Maryam Heidari, Samira Zad, Özlem Uzuner, James H. Jones","doi":"10.1109/uemcon53757.2021.9666633","DOIUrl":"https://doi.org/10.1109/uemcon53757.2021.9666633","url":null,"abstract":"Text classification is one of the fundamental problems in Natural Language Processing (NLP). Several research studies have used deep learning approaches such as Convolution Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) for text classification. Over the past decade, graph-based approaches have been used to solve various NLP tasks including text classification. This paper reviews the most recent state-of-the-art graph-based text classification, datasets, and performance evaluations versus baseline models.","PeriodicalId":127072,"journal":{"name":"2021 IEEE 12th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117298767","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 : 2021-12-01DOI: 10.1109/UEMCON53757.2021.9666483
Md. Abu Obaidah, Faria Soroni, Mohammad Monirujjaman Khan
This paper presents the design and the implementation of an onlinebased babysitting system. This is a web-based babysitting service and information storage system created specifically for urban working families. Since the rate of working women in the country is increasing; Parents are in desperate need of help when it comes to taking care of kids or homeschooling them. This system is designed in an efficient way that connects children or adolescents with parents who need childcare or babysitter services, want to lend a hand. The unique process in our country is capable of providing babysitters as well as there is easy and effective storage of information of all the babysitters and parents who register on the system. The system has a great socio-economic impact on society.
{"title":"Development of an Online Based Babysitting System: Bonne","authors":"Md. Abu Obaidah, Faria Soroni, Mohammad Monirujjaman Khan","doi":"10.1109/UEMCON53757.2021.9666483","DOIUrl":"https://doi.org/10.1109/UEMCON53757.2021.9666483","url":null,"abstract":"This paper presents the design and the implementation of an onlinebased babysitting system. This is a web-based babysitting service and information storage system created specifically for urban working families. Since the rate of working women in the country is increasing; Parents are in desperate need of help when it comes to taking care of kids or homeschooling them. This system is designed in an efficient way that connects children or adolescents with parents who need childcare or babysitter services, want to lend a hand. The unique process in our country is capable of providing babysitters as well as there is easy and effective storage of information of all the babysitters and parents who register on the system. The system has a great socio-economic impact on society.","PeriodicalId":127072,"journal":{"name":"2021 IEEE 12th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117152241","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 : 2021-12-01DOI: 10.1109/UEMCON53757.2021.9666709
Robin Chataut, R. Akl, U. K. Dey
Massive MIMO (multiple-input multiple-output) is a sub-6GHz wireless access technology that can provide high spectral and energy efficiency and is considered as one of the key enabling technology for 5G, 6G, and beyond networks. The user signal detection during the uplink is one of the major challenges in massive MIMO systems due to the large number of antennas working together at both the user terminal and the base station. The current iterative methods do not offer great efficiency, and the conventional matrix inversion methods are computationally complex due to the large antennas involved in massive MIMO systems. In this paper, we propose a fast and efficient preconditioned iterative method by introducing a preconditioner based on ICF (Incomplete Cholesky Factorization). Additionally, we introduce a novel matrix initializer to further improve the convergence of the proposed algorithm. The numerical results, when compared to conventional methods, show that the proposed algorithm provides better error performance with optimal computational complexity.
{"title":"An Efficient and Fast-convergent Detector for 5G and Beyond Massive MIMO Systems","authors":"Robin Chataut, R. Akl, U. K. Dey","doi":"10.1109/UEMCON53757.2021.9666709","DOIUrl":"https://doi.org/10.1109/UEMCON53757.2021.9666709","url":null,"abstract":"Massive MIMO (multiple-input multiple-output) is a sub-6GHz wireless access technology that can provide high spectral and energy efficiency and is considered as one of the key enabling technology for 5G, 6G, and beyond networks. The user signal detection during the uplink is one of the major challenges in massive MIMO systems due to the large number of antennas working together at both the user terminal and the base station. The current iterative methods do not offer great efficiency, and the conventional matrix inversion methods are computationally complex due to the large antennas involved in massive MIMO systems. In this paper, we propose a fast and efficient preconditioned iterative method by introducing a preconditioner based on ICF (Incomplete Cholesky Factorization). Additionally, we introduce a novel matrix initializer to further improve the convergence of the proposed algorithm. The numerical results, when compared to conventional methods, show that the proposed algorithm provides better error performance with optimal computational complexity.","PeriodicalId":127072,"journal":{"name":"2021 IEEE 12th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124000380","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 : 2021-12-01DOI: 10.1109/UEMCON53757.2021.9666508
Kazi Nabiul Alam, Mohammad Monirujjaman Khan
Coronavirus disease COVID-19 is an infectious disease caused by a newly discovered coronavirus. COVID-19 virus affects the respiratory system of healthy individuals. Chest X-ray is one of the important imaging methods to identify the coronavirus. In deep learning, a convolutional neural network (CNN), is a class of deep learning models, most commonly applied for better outcomes to analyzing visual imagery. Automated covid-19 using Deep Learning techniques could, therefore, serve as an effective diagnostic aid. In this study, we used a convolutional neural network (CNN) for detecting COVID-19 from chest X-ray images. The overall project comprises various convolutional layers. The Max-pooling layers diminish the size of the picture significantly and by joining convolutional and pooling layers, the net is able to combine its features to learn more global features of the Image. Eventually, we utilize the highlights in two completely associated (Dense) layers. Dropout is a regularization strategy, where the layer arbitrarily replaces an extent of its weights to zero for each training sample. This forces the net to learn features in an appropriate way, not depending a lot on specific weight, and thus improves speculation and 'relu' is the activation function. Applying convolutional neural network which is a Deep Learning algorithm that can take in an input image, relegate significance to different perspectives in the images and have the option to separate one from the other.
{"title":"CNN Based COVID-19 Prediction from Chest X-ray Images","authors":"Kazi Nabiul Alam, Mohammad Monirujjaman Khan","doi":"10.1109/UEMCON53757.2021.9666508","DOIUrl":"https://doi.org/10.1109/UEMCON53757.2021.9666508","url":null,"abstract":"Coronavirus disease COVID-19 is an infectious disease caused by a newly discovered coronavirus. COVID-19 virus affects the respiratory system of healthy individuals. Chest X-ray is one of the important imaging methods to identify the coronavirus. In deep learning, a convolutional neural network (CNN), is a class of deep learning models, most commonly applied for better outcomes to analyzing visual imagery. Automated covid-19 using Deep Learning techniques could, therefore, serve as an effective diagnostic aid. In this study, we used a convolutional neural network (CNN) for detecting COVID-19 from chest X-ray images. The overall project comprises various convolutional layers. The Max-pooling layers diminish the size of the picture significantly and by joining convolutional and pooling layers, the net is able to combine its features to learn more global features of the Image. Eventually, we utilize the highlights in two completely associated (Dense) layers. Dropout is a regularization strategy, where the layer arbitrarily replaces an extent of its weights to zero for each training sample. This forces the net to learn features in an appropriate way, not depending a lot on specific weight, and thus improves speculation and 'relu' is the activation function. Applying convolutional neural network which is a Deep Learning algorithm that can take in an input image, relegate significance to different perspectives in the images and have the option to separate one from the other.","PeriodicalId":127072,"journal":{"name":"2021 IEEE 12th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121493290","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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