Pub Date : 2022-10-22DOI: 10.5121/csit.2022.121714
Sarah Fan, Kevin Guo, Yu Sun
Human Pose Estimation has proven versatility in improving real-world applications in healthcare, sports, etc. [1]. Proper stance, form and movement is instrumental to succeeding in these activities. This paper will explain the research process behind the deep learning mobile ballet app, LaBelle [2]. LaBelle takes in two short videos: one of a teacher, and one of a student. Utilizing MediaPipe Pose to identify, analyze, and store data about the poses and movements of both dancers, the app calculates the angles created between different joints and major body parts. The app’s AI Model uses a K-means clustering algorithm to create a group of clusters for both the student dataset and the teacher dataset [3]. Using the two sets of clusters, LaBelle identifies the key frames in the student-video and searches the teacher cluster set for a matching set of properties and frames. It evaluates the differences between the paired frames and produces a final score as well as feedback on the poses that need improving. We propose an unsupervised guided-learning approach with improved efficiency in video comparison, which is usually both time and resource consuming. This efficient model can be used not just in dance, but athletics and medicine (physical therapy like activities) as well, where stance, form, and movements are often hard to track with the naked eye.
{"title":"LaBelle: A Deep Learning APP that Helps You Learn Ballet","authors":"Sarah Fan, Kevin Guo, Yu Sun","doi":"10.5121/csit.2022.121714","DOIUrl":"https://doi.org/10.5121/csit.2022.121714","url":null,"abstract":"Human Pose Estimation has proven versatility in improving real-world applications in healthcare, sports, etc. [1]. Proper stance, form and movement is instrumental to succeeding in these activities. This paper will explain the research process behind the deep learning mobile ballet app, LaBelle [2]. LaBelle takes in two short videos: one of a teacher, and one of a student. Utilizing MediaPipe Pose to identify, analyze, and store data about the poses and movements of both dancers, the app calculates the angles created between different joints and major body parts. The app’s AI Model uses a K-means clustering algorithm to create a group of clusters for both the student dataset and the teacher dataset [3]. Using the two sets of clusters, LaBelle identifies the key frames in the student-video and searches the teacher cluster set for a matching set of properties and frames. It evaluates the differences between the paired frames and produces a final score as well as feedback on the poses that need improving. We propose an unsupervised guided-learning approach with improved efficiency in video comparison, which is usually both time and resource consuming. This efficient model can be used not just in dance, but athletics and medicine (physical therapy like activities) as well, where stance, form, and movements are often hard to track with the naked eye.","PeriodicalId":170432,"journal":{"name":"Signal & Image Processing Trends","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115693460","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 : 2022-10-22DOI: 10.5121/csit.2022.121717
P. Ahadian, Maryam Babaei, K. Parand
A brain tumor consists of cells showing abnormal brain growth. The area of the brain tumor significantly affects choosing the type of treatment and following the course of the disease during the treatment. At the same time, pictures of Brain MRIs are accompanied by noise. Eliminating existing noises can significantly impact the better segmentation and diagnosis of brain tumors. In this work, we have tried using the analysis of eigenvalues. We have used the MSVD algorithm, reducing the image noise and then using the deep neural network to segment the tumor in the images. The proposed method's accuracy was increased by 2.4% compared to using the original images. With Using the MSVD method, convergence speed has also increased, showing the proposed method's effectiveness.
{"title":"Using Singular Value Decomposition in a Convolutional Neural Network to Improve Brain Tumor Segmentation Accuracy","authors":"P. Ahadian, Maryam Babaei, K. Parand","doi":"10.5121/csit.2022.121717","DOIUrl":"https://doi.org/10.5121/csit.2022.121717","url":null,"abstract":"A brain tumor consists of cells showing abnormal brain growth. The area of the brain tumor significantly affects choosing the type of treatment and following the course of the disease during the treatment. At the same time, pictures of Brain MRIs are accompanied by noise. Eliminating existing noises can significantly impact the better segmentation and diagnosis of brain tumors. In this work, we have tried using the analysis of eigenvalues. We have used the MSVD algorithm, reducing the image noise and then using the deep neural network to segment the tumor in the images. The proposed method's accuracy was increased by 2.4% compared to using the original images. With Using the MSVD method, convergence speed has also increased, showing the proposed method's effectiveness.","PeriodicalId":170432,"journal":{"name":"Signal & Image Processing Trends","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123440477","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 : 2022-10-22DOI: 10.5121/csit.2022.121707
Qinyuhan Zhao, Mingze Gao, Yu Sun
In the environment of online courses and online exams, cheating in online courses is prevalent [1]. To better ensure fairness in exams, schools and educational institutions need to use technology to detect and deter cheating [2]. Starting from practical application, this paper discusses 3 different methods to detect cheating behavior, and proposes a new way. for online exam supervision.
{"title":"An Intelligent System to Automate the Detection of Online Cheating Activities using AI and Context Aware Techniques","authors":"Qinyuhan Zhao, Mingze Gao, Yu Sun","doi":"10.5121/csit.2022.121707","DOIUrl":"https://doi.org/10.5121/csit.2022.121707","url":null,"abstract":"In the environment of online courses and online exams, cheating in online courses is prevalent [1]. To better ensure fairness in exams, schools and educational institutions need to use technology to detect and deter cheating [2]. Starting from practical application, this paper discusses 3 different methods to detect cheating behavior, and proposes a new way. for online exam supervision.","PeriodicalId":170432,"journal":{"name":"Signal & Image Processing Trends","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124547727","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 : 2022-10-22DOI: 10.5121/csit.2022.121704
Erick Gong, Yu Sun
Many people donate money to fund organizations, but very rarely do those donors have information about where those donations go. Donation platforms are both non-transparent and also leave a large portion of potential donors unnoticed: gamers [1]. This paper explores the concept of utilizing blockchain technology and its existence as a web3 token-based platform in order to provide transparency for donation routes, showing donors and other companies exactly where donations are coming from and where that money is going. Our application utilizes HTTP requests in order to greatly increase compatibility, and also uses multiple private key encryptions in order to ensure that any user data or information and monetary transactions are kept secure and private [2].
{"title":"ComputerBank: A Community-based Computer Donation Platform using Machine Learning and NFT","authors":"Erick Gong, Yu Sun","doi":"10.5121/csit.2022.121704","DOIUrl":"https://doi.org/10.5121/csit.2022.121704","url":null,"abstract":"Many people donate money to fund organizations, but very rarely do those donors have information about where those donations go. Donation platforms are both non-transparent and also leave a large portion of potential donors unnoticed: gamers [1]. This paper explores the concept of utilizing blockchain technology and its existence as a web3 token-based platform in order to provide transparency for donation routes, showing donors and other companies exactly where donations are coming from and where that money is going. Our application utilizes HTTP requests in order to greatly increase compatibility, and also uses multiple private key encryptions in order to ensure that any user data or information and monetary transactions are kept secure and private [2].","PeriodicalId":170432,"journal":{"name":"Signal & Image Processing Trends","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114757728","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 : 2022-10-22DOI: 10.5121/csit.2022.121711
Jasmin Liao, Yu Sun
Engaging online students is a challenge for many teachers. While I was a student, I saw teachers struggling to take attendance due to the number of students leaving their classes after attendance. Students would be held responsible for their work using facial recognition technology. To simplify the process of applying absences to students in each class, this paper proposes an application that would allow teachers to stay on top of their work. We applied our software to test “students” in the classroom and used various libraries/CSC styles to create a classroom that is easy for both the student and the teacher to read. Our designs are built upon OpenCV and PIL which are used as geometric classifiers to determine if the student is present. We tested several faces to see if the algorithm was suitable for the program. After conducting a qualitative evaluation of the approach, we’ve begun to implement registration, create new classrooms with different databases, and apply verification. With the addition of HTML code, we wereable to create a classroom that is safe, engaging, and easy to use.
{"title":"Artificial Intelligence Designed For Attendance","authors":"Jasmin Liao, Yu Sun","doi":"10.5121/csit.2022.121711","DOIUrl":"https://doi.org/10.5121/csit.2022.121711","url":null,"abstract":"Engaging online students is a challenge for many teachers. While I was a student, I saw teachers struggling to take attendance due to the number of students leaving their classes after attendance. Students would be held responsible for their work using facial recognition technology. To simplify the process of applying absences to students in each class, this paper proposes an application that would allow teachers to stay on top of their work. We applied our software to test “students” in the classroom and used various libraries/CSC styles to create a classroom that is easy for both the student and the teacher to read. Our designs are built upon OpenCV and PIL which are used as geometric classifiers to determine if the student is present. We tested several faces to see if the algorithm was suitable for the program. After conducting a qualitative evaluation of the approach, we’ve begun to implement registration, create new classrooms with different databases, and apply verification. With the addition of HTML code, we wereable to create a classroom that is safe, engaging, and easy to use.","PeriodicalId":170432,"journal":{"name":"Signal & Image Processing Trends","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127487971","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 : 2022-10-22DOI: 10.5121/csit.2022.121712
A. Tse, Yu Sun
There are numerous arguments as to what the best GPS software is [1]. However, there are no definitive answers as to which is the best. In this paper, we use multiple GPS applications tracking user locations to determine what GPS is best in terms of tracking users through a mobile app [3]. The app utilizes a GPS as well as a Google Firebase Realtime Database to manage, pinpoint, and track users’ locations [2]. The application is explicitly applied to track locations of people that need care, such as the elderly. This will allow concerned caretakers to help keep track and take care of people in need.
{"title":"A Data-Driven and Collaborative Mobile Application to Assist Sensors using Artificial Intelligence and Machine Learning","authors":"A. Tse, Yu Sun","doi":"10.5121/csit.2022.121712","DOIUrl":"https://doi.org/10.5121/csit.2022.121712","url":null,"abstract":"There are numerous arguments as to what the best GPS software is [1]. However, there are no definitive answers as to which is the best. In this paper, we use multiple GPS applications tracking user locations to determine what GPS is best in terms of tracking users through a mobile app [3]. The app utilizes a GPS as well as a Google Firebase Realtime Database to manage, pinpoint, and track users’ locations [2]. The application is explicitly applied to track locations of people that need care, such as the elderly. This will allow concerned caretakers to help keep track and take care of people in need.","PeriodicalId":170432,"journal":{"name":"Signal & Image Processing Trends","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134015414","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 : 2022-10-22DOI: 10.5121/csit.2022.121701
Yousra Berrich, Z. Guennoun
The recognition of handwritten digits has aroused the interest of the scientific community and is the subject of a large number of research works thanks to its various applications. The objective of this paper is to develop a system capable of recognizing handwritten digits using a convolutional neural network (CNN) combined with machine learning approaches to ensure diversity in automatic classification tools. In this work, we propose a classification method based on deep learning, in particular the convolutional neural network for feature extraction, it is a powerful tool that has had great success in image classification, followed by the support vector machine (SVM) for higher performance. We used the dataset (MNIST), and the results obtained showed that the combination of CNN with SVM improves the performance of the model as well as the classification accuracy with a rate of 99.12%.
{"title":"Handwritten Digit Recognition System based on CNN and SVM","authors":"Yousra Berrich, Z. Guennoun","doi":"10.5121/csit.2022.121701","DOIUrl":"https://doi.org/10.5121/csit.2022.121701","url":null,"abstract":"The recognition of handwritten digits has aroused the interest of the scientific community and is the subject of a large number of research works thanks to its various applications. The objective of this paper is to develop a system capable of recognizing handwritten digits using a convolutional neural network (CNN) combined with machine learning approaches to ensure diversity in automatic classification tools. In this work, we propose a classification method based on deep learning, in particular the convolutional neural network for feature extraction, it is a powerful tool that has had great success in image classification, followed by the support vector machine (SVM) for higher performance. We used the dataset (MNIST), and the results obtained showed that the combination of CNN with SVM improves the performance of the model as well as the classification accuracy with a rate of 99.12%.","PeriodicalId":170432,"journal":{"name":"Signal & Image Processing Trends","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134428353","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 : 2022-10-22DOI: 10.5121/csit.2022.121718
Deepakindresh N, Gauthum J, Jeffrin Harris, Harshavardhan J, Shivaditya Shivganesh
The paper is an analysis of class imbalance problems from various domains such as the medical field, sentiment analysis, software de-fects, water portability, and relationship status of students and summarizes the performance of data resampling techniques such as random undersampling and oversampling. Synthetic minority oversampling techniques combined with the power of ensemble methods such as bagging, boosting, and hybrid techniques are generally used to solve the class imbalance problem.
{"title":"Ensembles for Class Imbalance Problems in Various Domains","authors":"Deepakindresh N, Gauthum J, Jeffrin Harris, Harshavardhan J, Shivaditya Shivganesh","doi":"10.5121/csit.2022.121718","DOIUrl":"https://doi.org/10.5121/csit.2022.121718","url":null,"abstract":"The paper is an analysis of class imbalance problems from various domains such as the medical field, sentiment analysis, software de-fects, water portability, and relationship status of students and summarizes the performance of data resampling techniques such as random undersampling and oversampling. Synthetic minority oversampling techniques combined with the power of ensemble methods such as bagging, boosting, and hybrid techniques are generally used to solve the class imbalance problem.","PeriodicalId":170432,"journal":{"name":"Signal & Image Processing Trends","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124955771","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 : 2022-10-22DOI: 10.5121/csit.2022.121703
B. S. D. Silva, Iury da S. Batalha
The 5G NR network with the Non-Standalone (NSA) architecture aims to advance with regard to throughput. When compared to fourth-generation mobile communication (4G LTE), the 5G has a higher data exchange capability through the gNB and the UE (User Equipment). For evaluation and optimization, it is necessary to carry out practical studies on the behaviour of the system in different environmental conditions, subject to attenuation processes, such as large-scale fading (Shading) and small-scale fading (Multipath propagation). This work has analysed the effect of the MCS (Modulation and Coding Scheme) variation on Throughput/BLER for, initially, a channel degraded by default AWGN, then the analysis extends to the multipath fading effect, which emulates more realistically a mobile communication network. The analysis confirmed the need for robust decision process algorithms in terms of MCS switching to maintain adequate data rates according to the requirement of each scenario with specific QoS (Quality of service), considering both 64 QAM and 256 QAM. The throughput degradation effect was more evident in higher-order modulations due to the higher probability of error inherent in the symbol arrangement. This study can be a key for understanding and developing huge modulation and coding schemes for fifth generation communications.
{"title":"Measurement Study on 5G NSA Architecture over Fading Channel","authors":"B. S. D. Silva, Iury da S. Batalha","doi":"10.5121/csit.2022.121703","DOIUrl":"https://doi.org/10.5121/csit.2022.121703","url":null,"abstract":"The 5G NR network with the Non-Standalone (NSA) architecture aims to advance with regard to throughput. When compared to fourth-generation mobile communication (4G LTE), the 5G has a higher data exchange capability through the gNB and the UE (User Equipment). For evaluation and optimization, it is necessary to carry out practical studies on the behaviour of the system in different environmental conditions, subject to attenuation processes, such as large-scale fading (Shading) and small-scale fading (Multipath propagation). This work has analysed the effect of the MCS (Modulation and Coding Scheme) variation on Throughput/BLER for, initially, a channel degraded by default AWGN, then the analysis extends to the multipath fading effect, which emulates more realistically a mobile communication network. The analysis confirmed the need for robust decision process algorithms in terms of MCS switching to maintain adequate data rates according to the requirement of each scenario with specific QoS (Quality of service), considering both 64 QAM and 256 QAM. The throughput degradation effect was more evident in higher-order modulations due to the higher probability of error inherent in the symbol arrangement. This study can be a key for understanding and developing huge modulation and coding schemes for fifth generation communications.","PeriodicalId":170432,"journal":{"name":"Signal & Image Processing Trends","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132189632","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 : 2022-10-22DOI: 10.5121/csit.2022.121709
Daniel Wang, Yu Sun
As technology advances, we have found more practical uses for it. This ranges from such things as cleaning the house using machines to serving restaurants with robots. Using technology, what if we can use machines to automatically write sheet music for us, transcribing it from audio [1]. This paper designs an application to do exactly that. We used Java to write a program and app that would be able to transcribe audio into sheet music and store it on an app. We applied our application to multiple cases and conducted a qualitative evaluation of the approach. The results show that it is possible with some fine tuning and may be usable in the near future.
{"title":"MusicApp, A Music Sheet Transcribing Moblie Platform using Machine Learning and Nature Language Processing","authors":"Daniel Wang, Yu Sun","doi":"10.5121/csit.2022.121709","DOIUrl":"https://doi.org/10.5121/csit.2022.121709","url":null,"abstract":"As technology advances, we have found more practical uses for it. This ranges from such things as cleaning the house using machines to serving restaurants with robots. Using technology, what if we can use machines to automatically write sheet music for us, transcribing it from audio [1]. This paper designs an application to do exactly that. We used Java to write a program and app that would be able to transcribe audio into sheet music and store it on an app. We applied our application to multiple cases and conducted a qualitative evaluation of the approach. The results show that it is possible with some fine tuning and may be usable in the near future.","PeriodicalId":170432,"journal":{"name":"Signal & Image Processing Trends","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131019376","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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