Pub Date : 2023-02-15DOI: 10.13052/jmm1550-4646.19310
B. Shevchuk, O. Ivakhiv, O. Zastavnyy, M. Geraimchuk
Methodological and algorithmic bases of the local-regional and global wireless networks operation for functional states of people long-term monitoring are offered. The construction of efficient wireless networks and portable devices for monitoring biological objects is proposed. High-speed algorithms for processing, encoding, encrypting and transmitting samples of monitoring signals and video data frames using a signaling approach are proposed. The express analysis of monitoring signal samples is offered. The described methods of effective long-term monitoring of functional states of biological objects using wireless networks, algorithms of operational processing, coding, encryption and express analysis of signals are the basis of information technology of wide application. The technology is focused on evidence-based monitoring of the condition of operators of various human-machine complexes, systems, vehicles, aviation and space systems, monitoring of athletes and healthy people in order to prevent various diseases.
{"title":"Telemonitoring of Human Biomedical and Biomechanical Signals","authors":"B. Shevchuk, O. Ivakhiv, O. Zastavnyy, M. Geraimchuk","doi":"10.13052/jmm1550-4646.19310","DOIUrl":"https://doi.org/10.13052/jmm1550-4646.19310","url":null,"abstract":"Methodological and algorithmic bases of the local-regional and global wireless networks operation for functional states of people long-term monitoring are offered. The construction of efficient wireless networks and portable devices for monitoring biological objects is proposed. High-speed algorithms for processing, encoding, encrypting and transmitting samples of monitoring signals and video data frames using a signaling approach are proposed. The express analysis of monitoring signal samples is offered. The described methods of effective long-term monitoring of functional states of biological objects using wireless networks, algorithms of operational processing, coding, encryption and express analysis of signals are the basis of information technology of wide application. The technology is focused on evidence-based monitoring of the condition of operators of various human-machine complexes, systems, vehicles, aviation and space systems, monitoring of athletes and healthy people in order to prevent various diseases.","PeriodicalId":425561,"journal":{"name":"J. Mobile Multimedia","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132531164","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 : 2023-02-15DOI: 10.13052/jmm1550-4646.1936
Ara Miran, H. Yahia
Hate speech recently became a real threat in social media, and almost all social media users are intended to in different ways. Hate speech is not limited to a group or society. It affects many people and can be classified as abusive, offensive, sexism, racism, political affiliation, religious hate, nationality, skin color, disability, gender-based, ethnicity, sexual orientation, immigrants, and others. Many researchers and authorities attempt to discover new procedures to sense hate speech in social media, especially on Facebook and Twitter, and many methods, models, and algorithms are used for this purpose. One of the most valuable models for detecting hate speech is Convolutional Neural Network (CNN). This review aims to assort academic studies on hate speech detection in Twitter using CNN-based models summarize the results of each model to expand the understanding of the recent circumstances of hate speech detection in Twitter. For this purpose, we implemented a broad, automated search using Boolean and Snowballing searching methods to find academic works in this area. Studies and papers have been distinguished, and the following information was obtained and aggregated from each article: authors, publication’s year, the journal name or the conference name, proposed model/method, the aim of the study, the outcome, and the quality of each study. According to the findings, the CNN and CNN-based models are standard models for hate speech detection. Besides, the findings show that other new models have a great compact on hate speech detection, and there is good progress in this field. However, the problems that still exist with hate speech detection models mainly are; most of the models cannot detect hate speech automatically. The methods are not suitable with all the languages, and they are working only with one language; most are best suited with the English language, and when they are used with datasets with other languages. Besides, the models are suffering from confusion in speech classification. Finally, most models are not considering a user-to-user speech in social media.
{"title":"Hate Speech Detection in Social Media (Twitter) Using Neural Network","authors":"Ara Miran, H. Yahia","doi":"10.13052/jmm1550-4646.1936","DOIUrl":"https://doi.org/10.13052/jmm1550-4646.1936","url":null,"abstract":"Hate speech recently became a real threat in social media, and almost all social media users are intended to in different ways. Hate speech is not limited to a group or society. It affects many people and can be classified as abusive, offensive, sexism, racism, political affiliation, religious hate, nationality, skin color, disability, gender-based, ethnicity, sexual orientation, immigrants, and others. Many researchers and authorities attempt to discover new procedures to sense hate speech in social media, especially on Facebook and Twitter, and many methods, models, and algorithms are used for this purpose. One of the most valuable models for detecting hate speech is Convolutional Neural Network (CNN). This review aims to assort academic studies on hate speech detection in Twitter using CNN-based models summarize the results of each model to expand the understanding of the recent circumstances of hate speech detection in Twitter. For this purpose, we implemented a broad, automated search using Boolean and Snowballing searching methods to find academic works in this area. Studies and papers have been distinguished, and the following information was obtained and aggregated from each article: authors, publication’s year, the journal name or the conference name, proposed model/method, the aim of the study, the outcome, and the quality of each study. According to the findings, the CNN and CNN-based models are standard models for hate speech detection. Besides, the findings show that other new models have a great compact on hate speech detection, and there is good progress in this field. However, the problems that still exist with hate speech detection models mainly are; most of the models cannot detect hate speech automatically. The methods are not suitable with all the languages, and they are working only with one language; most are best suited with the English language, and when they are used with datasets with other languages. Besides, the models are suffering from confusion in speech classification. Finally, most models are not considering a user-to-user speech in social media.","PeriodicalId":425561,"journal":{"name":"J. Mobile Multimedia","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134602032","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 : 2023-02-15DOI: 10.13052/jmm1550-4646.1933
Shaiqa Nasreen, A. H. Mir
The investigation as well as recovery of data gathered as of digital devices associated with computer crime is involved in Digital Forensics (DF). In a distributed Cloud Server (CS), DF investigation is more complicated (during collecting, preserving, and reporting the evidence) as well as insecure during gathering evidence as of the cloud sources. Centered on the DF investigation system, numerous works were performed. However, lots of challenges still remain that bring about cybercrime. The work developed a robust cloud forensic investigation system centered upon distributed Cloud Computing (CC) for conquering the challenges. It is framed into the ‘3’ phase (i.e.) originally, Group Key Generation (GKG) phase that enables the authorized user to upload or download the evidence for maintaining the evidence’s trustworthiness. Distributed Key Cipher Policy Elliptic Curve Cryptography (DK-CP-ECC) algorithm performed the Secure Data Transfer (SDT) phase. It aids in maintaining the evidence’s privacy together with confidentiality. Exponential Membership Function Adaptive Neuro-Fuzzy Interference System (EK-ANFIS) carries out the CS selection with the aid of a deer hunting genetic algorithm that evades the reporting issues and renders secure evidence storage. 97% Security Level (SL) is obtained by the proposed work that is better analogized to the prevailing frameworks.
{"title":"Enhancing Cloud Forensic Investigation System in Distributed Cloud Computing Using DK-CP-ECC Algorithm and EK-ANFIS","authors":"Shaiqa Nasreen, A. H. Mir","doi":"10.13052/jmm1550-4646.1933","DOIUrl":"https://doi.org/10.13052/jmm1550-4646.1933","url":null,"abstract":"The investigation as well as recovery of data gathered as of digital devices associated with computer crime is involved in Digital Forensics (DF). In a distributed Cloud Server (CS), DF investigation is more complicated (during collecting, preserving, and reporting the evidence) as well as insecure during gathering evidence as of the cloud sources. Centered on the DF investigation system, numerous works were performed. However, lots of challenges still remain that bring about cybercrime. The work developed a robust cloud forensic investigation system centered upon distributed Cloud Computing (CC) for conquering the challenges. It is framed into the ‘3’ phase (i.e.) originally, Group Key Generation (GKG) phase that enables the authorized user to upload or download the evidence for maintaining the evidence’s trustworthiness. Distributed Key Cipher Policy Elliptic Curve Cryptography (DK-CP-ECC) algorithm performed the Secure Data Transfer (SDT) phase. It aids in maintaining the evidence’s privacy together with confidentiality. Exponential Membership Function Adaptive Neuro-Fuzzy Interference System (EK-ANFIS) carries out the CS selection with the aid of a deer hunting genetic algorithm that evades the reporting issues and renders secure evidence storage. 97% Security Level (SL) is obtained by the proposed work that is better analogized to the prevailing frameworks.","PeriodicalId":425561,"journal":{"name":"J. Mobile Multimedia","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117157733","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 : 2023-02-15DOI: 10.13052/jmm1550-4646.19311
I. Zhuravska, M. Dvoretskyi, I. Kulakovska, Anzhela P. Boiko, S. Dvoretska
The spread of the COVID-19 virus is challenging society to provide medical care to a growing number of patients in the hospital. It is important to determine the patient from whom an urgent appeal was received earlier and to automate the creation of the calls’ queue. Determining the direction of the sound source, which is the patient’s voice, can be realized using the passive acoustic location method. In this case, it is necessary to place sound sensors in the wards with patients. In such a case, these sensors it is expedient to build in the lighting equipment executed in the shape of Platonic polyhedra. The microcontroller system, located inside such a spatial structure, ensures the transmission of sound to a server computer system. The above system alternately records the receipt of urgent appeals, analyzes the location of the sound source, and sends the relevant data to the doctor’s smartphone. The mobile application visualizes information about the location of the patients who need consultation or help. The proposed solution for intelligent analysis of voice appeals by inpatients may also be useful for post-stroke, post-infarction, and other bedridden patients who are unable to call medical staff otherwise than a voice.
{"title":"The Queue's Automated Creation of Doctor's Calls by Patients in the Hospital with Visualization via the Mobile Application","authors":"I. Zhuravska, M. Dvoretskyi, I. Kulakovska, Anzhela P. Boiko, S. Dvoretska","doi":"10.13052/jmm1550-4646.19311","DOIUrl":"https://doi.org/10.13052/jmm1550-4646.19311","url":null,"abstract":"The spread of the COVID-19 virus is challenging society to provide medical care to a growing number of patients in the hospital. It is important to determine the patient from whom an urgent appeal was received earlier and to automate the creation of the calls’ queue. Determining the direction of the sound source, which is the patient’s voice, can be realized using the passive acoustic location method. In this case, it is necessary to place sound sensors in the wards with patients. In such a case, these sensors it is expedient to build in the lighting equipment executed in the shape of Platonic polyhedra. The microcontroller system, located inside such a spatial structure, ensures the transmission of sound to a server computer system. The above system alternately records the receipt of urgent appeals, analyzes the location of the sound source, and sends the relevant data to the doctor’s smartphone. The mobile application visualizes information about the location of the patients who need consultation or help. The proposed solution for intelligent analysis of voice appeals by inpatients may also be useful for post-stroke, post-infarction, and other bedridden patients who are unable to call medical staff otherwise than a voice.","PeriodicalId":425561,"journal":{"name":"J. Mobile Multimedia","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124812034","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 : 2023-02-15DOI: 10.13052/jmm1550-4646.1931
S. Kharche, J. Kharche
The Healthcare industry is experiencing a sea change due to the availability of disruptive communication technologies. Augmented reality, virtual reality, haptics, robotic assistance, and the ubiquitous, highly reliable low latency communication based on intelligence; support remote surgery in real-time. The sixth-generation (6G) mobile communication supporting disruptive technologies and intelligence; will realize remote healthcare for people of all ages in three-dimensional (3D) space. The discussion on the application of disruptive technologies in 6G intelligent healthcare is found insufficient in the literature. The objective of the paper is to propose a conceptual framework for 6G intelligent healthcare and elaborate on applications of the various disruptive technologies with their associated challenges. The state-of-the-art technologies viz; digital twin, blockchain, optical wireless communication, wireless energy transfer, tactile Internet, holographic communication, quantum communication, artificial intelligence, etc., are elaborated for their applications and challenges in healthcare. The fifth-generation (5G) lacks supporting disruptive technologies, intelligence, and ultra-low latency requirements of remote healthcare. 6G will support highly reliable (99.9999%), secure, ultra-low latency (<0.1 microseconds), and ultra-high-speed communication (>1 Tbps) required for remote and ubiquitous healthcare. Moreover, 6G supports the Internet of Everything, Internet of skills, and Internet of thinking to realize and optimize healthcare globally.
{"title":"6G Intelligent Healthcare Framework: A Review on Role of Technologies, Challenges and Future Directions","authors":"S. Kharche, J. Kharche","doi":"10.13052/jmm1550-4646.1931","DOIUrl":"https://doi.org/10.13052/jmm1550-4646.1931","url":null,"abstract":"The Healthcare industry is experiencing a sea change due to the availability of disruptive communication technologies. Augmented reality, virtual reality, haptics, robotic assistance, and the ubiquitous, highly reliable low latency communication based on intelligence; support remote surgery in real-time. The sixth-generation (6G) mobile communication supporting disruptive technologies and intelligence; will realize remote healthcare for people of all ages in three-dimensional (3D) space. The discussion on the application of disruptive technologies in 6G intelligent healthcare is found insufficient in the literature. The objective of the paper is to propose a conceptual framework for 6G intelligent healthcare and elaborate on applications of the various disruptive technologies with their associated challenges. The state-of-the-art technologies viz; digital twin, blockchain, optical wireless communication, wireless energy transfer, tactile Internet, holographic communication, quantum communication, artificial intelligence, etc., are elaborated for their applications and challenges in healthcare. The fifth-generation (5G) lacks supporting disruptive technologies, intelligence, and ultra-low latency requirements of remote healthcare. 6G will support highly reliable (99.9999%), secure, ultra-low latency (<0.1 microseconds), and ultra-high-speed communication (>1 Tbps) required for remote and ubiquitous healthcare. Moreover, 6G supports the Internet of Everything, Internet of skills, and Internet of thinking to realize and optimize healthcare globally.","PeriodicalId":425561,"journal":{"name":"J. Mobile Multimedia","volume":"134 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116335883","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 : 2023-02-15DOI: 10.13052/jmm1550-4646.1934
Aiman M. Ayyal Awwad
Mobile learning is becoming more and more popular today. It gained popularity recently due to the COVID-19 pandemic restrictions in 2020. However, to provide learners with appropriate educational materials in such a mobile environment, the characteristics and context of the learners must be considered. Therefore, in this paper, we propose a framework for providing an adaptive context-aware learning process considering a combination of student learning models and principles of Universal Design for Learning (UDL). The proposed system consists of components capable of detecting changes in context and adapting the way the application responds and behaves. The framework uses a machine-learning algorithm to predict learners’ characteristics and follow UDL principles to deliver enriched user experience and location-aware content and activities. An online survey was conducted with 20 undergraduate students. We analyzed their levels of satisfaction with the proposed m-learning system. From the analyzed data, we noticed that the average rating values are close to 4.5, which indicates that the proposed m-learning system complies with UDL principles and provides an adaptive and localized learning environment, thus enhancing the efficiency of the learning process and experiences. The study also investigated the impact of factors (i.e., noise level, physical activity, and location) on learners’ concentration towards the learning process. The results show that these factors have a significant impact on the learner’s concentration level.
{"title":"A Universal Design for an Adaptive Context-Aware Mobile Cloud Learning Framework Using Machine Learning","authors":"Aiman M. Ayyal Awwad","doi":"10.13052/jmm1550-4646.1934","DOIUrl":"https://doi.org/10.13052/jmm1550-4646.1934","url":null,"abstract":"Mobile learning is becoming more and more popular today. It gained popularity recently due to the COVID-19 pandemic restrictions in 2020. However, to provide learners with appropriate educational materials in such a mobile environment, the characteristics and context of the learners must be considered. Therefore, in this paper, we propose a framework for providing an adaptive context-aware learning process considering a combination of student learning models and principles of Universal Design for Learning (UDL). The proposed system consists of components capable of detecting changes in context and adapting the way the application responds and behaves. The framework uses a machine-learning algorithm to predict learners’ characteristics and follow UDL principles to deliver enriched user experience and location-aware content and activities. An online survey was conducted with 20 undergraduate students. We analyzed their levels of satisfaction with the proposed m-learning system. From the analyzed data, we noticed that the average rating values are close to 4.5, which indicates that the proposed m-learning system complies with UDL principles and provides an adaptive and localized learning environment, thus enhancing the efficiency of the learning process and experiences. The study also investigated the impact of factors (i.e., noise level, physical activity, and location) on learners’ concentration towards the learning process. The results show that these factors have a significant impact on the learner’s concentration level.","PeriodicalId":425561,"journal":{"name":"J. Mobile Multimedia","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121958024","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 : 2023-02-15DOI: 10.13052/jmm1550-4646.1938
Oleksandr Striuk, Yuriy Kondratenko
This article aims to explore and research GANs as a tool for mobile devices that can generate high-resolution images from low-resolution samples and reduce blurring. In addition, the authors also analyse the specifics of GAN, SRGAN, and ESRGAN loss functions and their features. GANs are widely used for a vast range of applied tasks for image manipulations. They’re able to synthesize, combine, and restore graphical samples of high quality that are almost indistinguishable from real data. The main scope of the research is to study the possibility to use GANs for the said tasks, and their potential implementation in mobile applications.
{"title":"Implementation of Generative Adversarial Networks in Mobile Applications for Image Data Enhancement","authors":"Oleksandr Striuk, Yuriy Kondratenko","doi":"10.13052/jmm1550-4646.1938","DOIUrl":"https://doi.org/10.13052/jmm1550-4646.1938","url":null,"abstract":"This article aims to explore and research GANs as a tool for mobile devices that can generate high-resolution images from low-resolution samples and reduce blurring. In addition, the authors also analyse the specifics of GAN, SRGAN, and ESRGAN loss functions and their features. GANs are widely used for a vast range of applied tasks for image manipulations. They’re able to synthesize, combine, and restore graphical samples of high quality that are almost indistinguishable from real data. The main scope of the research is to study the possibility to use GANs for the said tasks, and their potential implementation in mobile applications.","PeriodicalId":425561,"journal":{"name":"J. Mobile Multimedia","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129596010","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 : 2023-02-15DOI: 10.13052/jmm1550-4646.1939
O. Kozlov, Y. Kondratenko, O. Skakodub, O. Gerasin, A. Topalov
This paper is dedicated to the development and research of the advanced approach for optimization of fuzzy control systems (FCS) for mobile robots (MR) with remote control based on bioinspired swarm techniques. The proposed approach makes it possible to create effective intelligent control systems for MRs based on the principles of hierarchical multi-level control, remote IoT-based control, fuzzy logic control, and intelligent optimization of fuzzy control devices. The applied hybrid particle swarm optimization (PSO) techniques with elite strategy allow effectively optimizing various parameters of FCSs, finding the optimal solution to the problem, and, at the same time, have a higher convergence rate compared with the basic PSO algorithms. To evaluate the effectiveness of the obtained advanced approach based on hybrid swarm techniques, the optimization process of the FCS for the speed control of the multi-purpose caterpillar MR, which can move on inclined and vertical ferromagnetic surfaces, is carried out. The presented research results fully confirm the high efficiency of the proposed approach, as well as the expediency of its application for the optimization of fuzzy control systems for various remotely controlled mobile robots.
{"title":"Swarm Optimization of Fuzzy Systems for Mobile Robots with Remote Control","authors":"O. Kozlov, Y. Kondratenko, O. Skakodub, O. Gerasin, A. Topalov","doi":"10.13052/jmm1550-4646.1939","DOIUrl":"https://doi.org/10.13052/jmm1550-4646.1939","url":null,"abstract":"This paper is dedicated to the development and research of the advanced approach for optimization of fuzzy control systems (FCS) for mobile robots (MR) with remote control based on bioinspired swarm techniques. The proposed approach makes it possible to create effective intelligent control systems for MRs based on the principles of hierarchical multi-level control, remote IoT-based control, fuzzy logic control, and intelligent optimization of fuzzy control devices. The applied hybrid particle swarm optimization (PSO) techniques with elite strategy allow effectively optimizing various parameters of FCSs, finding the optimal solution to the problem, and, at the same time, have a higher convergence rate compared with the basic PSO algorithms. To evaluate the effectiveness of the obtained advanced approach based on hybrid swarm techniques, the optimization process of the FCS for the speed control of the multi-purpose caterpillar MR, which can move on inclined and vertical ferromagnetic surfaces, is carried out. The presented research results fully confirm the high efficiency of the proposed approach, as well as the expediency of its application for the optimization of fuzzy control systems for various remotely controlled mobile robots.","PeriodicalId":425561,"journal":{"name":"J. Mobile Multimedia","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130187727","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 : 2023-02-15DOI: 10.13052/jmm1550-4646.1932
Nisha P. Shetty, Balachandra, D. R. Teja, L. Maben, Tummala Srinag Vinil
Twitter is one of the most popular social networking sites today, and it has become a critical tool for gathering data from numerous individuals throughout the world. The platform hosts a variety of debates spanning from current events and news to entertainment, advertising, and technology. In contrast to earlier approaches, the proposed work employs the concept of both direct (via tweets) and indirect stance detection (via homophily elements) to infer sensitive attributes. Along with attribute-based inference, the proposed work also matches user profiles across cross platforms via user-generated posts. Unlike prior efforts, usernames are not included in the feature set here since they are a bit of a giveaway. Bio-inspired algorithms are used along with ensemble methods to extract the best set of features.
{"title":"Identity - Attribute Inference in Online Social Network(s) Using Bio-Inspired Algorithms and Machine Learning Approaches","authors":"Nisha P. Shetty, Balachandra, D. R. Teja, L. Maben, Tummala Srinag Vinil","doi":"10.13052/jmm1550-4646.1932","DOIUrl":"https://doi.org/10.13052/jmm1550-4646.1932","url":null,"abstract":"Twitter is one of the most popular social networking sites today, and it has become a critical tool for gathering data from numerous individuals throughout the world. The platform hosts a variety of debates spanning from current events and news to entertainment, advertising, and technology. In contrast to earlier approaches, the proposed work employs the concept of both direct (via tweets) and indirect stance detection (via homophily elements) to infer sensitive attributes. Along with attribute-based inference, the proposed work also matches user profiles across cross platforms via user-generated posts. Unlike prior efforts, usernames are not included in the feature set here since they are a bit of a giveaway. Bio-inspired algorithms are used along with ensemble methods to extract the best set of features.","PeriodicalId":425561,"journal":{"name":"J. Mobile Multimedia","volume":"216 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130666879","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 : 2022-11-15DOI: 10.13052/jmm1550-4646.1923
Hui Chen
Human motion capturing is frequently used in sports research. The focus of this research is to help the user choose an appropriate motion capture system for their experimental setup for sports activities that addresses the challenges of linear size and acceptable fits with non-linear futures. In this paper, the eigenvalue combination was used to represent different motion postures, so as to construct an index space related to the motion sequence; and according to this index space, fast and accurate motion retrieval was done; at the same time, a motion data segmentation method based on MVU nonlinear dimensionality reduction was proposed. MVU can overcome the shortcomings that the linear size is reduced and difficult to deal with nonlinear features, and can better fit human motion data, and achieve higher accuracy.
{"title":"Human Motion Capture Data Retrieval and Segmentation Technology for Professional Sports Training","authors":"Hui Chen","doi":"10.13052/jmm1550-4646.1923","DOIUrl":"https://doi.org/10.13052/jmm1550-4646.1923","url":null,"abstract":"Human motion capturing is frequently used in sports research. The focus of this research is to help the user choose an appropriate motion capture system for their experimental setup for sports activities that addresses the challenges of linear size and acceptable fits with non-linear futures. In this paper, the eigenvalue combination was used to represent different motion postures, so as to construct an index space related to the motion sequence; and according to this index space, fast and accurate motion retrieval was done; at the same time, a motion data segmentation method based on MVU nonlinear dimensionality reduction was proposed. MVU can overcome the shortcomings that the linear size is reduced and difficult to deal with nonlinear features, and can better fit human motion data, and achieve higher accuracy.","PeriodicalId":425561,"journal":{"name":"J. Mobile Multimedia","volume":"72 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130800074","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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