Pub Date : 2024-03-29DOI: 10.58346/jowua.2024.i1.014
M. Rajesh, S.R. Nagaraja, P. Suja
Exploration of an area by a group of robots is an active research field of robotics as multi-robot exploration is applied extensively in several real life scenarios. The major challenges in such exploration are the availability of communication infrastructure as communication plays a key role in the coordination of team of robots for effective coverage of the area under exploration. But in disaster affected scenarios, there will be no existing communication infrastructure available and this makes the exploration ineffective and time consuming. Another challenge is in the localization process each robot is carrying out to update the map as well as for exchange of information with other robots. In this paper, an enhanced Multi-robot exploration strategy is introduced. The base of the exploration strategy is two techniques. The first one being localization of each robot involved in the exploration and this is done with the help of trilateration where three anchors are required which will be setup before the exploration starts. The second part is navigation and avoiding overlapping or missing out sectors while exploring. This is done with help of a navigation policy called frontier cell based approach. Further to this, the exploration strategy is supported with localization error reduction scheme in which the localization error is reduced with the help of Particle Swarm Optimization (PSO). The entire scheme is simulated and exploration time is analyzed for the same environment in different obstacle density and different number of robots to perform exploration. The results show the scheme is better than many existing multi-robot exploration strategies. Precisely, the proposed scheme is able to reduce the localization error to a threshold level of 0.02cm or below which can be considered as novel contribution towards the exploration strategies.
{"title":"Multi – Robot Exploration Supported by Enhanced Localization with Reduction of Localization Error Using Particle Swarm Optimization","authors":"M. Rajesh, S.R. Nagaraja, P. Suja","doi":"10.58346/jowua.2024.i1.014","DOIUrl":"https://doi.org/10.58346/jowua.2024.i1.014","url":null,"abstract":"Exploration of an area by a group of robots is an active research field of robotics as multi-robot exploration is applied extensively in several real life scenarios. The major challenges in such exploration are the availability of communication infrastructure as communication plays a key role in the coordination of team of robots for effective coverage of the area under exploration. But in disaster affected scenarios, there will be no existing communication infrastructure available and this makes the exploration ineffective and time consuming. Another challenge is in the localization process each robot is carrying out to update the map as well as for exchange of information with other robots. In this paper, an enhanced Multi-robot exploration strategy is introduced. The base of the exploration strategy is two techniques. The first one being localization of each robot involved in the exploration and this is done with the help of trilateration where three anchors are required which will be setup before the exploration starts. The second part is navigation and avoiding overlapping or missing out sectors while exploring. This is done with help of a navigation policy called frontier cell based approach. Further to this, the exploration strategy is supported with localization error reduction scheme in which the localization error is reduced with the help of Particle Swarm Optimization (PSO). The entire scheme is simulated and exploration time is analyzed for the same environment in different obstacle density and different number of robots to perform exploration. The results show the scheme is better than many existing multi-robot exploration strategies. Precisely, the proposed scheme is able to reduce the localization error to a threshold level of 0.02cm or below which can be considered as novel contribution towards the exploration strategies.","PeriodicalId":38235,"journal":{"name":"Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications","volume":"39 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140366862","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 : 2024-03-29DOI: 10.58346/jowua.2024.i1.007
Hyun Woo Song, Jaeho Yu
This study compared augmented reality (AR)-based proprioception training to traditional therapy to determine if the two tasks together were effective in improving postural stability, proprioception, and cognition. Forty-five healthy adults in their 20s were randomized into three groups: AR-based DT, AR-based proprioceptive exercise, and therapist-supervised exercise. Paired t-test and independent t-test were used to determine the within and between group effects, and the three groups were subjected to one-way ANOVA and Bonferroni's post hoc analysis. For postural stability, stability index and postural stability improved post-intervention in all groups (p<.05), with no differences between groups (p>.05). positioning sensation improved in all groups (p<.05), with no difference between groups (p>.05). Cognitive parameters showed significant differences in recognition and calculation in all groups after the intervention (p<.05), and no significant differences in ordering (p<.05). Thus, AR-based interventions have shown similar effects to therapists, improving cognitive performance on both tasks, and can be selected in some cases.
这项研究将基于增强现实（AR）的本体感觉训练与传统疗法进行了比较，以确定这两项任务结合在一起是否能有效改善姿势稳定性、本体感觉和认知能力。45 名 20 多岁的健康成年人被随机分为三组：基于 AR 的 DT 组、基于 AR 的本体感觉训练组和治疗师指导的训练组。采用配对 t 检验和独立 t 检验来确定组内和组间效应，并对三组进行单因素方差分析和 Bonferroni 事后分析。在姿势稳定性方面，所有组的稳定性指数和姿势稳定性在干预后都有所改善（P.05）。认知参数显示，干预后各组在识别和计算方面均有显著差异（P<.05），而在排序方面无显著差异（P<.05）。因此，基于增强现实技术的干预显示出与治疗师相似的效果，提高了这两项任务的认知表现，在某些情况下可以选用。
{"title":"Effects of Augmented Reality based Dual-Task Proprioceptive Training on Postural Stability, Positioning Sensation and Cognition","authors":"Hyun Woo Song, Jaeho Yu","doi":"10.58346/jowua.2024.i1.007","DOIUrl":"https://doi.org/10.58346/jowua.2024.i1.007","url":null,"abstract":"This study compared augmented reality (AR)-based proprioception training to traditional therapy to determine if the two tasks together were effective in improving postural stability, proprioception, and cognition. Forty-five healthy adults in their 20s were randomized into three groups: AR-based DT, AR-based proprioceptive exercise, and therapist-supervised exercise. Paired t-test and independent t-test were used to determine the within and between group effects, and the three groups were subjected to one-way ANOVA and Bonferroni's post hoc analysis. For postural stability, stability index and postural stability improved post-intervention in all groups (p<.05), with no differences between groups (p>.05). positioning sensation improved in all groups (p<.05), with no difference between groups (p>.05). Cognitive parameters showed significant differences in recognition and calculation in all groups after the intervention (p<.05), and no significant differences in ordering (p<.05). Thus, AR-based interventions have shown similar effects to therapists, improving cognitive performance on both tasks, and can be selected in some cases.","PeriodicalId":38235,"journal":{"name":"Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications","volume":"40 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140368278","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 : 2024-03-29DOI: 10.58346/jowua.2024.i1.009
Dr. Agung Triayudi, Rima Tamara Aldisa, S. Sumiati
Educational systems designed to meet the needs of academic advisors about adaptive learning will always be an essential issue, as this will be the beginning of the development of intelligent learning methods. In an educational institution, such as in a university environment, academic guidance carried out by a teacher to his students significantly affects the student's performance in the lecture stage, where educational guidance that goes poorly is allegedly causing difficulties for the student in carrying out his studies, or worst chance of dropping out of school. Therefore, this study aims to explore the potential and capabilities contained in the features of Educational Data Mining to predict students' learning performance which will later present various recommendations for academic guidance methods based on data analysis related to academic records and social and economic related data. In this study, we will propose data analysis and testing from recorded student data in an information technology class from a private university in Jakarta. The modelling presented in this study uses the Decision Tree, Neural Networks, and Naïve Bayes methods, which then implement these algorithms on academic data from 300 students of the 2017-2019 and 2018-2020 Information Systems and Informatics study program. From the implementation of data mining techniques in this study, performance results were obtained, which stated that the designed framework provided accurate predictions related to student performance.
{"title":"New Framework of Educational Data Mining to Predict Student Learning Performance","authors":"Dr. Agung Triayudi, Rima Tamara Aldisa, S. Sumiati","doi":"10.58346/jowua.2024.i1.009","DOIUrl":"https://doi.org/10.58346/jowua.2024.i1.009","url":null,"abstract":"Educational systems designed to meet the needs of academic advisors about adaptive learning will always be an essential issue, as this will be the beginning of the development of intelligent learning methods. In an educational institution, such as in a university environment, academic guidance carried out by a teacher to his students significantly affects the student's performance in the lecture stage, where educational guidance that goes poorly is allegedly causing difficulties for the student in carrying out his studies, or worst chance of dropping out of school. Therefore, this study aims to explore the potential and capabilities contained in the features of Educational Data Mining to predict students' learning performance which will later present various recommendations for academic guidance methods based on data analysis related to academic records and social and economic related data. In this study, we will propose data analysis and testing from recorded student data in an information technology class from a private university in Jakarta. The modelling presented in this study uses the Decision Tree, Neural Networks, and Naïve Bayes methods, which then implement these algorithms on academic data from 300 students of the 2017-2019 and 2018-2020 Information Systems and Informatics study program. From the implementation of data mining techniques in this study, performance results were obtained, which stated that the designed framework provided accurate predictions related to student performance.","PeriodicalId":38235,"journal":{"name":"Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications","volume":"56 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140365899","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 : 2024-03-29DOI: 10.58346/jowua.2024.i1.012
Dr. Walaa Saber Ismail
AI has significantly altered the way humans interact with technology. It is important to observe the impact of Natural Language Interfaces (NLIs) on user experiences in Human-Centric AI across various industries. Therefore, we specifically focus on the influence of Human-Centric AI and user interactions within AI chatbots in the United Arab Emirates (UAE). The aim of this study is to assess the factors that influence the acceptance of AI, examine its practical implications across different industries, and offer valuable insights for the responsible development of AI. A quantitative survey methodology was employed, involving 230 participants in the UAE. The research design, data collection, and analysis followed the Unified Theory of Acceptance and Use of Technology (UTAUT) model, which emphasizes performance expectancy, effort expectancy, social influence, and facilitating conditions. The survey encompassed a variety of participants from various organizations, with a majority expressing positive attitudes towards AI chatbots. The survey found that 80% of users agreed that AI systems improve task efficiency, 84% believe they help achieve goals, and 84% view them as practical. According to 75% of participants, the social impact is strongly influenced by AI chatbot system adoption. However, 80% understood the relevance of organizational infrastructure and favorable conditions. In particular, 72% of users stated that Natural Language Interfaces transform, indicating satisfactory user experiences. These features demonstrate the influence of Human-Centric AI adoption and its use in different organizations. Natural language interfaces play a critical role in improving human-centered AI user experiences, investigating theoretical issues and real-world applications, and providing guidance for the ethical use of AI.
{"title":"Human-Centric AI : Enhancing User Experience through Natural Language Interfaces","authors":"Dr. Walaa Saber Ismail","doi":"10.58346/jowua.2024.i1.012","DOIUrl":"https://doi.org/10.58346/jowua.2024.i1.012","url":null,"abstract":"AI has significantly altered the way humans interact with technology. It is important to observe the impact of Natural Language Interfaces (NLIs) on user experiences in Human-Centric AI across various industries. Therefore, we specifically focus on the influence of Human-Centric AI and user interactions within AI chatbots in the United Arab Emirates (UAE). The aim of this study is to assess the factors that influence the acceptance of AI, examine its practical implications across different industries, and offer valuable insights for the responsible development of AI. A quantitative survey methodology was employed, involving 230 participants in the UAE. The research design, data collection, and analysis followed the Unified Theory of Acceptance and Use of Technology (UTAUT) model, which emphasizes performance expectancy, effort expectancy, social influence, and facilitating conditions. The survey encompassed a variety of participants from various organizations, with a majority expressing positive attitudes towards AI chatbots. The survey found that 80% of users agreed that AI systems improve task efficiency, 84% believe they help achieve goals, and 84% view them as practical. According to 75% of participants, the social impact is strongly influenced by AI chatbot system adoption. However, 80% understood the relevance of organizational infrastructure and favorable conditions. In particular, 72% of users stated that Natural Language Interfaces transform, indicating satisfactory user experiences. These features demonstrate the influence of Human-Centric AI adoption and its use in different organizations. Natural language interfaces play a critical role in improving human-centered AI user experiences, investigating theoretical issues and real-world applications, and providing guidance for the ethical use of AI.","PeriodicalId":38235,"journal":{"name":"Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications","volume":"61 41","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140365029","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 : 2024-03-29DOI: 10.58346/jowua.2024.i1.008
B. Fakiha
As critical infrastructure networks become more interconnected and digitalized, they confront increased cyber threats fueled by the growing adoption of digitalized working and general operation methods around the world. This research delves into the complex topic of critical infrastructure network security by examining the hidden challenges and best forensic techniques used to safeguard these crucial systems. The study utilizes a comprehensive data collection approach that integrates an experiment and a case study to provide an in-depth understanding of this essential subject. It assesses the efficacy of various digital forensic procedures customized for critical infrastructure network protection by using meticulously designed experiments within controlled simulated environments. The findings highlight the wide range of challenges and threats that organizations tasked with maintaining and securing these networks encounter. The case study illuminates how forensic practices can be used in incident response and recovery situations. The results highlight the significance of a diversified approach to safeguarding critical infrastructure networks. They emphasize the need for modern methods and practices, such as blockchain technology and Artificial intelligence, by analyzing findings from the experiment and the case study.
{"title":"Investigating the Secrets, New Challenges, and Best Forensic Methods for Securing Critical Infrastructure Networks","authors":"B. Fakiha","doi":"10.58346/jowua.2024.i1.008","DOIUrl":"https://doi.org/10.58346/jowua.2024.i1.008","url":null,"abstract":"As critical infrastructure networks become more interconnected and digitalized, they confront increased cyber threats fueled by the growing adoption of digitalized working and general operation methods around the world. This research delves into the complex topic of critical infrastructure network security by examining the hidden challenges and best forensic techniques used to safeguard these crucial systems. The study utilizes a comprehensive data collection approach that integrates an experiment and a case study to provide an in-depth understanding of this essential subject. It assesses the efficacy of various digital forensic procedures customized for critical infrastructure network protection by using meticulously designed experiments within controlled simulated environments. The findings highlight the wide range of challenges and threats that organizations tasked with maintaining and securing these networks encounter. The case study illuminates how forensic practices can be used in incident response and recovery situations. The results highlight the significance of a diversified approach to safeguarding critical infrastructure networks. They emphasize the need for modern methods and practices, such as blockchain technology and Artificial intelligence, by analyzing findings from the experiment and the case study.","PeriodicalId":38235,"journal":{"name":"Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications","volume":"6 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140365373","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 : 2024-03-29DOI: 10.58346/jowua.2024.i1.004
Dr. Fernando Escobedo, Dr. Henry Bernardo Garay Canales, Fernando Willy Morillo Galarza, Dr. Carlos Miguel Aguilar Saldaña, Dr. Eddy Miguel Aguirre Reyes, Dr. César Augusto Flores Tananta
Assuring the safe and effective operation of a company's technological infrastructure is an essential part of business management. Monitoring, administering, and troubleshooting the many components and systems of the network are all part of the process. Additionally, it is the responsibility of business administrators to find methods to enhance the existing system, as well as to make certain that all resources are distributed in such a manner that their use may be maximized. Management of the business is a key component of the technological infrastructure of any organization, and it is of the utmost importance that it be carried out effectively in order to guarantee a safe and effective operation. It is essential for network operators to discover strategies to improve the energy efficiency of their networks without adversely affecting the quality of service (QoS) for reasons related to both cost and sustainability. In this research, an in-depth analysis of business management techniques for effective resource utilization is presented. This analysis begins with the design of the network and continues all the way through to the delivery of accurate data. An Energy Efficient Network (EEN) must be able to give programmability and flexibility to network infrastructures, as well as the ability to operate networks in a rapid way and provide operators with more control. It is impossible to ignore energy throughout the process of meeting the needs and requirements of network services, especially when taking into consideration the consequences on the long-term viability of both the environment and businesses. Energy efficiency in both current and future networks is the topic of discussion in this research. The findings indicate that energy efficient networks are successful in overcoming the present issues that stand in the way of the application of energy efficiency techniques, also the discussed model are effective in addressing some of the obstacles that are encountered by small and medium-sized businesses.
{"title":"Energy Efficient Business Management System for Improving QoS in Network Model","authors":"Dr. Fernando Escobedo, Dr. Henry Bernardo Garay Canales, Fernando Willy Morillo Galarza, Dr. Carlos Miguel Aguilar Saldaña, Dr. Eddy Miguel Aguirre Reyes, Dr. César Augusto Flores Tananta","doi":"10.58346/jowua.2024.i1.004","DOIUrl":"https://doi.org/10.58346/jowua.2024.i1.004","url":null,"abstract":"Assuring the safe and effective operation of a company's technological infrastructure is an essential part of business management. Monitoring, administering, and troubleshooting the many components and systems of the network are all part of the process. Additionally, it is the responsibility of business administrators to find methods to enhance the existing system, as well as to make certain that all resources are distributed in such a manner that their use may be maximized. Management of the business is a key component of the technological infrastructure of any organization, and it is of the utmost importance that it be carried out effectively in order to guarantee a safe and effective operation. It is essential for network operators to discover strategies to improve the energy efficiency of their networks without adversely affecting the quality of service (QoS) for reasons related to both cost and sustainability. In this research, an in-depth analysis of business management techniques for effective resource utilization is presented. This analysis begins with the design of the network and continues all the way through to the delivery of accurate data. An Energy Efficient Network (EEN) must be able to give programmability and flexibility to network infrastructures, as well as the ability to operate networks in a rapid way and provide operators with more control. It is impossible to ignore energy throughout the process of meeting the needs and requirements of network services, especially when taking into consideration the consequences on the long-term viability of both the environment and businesses. Energy efficiency in both current and future networks is the topic of discussion in this research. The findings indicate that energy efficient networks are successful in overcoming the present issues that stand in the way of the application of energy efficiency techniques, also the discussed model are effective in addressing some of the obstacles that are encountered by small and medium-sized businesses.","PeriodicalId":38235,"journal":{"name":"Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications","volume":"62 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140366440","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 : 2024-03-29DOI: 10.58346/jowua.2024.i1.006
Milind Bhilavade, Dr.K.S. Shivaprakasha, Dr. Meenakshi R. Patil, D. L. Admuthe
Fingerprint reconstruction methods have been initially proposed to spoof the fingerprint identification systems, wherein the fingerprints are generated from the fingerprint features stored in the database for template matching/identification purpose. The reconstructed fingerprints attempt to validate in the absence of the user/person. The poor fingerprint Images with scratches on fingerprint image or latent fingerprints or overlapping fingerprints shall also be reconstructed for personality identification. In this paper we discuss the two fingerprint reconstruction methods, one which uses minutiae features for reconstruction and the other one uses deep learning methods to reconstruct the fingerprint images. The poor fingerprint image which fails to validate the identity due to various reasons like poor skin condition/large cuts on the fingers/wet fingers/poor scanning of images shall be reconstructed for increasing the matching accuracy. The requirement of performance measure parameters used for evaluation of these systems are equal error rate, false acceptance rate, false rejection rate and average matching score. The deep learning methods are more suitable for reconstructing the fingerprint images that appear damaged due to poor skin condition/large cuts on the fingers/wet fingers/poor scanning of images. In terms of matching score comparison, the deep learning methods have matching scores in between 23-94% whereas for minutiae-based techniques the matching score is between 82 and 99.99%. The other performance parameter is the equal error rate (ERR) required to meet has to be closer to 0. The matching score is computed with the assumptions of false acceptance rate (FAR) ranging from 1% to 0%.
{"title":"Fingerprint Reconstruction: Approaches to Improve Fingerprint Images","authors":"Milind Bhilavade, Dr.K.S. Shivaprakasha, Dr. Meenakshi R. Patil, D. L. Admuthe","doi":"10.58346/jowua.2024.i1.006","DOIUrl":"https://doi.org/10.58346/jowua.2024.i1.006","url":null,"abstract":"Fingerprint reconstruction methods have been initially proposed to spoof the fingerprint identification systems, wherein the fingerprints are generated from the fingerprint features stored in the database for template matching/identification purpose. The reconstructed fingerprints attempt to validate in the absence of the user/person. The poor fingerprint Images with scratches on fingerprint image or latent fingerprints or overlapping fingerprints shall also be reconstructed for personality identification. In this paper we discuss the two fingerprint reconstruction methods, one which uses minutiae features for reconstruction and the other one uses deep learning methods to reconstruct the fingerprint images. The poor fingerprint image which fails to validate the identity due to various reasons like poor skin condition/large cuts on the fingers/wet fingers/poor scanning of images shall be reconstructed for increasing the matching accuracy. The requirement of performance measure parameters used for evaluation of these systems are equal error rate, false acceptance rate, false rejection rate and average matching score. The deep learning methods are more suitable for reconstructing the fingerprint images that appear damaged due to poor skin condition/large cuts on the fingers/wet fingers/poor scanning of images. In terms of matching score comparison, the deep learning methods have matching scores in between 23-94% whereas for minutiae-based techniques the matching score is between 82 and 99.99%. The other performance parameter is the equal error rate (ERR) required to meet has to be closer to 0. The matching score is computed with the assumptions of false acceptance rate (FAR) ranging from 1% to 0%.","PeriodicalId":38235,"journal":{"name":"Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications","volume":"41 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140366695","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 : 2024-03-29DOI: 10.58346/jowua.2024.i1.010
Ahmad AL Smadi, Dr. Ahed Abugabah, Mutasem K. Al-smadi, A. Al-Smadi
Fighting the outbreak of COVID-19 is now one of humanity's most critical matters. Rapid detection and isolation of infected people are crucial for decelerating the disease's spread. Due to the pandemic, the conventional technique for COVID-19 detection, reverse transcription-polymerase chain reaction, is time-consuming and in small abundance. Therefore, studies have been searching for alternate methods for detecting COVID-19, and thus applying deep learning methods to patients' chest images has been rendering impressive performance. The primary objective of this study is to suggest a technique for COVID-19 detection in chest images that is both efficient and reliable. We propose a deep learning method for COVID-19 classification based on a modified UNet called (Covid-MUNet). The Covid-MUNet model is trained using publicly available datasets, including chest X-ray images for multi-class classification (3-class and 4-classes) and CT scans images for binary/multi-class classification (2-classes and 3-classes). Using chest images, the Covid-MUNet is a successful methodology that helps physicians rapidly identify patients with COVID-19, thereby delaying the fast spread of COVID-19. The proposed model achieved an overall accuracy of 97.44% in classifying three categories (COVID-19, Normal, and Pneumonia) and an accuracy of 96.57% in classifying two categories (COVID-19 and Normal).
{"title":"Smart Medical Application of Deep Learning (MUNet) for Detection of COVID-19 from Chest Images","authors":"Ahmad AL Smadi, Dr. Ahed Abugabah, Mutasem K. Al-smadi, A. Al-Smadi","doi":"10.58346/jowua.2024.i1.010","DOIUrl":"https://doi.org/10.58346/jowua.2024.i1.010","url":null,"abstract":"Fighting the outbreak of COVID-19 is now one of humanity's most critical matters. Rapid detection and isolation of infected people are crucial for decelerating the disease's spread. Due to the pandemic, the conventional technique for COVID-19 detection, reverse transcription-polymerase chain reaction, is time-consuming and in small abundance. Therefore, studies have been searching for alternate methods for detecting COVID-19, and thus applying deep learning methods to patients' chest images has been rendering impressive performance. The primary objective of this study is to suggest a technique for COVID-19 detection in chest images that is both efficient and reliable. We propose a deep learning method for COVID-19 classification based on a modified UNet called (Covid-MUNet). The Covid-MUNet model is trained using publicly available datasets, including chest X-ray images for multi-class classification (3-class and 4-classes) and CT scans images for binary/multi-class classification (2-classes and 3-classes). Using chest images, the Covid-MUNet is a successful methodology that helps physicians rapidly identify patients with COVID-19, thereby delaying the fast spread of COVID-19. The proposed model achieved an overall accuracy of 97.44% in classifying three categories (COVID-19, Normal, and Pneumonia) and an accuracy of 96.57% in classifying two categories (COVID-19 and Normal).","PeriodicalId":38235,"journal":{"name":"Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications","volume":"19 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140368363","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 : 2024-03-29DOI: 10.58346/jowua.2024.i1.016
Ronald M. Hernández, Dr. Walter Antonio Campos Ugaz, Dr. Segundo Juan Sanchez Tarrillo, Dr. Silvia Josefina Aguinaga Vasquez, Sara Esther Liza Ordoñez, Ronald Avellaneda Montenegro, Dr. Dora Elisa Elías Martínez, Dr. Doris E. Fuster- Guillen
Recently, the education sector has undergone a notable change due to the incorporation of technology, resulting in the emergence of Educational Technology (EdTech). This new trend has completely transformed the learning process for students, teaching methods for educators, and operations of educational institutions. Due to EdTech, education has become simpler to access, engaging, and efficient, providing customized and diverse learning experiences. This article explores into the significant influence of EdTech on the field of education and the promising opportunities it offers for the future. EdTech has become a significant enabler, allowing institutions to meet evolving student needs and cultivate new skillsets without being limited by geographical barriers. EdTech integrates digital and technological media with conventional methods of instruction to enable various forms of learning, offering adaptability, enhancing engagement, and providing high-quality educational solutions. EdTech tools empower educators to track student engagement, encourage interactive and creative learning experiences, and stand for human-centred education focusing on critical thinking, innovation, and entrepreneurial activity. Exploring deeper relationships between educational data as well as predicting how well students do in school has been made possible through educational data mining. Presented is a novel model utilizing machine learning computational methods to forecast the EdTech for the students by using their midterm exam results. Various machine learning algorithms were assessed and providing the EdTech for improving their performance in final exam. This study comprehensively examines different EdTech technologies and recommend a unified model that could serve as a solid framework for classroom teaching.
{"title":"Exploring Software Infrastructures for Enhanced Learning Environments to Empowering Education","authors":"Ronald M. Hernández, Dr. Walter Antonio Campos Ugaz, Dr. Segundo Juan Sanchez Tarrillo, Dr. Silvia Josefina Aguinaga Vasquez, Sara Esther Liza Ordoñez, Ronald Avellaneda Montenegro, Dr. Dora Elisa Elías Martínez, Dr. Doris E. Fuster- Guillen","doi":"10.58346/jowua.2024.i1.016","DOIUrl":"https://doi.org/10.58346/jowua.2024.i1.016","url":null,"abstract":"Recently, the education sector has undergone a notable change due to the incorporation of technology, resulting in the emergence of Educational Technology (EdTech). This new trend has completely transformed the learning process for students, teaching methods for educators, and operations of educational institutions. Due to EdTech, education has become simpler to access, engaging, and efficient, providing customized and diverse learning experiences. This article explores into the significant influence of EdTech on the field of education and the promising opportunities it offers for the future. EdTech has become a significant enabler, allowing institutions to meet evolving student needs and cultivate new skillsets without being limited by geographical barriers. EdTech integrates digital and technological media with conventional methods of instruction to enable various forms of learning, offering adaptability, enhancing engagement, and providing high-quality educational solutions. EdTech tools empower educators to track student engagement, encourage interactive and creative learning experiences, and stand for human-centred education focusing on critical thinking, innovation, and entrepreneurial activity. Exploring deeper relationships between educational data as well as predicting how well students do in school has been made possible through educational data mining. Presented is a novel model utilizing machine learning computational methods to forecast the EdTech for the students by using their midterm exam results. Various machine learning algorithms were assessed and providing the EdTech for improving their performance in final exam. This study comprehensively examines different EdTech technologies and recommend a unified model that could serve as a solid framework for classroom teaching.","PeriodicalId":38235,"journal":{"name":"Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications","volume":"52 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140367869","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 : 2024-03-29DOI: 10.58346/jowua.2024.i1.013
Daehyeon Son, Youngshin Park, Bonam Kim, Ilsun You
The threat posed by false base stations remains pertinent across the 4G, 5G, and forthcoming 6G generations of mobile communication. In response, this paper introduces a real-time detection method for false base stations employing two approaches: machine learning and specification-based. Utilizing the UERANSIM open 5G RAN (Radio-Access Network) test platform, we assess the feasibility and practicality of applying these techniques within a 5G network environment. Emulating real-world 5G conditions, we implement a functional split in the 5G base station and deploy the False Base Station Detection Function (FDF) as a 5G NF (Network Function) within the CU (Central Unit). This setup enables real-time detection seamlessly integrated into the network. Experimental results indicate that specification-based detection outperforms machine learning, achieving a detection accuracy of 99.6% compared to 96.75% for the highest-performing machine learning model XGBoost. Furthermore, specification-based detection demonstrates superior efficiency, with CPU usage of 1.2% and memory usage of 16.12MB, compared to 1.6% CPU usage and 182.4MB memory usage for machine learning on average. Consequently, the implementation of specification-based detection using the proposed method emerges as the most effective technique in the 5G environment.
{"title":"A Study on the Implementation of a Network Function for Real-time False Base Station Detection for the Next Generation Mobile Communication Environment","authors":"Daehyeon Son, Youngshin Park, Bonam Kim, Ilsun You","doi":"10.58346/jowua.2024.i1.013","DOIUrl":"https://doi.org/10.58346/jowua.2024.i1.013","url":null,"abstract":"The threat posed by false base stations remains pertinent across the 4G, 5G, and forthcoming 6G generations of mobile communication. In response, this paper introduces a real-time detection method for false base stations employing two approaches: machine learning and specification-based. Utilizing the UERANSIM open 5G RAN (Radio-Access Network) test platform, we assess the feasibility and practicality of applying these techniques within a 5G network environment. Emulating real-world 5G conditions, we implement a functional split in the 5G base station and deploy the False Base Station Detection Function (FDF) as a 5G NF (Network Function) within the CU (Central Unit). This setup enables real-time detection seamlessly integrated into the network. Experimental results indicate that specification-based detection outperforms machine learning, achieving a detection accuracy of 99.6% compared to 96.75% for the highest-performing machine learning model XGBoost. Furthermore, specification-based detection demonstrates superior efficiency, with CPU usage of 1.2% and memory usage of 16.12MB, compared to 1.6% CPU usage and 182.4MB memory usage for machine learning on average. Consequently, the implementation of specification-based detection using the proposed method emerges as the most effective technique in the 5G environment.","PeriodicalId":38235,"journal":{"name":"Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications","volume":"59 17","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140365099","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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