Pub Date : 2022-11-14DOI: 10.1109/ICOCO56118.2022.10031639
Muhammad Syahir Nazarudin, H. M. Hanum, S. A. Rahman, S. Mutalib
There are many tourist places in Malaysia, and still many to be discovered. The Internet is an agent to find web documents describing a place or a location. The precise location or place is always connected to the geotagging process. However, a limited geotagging approach is applied in Malay documents. Hence, a geotagging application is developed using the name-place entity recognition approach. A geotagging algorithm is developed for tagging geographic information in Malay documents. The algorithm performs word filtering and name-place extraction from both the MyGazetteer resources and tourism web resources. An application is built to demonstrate the tagging of geographic information onto each web document. The geotagging prototype (MyGeo-NER) allows users to edit tags of existing web documents, add new ones, and search for documents containing the names of places or locations they have entered.
{"title":"Geotagging for Malay documents using Name-Entity Recognition Approach","authors":"Muhammad Syahir Nazarudin, H. M. Hanum, S. A. Rahman, S. Mutalib","doi":"10.1109/ICOCO56118.2022.10031639","DOIUrl":"https://doi.org/10.1109/ICOCO56118.2022.10031639","url":null,"abstract":"There are many tourist places in Malaysia, and still many to be discovered. The Internet is an agent to find web documents describing a place or a location. The precise location or place is always connected to the geotagging process. However, a limited geotagging approach is applied in Malay documents. Hence, a geotagging application is developed using the name-place entity recognition approach. A geotagging algorithm is developed for tagging geographic information in Malay documents. The algorithm performs word filtering and name-place extraction from both the MyGazetteer resources and tourism web resources. An application is built to demonstrate the tagging of geographic information onto each web document. The geotagging prototype (MyGeo-NER) allows users to edit tags of existing web documents, add new ones, and search for documents containing the names of places or locations they have entered.","PeriodicalId":319652,"journal":{"name":"2022 IEEE International Conference on Computing (ICOCO)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116820037","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 : 2022-11-14DOI: 10.1109/ICOCO56118.2022.10031920
Shoichi Nakamura, H. Nakayama, R. Onuma, Junichi Tachibana, H. Kaminaga, Y. Miyadera
In programing exercises, it is important to identify the stumbling of each student and to provide appropriate guidance. However, there are circumstances in which students’ stumbling cannot be fully understood due to the diverse nature of their stumbling and the limited number of instructors. In particular, students tend to stumble when trying to shape their programs to match the target processing (“stumbling in construction of a program logic”). However, it is difficult to identify this stumbling since it does not appear as a compilation error. We have developed a system for automatically estimating the stumbling in the construction of a program logic. An experiment to evaluate the effectiveness of the proposed system produced promising results regarding its effectiveness for identifying stumbling in logic construction.
{"title":"A System for Identification of Stumbles in Construction of Program Logic that Does Not Appear as Compilation Errors","authors":"Shoichi Nakamura, H. Nakayama, R. Onuma, Junichi Tachibana, H. Kaminaga, Y. Miyadera","doi":"10.1109/ICOCO56118.2022.10031920","DOIUrl":"https://doi.org/10.1109/ICOCO56118.2022.10031920","url":null,"abstract":"In programing exercises, it is important to identify the stumbling of each student and to provide appropriate guidance. However, there are circumstances in which students’ stumbling cannot be fully understood due to the diverse nature of their stumbling and the limited number of instructors. In particular, students tend to stumble when trying to shape their programs to match the target processing (“stumbling in construction of a program logic”). However, it is difficult to identify this stumbling since it does not appear as a compilation error. We have developed a system for automatically estimating the stumbling in the construction of a program logic. An experiment to evaluate the effectiveness of the proposed system produced promising results regarding its effectiveness for identifying stumbling in logic construction.","PeriodicalId":319652,"journal":{"name":"2022 IEEE International Conference on Computing (ICOCO)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116775467","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 : 2022-11-14DOI: 10.1109/ICOCO56118.2022.10031943
M. A. Faudzi, Masitah Ghazali, Z. C. Cob, Ridha Omar, Sharul Azim Sharudin
COVID-19 has changed how the world operates, and education is one of the sectors that are highly affected by these changes. Previously, mobile learning is just an optional or a supplementary learning method. However, with the increased in the number of COVID-19 cases around the world, education system has switched from the traditional face-to-face mode in a classroom setting, to an online learning environment. Learning using a mobile device or mobile learning is a concept that is new to most learners, especially those who have never before experienced an online learning setting. One of the prevalent factors that leads to ineffective mobile learning process is badly designed user interfaces that will disengage learners from learning materials presented, and increase the cognitive load of the learners. Among the factors that results in bad user interface is the violation of a user interface guideline/framework. Therefore, the main objective of this research-work is to evaluate the learners’ cognitive load significance for mobile learning application by identifying Nielsen’s Heuristics’ violation. By implementing this study, important user interface design (UID) attributes that increase learner’s cognitive load can be identified. Understanding how UID can affect the learners’ cognitive load can assist designers in deciding which user interface designs that can improve or minimize learners’ cognitive load. The outcome of this research will enable mobile learning application designers, developers, educators, teachers and people who are interested in developing a mobile learning application to deliver an effective mobile learning experience to learners.
{"title":"The Evaluation of Cognitive Load Significance for Mobile Learning Application via User Interface Design Violations","authors":"M. A. Faudzi, Masitah Ghazali, Z. C. Cob, Ridha Omar, Sharul Azim Sharudin","doi":"10.1109/ICOCO56118.2022.10031943","DOIUrl":"https://doi.org/10.1109/ICOCO56118.2022.10031943","url":null,"abstract":"COVID-19 has changed how the world operates, and education is one of the sectors that are highly affected by these changes. Previously, mobile learning is just an optional or a supplementary learning method. However, with the increased in the number of COVID-19 cases around the world, education system has switched from the traditional face-to-face mode in a classroom setting, to an online learning environment. Learning using a mobile device or mobile learning is a concept that is new to most learners, especially those who have never before experienced an online learning setting. One of the prevalent factors that leads to ineffective mobile learning process is badly designed user interfaces that will disengage learners from learning materials presented, and increase the cognitive load of the learners. Among the factors that results in bad user interface is the violation of a user interface guideline/framework. Therefore, the main objective of this research-work is to evaluate the learners’ cognitive load significance for mobile learning application by identifying Nielsen’s Heuristics’ violation. By implementing this study, important user interface design (UID) attributes that increase learner’s cognitive load can be identified. Understanding how UID can affect the learners’ cognitive load can assist designers in deciding which user interface designs that can improve or minimize learners’ cognitive load. The outcome of this research will enable mobile learning application designers, developers, educators, teachers and people who are interested in developing a mobile learning application to deliver an effective mobile learning experience to learners.","PeriodicalId":319652,"journal":{"name":"2022 IEEE International Conference on Computing (ICOCO)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123883822","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 : 2022-11-14DOI: 10.1109/ICOCO56118.2022.10032026
Long Doan, Tho Nguyen Duc, Chuanzhe Jing, E. Kamioka, Phan Xuan Tan
In 360-degree streaming videos, viewport prediction can reduce the bandwidth needed during the stream while still maintaining a quality experience for the users by streaming only the area that is visible to the user. Existing research in viewport prediction aims to predict the user’s viewport with data from the user’s head movement trajectory, video saliency, and subtitles of the video. While these subtitles can contain much information necessary for viewport prediction, previous studies can only extract these information manually, which requires in-depth knowledge about the topic of the video. Moreover, extracting these information by hand can still miss some important keywords from the subtitles and limit the accuracy of the viewport prediction. In this paper, we focus on automate this extraction process by proposing three types of automatic keyword extraction methods, namely Adverb, NER (Named entity recognition) and Adverb+NER. We provide an analysis to demonstrate the effectiveness of our automatic methods compared to extracting important keywords by hand. We also incorporate our methods into an existing viewport prediction model to improve prediction accuracy. The experimental results show that the model with our automatic keyword extraction methods outperforms baseline methods which only use manually extracted information.
{"title":"Automatic Keyword Extraction for Viewport Prediction of 360-degree Virtual Tourism Video","authors":"Long Doan, Tho Nguyen Duc, Chuanzhe Jing, E. Kamioka, Phan Xuan Tan","doi":"10.1109/ICOCO56118.2022.10032026","DOIUrl":"https://doi.org/10.1109/ICOCO56118.2022.10032026","url":null,"abstract":"In 360-degree streaming videos, viewport prediction can reduce the bandwidth needed during the stream while still maintaining a quality experience for the users by streaming only the area that is visible to the user. Existing research in viewport prediction aims to predict the user’s viewport with data from the user’s head movement trajectory, video saliency, and subtitles of the video. While these subtitles can contain much information necessary for viewport prediction, previous studies can only extract these information manually, which requires in-depth knowledge about the topic of the video. Moreover, extracting these information by hand can still miss some important keywords from the subtitles and limit the accuracy of the viewport prediction. In this paper, we focus on automate this extraction process by proposing three types of automatic keyword extraction methods, namely Adverb, NER (Named entity recognition) and Adverb+NER. We provide an analysis to demonstrate the effectiveness of our automatic methods compared to extracting important keywords by hand. We also incorporate our methods into an existing viewport prediction model to improve prediction accuracy. The experimental results show that the model with our automatic keyword extraction methods outperforms baseline methods which only use manually extracted information.","PeriodicalId":319652,"journal":{"name":"2022 IEEE International Conference on Computing (ICOCO)","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128943101","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 : 2022-11-14DOI: 10.1109/ICOCO56118.2022.10031846
Mashitah Ibrahim, Muzaffar Hamzah, M. F. Asli
In recent years, the rapid development of environmental sensors and artificial intelligence is changing the traditional mode of agricultural production and moving towards intelligent and efficient precision agriculture. According to the demand of developing precision agriculture, this study plans to carry out comprehensive improvise research on the intelligent unmanned plant disease detection technology for agricultural ecosystems. The production can be adversely affected if plant disease problems cannot be detected in the early stage. Therefore, the biggest challenge in disease detection is the accurate early diagnosis for loss prevention. However, achieving high accuracy requires a computationally intensive approach to the system, which can cause overhead and high technical costs. Random Forest is a special kind of ensemble learning technique and it turns out to perform very well compared to other classification algorithms such as Support Vector Machines (SVM) and Artificial Neural Networks (ANN). In this study, we modified the structure of, RF model to improve the overall accuracy and accessibility, to transform it into a lightweight detection system. This lightweight framework is for cost-effective distribution with high performance without requiring extensive computational resources or complex algorithms. With that, this system can be more practical and easier to use.
{"title":"A Preliminary Lightweight Random Forest Approach-Based Image Classification for Plant Disease Detection","authors":"Mashitah Ibrahim, Muzaffar Hamzah, M. F. Asli","doi":"10.1109/ICOCO56118.2022.10031846","DOIUrl":"https://doi.org/10.1109/ICOCO56118.2022.10031846","url":null,"abstract":"In recent years, the rapid development of environmental sensors and artificial intelligence is changing the traditional mode of agricultural production and moving towards intelligent and efficient precision agriculture. According to the demand of developing precision agriculture, this study plans to carry out comprehensive improvise research on the intelligent unmanned plant disease detection technology for agricultural ecosystems. The production can be adversely affected if plant disease problems cannot be detected in the early stage. Therefore, the biggest challenge in disease detection is the accurate early diagnosis for loss prevention. However, achieving high accuracy requires a computationally intensive approach to the system, which can cause overhead and high technical costs. Random Forest is a special kind of ensemble learning technique and it turns out to perform very well compared to other classification algorithms such as Support Vector Machines (SVM) and Artificial Neural Networks (ANN). In this study, we modified the structure of, RF model to improve the overall accuracy and accessibility, to transform it into a lightweight detection system. This lightweight framework is for cost-effective distribution with high performance without requiring extensive computational resources or complex algorithms. With that, this system can be more practical and easier to use.","PeriodicalId":319652,"journal":{"name":"2022 IEEE International Conference on Computing (ICOCO)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129708748","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 : 2022-11-14DOI: 10.1109/ICOCO56118.2022.10031694
H. Biswas, Sian Lun Lau, Tze Ying Sim
3D Terrestrial Laser Scanning (TLS) technology has achieved massive acceptance to produce and visualise the 3D point clouds of existing facilities in the Architectural, Engineering and construction (AEC) industry. In addition, Building Information Modelling (BIM) is also well recognised to digitalise every component of actual buildings. Integration of TLS and BIM has been established as a disruptive technology to increase the quality and performance of the construction industry. Apart from all these recognised contributions of using the technologies, significant flaws in creating 3D BIM models of existing facilities must be addressed to improve the functionalities and operations in the facility management system. It is worth mentioning that the effective geometric modelling and semantic-rich object recognitions of existing construction buildings (behind the concrete wall) and point cloud overlapping are challenging issues in laser scanning technologies that ultimately lead to incomplete BIM models of as-built facilities. In this study, the contribution to the knowledge gap is represented by considering the two real case studies of existing facilities to optimise facility management and operations in the construction industry. Nevertheless, the main focus of this research is to introduce the current challenges in generating an effective 3D BIM model for the existing buildings satisfying the specified specifications and standards. Further research should consider the issues and more robust and effective evaluation procedures for the larger-scale real case containing more complex and hidden objects.
{"title":"Impact of Digitalization in Construction: Enriching As-built Facilities and Operations using BIM","authors":"H. Biswas, Sian Lun Lau, Tze Ying Sim","doi":"10.1109/ICOCO56118.2022.10031694","DOIUrl":"https://doi.org/10.1109/ICOCO56118.2022.10031694","url":null,"abstract":"3D Terrestrial Laser Scanning (TLS) technology has achieved massive acceptance to produce and visualise the 3D point clouds of existing facilities in the Architectural, Engineering and construction (AEC) industry. In addition, Building Information Modelling (BIM) is also well recognised to digitalise every component of actual buildings. Integration of TLS and BIM has been established as a disruptive technology to increase the quality and performance of the construction industry. Apart from all these recognised contributions of using the technologies, significant flaws in creating 3D BIM models of existing facilities must be addressed to improve the functionalities and operations in the facility management system. It is worth mentioning that the effective geometric modelling and semantic-rich object recognitions of existing construction buildings (behind the concrete wall) and point cloud overlapping are challenging issues in laser scanning technologies that ultimately lead to incomplete BIM models of as-built facilities. In this study, the contribution to the knowledge gap is represented by considering the two real case studies of existing facilities to optimise facility management and operations in the construction industry. Nevertheless, the main focus of this research is to introduce the current challenges in generating an effective 3D BIM model for the existing buildings satisfying the specified specifications and standards. Further research should consider the issues and more robust and effective evaluation procedures for the larger-scale real case containing more complex and hidden objects.","PeriodicalId":319652,"journal":{"name":"2022 IEEE International Conference on Computing (ICOCO)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127232612","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 : 2022-11-14DOI: 10.1109/ICOCO56118.2022.10031634
Salfarina Abdullah, Al Dulaimi Moatasem Abdulmajeed Alwan, Y. Y. Jusoh
The efficiency of blockchain technologies in managing transactions using distributed ledgers provides new age of government services. This enhances citizen-government transparency to establish public-sector trust by preventing fraud. However, using and adopting blockchain in e-Government context was insufficiently explored in previously published literatures. This paper systematically reviews relevant works to illustrate blockchain issues, challenges, while detecting the new directions for future research of using blockchain applications in e-Government. Discussed literatures explained that adopting blockchain applications to build e-Government services still has obvious lack in empirical evidence. Predominantly, major challenges facing blockchain adoption are summarized in scalability, flexibility and security aspects. organizationally, acceptability issues in blockchain and demanding new models of e-government are illustrated as major obstacles against the adoption. Furthermore, the lack in legislations and helping regulatory represents the major environmental obstacles against the adoption.
{"title":"Blockchain Technologies in e-Government Services: A Literature Review","authors":"Salfarina Abdullah, Al Dulaimi Moatasem Abdulmajeed Alwan, Y. Y. Jusoh","doi":"10.1109/ICOCO56118.2022.10031634","DOIUrl":"https://doi.org/10.1109/ICOCO56118.2022.10031634","url":null,"abstract":"The efficiency of blockchain technologies in managing transactions using distributed ledgers provides new age of government services. This enhances citizen-government transparency to establish public-sector trust by preventing fraud. However, using and adopting blockchain in e-Government context was insufficiently explored in previously published literatures. This paper systematically reviews relevant works to illustrate blockchain issues, challenges, while detecting the new directions for future research of using blockchain applications in e-Government. Discussed literatures explained that adopting blockchain applications to build e-Government services still has obvious lack in empirical evidence. Predominantly, major challenges facing blockchain adoption are summarized in scalability, flexibility and security aspects. organizationally, acceptability issues in blockchain and demanding new models of e-government are illustrated as major obstacles against the adoption. Furthermore, the lack in legislations and helping regulatory represents the major environmental obstacles against the adoption.","PeriodicalId":319652,"journal":{"name":"2022 IEEE International Conference on Computing (ICOCO)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127452316","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 : 2022-11-14DOI: 10.1109/ICOCO56118.2022.10031699
Siti Rohana Ahmad Ibrahim, J. Yahaya, H. Sallehudin
Green technology is the solution that responds to the environment and meets development needs. Climate change has led to an increasing global need to develop green and sustainable technology with economic development, employment opportunities, and long-term investment. Nowadays, people are increasingly responsive to the importance of green and sustainability. It has become progressively crucial to the government, business, products, and software industry. Green software engineering is proposed for software development to provide environmental awareness and also less waste generation throughout the development process. The current green software process model only concentrated on environmental and economic elements and was not integrated with waste elements in the development phase. Model development often consists of smaller component models, each representing a specific domain based on sustainability elements and waste elimination. Organisations and industries conducted assessments to determine the level of the greenness of a development process in their real environment. This paper presents the concept of the theory evaluation approach in the green software process, which focuses on the components and factors embedded in this model.
{"title":"Green Software Process Based on Sustainability, Waste and Evaluation Theory Approach: The Conceptual Model","authors":"Siti Rohana Ahmad Ibrahim, J. Yahaya, H. Sallehudin","doi":"10.1109/ICOCO56118.2022.10031699","DOIUrl":"https://doi.org/10.1109/ICOCO56118.2022.10031699","url":null,"abstract":"Green technology is the solution that responds to the environment and meets development needs. Climate change has led to an increasing global need to develop green and sustainable technology with economic development, employment opportunities, and long-term investment. Nowadays, people are increasingly responsive to the importance of green and sustainability. It has become progressively crucial to the government, business, products, and software industry. Green software engineering is proposed for software development to provide environmental awareness and also less waste generation throughout the development process. The current green software process model only concentrated on environmental and economic elements and was not integrated with waste elements in the development phase. Model development often consists of smaller component models, each representing a specific domain based on sustainability elements and waste elimination. Organisations and industries conducted assessments to determine the level of the greenness of a development process in their real environment. This paper presents the concept of the theory evaluation approach in the green software process, which focuses on the components and factors embedded in this model.","PeriodicalId":319652,"journal":{"name":"2022 IEEE International Conference on Computing (ICOCO)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126773444","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 : 2022-11-14DOI: 10.1109/ICOCO56118.2022.10031709
Evelyn Yeap Ewe Lin, Kok Cheng Lim, M. A. Faudzi, Mohd Hazli Mohamed Zabil, Ridha Omar, A. Selamat, O. Krejcar
Language e-learning technologies (LELT) is an emerging area focusing on application of computing technologies in language learning of all kinds. Being a communication skill that requires much face to face interactions, electronic learning approach is definitely a challenge since e-learning focuses on self-paced remote engagements. This paper collects paper pertaining technologies in language learning from year 2019 to 2022 seeking into five research questions on finding out the current trends of research types, evaluation methods, contributions and correlations of between them. This research through a filtration process has found 27 relevant papers. This paper will first present all descriptive findings, data analysis, correlations and lastly insights of the review analysis processes. This paper identified 3 research domain gaps and 3 recommendations of novel future works.
{"title":"A Systematic Review on English Language e-Learning Technologies","authors":"Evelyn Yeap Ewe Lin, Kok Cheng Lim, M. A. Faudzi, Mohd Hazli Mohamed Zabil, Ridha Omar, A. Selamat, O. Krejcar","doi":"10.1109/ICOCO56118.2022.10031709","DOIUrl":"https://doi.org/10.1109/ICOCO56118.2022.10031709","url":null,"abstract":"Language e-learning technologies (LELT) is an emerging area focusing on application of computing technologies in language learning of all kinds. Being a communication skill that requires much face to face interactions, electronic learning approach is definitely a challenge since e-learning focuses on self-paced remote engagements. This paper collects paper pertaining technologies in language learning from year 2019 to 2022 seeking into five research questions on finding out the current trends of research types, evaluation methods, contributions and correlations of between them. This research through a filtration process has found 27 relevant papers. This paper will first present all descriptive findings, data analysis, correlations and lastly insights of the review analysis processes. This paper identified 3 research domain gaps and 3 recommendations of novel future works.","PeriodicalId":319652,"journal":{"name":"2022 IEEE International Conference on Computing (ICOCO)","volume":"475 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133039992","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 : 2022-11-14DOI: 10.1109/ICOCO56118.2022.10031837
Tomoya Oshio, Satoshi Okada, Takuho Mitsunaga
With the development of information technology, IoT devices are spreading rapidly. ZigBee is one of the short-range wireless communication standards used in IoT devices and is expected to be used in smart homes and industrial control systems because of its low power consumption and low-cost operation despite its low communication speed. However, ZigBee can be subject to cyber-attacks because eavesdropping on packets and sending forged packets against wireless communication is easier than wired ones. In order to use ZigBee safely in smart home and industrial control systems, it is necessary to develop a method to detect cyber-attacks quickly. In this paper, we propose a machine learning-based anomaly detection system for Zigbee networks. We focus on characteristics of ZigBee communication and investigate a method to detect network anomalies and cyber attacks on ZigBee networks using machine learning. Furthermore, since we primarily put emphasis on practicality, our proposed system is simple and consists of widely used tools such as Wireshark. To evaluate the detection accuracy of our proposed system, we conduct some experiments. As a result, it is shown that our proposed system can detect attacks with high accuracy. In addition, we varied the features used in machine learning and discuss which feature has a high contribution to anomaly detection.
{"title":"Machine Learning-based Anomaly Detection in ZigBee Networks","authors":"Tomoya Oshio, Satoshi Okada, Takuho Mitsunaga","doi":"10.1109/ICOCO56118.2022.10031837","DOIUrl":"https://doi.org/10.1109/ICOCO56118.2022.10031837","url":null,"abstract":"With the development of information technology, IoT devices are spreading rapidly. ZigBee is one of the short-range wireless communication standards used in IoT devices and is expected to be used in smart homes and industrial control systems because of its low power consumption and low-cost operation despite its low communication speed. However, ZigBee can be subject to cyber-attacks because eavesdropping on packets and sending forged packets against wireless communication is easier than wired ones. In order to use ZigBee safely in smart home and industrial control systems, it is necessary to develop a method to detect cyber-attacks quickly. In this paper, we propose a machine learning-based anomaly detection system for Zigbee networks. We focus on characteristics of ZigBee communication and investigate a method to detect network anomalies and cyber attacks on ZigBee networks using machine learning. Furthermore, since we primarily put emphasis on practicality, our proposed system is simple and consists of widely used tools such as Wireshark. To evaluate the detection accuracy of our proposed system, we conduct some experiments. As a result, it is shown that our proposed system can detect attacks with high accuracy. In addition, we varied the features used in machine learning and discuss which feature has a high contribution to anomaly detection.","PeriodicalId":319652,"journal":{"name":"2022 IEEE International Conference on Computing (ICOCO)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133442135","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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