Pub Date : 2022-11-14DOI: 10.1109/ICOCO56118.2022.10031274
Nuraina Daud, Nurulhuda Noordin, N. Teng
This paper presents a data conversion process involving household grocery data. The household grocery data were gathered from the primary source which is directly from 50 selected household in Shah Alam for 5 consecutive months. The data transformation was done to convert the grocery data into the nutrition data. The converted nutrition data will be tested using data mining classification algorithms, and the patterns generated from it will be explored for obesity prediction purposes. In the data transformation process, the raw grocery data has undergone several data pre-processing and conversion methods. These processes have been done by the nutritionists as the knowledge on nutrition field are needed in performing this task. The processes involved are calorie conversion, macronutrient grouping, food pyramid grouping, and food categorization. There were five methods have been conducted to perform the conversion task which are food composition database, offline and online market survey, food pyramid and knowledge theory on nutrition. The conversion process has been gathered to form Data Conversion Process Framework. This paper also introduced the use of estimation formula using BMI weightage as a method to generate the individual-level nutrition data. The nutrition data generated from the grocery data processing and the conversion process using the BMI weightage highlight the significance of the study. The output from this study (nutrition data) will be used in the later stage of the study as the input data in the development of obesity prediction modelling.
{"title":"Data Conversion Process Framework to Generate Individual-Level Nutrition Data from Household-Level Grocery Data","authors":"Nuraina Daud, Nurulhuda Noordin, N. Teng","doi":"10.1109/ICOCO56118.2022.10031274","DOIUrl":"https://doi.org/10.1109/ICOCO56118.2022.10031274","url":null,"abstract":"This paper presents a data conversion process involving household grocery data. The household grocery data were gathered from the primary source which is directly from 50 selected household in Shah Alam for 5 consecutive months. The data transformation was done to convert the grocery data into the nutrition data. The converted nutrition data will be tested using data mining classification algorithms, and the patterns generated from it will be explored for obesity prediction purposes. In the data transformation process, the raw grocery data has undergone several data pre-processing and conversion methods. These processes have been done by the nutritionists as the knowledge on nutrition field are needed in performing this task. The processes involved are calorie conversion, macronutrient grouping, food pyramid grouping, and food categorization. There were five methods have been conducted to perform the conversion task which are food composition database, offline and online market survey, food pyramid and knowledge theory on nutrition. The conversion process has been gathered to form Data Conversion Process Framework. This paper also introduced the use of estimation formula using BMI weightage as a method to generate the individual-level nutrition data. The nutrition data generated from the grocery data processing and the conversion process using the BMI weightage highlight the significance of the study. The output from this study (nutrition data) will be used in the later stage of the study as the input data in the development of obesity prediction modelling.","PeriodicalId":319652,"journal":{"name":"2022 IEEE International Conference on Computing (ICOCO)","volume":"54 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":"131366245","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/ICOCO56118.2022.10031637
Zan Azma Nasruddin, Norafifa Mohd Ariffin
Nowadays, students are the main stakeholders in any educational setting. They actively participate in the transmission of knowledge. The university’s future planning heavily relies on their feedback on the current teaching and learning techniques. The Learning Management System (LMS), an i-Learn system, has been used by Universiti Teknologi MARA (UiTM) to implement blended learning in their teaching and learning methods. Student Feedback Online (SuFo), which UiTM has made available to its students, allows them to evaluate the teaching and learning process. However, there are issues with the validity and reliability of the SuFo question in evaluating the course, the lecturer’s performance, the classroom environment, and scepticism in the students’ responses brought up by the previous study. Therefore, this study aims to understand how students perceive SuFo questions and investigate how students feel about SuFo questions and any potential biases in student evaluations. This study analyses data using quantitative methods and a survey along with SPSS. One hundred students from various faculties have fully responded to the survey. The results demonstrate that there are no gender-based biases in student ratings and that the students strongly agree if the SuFo questions are modified and changed. There are also some suggestions for future planning such as changes of SuFo questions and reduced the number of SuFo questions.
如今，学生是任何教育环境中的主要利益相关者。他们积极参与知识的传播。大学的未来规划在很大程度上依赖于他们对当前教学技术的反馈。学习管理系统(LMS)是一种i-Learn系统，已被Universiti teknologii MARA (UiTM)用于在其教学方法中实施混合学习。麻省理工大学为学生提供了学生在线反馈(SuFo)，使他们能够评估教学过程。然而，SuFo问题在评估课程、讲师的表现、课堂环境和先前研究提出的学生回答中的怀疑态度方面存在效度和信度问题。因此，本研究旨在了解学生对SuFo问题的看法，并调查学生对SuFo问题的感受以及学生评价中可能存在的偏见。本研究使用定量方法和调查以及SPSS分析数据。来自不同院系的100名学生对这项调查进行了全面回应。结果表明，学生评分中不存在基于性别的偏见，并且学生对SuFo问题的修改和更改表示强烈同意。也有一些关于未来规划的建议，比如改变SuFo题目，减少SuFo题目的数量。
{"title":"A Student’s Perspective On The Evaluation Of Teaching And Learning Using Student Feedback Online (SuFO)","authors":"Zan Azma Nasruddin, Norafifa Mohd Ariffin","doi":"10.1109/ICOCO56118.2022.10031637","DOIUrl":"https://doi.org/10.1109/ICOCO56118.2022.10031637","url":null,"abstract":"Nowadays, students are the main stakeholders in any educational setting. They actively participate in the transmission of knowledge. The university’s future planning heavily relies on their feedback on the current teaching and learning techniques. The Learning Management System (LMS), an i-Learn system, has been used by Universiti Teknologi MARA (UiTM) to implement blended learning in their teaching and learning methods. Student Feedback Online (SuFo), which UiTM has made available to its students, allows them to evaluate the teaching and learning process. However, there are issues with the validity and reliability of the SuFo question in evaluating the course, the lecturer’s performance, the classroom environment, and scepticism in the students’ responses brought up by the previous study. Therefore, this study aims to understand how students perceive SuFo questions and investigate how students feel about SuFo questions and any potential biases in student evaluations. This study analyses data using quantitative methods and a survey along with SPSS. One hundred students from various faculties have fully responded to the survey. The results demonstrate that there are no gender-based biases in student ratings and that the students strongly agree if the SuFo questions are modified and changed. There are also some suggestions for future planning such as changes of SuFo questions and reduced the number of SuFo questions.","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":"130242192","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/ICOCO56118.2022.10031788
M. Ati, Arooba Khalid
With lifestyle changes, digital transformations, and increasing use of the internet, technology has become a part of our daily lives. It has become an essential factor used in all fields such as industry, commerce, education, and entertainment. Technology has also touched areas directly related to human life, such as health, medicine, and food. We are constantly engaging with technology: through smartphones and applications or even in-home devices and systems. We can control and give orders to various electrical smart devices from inside or outside the home; they all work in an interconnected way. For example, we can control the temperature of the house and water during the shower, raise the curtains, or turn on and off modern media and communication devices. This technology contributes to facilitating the user’s daily life and saving time and effort. The smart home contains remote control devices to operate and monitor electrical and electronic devices such as interior lighting, outdoor garden lighting, electric blinds, air conditioning, and TV, in addition to controlling audio systems, cameras, and electric doors using smartphones or screens on the wall. Also, this house contains remote control devices to protect against theft and fire. This study focuses on exploring the idea of smart homes and the related security techniques whilst shedding light on some work related to privacy and security in smart homes.
{"title":"Smart Homes in The Age of IoT","authors":"M. Ati, Arooba Khalid","doi":"10.1109/ICOCO56118.2022.10031788","DOIUrl":"https://doi.org/10.1109/ICOCO56118.2022.10031788","url":null,"abstract":"With lifestyle changes, digital transformations, and increasing use of the internet, technology has become a part of our daily lives. It has become an essential factor used in all fields such as industry, commerce, education, and entertainment. Technology has also touched areas directly related to human life, such as health, medicine, and food. We are constantly engaging with technology: through smartphones and applications or even in-home devices and systems. We can control and give orders to various electrical smart devices from inside or outside the home; they all work in an interconnected way. For example, we can control the temperature of the house and water during the shower, raise the curtains, or turn on and off modern media and communication devices. This technology contributes to facilitating the user’s daily life and saving time and effort. The smart home contains remote control devices to operate and monitor electrical and electronic devices such as interior lighting, outdoor garden lighting, electric blinds, air conditioning, and TV, in addition to controlling audio systems, cameras, and electric doors using smartphones or screens on the wall. Also, this house contains remote control devices to protect against theft and fire. This study focuses on exploring the idea of smart homes and the related security techniques whilst shedding light on some work related to privacy and security in smart homes.","PeriodicalId":319652,"journal":{"name":"2022 IEEE International Conference on Computing (ICOCO)","volume":"83 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":"130405189","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/ICOCO56118.2022.10031731
R. Onuma, H. Kaminaga, H. Nakayama, Y. Miyadera, Keito Suzuki, Shoichi Nakamura
Social media is increasingly being used as a tool to gather a wide variety of information. However, there are fake articles on social networking services mixed in with useful posts. It is desirable for users to use social networking services while determining the truth or falsity of articles. However, such judgement is difficult for inexperienced users since the skills to determine the authenticity of articles should be obtained by a stacking of experiences. In this research, we aim to develop methods for gaining experience with examining fake articles by suggesting noteworthy articles on the basis of an analysis of others’ responses to the articles. This paper describes methods for extracting articles that correct other posts on the basis of the characteristics of people’s responses to articles on social networking services and for extracting candidates for fake articles by analyzing such articles. Finally, we describe an experiment using a prototype system and discuss the effectiveness of our system as based on its results.
{"title":"Analysis of Articles that Correct Other Posts on Social Media Aimed at Promoting the Experience in Examining Fakes","authors":"R. Onuma, H. Kaminaga, H. Nakayama, Y. Miyadera, Keito Suzuki, Shoichi Nakamura","doi":"10.1109/ICOCO56118.2022.10031731","DOIUrl":"https://doi.org/10.1109/ICOCO56118.2022.10031731","url":null,"abstract":"Social media is increasingly being used as a tool to gather a wide variety of information. However, there are fake articles on social networking services mixed in with useful posts. It is desirable for users to use social networking services while determining the truth or falsity of articles. However, such judgement is difficult for inexperienced users since the skills to determine the authenticity of articles should be obtained by a stacking of experiences. In this research, we aim to develop methods for gaining experience with examining fake articles by suggesting noteworthy articles on the basis of an analysis of others’ responses to the articles. This paper describes methods for extracting articles that correct other posts on the basis of the characteristics of people’s responses to articles on social networking services and for extracting candidates for fake articles by analyzing such articles. Finally, we describe an experiment using a prototype system and discuss the effectiveness of our system as based on its results.","PeriodicalId":319652,"journal":{"name":"2022 IEEE International Conference on Computing (ICOCO)","volume":"22 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":"132791689","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}
Cyber attacks are causing tremendous damage around the world. To protect against attacks, many organizations have established or outsourced Security Operation Centers (SOCs) to check a large number of logs daily. Since there is no perfect countermeasure against cyber attacks, it is necessary to detect signs of intrusion quickly to mitigate damage caused by them. However, it is challenging to analyze a lot of logs obtained from PCs and servers inside an organization. Therefore, there is a need for a method of efficiently analyzing logs. In this paper, we propose a recommendation system using the ATT&CK technique, which predicts and visualizes attackers’ behaviors using collaborative filtering so that security analysts can analyze logs efficiently.
{"title":"ATT&CK Behavior Forecasting based on Collaborative Filtering and Graph Databases","authors":"Masaki Kuwano, Momoka Okuma, Satoshi Okada, Takuho Mitsunaga","doi":"10.1109/ICOCO56118.2022.10032036","DOIUrl":"https://doi.org/10.1109/ICOCO56118.2022.10032036","url":null,"abstract":"Cyber attacks are causing tremendous damage around the world. To protect against attacks, many organizations have established or outsourced Security Operation Centers (SOCs) to check a large number of logs daily. Since there is no perfect countermeasure against cyber attacks, it is necessary to detect signs of intrusion quickly to mitigate damage caused by them. However, it is challenging to analyze a lot of logs obtained from PCs and servers inside an organization. Therefore, there is a need for a method of efficiently analyzing logs. In this paper, we propose a recommendation system using the ATT&CK technique, which predicts and visualizes attackers’ behaviors using collaborative filtering so that security analysts can analyze logs efficiently.","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":"130634621","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/ICOCO56118.2022.10031648
S. Kleftakis, Argyro Mavrogiorgou, N. Zafeiropoulos, Konstantinos Mavrogiorgos, Athanasios Kiourtis, D. Kyriazis
Choosing the most suitable architecture for applications is not an easy decision. While the software giants have almost all put in place the microservices architecture, on smaller platforms such decision it is not so obvious. In the healthcare domain and specifically when accomplishing Machine Learning (ML) tasks in this domain, considering its special characteristics, the decision should be made based on specific metrics. In the context of the beHEALTHIER platform, a platform that is able to handle heterogeneous healthcare data towards their successful management and analysis by applying various ML tasks, such research gap was fully investigated. There has been conducted an experiment by installing the platform in three (3) different architectural ways, referring to the monolithic architecture, the clustered microservices architecture exploiting docker compose, and the microservices architecture exploiting Kubernetes cluster. For these three (3) environments, time-based measurements were made for each Application Programming Interface (API) of the diverse platform’s functionalities (i.e., components) and useful conclusions were drawn towards the adoption of the most suitable software architecture.
{"title":"A Comparative Study of Monolithic and Microservices Architectures in Machine Learning Scenarios","authors":"S. Kleftakis, Argyro Mavrogiorgou, N. Zafeiropoulos, Konstantinos Mavrogiorgos, Athanasios Kiourtis, D. Kyriazis","doi":"10.1109/ICOCO56118.2022.10031648","DOIUrl":"https://doi.org/10.1109/ICOCO56118.2022.10031648","url":null,"abstract":"Choosing the most suitable architecture for applications is not an easy decision. While the software giants have almost all put in place the microservices architecture, on smaller platforms such decision it is not so obvious. In the healthcare domain and specifically when accomplishing Machine Learning (ML) tasks in this domain, considering its special characteristics, the decision should be made based on specific metrics. In the context of the beHEALTHIER platform, a platform that is able to handle heterogeneous healthcare data towards their successful management and analysis by applying various ML tasks, such research gap was fully investigated. There has been conducted an experiment by installing the platform in three (3) different architectural ways, referring to the monolithic architecture, the clustered microservices architecture exploiting docker compose, and the microservices architecture exploiting Kubernetes cluster. For these three (3) environments, time-based measurements were made for each Application Programming Interface (API) of the diverse platform’s functionalities (i.e., components) and useful conclusions were drawn towards the adoption of the most suitable software architecture.","PeriodicalId":319652,"journal":{"name":"2022 IEEE International Conference on Computing (ICOCO)","volume":"72 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":"114150868","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/ICOCO56118.2022.10031951
Refat Khan Pathan, Wei Lun Lim, Sian Lun Lau, C. Ho, P. Khare, R. Koneru
In digital image processing, segmentation is a process by which we can partition an image based on some variables to extract necessary elements. Unlike typical objects, it is complicated to segment dynamic objects from a synthetic fluid dataset where properties like position and shape change over time. Experiments on image segmentation over this dataset are conducted using U-Net (semantic segmentation) and Mask R-CNN (instance segmentation) to compare their results. The training dataset is generated from seven labelled images through data augmentation. Training on 1000 and validating on 200 images, Mask R-CNN achieved more epochs quickly. Around 1000 epochs for Mask R-CNN and 500 epochs for U-Net, both models reached a similar result in terms of F1 score and can segment the object in the new images.
{"title":"Experimental Analysis of U-Net and Mask R-CNN for Segmentation of Synthetic Liquid Spray","authors":"Refat Khan Pathan, Wei Lun Lim, Sian Lun Lau, C. Ho, P. Khare, R. Koneru","doi":"10.1109/ICOCO56118.2022.10031951","DOIUrl":"https://doi.org/10.1109/ICOCO56118.2022.10031951","url":null,"abstract":"In digital image processing, segmentation is a process by which we can partition an image based on some variables to extract necessary elements. Unlike typical objects, it is complicated to segment dynamic objects from a synthetic fluid dataset where properties like position and shape change over time. Experiments on image segmentation over this dataset are conducted using U-Net (semantic segmentation) and Mask R-CNN (instance segmentation) to compare their results. The training dataset is generated from seven labelled images through data augmentation. Training on 1000 and validating on 200 images, Mask R-CNN achieved more epochs quickly. Around 1000 epochs for Mask R-CNN and 500 epochs for U-Net, both models reached a similar result in terms of F1 score and can segment the object in the new images.","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":"125851194","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/ICOCO56118.2022.10031863
N. Borhan, H. Zulzalil, Sa’adah Hassan, Norhayati Mohd. Ali
Due to its significance in the creation of software projects, the Agile-Scrum methodology has only lately become well-known in the field of software development. The Scrum technique, which is a process that gradually, iteratively, and continuously provides software based on time boxes, is another study that supports agile practitioners’ recent shift towards this method (sprints). It consists of user stories that are delivered during sprints by a Scrum team made up of team members, a Scrum Master, and a Product Owner. User stories are kept in product backlogs. The integrated needs prioritization methodologies have been created by a small number of scholars. However, the majority of the research is on non-agile software development. The prioritization of both functional and nonfunctional user stories simultaneously during Agile-Scrum Software Development (ASSD) is one of the main gaps that has been ignored by all of these methodologies, according to the literature review conducted by the researchers. The purpose of this study is to outline a research plan for creating an integrated user story prioritizing approach that combines non-functional and functional user stories of the ASSD (i-USPA). The preliminary findings demonstrate the critical importance of understanding the significance of non-functional user stories or requirements during the early stages of software development using the agile methodology, particularly in ASSD, in order to produce high-quality software while staying within budget and time constraints. The data was acquired from a small group of experts or software practitioners who use Agile, particularly the Scrum technique, in their organizations.
{"title":"A Hybrid Prioritization Approach by integrating non-Functional and Functional User Stories in Agile-Scrum Software Development (i-USPA):A preliminary study","authors":"N. Borhan, H. Zulzalil, Sa’adah Hassan, Norhayati Mohd. Ali","doi":"10.1109/ICOCO56118.2022.10031863","DOIUrl":"https://doi.org/10.1109/ICOCO56118.2022.10031863","url":null,"abstract":"Due to its significance in the creation of software projects, the Agile-Scrum methodology has only lately become well-known in the field of software development. The Scrum technique, which is a process that gradually, iteratively, and continuously provides software based on time boxes, is another study that supports agile practitioners’ recent shift towards this method (sprints). It consists of user stories that are delivered during sprints by a Scrum team made up of team members, a Scrum Master, and a Product Owner. User stories are kept in product backlogs. The integrated needs prioritization methodologies have been created by a small number of scholars. However, the majority of the research is on non-agile software development. The prioritization of both functional and nonfunctional user stories simultaneously during Agile-Scrum Software Development (ASSD) is one of the main gaps that has been ignored by all of these methodologies, according to the literature review conducted by the researchers. The purpose of this study is to outline a research plan for creating an integrated user story prioritizing approach that combines non-functional and functional user stories of the ASSD (i-USPA). The preliminary findings demonstrate the critical importance of understanding the significance of non-functional user stories or requirements during the early stages of software development using the agile methodology, particularly in ASSD, in order to produce high-quality software while staying within budget and time constraints. The data was acquired from a small group of experts or software practitioners who use Agile, particularly the Scrum technique, in their organizations.","PeriodicalId":319652,"journal":{"name":"2022 IEEE International Conference on Computing (ICOCO)","volume":"37 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":"126396155","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/ICOCO56118.2022.10031980
Intan Norsyafiqa Kamalbahrin, H. M. Hanum, N. Abdullah, Noor Latiffah Adam, N. Kamal, Z. Bakar
The process of matching student profiles to industry profiles is critical to ensuring that students are placed in industries that are a good fit for their program. Therefore, to solve this problem, a system is presented that will give suggestions on suitable industrial types and internship placement from companies in the suggested industry for undergraduate students. This project maps student profiles from seven computer science programs and seven industrial types. There are 284 sample profiles collected from undergraduate students of Universiti Teknologi MARA. The profiles are gathered from previous records of placement for internship training. A decision tree model is constructed based on the sample profiles. The student’s Cumulative Grade Point Average (CGPA) and registered program are used as the main feature of industry recommendation. As a result, a web-based system for mapping students’ profiles to industries’ profiles has been developed. The application stores students’ and industries’ profiles and recommends suitable industries for each student’s profile.
{"title":"Industry Recommendation for Undergraduate Internship using Decision Tree","authors":"Intan Norsyafiqa Kamalbahrin, H. M. Hanum, N. Abdullah, Noor Latiffah Adam, N. Kamal, Z. Bakar","doi":"10.1109/ICOCO56118.2022.10031980","DOIUrl":"https://doi.org/10.1109/ICOCO56118.2022.10031980","url":null,"abstract":"The process of matching student profiles to industry profiles is critical to ensuring that students are placed in industries that are a good fit for their program. Therefore, to solve this problem, a system is presented that will give suggestions on suitable industrial types and internship placement from companies in the suggested industry for undergraduate students. This project maps student profiles from seven computer science programs and seven industrial types. There are 284 sample profiles collected from undergraduate students of Universiti Teknologi MARA. The profiles are gathered from previous records of placement for internship training. A decision tree model is constructed based on the sample profiles. The student’s Cumulative Grade Point Average (CGPA) and registered program are used as the main feature of industry recommendation. As a result, a web-based system for mapping students’ profiles to industries’ profiles has been developed. The application stores students’ and industries’ profiles and recommends suitable industries for each student’s profile.","PeriodicalId":319652,"journal":{"name":"2022 IEEE International Conference on Computing (ICOCO)","volume":"547 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":"116376108","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/ICOCO56118.2022.10031868
Nurul Nabilah Izzati Binti Ridzuan, Nurfauza Binti Jali, S. K. Jali, Mohamad Imran Bandan, Adrus Bin Mohamad Tazuddin, Lim Phei Chin
Despite continuous growth in STEM-associated industries, the number of students pursuing Science, Technology, Engineering, and Mathematics (STEM) related subjects is declining. The implementation of Augmented Reality (AR) in education has the potential to improve not just the students’ conceptual comprehension and knowledge but also critical abilities like problem-solving, cooperation, and communication. This study intends to demonstrate how a mobile application embedded with AR that uses an educational scrapbook as its AR marker platform can improve the learning experience of secondary Malaysian secondary school students studying Science. 30 students across Malaysia were recruited. Their responses were analysed to determine whether the notion of employing a mobile application and scrapbook was feasible. Overall, the System Usability Scale (SUS) results were encouraging (mean=69.83, SD=13.36, n=30), suggesting the possibility of integrating AR as part of the learning medium that could improve the learning experience in Science subjects.
{"title":"ARventure: Edutainment Meets Science Mobile Application","authors":"Nurul Nabilah Izzati Binti Ridzuan, Nurfauza Binti Jali, S. K. Jali, Mohamad Imran Bandan, Adrus Bin Mohamad Tazuddin, Lim Phei Chin","doi":"10.1109/ICOCO56118.2022.10031868","DOIUrl":"https://doi.org/10.1109/ICOCO56118.2022.10031868","url":null,"abstract":"Despite continuous growth in STEM-associated industries, the number of students pursuing Science, Technology, Engineering, and Mathematics (STEM) related subjects is declining. The implementation of Augmented Reality (AR) in education has the potential to improve not just the students’ conceptual comprehension and knowledge but also critical abilities like problem-solving, cooperation, and communication. This study intends to demonstrate how a mobile application embedded with AR that uses an educational scrapbook as its AR marker platform can improve the learning experience of secondary Malaysian secondary school students studying Science. 30 students across Malaysia were recruited. Their responses were analysed to determine whether the notion of employing a mobile application and scrapbook was feasible. Overall, the System Usability Scale (SUS) results were encouraging (mean=69.83, SD=13.36, n=30), suggesting the possibility of integrating AR as part of the learning medium that could improve the learning experience in Science subjects.","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":"123497878","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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