Pub Date : 2022-11-14DOI: 10.1109/ICOCO56118.2022.10031900
A. Selamat, Mohamed Noordin Yusuff Marican, S. H. Othman, S. Razak
Cybersecurity is increasingly becoming an important discussion topic in the boardroom of companies, regardless of the size or industry. Hackers nowadays are becoming increasingly smart. Instead of attacking big multi-national companies, international banks and government organisations which have built strong protection against cyber threats, the perpetrators now placed their focus on smaller and medium size businesses like technology start-ups through a variety of attacks from phishing, ransomware to the exploitation of vulnerabilities in the web or mobile applications. Therefore, it is imperative that technology start-ups have the capability in assessing their cyber security maturity to combat against cyber threats. However, for technology start-ups, it is especially imperative as cyber-attacks or data breaches could undeniably result in the loss of customers’ confidence, regulatory implications and revenue loss which could eventually result in the start-up untimely closure. Although there are available security frameworks commonly used in the industry by cyber security practitioners, these frameworks are not suitable for technology start-ups as they tend to be broad and generic, taking a long time to conduct the assessment requiring adequate manpower or even the need for a budget to hire external consultants to help in conducting the assessment. This study seeks to analyse the current cyber security frameworks and introduce an end-to-end Cyber Security Maturity Model, which can be used specifically for technology start-ups. The proposed model not only provides an end-to-end maturity assessment of the start-up’s cyber security posture but also coupled with an existing quantification model to justify the investments allocated in implementing cyber security measures for the start-up. Right-sizing the cyber security measures for the start-up in the different stages of the start-up lifecycle could allow reasonable controls to be implemented at the appropriate phase.
{"title":"An End-To-End Cyber Security Maturity Model For Technology Startups","authors":"A. Selamat, Mohamed Noordin Yusuff Marican, S. H. Othman, S. Razak","doi":"10.1109/ICOCO56118.2022.10031900","DOIUrl":"https://doi.org/10.1109/ICOCO56118.2022.10031900","url":null,"abstract":"Cybersecurity is increasingly becoming an important discussion topic in the boardroom of companies, regardless of the size or industry. Hackers nowadays are becoming increasingly smart. Instead of attacking big multi-national companies, international banks and government organisations which have built strong protection against cyber threats, the perpetrators now placed their focus on smaller and medium size businesses like technology start-ups through a variety of attacks from phishing, ransomware to the exploitation of vulnerabilities in the web or mobile applications. Therefore, it is imperative that technology start-ups have the capability in assessing their cyber security maturity to combat against cyber threats. However, for technology start-ups, it is especially imperative as cyber-attacks or data breaches could undeniably result in the loss of customers’ confidence, regulatory implications and revenue loss which could eventually result in the start-up untimely closure. Although there are available security frameworks commonly used in the industry by cyber security practitioners, these frameworks are not suitable for technology start-ups as they tend to be broad and generic, taking a long time to conduct the assessment requiring adequate manpower or even the need for a budget to hire external consultants to help in conducting the assessment. This study seeks to analyse the current cyber security frameworks and introduce an end-to-end Cyber Security Maturity Model, which can be used specifically for technology start-ups. The proposed model not only provides an end-to-end maturity assessment of the start-up’s cyber security posture but also coupled with an existing quantification model to justify the investments allocated in implementing cyber security measures for the start-up. Right-sizing the cyber security measures for the start-up in the different stages of the start-up lifecycle could allow reasonable controls to be implemented at the appropriate phase.","PeriodicalId":319652,"journal":{"name":"2022 IEEE International Conference on Computing (ICOCO)","volume":"6 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":"115398906","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/ICOCO56118.2022.10031660
Noor Izati Abdul Hamid, N. Kamal, H. M. Hanum, Noor Latiffah Adam, Z. Ibrahim
Due to the rapid growth of online e-commerce, customers can now voice and express their reviews and thoughts on online products. Therefore, companies who market their product on the e-commerce website will receive thousands of reviews and feedback from their end-users directly on this platform. As the amount of textual data grows tremendously, developing sentiment analysis that automatically analyses text data becomes increasingly vital. It is because reading every review manually can be a tedious task and time-consuming. Analyzing the sentiment for all reviews can provide the companies with an overview of how positive or negative the customers are about their products. The convolutional neural network (CNN) has recently been used for text classification tasks and has achieved impressive results. Hence, this study proposes a CNN method for sentiment analysis to classify the reviews on mobile phone brands. The customer reviews dataset is obtained from the Amazon website. This study combined the Word2Vec-CNN model to predict the sentiment of mobile phone reviews effectively. Pre-trained Word2Vec model is utilized to generate word vectors in word embedding. CNN layers are used to extract better features for sentence categorization to identify the sentiment polarity of the reviews, whether positive or negative. The obtained results give us 88% accuracy and the developed application can also function well in analyzing the sentiment of customers’ reviews.
{"title":"fProSentiment Analysis on Mobile Phone Brands Reviews using Convolutional Neural Network (CNN)","authors":"Noor Izati Abdul Hamid, N. Kamal, H. M. Hanum, Noor Latiffah Adam, Z. Ibrahim","doi":"10.1109/ICOCO56118.2022.10031660","DOIUrl":"https://doi.org/10.1109/ICOCO56118.2022.10031660","url":null,"abstract":"Due to the rapid growth of online e-commerce, customers can now voice and express their reviews and thoughts on online products. Therefore, companies who market their product on the e-commerce website will receive thousands of reviews and feedback from their end-users directly on this platform. As the amount of textual data grows tremendously, developing sentiment analysis that automatically analyses text data becomes increasingly vital. It is because reading every review manually can be a tedious task and time-consuming. Analyzing the sentiment for all reviews can provide the companies with an overview of how positive or negative the customers are about their products. The convolutional neural network (CNN) has recently been used for text classification tasks and has achieved impressive results. Hence, this study proposes a CNN method for sentiment analysis to classify the reviews on mobile phone brands. The customer reviews dataset is obtained from the Amazon website. This study combined the Word2Vec-CNN model to predict the sentiment of mobile phone reviews effectively. Pre-trained Word2Vec model is utilized to generate word vectors in word embedding. CNN layers are used to extract better features for sentence categorization to identify the sentiment polarity of the reviews, whether positive or negative. The obtained results give us 88% accuracy and the developed application can also function well in analyzing the sentiment of customers’ reviews.","PeriodicalId":319652,"journal":{"name":"2022 IEEE International Conference on Computing (ICOCO)","volume":"28 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":"121627746","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/ICOCO56118.2022.10031882
Vyankatesh Adke, Priti Bakhshi, Muniza Askari
The financial services sector in the Association of South East Asian Nations (ASEAN) region has seen significant growth, driven by digitalization and the rise of fintech firms. Financial services accounted for about 8% of the overall Gross Domestic Product (GDP) at around ${$}$ 3 Trillion in 2021 [1]. While the GDP contracted slightly due to the COVID-19 pandemic, the overall outlook over the next five years remains positive.To further boost this growth, and foster innovation, regulators across ASEAN are establishing foundations for open finance, as is clear from policies in Singapore [2], the Philippines [3], and Indonesia [4].The main objectives of the open finance framework are to offer integrated financial services by making customer experiences that are fully digital, frictionless, empathetic, and anticipatory to customer needs.Customers today are more digitally empowered, expect personalized service, and often maintain relationships with multiple retail banks. As such, Customer Experience (CX) management is a top priority for retail banks to ensure overall brand recall, customer loyalty, and growth.This however also poses a new challenge to incumbent banks, as they need to embark on complex digital transformation journeys to stay relevant and competitive with due consideration for costs and accrued benefits.In this context, this study explores the impact of cloud, Artificial Intelligence (AI), and digital channels, collectively referred to as disruptive technologies, on customer experience management.It does so by critically examining existing literature on the evolution of digital technologies, their applications for customer engagement and the consequent impact on customer behaviours, and customer experience measures such as the Customer Satisfaction Score (CSAT) and Net Promoter Score (NPS). Based on the review, the study identifies opportunities for future research in the form of research questions, which include factors like experience quality, behaviour traits, and customer segmentation attributes that impact customer experience.The study contributes by providing insights to retail banks on key factors to consider while embarking on digital transformation projects to improve customer experience. While the study focuses on retail banking, its contributions could be beneficial to adjacent financial services like lending and insurance in ASEAN.
{"title":"Impact of Disruptive Technologies on Customer Experience Management In ASEAN: A Review","authors":"Vyankatesh Adke, Priti Bakhshi, Muniza Askari","doi":"10.1109/ICOCO56118.2022.10031882","DOIUrl":"https://doi.org/10.1109/ICOCO56118.2022.10031882","url":null,"abstract":"The financial services sector in the Association of South East Asian Nations (ASEAN) region has seen significant growth, driven by digitalization and the rise of fintech firms. Financial services accounted for about 8% of the overall Gross Domestic Product (GDP) at around ${$}$ 3 Trillion in 2021 [1]. While the GDP contracted slightly due to the COVID-19 pandemic, the overall outlook over the next five years remains positive.To further boost this growth, and foster innovation, regulators across ASEAN are establishing foundations for open finance, as is clear from policies in Singapore [2], the Philippines [3], and Indonesia [4].The main objectives of the open finance framework are to offer integrated financial services by making customer experiences that are fully digital, frictionless, empathetic, and anticipatory to customer needs.Customers today are more digitally empowered, expect personalized service, and often maintain relationships with multiple retail banks. As such, Customer Experience (CX) management is a top priority for retail banks to ensure overall brand recall, customer loyalty, and growth.This however also poses a new challenge to incumbent banks, as they need to embark on complex digital transformation journeys to stay relevant and competitive with due consideration for costs and accrued benefits.In this context, this study explores the impact of cloud, Artificial Intelligence (AI), and digital channels, collectively referred to as disruptive technologies, on customer experience management.It does so by critically examining existing literature on the evolution of digital technologies, their applications for customer engagement and the consequent impact on customer behaviours, and customer experience measures such as the Customer Satisfaction Score (CSAT) and Net Promoter Score (NPS). Based on the review, the study identifies opportunities for future research in the form of research questions, which include factors like experience quality, behaviour traits, and customer segmentation attributes that impact customer experience.The study contributes by providing insights to retail banks on key factors to consider while embarking on digital transformation projects to improve customer experience. While the study focuses on retail banking, its contributions could be beneficial to adjacent financial services like lending and insurance in ASEAN.","PeriodicalId":319652,"journal":{"name":"2022 IEEE International Conference on Computing (ICOCO)","volume":"496 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":"126563056","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/ICOCO56118.2022.10031692
Sian Lun Lau, Marvin J. H. Lee, Min Xuan Teoh
The use of social media for analysing behaviours and trends of the public is a growing research area. In 2021, the #LAWAN social movement emerged in Malaysia as a result of discontentment of the people towards the government. This study intends to study and discover the public perception and sentiment towards the #LAWAN rally. The investigation also questions whether is the civil society supportive of such social movement and rally, especially when rally, protests and demonstration are traditionally and often seen as a negative and non-constructive approach in the country. Tweets with the hashtag #LAWAN over a day on and before the rally day on 31st July 2021 have been collected and analysed. Sentiment analysis helped to identify the public sentiments towards the rally, and topic modelling helped to discover common topics from the 5000+ tweets scrapped from the social media.
{"title":"Understanding Public Sentiment Towards a Public Rally Using Text and Social Media Analytic","authors":"Sian Lun Lau, Marvin J. H. Lee, Min Xuan Teoh","doi":"10.1109/ICOCO56118.2022.10031692","DOIUrl":"https://doi.org/10.1109/ICOCO56118.2022.10031692","url":null,"abstract":"The use of social media for analysing behaviours and trends of the public is a growing research area. In 2021, the #LAWAN social movement emerged in Malaysia as a result of discontentment of the people towards the government. This study intends to study and discover the public perception and sentiment towards the #LAWAN rally. The investigation also questions whether is the civil society supportive of such social movement and rally, especially when rally, protests and demonstration are traditionally and often seen as a negative and non-constructive approach in the country. Tweets with the hashtag #LAWAN over a day on and before the rally day on 31st July 2021 have been collected and analysed. Sentiment analysis helped to identify the public sentiments towards the rally, and topic modelling helped to discover common topics from the 5000+ tweets scrapped from the social media.","PeriodicalId":319652,"journal":{"name":"2022 IEEE International Conference on Computing (ICOCO)","volume":"16 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":"127706858","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/ICOCO56118.2022.10031676
Tengku Nurul Aimi Balqis Tengku, Malim Busu, Saadi Ahmad Kamarudin, N. Ahad, Norazlina Mamat
The study aims to present a bibliographic review of publications from the Scopus database related to the robust neural network topic. As of 13th September 2022, this study managed to gather 16 articles from 2019-2023 based on the keywords of robust neural network used for the searching process. The three tools have been used to analyze the gathered Scopus database, which are Microsoft Excel, VOSviewer software and Harzing’s Publish and Perish software. This study reports the findings in terms of the current trend and the impact of publications of robust neural network studies. According to bibliometrics analysis, the number of publications has been increasing over time. This study focuses only on the Scopus database. For future research, other databases like PubMed, Lens, Dimensions, and Web of Science could be considered so the findings will be more meaningful and impactful. This study is the first article to do a bibliographic review related to the neural network.
该研究旨在对Scopus数据库中与鲁棒神经网络主题相关的出版物进行书目综述。截至2022年9月13日，本研究基于鲁棒神经网络搜索过程中的关键词，成功收集了2019-2023年的16篇文章。这三种工具被用来分析收集到的Scopus数据库，它们是Microsoft Excel, VOSviewer软件和Harzing的Publish and Perish软件。本研究报告了鲁棒神经网络研究的当前趋势和出版物的影响。根据文献计量学分析，出版物的数量一直在增加。本研究仅关注Scopus数据库。对于未来的研究，可以考虑其他数据库，如PubMed, Lens, Dimensions和Web of Science，这样研究结果将更有意义和影响力。本研究首次对神经网络相关文献进行综述。
{"title":"Bibliometric Analysis of Global Scientific Literature on Robust Neural Network","authors":"Tengku Nurul Aimi Balqis Tengku, Malim Busu, Saadi Ahmad Kamarudin, N. Ahad, Norazlina Mamat","doi":"10.1109/ICOCO56118.2022.10031676","DOIUrl":"https://doi.org/10.1109/ICOCO56118.2022.10031676","url":null,"abstract":"The study aims to present a bibliographic review of publications from the Scopus database related to the robust neural network topic. As of 13th September 2022, this study managed to gather 16 articles from 2019-2023 based on the keywords of robust neural network used for the searching process. The three tools have been used to analyze the gathered Scopus database, which are Microsoft Excel, VOSviewer software and Harzing’s Publish and Perish software. This study reports the findings in terms of the current trend and the impact of publications of robust neural network studies. According to bibliometrics analysis, the number of publications has been increasing over time. This study focuses only on the Scopus database. For future research, other databases like PubMed, Lens, Dimensions, and Web of Science could be considered so the findings will be more meaningful and impactful. This study is the first article to do a bibliographic review related to the neural network.","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":"129712170","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/ICOCO56118.2022.10031710
A. Lit, Popoola Oluwaseun Lydia, S. Suhaili, R. Sapawi, K. Kipli, D. N. S. Dharmiza
Wireless Network-on-Chip (NoC) is envisioned as complementary to the conventional NoC due to its CMOS compatibility and architectural flexibility, which is advantageous as no wiring infrastructure is required for wireless transmission. On-chip wireless channels are used to actually minimize the communication latency between the distant processing cores because of its ability to communicate with long-distance communication processing cores in a single-hop. This paper investigates the effect of the single-, dual-, and triple-channels on the mesh-WiNoC architecture. Additionally, four and nine radio hubs are evenly distributed throughout the mesh-WiNoC topological structure to evaluate its global transmission latency, network throughput, and energy characteristics. The investigated architectures under test are simulated on the cycle-accurate systemC based Noxim simulator under a random traffic workload scenario for WiNoC performance evaluation. This study’s contribution is that it looks into the best number of wireless channels to use in a 10 × 10 mesh WiNoC architecture for 4 and 9 radio hub scenarios to get the best performance in transmission latency and energy consumption. Experimental results show that for both investigated number of radio hub on mesh-WiNoC architecture demonstrates nearly identical system performance in terms of transmission latency and throughput. However, the meshWiNoC architecture with 4 radio hub demonstrates better energy characteristics, saving 9.63% and 13.60% of energy, respectively, when compared to the architecture with 6 and 9 radio hub.
{"title":"Performance Evaluation of Multi-Channel for 10×10 Mesh Wireless Network-on-Chip Architecture","authors":"A. Lit, Popoola Oluwaseun Lydia, S. Suhaili, R. Sapawi, K. Kipli, D. N. S. Dharmiza","doi":"10.1109/ICOCO56118.2022.10031710","DOIUrl":"https://doi.org/10.1109/ICOCO56118.2022.10031710","url":null,"abstract":"Wireless Network-on-Chip (NoC) is envisioned as complementary to the conventional NoC due to its CMOS compatibility and architectural flexibility, which is advantageous as no wiring infrastructure is required for wireless transmission. On-chip wireless channels are used to actually minimize the communication latency between the distant processing cores because of its ability to communicate with long-distance communication processing cores in a single-hop. This paper investigates the effect of the single-, dual-, and triple-channels on the mesh-WiNoC architecture. Additionally, four and nine radio hubs are evenly distributed throughout the mesh-WiNoC topological structure to evaluate its global transmission latency, network throughput, and energy characteristics. The investigated architectures under test are simulated on the cycle-accurate systemC based Noxim simulator under a random traffic workload scenario for WiNoC performance evaluation. This study’s contribution is that it looks into the best number of wireless channels to use in a 10 × 10 mesh WiNoC architecture for 4 and 9 radio hub scenarios to get the best performance in transmission latency and energy consumption. Experimental results show that for both investigated number of radio hub on mesh-WiNoC architecture demonstrates nearly identical system performance in terms of transmission latency and throughput. However, the meshWiNoC architecture with 4 radio hub demonstrates better energy characteristics, saving 9.63% and 13.60% of energy, respectively, when compared to the architecture with 6 and 9 radio hub.","PeriodicalId":319652,"journal":{"name":"2022 IEEE International Conference on Computing (ICOCO)","volume":"314 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":"122966221","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/ICOCO56118.2022.10031901
T. M. Busu, Saadi Ahmad Kamarudin, N. Ahad, Norazlina Mamat
Stock market prediction is vital in the financial world. Investors and people interested in investing would be interested in the future value of the stock market before they invest in it. By using the method of time series, this research gives a contribution to forecast and modelling the FTSE Bursa Malaysia KLCI (FBM KLCI) stock market. In this research, the stock market is forecasted to identify the stock market trend in the future. The FBM KLCI closing prices data was utilized to build Long Short-Term Memory (LSTM) models to predict the stock market. The performance of the model has been evaluated using the root mean squared error (RMSE) and the mean absolute error (MAE) in order to choose the best model. The researcher used the Bursa Malaysia data to forecast the stock market for five years, from October 20, 2016, to October 20, 2021, which has been scrapped from the Yahoo Finance website. The data is analyzed by running Python coding in Google Colab. The result proves that the accuration of the LSTM model by using Recurrent Neural Network (RNN) approach is accurate and the predicted value of the stock market at the date 2021-10-05 is increased by 1.87%. It can be used to predict the future closing stock prices in stock market prediction in FBM KLCI stock market. The results are expected to provide an accurate prediction for a better profit. Thus, prediction in stock market investment can support long-term economic growth, or in other words, it can help economic sustainability.
{"title":"Prediction of FTSE Bursa Malaysia KLCI Stock Market using LSTM Recurrent Neural Network","authors":"T. M. Busu, Saadi Ahmad Kamarudin, N. Ahad, Norazlina Mamat","doi":"10.1109/ICOCO56118.2022.10031901","DOIUrl":"https://doi.org/10.1109/ICOCO56118.2022.10031901","url":null,"abstract":"Stock market prediction is vital in the financial world. Investors and people interested in investing would be interested in the future value of the stock market before they invest in it. By using the method of time series, this research gives a contribution to forecast and modelling the FTSE Bursa Malaysia KLCI (FBM KLCI) stock market. In this research, the stock market is forecasted to identify the stock market trend in the future. The FBM KLCI closing prices data was utilized to build Long Short-Term Memory (LSTM) models to predict the stock market. The performance of the model has been evaluated using the root mean squared error (RMSE) and the mean absolute error (MAE) in order to choose the best model. The researcher used the Bursa Malaysia data to forecast the stock market for five years, from October 20, 2016, to October 20, 2021, which has been scrapped from the Yahoo Finance website. The data is analyzed by running Python coding in Google Colab. The result proves that the accuration of the LSTM model by using Recurrent Neural Network (RNN) approach is accurate and the predicted value of the stock market at the date 2021-10-05 is increased by 1.87%. It can be used to predict the future closing stock prices in stock market prediction in FBM KLCI stock market. The results are expected to provide an accurate prediction for a better profit. Thus, prediction in stock market investment can support long-term economic growth, or in other words, it can help economic sustainability.","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":"129378508","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/ICOCO56118.2022.10031962
J. Labadin, B. H. Hong, W. Tiong, B. Gill, D. Perera, A. Rigit, Sarbhan Singh, Tan Cia Vei, S. M. Ghazali, J. Jelip, Norhayati Mokhtar, Wan Ming Keong
This paper presents the predictive power analysis of the bipartite dengue contact (BDC) network model for identifying the source of dengue infection, defined as dengue hotspot. This BDC network model was earlier formulated, verified and validated using data collected in Sarawak, Malaysia. Then, a web-based BDC network system was implemented and subsequently tested by 7 other areas in Malaysia. The data collected using the system was then used to further evaluate the predictive ability of the BDC network model. The validity period of the dengue hotspots identified by the BDC network model was measured based on the accuracy of the predictive power analysis and Spearman’s Rank Correlation Coefficient (SRCC). Based on the results, using prior one-week data was sufficient to predict the dengue hotspot for the following week and subsequent two weeks. This shows that the hotspots are valid for two weeks. The accuracy for the outbreak areas is above 60%. Most of the model reported an SRCC above 0.70 which indicated a strong positive relationship between the hotspots in the targeted model and the validated model. Due to the accuracy and SRCC values obtained, it is suggested that the BDC network model can proceed further with retrospective data for other dengue outbreak areas in Malaysia and a prospective study for the areas that participated in this study.
{"title":"Evaluating the Predictive Ability of the Bipartite Dengue Contact Network Model","authors":"J. Labadin, B. H. Hong, W. Tiong, B. Gill, D. Perera, A. Rigit, Sarbhan Singh, Tan Cia Vei, S. M. Ghazali, J. Jelip, Norhayati Mokhtar, Wan Ming Keong","doi":"10.1109/ICOCO56118.2022.10031962","DOIUrl":"https://doi.org/10.1109/ICOCO56118.2022.10031962","url":null,"abstract":"This paper presents the predictive power analysis of the bipartite dengue contact (BDC) network model for identifying the source of dengue infection, defined as dengue hotspot. This BDC network model was earlier formulated, verified and validated using data collected in Sarawak, Malaysia. Then, a web-based BDC network system was implemented and subsequently tested by 7 other areas in Malaysia. The data collected using the system was then used to further evaluate the predictive ability of the BDC network model. The validity period of the dengue hotspots identified by the BDC network model was measured based on the accuracy of the predictive power analysis and Spearman’s Rank Correlation Coefficient (SRCC). Based on the results, using prior one-week data was sufficient to predict the dengue hotspot for the following week and subsequent two weeks. This shows that the hotspots are valid for two weeks. The accuracy for the outbreak areas is above 60%. Most of the model reported an SRCC above 0.70 which indicated a strong positive relationship between the hotspots in the targeted model and the validated model. Due to the accuracy and SRCC values obtained, it is suggested that the BDC network model can proceed further with retrospective data for other dengue outbreak areas in Malaysia and a prospective study for the areas that participated in this study.","PeriodicalId":319652,"journal":{"name":"2022 IEEE International Conference on Computing (ICOCO)","volume":"73 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":"128783399","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/ICOCO56118.2022.10031696
Najwa Ayuni Jamaludin, Farhan Mohamed, M. Sunar, A. Selamat, O. Krejcar
Immersive Analytics (IA) is a fast-growing research field that concerns improving and facilitating human sensemaking and data understanding through an immersive experience. Understanding the suitable application scenario that will benefit from IA enables a shift towards developing effective and meaningful applications. This paper aims to explore tasks and scenarios that can benefit from IA by conducting a systematic review of existing studies and mapping them according to the multi-level typology for abstract visualization tasks, which is also known as the What-Why-How framework. The study synthesized several works to answer the Why within the context of multiple levels of specificity. Finally, the limitations and future works are discussed.
{"title":"Answering Why? An Overview of Immersive Data Visualization Applications Using Multi-Level Typology of Visualization Task","authors":"Najwa Ayuni Jamaludin, Farhan Mohamed, M. Sunar, A. Selamat, O. Krejcar","doi":"10.1109/ICOCO56118.2022.10031696","DOIUrl":"https://doi.org/10.1109/ICOCO56118.2022.10031696","url":null,"abstract":"Immersive Analytics (IA) is a fast-growing research field that concerns improving and facilitating human sensemaking and data understanding through an immersive experience. Understanding the suitable application scenario that will benefit from IA enables a shift towards developing effective and meaningful applications. This paper aims to explore tasks and scenarios that can benefit from IA by conducting a systematic review of existing studies and mapping them according to the multi-level typology for abstract visualization tasks, which is also known as the What-Why-How framework. The study synthesized several works to answer the Why within the context of multiple levels of specificity. Finally, the limitations and future works are discussed.","PeriodicalId":319652,"journal":{"name":"2022 IEEE International Conference on Computing (ICOCO)","volume":"6 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":"125401442","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/ICOCO56118.2022.10031797
Woan Ning Lim, Yunli Lee, K. Yap, Ching-Chiuan Yen
There is a rise of interest in promoting Virtual Reality (VR) in many industries since the VR headsets released as consumer products and getting affordable. The advantage of VR lies in its capability in creating a sense of presence and immersion, however it is still a major challenge to enable humans to feel the weight of the object in VR. There have been remarkable advancements in the development of haptic interfaces throughout the years. However, a number of challenges limit the progression to enable humans to sense the weight of virtual objects. Pseudo-haptic approach is a less costly alternative with better mobility compared to haptic interfaces. It is a software approach seeks to use the overall dominance of the visual system to create haptic illusions to render the perception of weight. In this paper, a pseudo-haptic model using passive force to simulate weight perception is proposed. The hand pressures are captured during the interaction to simulate the objects’ behavior to create the pseudo-weight illusion. The design and implementation of the force detection and visual feedback modules are discussed, and the preliminary evaluations of the force sensing resistors are presented.
{"title":"Weight Perception Simulation in Virtual Reality with Passive Force using Force Sensing Resistors","authors":"Woan Ning Lim, Yunli Lee, K. Yap, Ching-Chiuan Yen","doi":"10.1109/ICOCO56118.2022.10031797","DOIUrl":"https://doi.org/10.1109/ICOCO56118.2022.10031797","url":null,"abstract":"There is a rise of interest in promoting Virtual Reality (VR) in many industries since the VR headsets released as consumer products and getting affordable. The advantage of VR lies in its capability in creating a sense of presence and immersion, however it is still a major challenge to enable humans to feel the weight of the object in VR. There have been remarkable advancements in the development of haptic interfaces throughout the years. However, a number of challenges limit the progression to enable humans to sense the weight of virtual objects. Pseudo-haptic approach is a less costly alternative with better mobility compared to haptic interfaces. It is a software approach seeks to use the overall dominance of the visual system to create haptic illusions to render the perception of weight. In this paper, a pseudo-haptic model using passive force to simulate weight perception is proposed. The hand pressures are captured during the interaction to simulate the objects’ behavior to create the pseudo-weight illusion. The design and implementation of the force detection and visual feedback modules are discussed, and the preliminary evaluations of the force sensing resistors are presented.","PeriodicalId":319652,"journal":{"name":"2022 IEEE International Conference on Computing (ICOCO)","volume":"86 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":"116400489","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：