2021 International Congress of Advanced Technology and Engineering (ICOTEN)最新文献

{"title":"The Prediction of E-Money Circulation: Backpropagation with Genetic Algorithm Adoption","authors":"Elvia Budianita, O. Okfalisa, Muhammad Rizki Assiddiki","doi":"10.1109/ICOTEN52080.2021.9493468","DOIUrl":"https://doi.org/10.1109/ICOTEN52080.2021.9493468","url":null,"abstract":"Digital transformation forces the utilization of e-money during the economic transaction. Behind its advantages, e-money has been influenced by the inflation rate, thus accelerating the country’s money circulation. Moreover, the fragile Covid-19 economy triggers each country’s need to anticipate the circulation of e-money to deter future inflation. Therefore, this paper deployed the Backpropagation approach integrated with the Genetic Algorithm to forecast the dissemination of e-money in Indonesia by exploiting time-series Bank Indonesia (BI) data from January 2009 to December 2019. Here, 120 data with 12 variables are considered to thoroughly predict the Year 2020 circulation focusing on the previous 12 months. This study reveals that e-money circulation in Indonesia is increasing monthly in 2020. The testing result shows that the lowest mean square error (MSE) is found at 0.000035 for data training division at 90%:10%, learning rate parameter at 0.8, the combination of crossover probability and mutation at 0.4:0.6, and the total generation and population at 350 and 200, respectively. In a nutshell, Backpropagation with a Genetic Algorithm has been expected to a successful outcome for e-money circulation and provides large values compared with actual data and original BPNN.","PeriodicalId":308802,"journal":{"name":"2021 International Congress of Advanced Technology and Engineering (ICOTEN)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122647518","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}

{"title":"Gamification Elements in E-commerce – A Review","authors":"Lina Fatini Azmi, Norasnita Ahmad, N. A. Iahad","doi":"10.1109/ICOTEN52080.2021.9493475","DOIUrl":"https://doi.org/10.1109/ICOTEN52080.2021.9493475","url":null,"abstract":"The implementation of gamification is driven by a range of factors, including the environment of the application, the elements involved and the types of users. The best practical method for effective gamification application still remains unclear, making it difficult to determine the most effective elements for an e-commerce website. This paper aims to present the literature review conducted to classify the gamification elements in e-commerce that have been investigated in previous studies which were published within the last four years (20182021). This is done to identify the most appropriate and relevant gamification elements to use in our future study. The findings from previous studies showed that gamification improved positive consumer behaviour in e-commerce, particularly in terms of engagement, and at the same time helped to boost business profitability. Furthermore, previous studies in this field have also found that rewards, badges and leaderboards were the most widely used gamification elements. This study may be used as a foundation for the researchers to build and develop a gamification framework for e-commerce in the future.","PeriodicalId":308802,"journal":{"name":"2021 International Congress of Advanced Technology and Engineering (ICOTEN)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115901177","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}

{"title":"ST-MLR: A Spatio-temporal Multiple Linear Regression Missing Data Reconstruction Approach for Improving WSN Data Reliability","authors":"Yemeni Zaid, Bo Zhang, Waleed M. Ismael, Yingjuan Xie, G. Surname, Haibin Wang","doi":"10.1109/ICOTEN52080.2021.9493512","DOIUrl":"https://doi.org/10.1109/ICOTEN52080.2021.9493512","url":null,"abstract":"Missing data is one of the unavoidable issues in Wireless Sensor Networks (WSNs) due to various reasons, including communication failure, unreliable communication links, unexpected damage, etc. WSNs are the base of many critical and non-critical applications, such as nuclear applications, medical applications, weather forecasting, etc. Therefore missing data reconstruction before their application or further analysis plays a vital role in data reliability. This paper proposed a missing data reconstruction approach based on the Multiple Linear Regression model (MLR) using Spatio-temporal correlation. The experimental results reveal that the proposed approach is effective and efficient in reconstructing missing data of different scales.","PeriodicalId":308802,"journal":{"name":"2021 International Congress of Advanced Technology and Engineering (ICOTEN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131138473","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}

{"title":"Experimental study of a real-time control by backstepping technique of an induction motor drive","authors":"Meryem Benakcha, A. Benakcha, S. Zouzou, A. Ammar","doi":"10.1109/ICOTEN52080.2021.9493526","DOIUrl":"https://doi.org/10.1109/ICOTEN52080.2021.9493526","url":null,"abstract":"Induction machine, associated with a static converter, constitutes a variable speed drive whose industrial uses are increasingly important. To achieve good dynamic performances, it is therefore necessary to develop robust control laws. The aim of this paper is the experimental validation of a Backstepping vector control strategy applied to the three-phase induction machine (IM). This approach consists in replacing the conventional controller proportional/integral (PI) by an algorithm using the Backstepping technique. The PI controller has the drawbacks of a strong dependence on the machine parameters in their gains synthesis. The system development is based on Lyapunov's stability theory. The results show good dynamic performances, because the system perfectly follows the speed reference, ensuring the decoupling of the two fluxes. The design of the control and its experimental implementation in real time are carried out on a dSPACE 1104 acquisition card and in a MATLAB / Simulink environment. The machine used is a three-phase 1.1 kW induction machine.","PeriodicalId":308802,"journal":{"name":"2021 International Congress of Advanced Technology and Engineering (ICOTEN)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127073057","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}

{"title":"A Conceptual Framework For Malay-English Mixed-language Question Answering System","authors":"H. T. Lim, S. Huspi, R. Ibrahim","doi":"10.1109/ICOTEN52080.2021.9493503","DOIUrl":"https://doi.org/10.1109/ICOTEN52080.2021.9493503","url":null,"abstract":"Mixed language has turned into a current trend of language which refers to combining two or more languages either in spoken or written form. It has been widely used in social media forums to improve communication and for a greater range of expression. The current question answering (QA) system only supports monolingual queries, which restricts the capability of multilingual users to have a natural interaction with the system. In recent years, there has been a rise of interest in multilingual QA systems where translation models merged with machine learning algorithms in question classification are the commonly used solution. However, using words from other languages in a single sentence has led to the problem of the inability to identify code-switch from the monolingual sentence; this has also caused the problem of limited captured language context from machine translation processed mistranslated questions. The informal mixed-language representation that disobeys the natural linguistic rule in particular languages provides a challenge for automated QA systems, as the systems would need to translate and extract answers for the given questions. Additionally, lack of public resources such as Chunker, POS Tagger, and WordNet for mixed-language, especially for Malay-English, leads to low performance of the translation and classification model. Furthermore, the use of machine learning algorithms in question classification requires a large number of structured training data to ensure performance. This is impracticable in the Malay-English mixed-language domain since the availability of the mixed-language dataset is still an issue. To solve these problems, we aim to propose a framework consisting of the combination of enhanced translation models with deep learning; by using Convolutional Neural Networks (CNN) to address the Malay-English mixed-language question classification to generate the best answer. The first part will study the machine translation model, where word-level language identification and text normalization towards Malay-English mixed-language questions will be developed. The second part will focus on the deep learning algorithm, where we will explore CNN as the classification model to assist in the translated questions to provide the best answer. Thus, in this paper, a framework consisting of an enhanced translation model for Malay-English, and also an end-to-end mixed-language question answering system for the Malay-English Q&A system, is presented. This research will provide a significant contribution to a multilingual forum platform and also to intelligent Q&A systems (chatbots).","PeriodicalId":308802,"journal":{"name":"2021 International Congress of Advanced Technology and Engineering (ICOTEN)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116900235","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}

{"title":"A Survey on Prediction based Routing for Vehicular Ad-hoc Networks","authors":"S. Rashid, M. Khan, A. Saeed, Ch Muhammad Hamza","doi":"10.1109/ICOTEN52080.2021.9493428","DOIUrl":"https://doi.org/10.1109/ICOTEN52080.2021.9493428","url":null,"abstract":"Vehicular Ad-hoc network (VANET) is a type of Mobile Ad-hoc network (MANET), where vehicle mobility is high and topology changes are frequent. VANETs have many applications in Intelligent Transportation System (ITS) e.g. traffic safety, vigilance control, active prediction and infotainment applications. Routing in VANETs has emerged as an interesting topic due to popularity of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. Due to frequent topology changes in VANETs, a topology independent protocol is desired to predict collisions and route failures. In this paper, we provide recent research progress from 2015-2020 in prediction-based routing for VANETs. For this purpose, we have followed Kitchenham guidelines to survey research articles, performed classification based on their evaluation parameters and explored their applications. The research challenges have been identified for prediction-based routing. This paper provides a quick review of recent predictive protocols designed for VANETs.","PeriodicalId":308802,"journal":{"name":"2021 International Congress of Advanced Technology and Engineering (ICOTEN)","volume":"153 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114433665","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}

{"title":"B5G and 6G: Next Generation Wireless Communications Technologies, Demand and Challenges","authors":"E. A. Kadir, R. Shubair, S. K. Abdul Rahim, M. Himdi, M. Kamarudin, S. Rosa","doi":"10.1109/ICOTEN52080.2021.9493470","DOIUrl":"https://doi.org/10.1109/ICOTEN52080.2021.9493470","url":null,"abstract":"The Fifth Generation (5G) is now have been implemented in some countries and will be progressing according to its plan to be commercialized worldwide soon. Nevertheless, many research institutions around the world have now started to look Beyond 5G (B5G) and Sixth-Generation (6G) where these could be the next generation of wireless communications technologies. The demand for wireless connectivity has grown exponentially over the last few decades, to meet the demands of future connectivity a significant improvement needs to be made in communications technologies. A new paradigm of wireless communication, the 6G system, with the full support of massive multiple inputs multiple-output (MIMO) system and millimeter-Wave (mmWave), is expected to be implemented between 2027 and 2030. B5G, some fundamental issues that need to be addressed are higher system capacity, higher data rate, lower latency, higher security, and improved quality of service (QoS) compared to the 5G system. This paper focusses on the discussion of the potential of 6G wireless communication and its network demands and challenges including mmWave, terahertz communications and massive MIMO systems.","PeriodicalId":308802,"journal":{"name":"2021 International Congress of Advanced Technology and Engineering (ICOTEN)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114498817","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}

{"title":"Intelligent Methods for flood forecasting in Wadi al Wala, Jordan","authors":"Mohammad Al-Fawa'reh, Alaa Hawamdeh, Rana Alrawashdeh, Mousa Tayseer Jafar","doi":"10.1109/ICOTEN52080.2021.9493425","DOIUrl":"https://doi.org/10.1109/ICOTEN52080.2021.9493425","url":null,"abstract":"Increasing water scarcity and rising demand throughout the Middle East and North Africa pose a major problem, and flood forecasting has been an open issue for a long time, attracting significant attention. Jordan seeks to use smart methods to solve the problem. Therefore, a real-world case study was conducted in Wadi al Wala for real-time rainfall forecasting and flood control, using 38 years of daily data from 13 rain gauge stations in the region. Different Machine Learning (ML) models were evaluated with various input information types to provide predictions in an almost real-time schedule. Preliminary tests showed that the decision tree (DT) and random forest (RF) techniques achieved the best generalized flood forecasting. In particular, the model was able to produce forecasts at any time, with the use of a mixture of meteorological parameters (relative humidity, air pressure, wet bulb temperature, and cloudiness), the precipitation at the forecasting point, and precipitation at the appropriate stations as input data, and the advanced ML model to be used with continuous data containing rainy and non-rainy cycles. Experiments showed the dominance of DT forecasts over those produced by the persistent model.","PeriodicalId":308802,"journal":{"name":"2021 International Congress of Advanced Technology and Engineering (ICOTEN)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123023325","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}

{"title":"Awareness to Deepfake: A resistance mechanism to Deepfake","authors":"Mohammad Faisal Bin Ahmed, M. Miah, Abhijit Bhowmik, Juniada Binti Sulaiman","doi":"10.1109/ICOTEN52080.2021.9493549","DOIUrl":"https://doi.org/10.1109/ICOTEN52080.2021.9493549","url":null,"abstract":"The goal of this study is to find whether exposure to Deepfake videos makes people better at detecting Deepfake videos and whether it is a better strategy against fighting Deepfake. For this study a group of people from Bangladesh has volunteered. This group were exposed to a number of Deepfake videos and asked subsequent questions to verify improvement on their level of awareness and detection in context of Deepfake videos. This study has been performed in two phases, where second phase was performed to validate any generalization. The fake videos are tailored for the specific audience and where suited, are created from scratch. Finally, the results are analyzed, and the study’s goals are inferred from the obtained data.","PeriodicalId":308802,"journal":{"name":"2021 International Congress of Advanced Technology and Engineering (ICOTEN)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125375569","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}

{"title":"Brain Tumour Classification using Deep Learning with Residual Attention Network: A Comparative Study","authors":"Abdulrazak Yahya Saleh, Sashwini A-P S Thiagaraju","doi":"10.1109/ICOTEN52080.2021.9493544","DOIUrl":"https://doi.org/10.1109/ICOTEN52080.2021.9493544","url":null,"abstract":"The main goal of this paper is to evaluate the performance of deep learning with Residual Attention Network (RAN) for brain tumour classification. Digitalised Magnetic Resonance Image (MRI) datasets obtained from Malaysian hospitals and other sources are utilised in this paper. The MRI datasets consist of information of those patients who are 20 years old and above, both male and female. The RAN algorithm is trained and tested using the MRI datasets. The algorithm performance is evaluated based on training accuracy, testing accuracy, validation accuracy, and validation loss metrices. Moreover, a comparative analysis is done with Residual Neural Network (ResNet) and Convolutional Neural Network (CNN) using the same datasets. The findings from this study prove that RAN provides the best performance among the three algorithms. ResNet has good performance, with an accuracy ranging from 67% to 87%. The standard CNN algorithm does not perform well, with a very inconsistent accuracy of between 57% and 71%. RAN produces the highest and most consistent accuracy, which is 94% and above. Further explanation is provided in this paper to prove the efficiency of RAN for the classification of brain tumours.","PeriodicalId":308802,"journal":{"name":"2021 International Congress of Advanced Technology and Engineering (ICOTEN)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121396279","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}