Pub Date : 2020-12-21DOI: 10.1109/ETCCE51779.2020.9350889
Md Mahmudur Rahman, A. Mallick
Bangladesh is a developing country with a severe power crisis. Like all the developing countries, power demand is increasing rapidly for the past couple of years. The energy system of Bangladesh is fossil fuel-based (98% of capacity), and fossil fuel is main responsible for high greenhouse gases (GHG) emissions referred to as the carbon footprint. During electricity generation, fossil fuel combustion produces a significant amount of greenhouse gases (GHG), and CO2 has the highest share among those. In this study, the total carbon footprint produced by electricity generation in Bangladesh is calculated based on the Intergovernmental Panel on Climate Change (IPCC) methodology using fossil-fueled power plants' data for 2009–15. In 2014–15, over 23 million tons of greenhouse gasses had been emitted in Bangladesh for 43 TWh of electricity generation. The Emission factor, the amount of produced carbon emission for unit electricity generation, is computed for every existing power plant (105 power plants in 2015) as well as the national grid. The National grid emission factor is calculated as 530–570 tCO2/GWh over six years, which is too high compared to that of developed countries. Fuel-specific CO2 emission factors are calculated to know how intense the fuel is. Coal claimed the highest emission factor as 1158.28 tCO2/GWh over six years.
{"title":"Measurement of the carbon footprint for Bangladesh's electricity generation in 2009-15","authors":"Md Mahmudur Rahman, A. Mallick","doi":"10.1109/ETCCE51779.2020.9350889","DOIUrl":"https://doi.org/10.1109/ETCCE51779.2020.9350889","url":null,"abstract":"Bangladesh is a developing country with a severe power crisis. Like all the developing countries, power demand is increasing rapidly for the past couple of years. The energy system of Bangladesh is fossil fuel-based (98% of capacity), and fossil fuel is main responsible for high greenhouse gases (GHG) emissions referred to as the carbon footprint. During electricity generation, fossil fuel combustion produces a significant amount of greenhouse gases (GHG), and CO2 has the highest share among those. In this study, the total carbon footprint produced by electricity generation in Bangladesh is calculated based on the Intergovernmental Panel on Climate Change (IPCC) methodology using fossil-fueled power plants' data for 2009–15. In 2014–15, over 23 million tons of greenhouse gasses had been emitted in Bangladesh for 43 TWh of electricity generation. The Emission factor, the amount of produced carbon emission for unit electricity generation, is computed for every existing power plant (105 power plants in 2015) as well as the national grid. The National grid emission factor is calculated as 530–570 tCO2/GWh over six years, which is too high compared to that of developed countries. Fuel-specific CO2 emission factors are calculated to know how intense the fuel is. Coal claimed the highest emission factor as 1158.28 tCO2/GWh over six years.","PeriodicalId":234459,"journal":{"name":"2020 Emerging Technology in Computing, Communication and Electronics (ETCCE)","volume":"60 11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133170642","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 : 2020-12-21DOI: 10.1109/etcce51779.2020.9350877
{"title":"Copyright","authors":"","doi":"10.1109/etcce51779.2020.9350877","DOIUrl":"https://doi.org/10.1109/etcce51779.2020.9350877","url":null,"abstract":"","PeriodicalId":234459,"journal":{"name":"2020 Emerging Technology in Computing, Communication and Electronics (ETCCE)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114798401","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 : 2020-12-21DOI: 10.1109/ETCCE51779.2020.9350875
Prosenjit Barua, Bikram Ghosh
An uninterrupted power supply is the most significant issue behind a country's development. Maximum production of electricity comes from using fossil fuel in Bangladesh. It has adequate renewable energy resources due to geographical conditions to produce electricity instead of using fossil fuel. This study is performed to evaluate the optimum feasibility of a grid-connected renewable-based power system in a seashore region of Bangladesh. The design and simulation are operated by the Hybrid Optimization of Multiple Energy Resources (HOMER) software to get the best economical, energy balancing and environment-friendly solution by comparing each of the optimum models. HOMER simulation has been conducted in the sense of lowest cost of energy (COE), net present cost (NPC), short payback period, largest renewable fraction and internal rate of return (IRR) for various forms of off-grid and on-grid hybrid grid models. Our optimal solution is carried out by the PV -Bio-Grid system with $0.0451/kWh (3.83 BDT/kWh) COE, 10.3% IRR and lowest greenhouse gas (GHG) emission rate. The excess electricity produced from this system can be sold back to the grid. This type of Hybrid Renewable system ensures to get continuous uninterrupted electricity service, reduce GHG gases and abate power dearth of the national grid.
{"title":"Feasibility Analysis of Renewable Energy Based Hybrid Power System in a Coastal Area, Bangladesh","authors":"Prosenjit Barua, Bikram Ghosh","doi":"10.1109/ETCCE51779.2020.9350875","DOIUrl":"https://doi.org/10.1109/ETCCE51779.2020.9350875","url":null,"abstract":"An uninterrupted power supply is the most significant issue behind a country's development. Maximum production of electricity comes from using fossil fuel in Bangladesh. It has adequate renewable energy resources due to geographical conditions to produce electricity instead of using fossil fuel. This study is performed to evaluate the optimum feasibility of a grid-connected renewable-based power system in a seashore region of Bangladesh. The design and simulation are operated by the Hybrid Optimization of Multiple Energy Resources (HOMER) software to get the best economical, energy balancing and environment-friendly solution by comparing each of the optimum models. HOMER simulation has been conducted in the sense of lowest cost of energy (COE), net present cost (NPC), short payback period, largest renewable fraction and internal rate of return (IRR) for various forms of off-grid and on-grid hybrid grid models. Our optimal solution is carried out by the PV -Bio-Grid system with $0.0451/kWh (3.83 BDT/kWh) COE, 10.3% IRR and lowest greenhouse gas (GHG) emission rate. The excess electricity produced from this system can be sold back to the grid. This type of Hybrid Renewable system ensures to get continuous uninterrupted electricity service, reduce GHG gases and abate power dearth of the national grid.","PeriodicalId":234459,"journal":{"name":"2020 Emerging Technology in Computing, Communication and Electronics (ETCCE)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124753355","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 : 2020-12-21DOI: 10.1109/ETCCE51779.2020.9350904
S. Chowdhury, Sudipta Nath, Ashim Dey, Annesha Das
We are living in the 21st century which is the era of modern technology. Many traditional problems are being solved using new innovative technologies. Taking attendance daily is an indispensable part of educational institutions as well as offices. It is both exhausting and time-consuming if done manually. Biometric attendance systems through voice, iris, and fingerprint recognition require complex and expensive hardware support. An auto attendance system using face recognition, which is another biometric trait, can resolve all these problems. This paper represents the development of a face recognition based automatic student attendance system using Convolutional Neural Networks which includes data entry, dataset training, face recognition and attendance entry. The system can detect and recognize multiple person's face from video stream and automatically record daily attendance. The proposed system achieved an average recognition accuracy of about 92 %. Using this system, daily attendance can be recorded effortlessly avoiding the risk of human error.
{"title":"Development of an Automatic Class Attendance System using CNN-based Face Recognition","authors":"S. Chowdhury, Sudipta Nath, Ashim Dey, Annesha Das","doi":"10.1109/ETCCE51779.2020.9350904","DOIUrl":"https://doi.org/10.1109/ETCCE51779.2020.9350904","url":null,"abstract":"We are living in the 21st century which is the era of modern technology. Many traditional problems are being solved using new innovative technologies. Taking attendance daily is an indispensable part of educational institutions as well as offices. It is both exhausting and time-consuming if done manually. Biometric attendance systems through voice, iris, and fingerprint recognition require complex and expensive hardware support. An auto attendance system using face recognition, which is another biometric trait, can resolve all these problems. This paper represents the development of a face recognition based automatic student attendance system using Convolutional Neural Networks which includes data entry, dataset training, face recognition and attendance entry. The system can detect and recognize multiple person's face from video stream and automatically record daily attendance. The proposed system achieved an average recognition accuracy of about 92 %. Using this system, daily attendance can be recorded effortlessly avoiding the risk of human error.","PeriodicalId":234459,"journal":{"name":"2020 Emerging Technology in Computing, Communication and Electronics (ETCCE)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124943924","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 : 2020-12-21DOI: 10.1109/ETCCE51779.2020.9350886
I. Kurniawan, A. Asyhari, Fei He
Joint computation is a form of collaborative job execution running at separate physical units, which are previously grouped by their unique functionalities. While existing studies have mainly utilized joint computation with direct coordination between nodes in different segments, it is worth considering another scenario where an additional node within a layer relays data to another layer. As a consequence, the node can serve as an aggregation point for data capture units prior to transmission to the sink node. However, this new arrangement produces additional transmission paths and can thus cause additional energy spending. This pilot study investigates the joint computation problem aiming at optimizing energy consumption. Relevant components, such as computation and communication, are taken into account and modeled into formal representation. A genetic algorithm-based solution is then used as a tool to optimize parameter setup. According to the experiment results, the metaheuristic algorithm has potential to achieve the optimal system configuration, emphasizing the data length that affects the final energy spending on communications. However, the algorithm cannot always guarantee the optimality as it relies on the random variable used in the process.
{"title":"Energy Optimization on Joint Task Computation Using Genetic Algorithm","authors":"I. Kurniawan, A. Asyhari, Fei He","doi":"10.1109/ETCCE51779.2020.9350886","DOIUrl":"https://doi.org/10.1109/ETCCE51779.2020.9350886","url":null,"abstract":"Joint computation is a form of collaborative job execution running at separate physical units, which are previously grouped by their unique functionalities. While existing studies have mainly utilized joint computation with direct coordination between nodes in different segments, it is worth considering another scenario where an additional node within a layer relays data to another layer. As a consequence, the node can serve as an aggregation point for data capture units prior to transmission to the sink node. However, this new arrangement produces additional transmission paths and can thus cause additional energy spending. This pilot study investigates the joint computation problem aiming at optimizing energy consumption. Relevant components, such as computation and communication, are taken into account and modeled into formal representation. A genetic algorithm-based solution is then used as a tool to optimize parameter setup. According to the experiment results, the metaheuristic algorithm has potential to achieve the optimal system configuration, emphasizing the data length that affects the final energy spending on communications. However, the algorithm cannot always guarantee the optimality as it relies on the random variable used in the process.","PeriodicalId":234459,"journal":{"name":"2020 Emerging Technology in Computing, Communication and Electronics (ETCCE)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123588672","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 : 2020-12-21DOI: 10.1109/ETCCE51779.2020.9350881
A. Razak, A. Nasir, N. Ghani, Nurul Amira Mhd Rizal, M. Jusof, Ikhwan Hafiz Muhamad
This paper presents a MultiObjective Barnacle Mating Optimization (MOBMO) and its application to optimize controller parameters for an inverted pendulum system. The algorithm is an extended version of a single-objective Barnacle Mating Optimization (BMO). In terms of solving a complex problem that has two conflicting objectives, a multiobjective type BMO is needed. Therefore, in the proposed MOBMO, nondominated sorting and crowding distance approaches are incorporated into BMO as a technique to formulate the multiobjective algorithm. The proposed algorithm is tested on various multiobjective benchmark functions. Its performance in terms of accuracy and diversity attainment to find a theoretical pareto front solution is analyzed. Moreover the proposed MOBMO is applied to optimize control parameters for PD controls of a pendulum system. The performance of the proposed MOBMO is compared with Multiobjective Water Cycle Algorithm (MOWCA). Result of the benchmark functions test shows that the proposed algorithm has attained a higher accuracy and a competitive diversity in locating the theoretical front solution. For its application to optimize PD control parameters, both MOWCA and MOBMO have successfully attained a good pareto front solution and controlled the pendulum sufficiently good. Overall performance, the proposed MOBMO has outperformed MOWCA for accuracy attainment and achieved the same level of diversity performance.
{"title":"Multi Objective Barnacle Mating Optimization for Control Design of a Pendulum System","authors":"A. Razak, A. Nasir, N. Ghani, Nurul Amira Mhd Rizal, M. Jusof, Ikhwan Hafiz Muhamad","doi":"10.1109/ETCCE51779.2020.9350881","DOIUrl":"https://doi.org/10.1109/ETCCE51779.2020.9350881","url":null,"abstract":"This paper presents a MultiObjective Barnacle Mating Optimization (MOBMO) and its application to optimize controller parameters for an inverted pendulum system. The algorithm is an extended version of a single-objective Barnacle Mating Optimization (BMO). In terms of solving a complex problem that has two conflicting objectives, a multiobjective type BMO is needed. Therefore, in the proposed MOBMO, nondominated sorting and crowding distance approaches are incorporated into BMO as a technique to formulate the multiobjective algorithm. The proposed algorithm is tested on various multiobjective benchmark functions. Its performance in terms of accuracy and diversity attainment to find a theoretical pareto front solution is analyzed. Moreover the proposed MOBMO is applied to optimize control parameters for PD controls of a pendulum system. The performance of the proposed MOBMO is compared with Multiobjective Water Cycle Algorithm (MOWCA). Result of the benchmark functions test shows that the proposed algorithm has attained a higher accuracy and a competitive diversity in locating the theoretical front solution. For its application to optimize PD control parameters, both MOWCA and MOBMO have successfully attained a good pareto front solution and controlled the pendulum sufficiently good. Overall performance, the proposed MOBMO has outperformed MOWCA for accuracy attainment and achieved the same level of diversity performance.","PeriodicalId":234459,"journal":{"name":"2020 Emerging Technology in Computing, Communication and Electronics (ETCCE)","volume":"1 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113964511","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 : 2020-12-21DOI: 10.1109/ETCCE51779.2020.9350890
Husnul Ajra, M. K. Nahar, Lipika Sarkar, Md. Shohidul Islam
Agriculture is a very significant field for increasing population over the world to meet the basic needs of food. Meanwhile, nutrition and the world economy depend on the growth of grains and vegetables. Many farmers are cultivating in remote areas of the world with the lack of accurate knowledge and disease detection, however, they rely on manual observation on grains and vegetables, as a result, they are suffering from a great loss. Digital farming practices can be an interesting solution for easily and quickly detecting plant diseases. To address such issues, this paper proposes plants leaf disease detection and preventive measures technique in the agricultural field using image processing and two well-known convolutional neural network (CNN) models as AlexNet and ResNet-50. Firstly, this technique is applied on Kaggle datasets of potato and tomato leaves to investigate the symptoms of unhealthy leaf. Then, the feature extraction and classification process are performed in dataset images to detect leaf diseases using AlexNet and ResNet-50 models with applying image processing. The experimental results elicit the efficiency of the proposed approach where it achieves the overall 97% and 96.1 % accuracy of ResNet-50 and the overall 96.5% and 95.3% accuracy of AlexNet for the classification of healthy-unhealthy leaf and leaf diseases, respectively. Finally, a graphical layout is also demonstrated to provide a preventive measures technique for the detected leaf diseases and to acquire a rich awareness about plant health.
{"title":"Disease Detection of Plant Leaf using Image Processing and CNN with Preventive Measures","authors":"Husnul Ajra, M. K. Nahar, Lipika Sarkar, Md. Shohidul Islam","doi":"10.1109/ETCCE51779.2020.9350890","DOIUrl":"https://doi.org/10.1109/ETCCE51779.2020.9350890","url":null,"abstract":"Agriculture is a very significant field for increasing population over the world to meet the basic needs of food. Meanwhile, nutrition and the world economy depend on the growth of grains and vegetables. Many farmers are cultivating in remote areas of the world with the lack of accurate knowledge and disease detection, however, they rely on manual observation on grains and vegetables, as a result, they are suffering from a great loss. Digital farming practices can be an interesting solution for easily and quickly detecting plant diseases. To address such issues, this paper proposes plants leaf disease detection and preventive measures technique in the agricultural field using image processing and two well-known convolutional neural network (CNN) models as AlexNet and ResNet-50. Firstly, this technique is applied on Kaggle datasets of potato and tomato leaves to investigate the symptoms of unhealthy leaf. Then, the feature extraction and classification process are performed in dataset images to detect leaf diseases using AlexNet and ResNet-50 models with applying image processing. The experimental results elicit the efficiency of the proposed approach where it achieves the overall 97% and 96.1 % accuracy of ResNet-50 and the overall 96.5% and 95.3% accuracy of AlexNet for the classification of healthy-unhealthy leaf and leaf diseases, respectively. Finally, a graphical layout is also demonstrated to provide a preventive measures technique for the detected leaf diseases and to acquire a rich awareness about plant health.","PeriodicalId":234459,"journal":{"name":"2020 Emerging Technology in Computing, Communication and Electronics (ETCCE)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130272272","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 : 2020-12-21DOI: 10.1109/ETCCE51779.2020.9350898
Vivine Nurcahyawati, Z. Mustaffa
Online media has become an essential part of everyday life in modern society. Everyone or organization is free to share their opinions and feelings about any topic on it, including information or news about commodity price fluctuations. Commodity price data from the National Strategic Price Information Center (NSPIC) website is not real-time, so it is not sufficient as a basis for monitoring commodity price fluctuations. Meanwhile, the government needs to collect data and infor-mation quickly about these price fluctuations, hence immediately strategic decisions and policies can be made to stabilize the prices. This study explores the potential function of online media by extracting the text in it and analyzing text so that it can display the commodity price data sought. The commodities used as search keywords were com-modities that had the highest consumption level in 2016 in Indonesia. The texts analyzed were taken from three online media, namely Twit-ter, Liputan6.com, and Detik.com. It was analyzed using text extraction techniques and the application of the Jaro-Winkler algorithm to find commodity prices in the text collection. Then compare the results of text analysis with commodity prices from the NSPIC website. The experimental data were 99,007 with a data collection time of three months. From only 122 data that match the keywords, it consists of 100 training data and 22 testing data. The results of the text analysis show that the text from the Detik.com website shows the commodity prices closest to the price data from the NSPIC, while Twitter shows the farthest results. The accuracy test with the confusion matrix is 75%. Based on this research, online media texts are a viable source for moni-toring commodity price fluctuations.
{"title":"Online Media as a Price Monitor: Text Analysis using Text Extraction Technique and Jaro-Winkler Similarity Algorithm","authors":"Vivine Nurcahyawati, Z. Mustaffa","doi":"10.1109/ETCCE51779.2020.9350898","DOIUrl":"https://doi.org/10.1109/ETCCE51779.2020.9350898","url":null,"abstract":"Online media has become an essential part of everyday life in modern society. Everyone or organization is free to share their opinions and feelings about any topic on it, including information or news about commodity price fluctuations. Commodity price data from the National Strategic Price Information Center (NSPIC) website is not real-time, so it is not sufficient as a basis for monitoring commodity price fluctuations. Meanwhile, the government needs to collect data and infor-mation quickly about these price fluctuations, hence immediately strategic decisions and policies can be made to stabilize the prices. This study explores the potential function of online media by extracting the text in it and analyzing text so that it can display the commodity price data sought. The commodities used as search keywords were com-modities that had the highest consumption level in 2016 in Indonesia. The texts analyzed were taken from three online media, namely Twit-ter, Liputan6.com, and Detik.com. It was analyzed using text extraction techniques and the application of the Jaro-Winkler algorithm to find commodity prices in the text collection. Then compare the results of text analysis with commodity prices from the NSPIC website. The experimental data were 99,007 with a data collection time of three months. From only 122 data that match the keywords, it consists of 100 training data and 22 testing data. The results of the text analysis show that the text from the Detik.com website shows the commodity prices closest to the price data from the NSPIC, while Twitter shows the farthest results. The accuracy test with the confusion matrix is 75%. Based on this research, online media texts are a viable source for moni-toring commodity price fluctuations.","PeriodicalId":234459,"journal":{"name":"2020 Emerging Technology in Computing, Communication and Electronics (ETCCE)","volume":"240 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126791484","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 : 2020-12-21DOI: 10.1109/ETCCE51779.2020.9350864
Nadia Refat, Hafizoah Kassim, M. A. Rahman
Cognitive architecture and information processing for learning are related to each other because of the content and presentation of the content in instructional materials. If the instructional design of the materials overloads the working memory, it then causes a cognitive load that hampers the learning outcome. Therefore, instructional design has been an area of focus repeatedly to make learning more effective and manage different types of cognitive load. Few studies focused sequencing theory of content design or highlighted the impact of the design on over all cognitive load. However, no studies to date have covered a systematic cognitive approach-based instructional design on m-grammar learning to investigate the outcome of learning performance. Therefore, the present study shows a cognitive approach-based instructional design for m-grammar learning. Unlike the existing studies, it designs instructional material based on a theoretical foundation of simple to complex learning theories to enhance learning outcomes and manage cognitive load for the grammar learners. It also measures instructional efficiency by employing 2-dimensional manners (mental effort and learning outcome). We followed a quantitative research design to conduct the study. An experimental group consisting of 128 students is used as study participants. NASA TLX, evaluation module score and a self-reporting mental effort measuring scale are the research instruments considered to collect the data. The results revealed the effectiveness of the proposed instructional design highlighting the instructional efficiency due to maintaining cognitive approach based designing that lessened the cognitive load and enhanced learning outcome of the learners.
{"title":"A Cognitive Approach-Based Instructional Design for Managing Cognitive Load and Improving Learning Outcome","authors":"Nadia Refat, Hafizoah Kassim, M. A. Rahman","doi":"10.1109/ETCCE51779.2020.9350864","DOIUrl":"https://doi.org/10.1109/ETCCE51779.2020.9350864","url":null,"abstract":"Cognitive architecture and information processing for learning are related to each other because of the content and presentation of the content in instructional materials. If the instructional design of the materials overloads the working memory, it then causes a cognitive load that hampers the learning outcome. Therefore, instructional design has been an area of focus repeatedly to make learning more effective and manage different types of cognitive load. Few studies focused sequencing theory of content design or highlighted the impact of the design on over all cognitive load. However, no studies to date have covered a systematic cognitive approach-based instructional design on m-grammar learning to investigate the outcome of learning performance. Therefore, the present study shows a cognitive approach-based instructional design for m-grammar learning. Unlike the existing studies, it designs instructional material based on a theoretical foundation of simple to complex learning theories to enhance learning outcomes and manage cognitive load for the grammar learners. It also measures instructional efficiency by employing 2-dimensional manners (mental effort and learning outcome). We followed a quantitative research design to conduct the study. An experimental group consisting of 128 students is used as study participants. NASA TLX, evaluation module score and a self-reporting mental effort measuring scale are the research instruments considered to collect the data. The results revealed the effectiveness of the proposed instructional design highlighting the instructional efficiency due to maintaining cognitive approach based designing that lessened the cognitive load and enhanced learning outcome of the learners.","PeriodicalId":234459,"journal":{"name":"2020 Emerging Technology in Computing, Communication and Electronics (ETCCE)","volume":"61 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132604643","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 : 2020-12-21DOI: 10.1109/ETCCE51779.2020.9350895
Ali fattah Dakhil, Wafaa Mohammed Ali, Ali Atshan Abdulredah
Machine learning classification techniques play a significant role in engine failure issues and machinery maintenance. With the help of Internet of Things, IoT industry, connected sensors have a considerable impact on data collection and remote engine monitoring. Mechanical engineers and professionals have difficulties determining when an engine is going to have a malfunction. So, engine maintenance requires an adequate strategy to predict the closest time in which an incident would likely to occur. This research investigates a perfect solution so that engineers will have an earlier alert about the potential incident which might exist. This study gives a visualized time left for how long an engine lifetime is present, accordingly, the system notifies the engineers of the best time to implement the maintenance. The methodology that we follow is setting up an appropriate mechanism by collecting data with IoT, and analyzing such data with classification algorithms. These algorithms categorize the status of an engine into particular conditions, so they indicate how far an engine going to work in an optimal state. Experiments have proved that K-Near Neighbor is the best algorithm for this kind of work in between others like; decision tree and linear discriminant with accuracy 82.9%, 51.0%, and 64.9% respectively. Consequently, classification techniques confidently distinguish the engine condition and warning for necessity of maintenance at the right time and right status.
{"title":"Predicting Prior Engine Failure with Classification Algorithms and web-based IoT Sensors","authors":"Ali fattah Dakhil, Wafaa Mohammed Ali, Ali Atshan Abdulredah","doi":"10.1109/ETCCE51779.2020.9350895","DOIUrl":"https://doi.org/10.1109/ETCCE51779.2020.9350895","url":null,"abstract":"Machine learning classification techniques play a significant role in engine failure issues and machinery maintenance. With the help of Internet of Things, IoT industry, connected sensors have a considerable impact on data collection and remote engine monitoring. Mechanical engineers and professionals have difficulties determining when an engine is going to have a malfunction. So, engine maintenance requires an adequate strategy to predict the closest time in which an incident would likely to occur. This research investigates a perfect solution so that engineers will have an earlier alert about the potential incident which might exist. This study gives a visualized time left for how long an engine lifetime is present, accordingly, the system notifies the engineers of the best time to implement the maintenance. The methodology that we follow is setting up an appropriate mechanism by collecting data with IoT, and analyzing such data with classification algorithms. These algorithms categorize the status of an engine into particular conditions, so they indicate how far an engine going to work in an optimal state. Experiments have proved that K-Near Neighbor is the best algorithm for this kind of work in between others like; decision tree and linear discriminant with accuracy 82.9%, 51.0%, and 64.9% respectively. Consequently, classification techniques confidently distinguish the engine condition and warning for necessity of maintenance at the right time and right status.","PeriodicalId":234459,"journal":{"name":"2020 Emerging Technology in Computing, Communication and Electronics (ETCCE)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124176535","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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