Pub Date : 2021-09-29DOI: 10.1109/3ICT53449.2021.9581963
Amani Yahyaoui, N. Yumusak
Machine Learning is a branch of artificial intelligence widely used in the medical field to analyze high-dimensional medical data and the early detection of certain dangerous diseases. Lung diseases continue to increase the mortality rate in the world. The early and accurate prediction of lung diseases has become a primary necessity to save patient's lives and facilitate doctor's works. This paper focuses on predicting certain chest diseases such as Pneumonia and Asthma using Deep Learning (DL) and Machine Learning (ML) techniques, respectively, the Deep Neural Network (DNN), and the K-nearest Neighbors (KNN) methods. These approaches are evaluated using a private data set from the pulmonary diseases department of Diyarbakir hospital, Turkey. It consists of 212 samples, 38 input characteristics characterize each one. The results obtained showed the effectiveness of these methods to detect pulmonary diseases, particularly the KNN, by giving a detection accuracy of 95% and 94.3% by using the DNN method.
{"title":"Deep And Machine Learning Towards Pneumonia And Asthma Detection","authors":"Amani Yahyaoui, N. Yumusak","doi":"10.1109/3ICT53449.2021.9581963","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9581963","url":null,"abstract":"Machine Learning is a branch of artificial intelligence widely used in the medical field to analyze high-dimensional medical data and the early detection of certain dangerous diseases. Lung diseases continue to increase the mortality rate in the world. The early and accurate prediction of lung diseases has become a primary necessity to save patient's lives and facilitate doctor's works. This paper focuses on predicting certain chest diseases such as Pneumonia and Asthma using Deep Learning (DL) and Machine Learning (ML) techniques, respectively, the Deep Neural Network (DNN), and the K-nearest Neighbors (KNN) methods. These approaches are evaluated using a private data set from the pulmonary diseases department of Diyarbakir hospital, Turkey. It consists of 212 samples, 38 input characteristics characterize each one. The results obtained showed the effectiveness of these methods to detect pulmonary diseases, particularly the KNN, by giving a detection accuracy of 95% and 94.3% by using the DNN method.","PeriodicalId":133021,"journal":{"name":"2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123816588","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 : 2021-09-29DOI: 10.1109/3ICT53449.2021.9581905
Hitesh Mohapatra
The smart city concept is a solution to many problems that we are facing in our day-to-day life. Many countries have been started adopting many prototypes to solve daily challenges. Still, the smart city term does not have any universally accepted definition. The smart city is a ubiquitous term whose definition varies from person to person, city to city, and country to country. The lack of a common definition remains the term smart city is in a chaotic state. This paper has presented the socio-technical challenges during the implementation of smart city plans. It has analyzed the smart city execution problems in the context of developed and underdeveloped countries.
{"title":"Socio-technical Challenges in the Implementation of Smart City","authors":"Hitesh Mohapatra","doi":"10.1109/3ICT53449.2021.9581905","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9581905","url":null,"abstract":"The smart city concept is a solution to many problems that we are facing in our day-to-day life. Many countries have been started adopting many prototypes to solve daily challenges. Still, the smart city term does not have any universally accepted definition. The smart city is a ubiquitous term whose definition varies from person to person, city to city, and country to country. The lack of a common definition remains the term smart city is in a chaotic state. This paper has presented the socio-technical challenges during the implementation of smart city plans. It has analyzed the smart city execution problems in the context of developed and underdeveloped countries.","PeriodicalId":133021,"journal":{"name":"2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133876544","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 : 2021-09-29DOI: 10.1109/3ICT53449.2021.9581827
Neesha Khan Malik, Ghadeer Ismail Khalil, Asma Yahiya Al Amoodi, Mohamed A Salman Bakhsh, Mona Ramadhan Sahwan
Human mind thrives on distraction for a change. Yet, counterintuitively, any alteration from the regular or routine baffles mankind and is perceived by default as a problem that automates resistance. Conventionally defined problems generate conventional solutions which usually don't last. Contrarily, a problem defined by those most affected by it or by living the experience of the affected ones, yields richer insights providing far lasting solutions. The early 2020 quarantines and social distancing practices globally, in response to the spread of COVID-19 resulted in the major disruption of workflow worldwide across public and private sectors with the digitalized operations. To solve the problem resulting due to this scenario, the current study used a design thinking approach for innovative and lasting solutions with wide applicability. The human-centric core of this design investigates resistance to change due to the COVID-19 pandemic by understanding human mindsets, needs, and limitations. Engaging a purpose-led participatory research design, the qualitative data on why people resist change is collected using ethnographic tools with focus groups of employees from the Ministry of Education and the Ministry of Health. The quantitative data is collected including other public sectors using a survey. With a sample size of 34 participants who volunteered to take part in the study in a short span of time, the paper culminates in proposing solutions that can be prototyped for testing and refined before being generalized and acceptable for wider implementation. The design thinking approach adopted, thus aims to establish transition guidelines for managing future organizational change with minimal resistance.
{"title":"Combatting Resistance to Change During the COVID 19 Pandemic with Design Thinking Approach: Making a Case for the Public Sector","authors":"Neesha Khan Malik, Ghadeer Ismail Khalil, Asma Yahiya Al Amoodi, Mohamed A Salman Bakhsh, Mona Ramadhan Sahwan","doi":"10.1109/3ICT53449.2021.9581827","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9581827","url":null,"abstract":"Human mind thrives on distraction for a change. Yet, counterintuitively, any alteration from the regular or routine baffles mankind and is perceived by default as a problem that automates resistance. Conventionally defined problems generate conventional solutions which usually don't last. Contrarily, a problem defined by those most affected by it or by living the experience of the affected ones, yields richer insights providing far lasting solutions. The early 2020 quarantines and social distancing practices globally, in response to the spread of COVID-19 resulted in the major disruption of workflow worldwide across public and private sectors with the digitalized operations. To solve the problem resulting due to this scenario, the current study used a design thinking approach for innovative and lasting solutions with wide applicability. The human-centric core of this design investigates resistance to change due to the COVID-19 pandemic by understanding human mindsets, needs, and limitations. Engaging a purpose-led participatory research design, the qualitative data on why people resist change is collected using ethnographic tools with focus groups of employees from the Ministry of Education and the Ministry of Health. The quantitative data is collected including other public sectors using a survey. With a sample size of 34 participants who volunteered to take part in the study in a short span of time, the paper culminates in proposing solutions that can be prototyped for testing and refined before being generalized and acceptable for wider implementation. The design thinking approach adopted, thus aims to establish transition guidelines for managing future organizational change with minimal resistance.","PeriodicalId":133021,"journal":{"name":"2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)","volume":"121 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134162878","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 : 2021-09-29DOI: 10.1109/3ICT53449.2021.9581499
B. Alkhaldi, M. Hammad
Internet of Things (IoT) architecture, despite its strong functionality and compatibility with numerous smart devices, is limited by its vulnerability to security threats. To overcome this limitation, attempts to introduce blockchain and Artificial Intelligence (AI) to improve IoT architecture have been gaining traction in the past few years. While a significant number of iterations have been made in this regard, the complexity of the integration process has made it difficult to identify best practices that are suitable across different applications. This study analyses the issues and limitations of integrating Blockchain and AI in an IoT architecture, by looking at different iterations and implementations to arrive at a clear picture of existing trends involving research limitations and challenges. The overall results seem to indicate a positive trajectory, as the integration of IoT, blockchain, and AI has been successful across various implementation. While the extent of blockchain integration of different components depend upon the purpose of the system, the caveat is that there are possible issues involving increased complexity, compatibility, and efficiency. The use of AI algorithms has been instrumental in filling in the gaps and improving the overall efficiency of such systems.
{"title":"Exploring Blockchain-enabled Intelligent IoT Architecture","authors":"B. Alkhaldi, M. Hammad","doi":"10.1109/3ICT53449.2021.9581499","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9581499","url":null,"abstract":"Internet of Things (IoT) architecture, despite its strong functionality and compatibility with numerous smart devices, is limited by its vulnerability to security threats. To overcome this limitation, attempts to introduce blockchain and Artificial Intelligence (AI) to improve IoT architecture have been gaining traction in the past few years. While a significant number of iterations have been made in this regard, the complexity of the integration process has made it difficult to identify best practices that are suitable across different applications. This study analyses the issues and limitations of integrating Blockchain and AI in an IoT architecture, by looking at different iterations and implementations to arrive at a clear picture of existing trends involving research limitations and challenges. The overall results seem to indicate a positive trajectory, as the integration of IoT, blockchain, and AI has been successful across various implementation. While the extent of blockchain integration of different components depend upon the purpose of the system, the caveat is that there are possible issues involving increased complexity, compatibility, and efficiency. The use of AI algorithms has been instrumental in filling in the gaps and improving the overall efficiency of such systems.","PeriodicalId":133021,"journal":{"name":"2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)","volume":"109 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134133564","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 : 2021-09-29DOI: 10.1109/3ICT53449.2021.9581591
Insaf Achour, S. Ayed, H. Idoudi
Access control is a main component in Blockchain systems. It is usually implemented in smart contracts and defines the security policy, in other words, it determines who can access a protected resource in the network. In this paper, we present a review of the major implementations of access control in Ethereum platform. The latter is based on RBAC model (Role-Based Access Control). Implementations require to take into account the whole RBAC process, that is, user role assignment and permission assignment. Three implementations currently exist and are described and compared in this work according to several features: RBAC-SC, Smart policies and OpenZepplin contracts.
{"title":"On the Implementation of Access Control in Ethereum Blockchain","authors":"Insaf Achour, S. Ayed, H. Idoudi","doi":"10.1109/3ICT53449.2021.9581591","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9581591","url":null,"abstract":"Access control is a main component in Blockchain systems. It is usually implemented in smart contracts and defines the security policy, in other words, it determines who can access a protected resource in the network. In this paper, we present a review of the major implementations of access control in Ethereum platform. The latter is based on RBAC model (Role-Based Access Control). Implementations require to take into account the whole RBAC process, that is, user role assignment and permission assignment. Three implementations currently exist and are described and compared in this work according to several features: RBAC-SC, Smart policies and OpenZepplin contracts.","PeriodicalId":133021,"journal":{"name":"2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116133469","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 : 2021-09-29DOI: 10.1109/3ICT53449.2021.9582110
Lina Alzayer, F. Jahromi
Background: Unused and expired medications are continuously disposed of through toilets, drain, and household trash. This is potentially dangerous and polluting, posing risks to public health and the environment. Objective: This study investigated public awareness in the Kingdom of Bahrain regarding contamination of the environment by pharmaceutical waste and assessed patterns of household medication disposal as well as factors influencing the chosen disposal practice. Methods: A cross-sectional study was designed, using a self-administered online questionnaire that was sent publicly to all people living in Bahrain and aged above 18 years, through social media platforms. Results: The questionnaire was completed by a total of 450 participants; of whom 421 were Bahrainis (93.6%) and 29 were non-Bahrainis (6.4%). Almost two-thirds (60.9%) of the participants had good knowledge regarding environmental contamination by pharmaceutical wastes. The majority (73.3%) of the participants discarded the leftover medications in the household trash, and only 12.0% of them returned them to the pharmacy. More than three-quarters (79.6%) of the participants did not check if a disposal method was mentioned on the medication's packaging. Interestingly, most of the participants (85.1%) declared to be willing to participate in pharmaceutical waste minimizing programs if applied in the Kingdom of Bahrain. Conclusion: Environmental contamination by pharmaceutical waste can considerably be resolved by improving public awareness of household disposal of medications and stimulating their willingness to participate in pharmaceutical waste management interventions if established in the future.
{"title":"Household Disposal of Medications as a Pathway of Environmental Contamination in the Kingdom of Bahrain","authors":"Lina Alzayer, F. Jahromi","doi":"10.1109/3ICT53449.2021.9582110","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9582110","url":null,"abstract":"Background: Unused and expired medications are continuously disposed of through toilets, drain, and household trash. This is potentially dangerous and polluting, posing risks to public health and the environment. Objective: This study investigated public awareness in the Kingdom of Bahrain regarding contamination of the environment by pharmaceutical waste and assessed patterns of household medication disposal as well as factors influencing the chosen disposal practice. Methods: A cross-sectional study was designed, using a self-administered online questionnaire that was sent publicly to all people living in Bahrain and aged above 18 years, through social media platforms. Results: The questionnaire was completed by a total of 450 participants; of whom 421 were Bahrainis (93.6%) and 29 were non-Bahrainis (6.4%). Almost two-thirds (60.9%) of the participants had good knowledge regarding environmental contamination by pharmaceutical wastes. The majority (73.3%) of the participants discarded the leftover medications in the household trash, and only 12.0% of them returned them to the pharmacy. More than three-quarters (79.6%) of the participants did not check if a disposal method was mentioned on the medication's packaging. Interestingly, most of the participants (85.1%) declared to be willing to participate in pharmaceutical waste minimizing programs if applied in the Kingdom of Bahrain. Conclusion: Environmental contamination by pharmaceutical waste can considerably be resolved by improving public awareness of household disposal of medications and stimulating their willingness to participate in pharmaceutical waste management interventions if established in the future.","PeriodicalId":133021,"journal":{"name":"2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121678864","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 : 2021-09-29DOI: 10.1109/3ICT53449.2021.9582052
Soon Hwai Ing, A. Abdullah, Shigehiko Kanaya
The threatening Coronavirus which was assigned as the global pandemic concussed not only the public health but society, economy and every walks of life. Some measurements are taken to stifle the spread and one of the best ways is to carry out some precautions to prevent the contagion of SARS-CoV-2 virus to uninfected populaces. Injecting prevention vaccines is one of the precaution steps under the grandiose blueprint. Among all vaccines, it is found that mRNA vaccine which shows no side effect with marvelous effectiveness is the most preferable candidates to be considered. However, degradation had become its biggest drawback to be implemented. Hereby, this study is held with desideratum to develop prediction models specifically to predict the degradation rate of mRNA vaccine for COVID-19.3 machine learning algorithms, which are, Linear Regression (LR), Light Gradient Boosting Machine (LGBM) and Random Forest (RF) are proposed for 12 models development. Dataset comprises of thousands of RNA molecules that holds degradation rates at each position from Eterna platform is extracted, pre-processed and encoded with label encoding before loaded into algorithms. The results show that the LGBM-based model which is trained along with auxiliary bpps features and encoded with method 1 label encoding performs the best (RMSE = 0.24466), followed by the same criteria LGBM-based model but encoded with label encoding method 2, with a difference in 0.00003 in tow the topnotch model. The RF-based model with applaudable performance (RMSE = 0.25302) even without the ubieties of the riddled bpps features in contradistinction to the training and encoding criteria of the superb mellowed LGBM-based model is worth being further cultivated for the prediction study on COVID-19 mRNA vaccines' degradation rate.
{"title":"Development of COVID-19 mRNA Vaccine Degradation Prediction System","authors":"Soon Hwai Ing, A. Abdullah, Shigehiko Kanaya","doi":"10.1109/3ICT53449.2021.9582052","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9582052","url":null,"abstract":"The threatening Coronavirus which was assigned as the global pandemic concussed not only the public health but society, economy and every walks of life. Some measurements are taken to stifle the spread and one of the best ways is to carry out some precautions to prevent the contagion of SARS-CoV-2 virus to uninfected populaces. Injecting prevention vaccines is one of the precaution steps under the grandiose blueprint. Among all vaccines, it is found that mRNA vaccine which shows no side effect with marvelous effectiveness is the most preferable candidates to be considered. However, degradation had become its biggest drawback to be implemented. Hereby, this study is held with desideratum to develop prediction models specifically to predict the degradation rate of mRNA vaccine for COVID-19.3 machine learning algorithms, which are, Linear Regression (LR), Light Gradient Boosting Machine (LGBM) and Random Forest (RF) are proposed for 12 models development. Dataset comprises of thousands of RNA molecules that holds degradation rates at each position from Eterna platform is extracted, pre-processed and encoded with label encoding before loaded into algorithms. The results show that the LGBM-based model which is trained along with auxiliary bpps features and encoded with method 1 label encoding performs the best (RMSE = 0.24466), followed by the same criteria LGBM-based model but encoded with label encoding method 2, with a difference in 0.00003 in tow the topnotch model. The RF-based model with applaudable performance (RMSE = 0.25302) even without the ubieties of the riddled bpps features in contradistinction to the training and encoding criteria of the superb mellowed LGBM-based model is worth being further cultivated for the prediction study on COVID-19 mRNA vaccines' degradation rate.","PeriodicalId":133021,"journal":{"name":"2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123502102","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 : 2021-09-29DOI: 10.1109/3ICT53449.2021.9581462
Brahim Mefgouda, H. Idoudi
Network Interface Selection (NIS) aims to connect the user equipment to the best available network in the context of heterogeneous wireless networks environments (HWN). NIS is one of the main current issues in HWNs that raised great scientific interest in the last few years. Multi-attribute decision-making (MADM) are the most common approaches applied to solve the NIS problem as they are easy to understand, they can be used in real scenarios, and they perform fast networks' ranking. In this paper, we apply, for the first time, the Combined Compromise Solution (COCOSO) to model and solve the network interface selection problem. Simulation results showed that our proposed approach outperforms the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Simple Additive Weighting (SAW) in terms of reducing the rank reversal problem and meeting QoS requirements.
{"title":"COCOSO-based Network Interface Selection Algorithm for Heterogeneous Wireless Networks","authors":"Brahim Mefgouda, H. Idoudi","doi":"10.1109/3ICT53449.2021.9581462","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9581462","url":null,"abstract":"Network Interface Selection (NIS) aims to connect the user equipment to the best available network in the context of heterogeneous wireless networks environments (HWN). NIS is one of the main current issues in HWNs that raised great scientific interest in the last few years. Multi-attribute decision-making (MADM) are the most common approaches applied to solve the NIS problem as they are easy to understand, they can be used in real scenarios, and they perform fast networks' ranking. In this paper, we apply, for the first time, the Combined Compromise Solution (COCOSO) to model and solve the network interface selection problem. Simulation results showed that our proposed approach outperforms the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Simple Additive Weighting (SAW) in terms of reducing the rank reversal problem and meeting QoS requirements.","PeriodicalId":133021,"journal":{"name":"2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124709625","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 : 2021-09-29DOI: 10.1109/3ICT53449.2021.9581846
M. F. Adak, S. Ercan
Nowadays, online education has become widespread, and the search for new techniques has begun to increase. The high number of quotas in university education in Turkey increases the number of students per instructor. It is not at the desired level for the student to receive a good education in the presence of an advisor and choose the appropriate course for his / her field due to a large number of students. In this study, a suggestion system is proposed by analyzing the previous courses taken by university students in directing the elective course. In this study, which courses would be beneficial to choose and which would be useless are presented with a web interface in which Support Vector Machine and decision trees are used. In the pilot study that the model developed conducted in the Computer Engineering department, an average of 76% success was achieved in test data sets. This success shows that the student can examine the compulsory courses and suggest elective courses suitable for his/her field and that he/she will like.
{"title":"Support Vector Machine and Decision Tree-Based Elective Course Suggestion System: A Case Study","authors":"M. F. Adak, S. Ercan","doi":"10.1109/3ICT53449.2021.9581846","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9581846","url":null,"abstract":"Nowadays, online education has become widespread, and the search for new techniques has begun to increase. The high number of quotas in university education in Turkey increases the number of students per instructor. It is not at the desired level for the student to receive a good education in the presence of an advisor and choose the appropriate course for his / her field due to a large number of students. In this study, a suggestion system is proposed by analyzing the previous courses taken by university students in directing the elective course. In this study, which courses would be beneficial to choose and which would be useless are presented with a web interface in which Support Vector Machine and decision trees are used. In the pilot study that the model developed conducted in the Computer Engineering department, an average of 76% success was achieved in test data sets. This success shows that the student can examine the compulsory courses and suggest elective courses suitable for his/her field and that he/she will like.","PeriodicalId":133021,"journal":{"name":"2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121599897","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 : 2021-09-29DOI: 10.1109/3ICT53449.2021.9581586
D. Suryadi, Wellington
This paper proposes a methodology to predict the repurchase intention based on the reviews and the customer's stated intention. However, there is a large number of words in the reviews. Using those words as features in the prediction model tends to decrease the accuracy of the model and cause model overfitting. A methodology that is based on Genetic Algorithm is proposed to improve the selection iteratively. Each chromosome is encoded as a set of randomly selected indices of words in the vocabulary. The fitness of a chromosome is measured as the accuracy of the Decision Tree prediction model using the selected features (i.e., words). Decision Tree model also provides the feature importance values, which are used to rearrange the genes, such that the Crossover procedure ensures important genes are passed to the offspring. For the Mutation, the information about the Tendency Rank of the features is used alter a gene. Therefore, the Crossover and Mutation procedures are not merely combining and modifying the chromosomes. The proposed methodology is implemented to two data sets. For both data sets, the prediction accuracy of the proposed methodology is significantly higher than the baseline, i.e., random selection.
{"title":"Genetic Algorithm for Feature Selection in Predicting Repurchase Intention from Online Reviews","authors":"D. Suryadi, Wellington","doi":"10.1109/3ICT53449.2021.9581586","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9581586","url":null,"abstract":"This paper proposes a methodology to predict the repurchase intention based on the reviews and the customer's stated intention. However, there is a large number of words in the reviews. Using those words as features in the prediction model tends to decrease the accuracy of the model and cause model overfitting. A methodology that is based on Genetic Algorithm is proposed to improve the selection iteratively. Each chromosome is encoded as a set of randomly selected indices of words in the vocabulary. The fitness of a chromosome is measured as the accuracy of the Decision Tree prediction model using the selected features (i.e., words). Decision Tree model also provides the feature importance values, which are used to rearrange the genes, such that the Crossover procedure ensures important genes are passed to the offspring. For the Mutation, the information about the Tendency Rank of the features is used alter a gene. Therefore, the Crossover and Mutation procedures are not merely combining and modifying the chromosomes. The proposed methodology is implemented to two data sets. For both data sets, the prediction accuracy of the proposed methodology is significantly higher than the baseline, i.e., random selection.","PeriodicalId":133021,"journal":{"name":"2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130445571","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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