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: 10.1109/3ICT53449.2021.9581586
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}
Twitter, for example, offers a wealth of information on people's choices. Because of social media's growing acceptability and popularity, extracting information from data produced on social media has emerged as a prominent study issue. These massive amounts of data are used to build models that anticipate behavior and trends. On Twitter, people express their opinions regarding movies. In this study, a Long Short-Term Memory (LSTM) and ensemble based approach was proposed predicting the success of movies using metadata and social media. In this research, both social media data and movie metadata were consumed to predict the success of the movies. The metadata of the movie also plays an important role, which can be utilized to predict the success of the movies. IMDb ratings, the genre of the movies, and details about the awards that the movies won or nominated are some of the metadata used in addition to the tweets. LSTM, a neural network (NN) model, was applied to identify the sentiment value of the Twitter posts. Then, the ensemble approach was employed to predict the success of movies using movie metadata and results from the LSTM based NN model. This combined model was able to obtain 81.2% accuracy and outperformed the other implemented models.
{"title":"LSTM and Ensemble Based Approach for Predicting the Success of Movies Using Metadata and Social Media","authors":"W.M.D.R. Ruwantha, Kuhaneswaran Banujan, Kumara Btgs","doi":"10.1109/3ICT53449.2021.9581601","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9581601","url":null,"abstract":"Twitter, for example, offers a wealth of information on people's choices. Because of social media's growing acceptability and popularity, extracting information from data produced on social media has emerged as a prominent study issue. These massive amounts of data are used to build models that anticipate behavior and trends. On Twitter, people express their opinions regarding movies. In this study, a Long Short-Term Memory (LSTM) and ensemble based approach was proposed predicting the success of movies using metadata and social media. In this research, both social media data and movie metadata were consumed to predict the success of the movies. The metadata of the movie also plays an important role, which can be utilized to predict the success of the movies. IMDb ratings, the genre of the movies, and details about the awards that the movies won or nominated are some of the metadata used in addition to the tweets. LSTM, a neural network (NN) model, was applied to identify the sentiment value of the Twitter posts. Then, the ensemble approach was employed to predict the success of movies using movie metadata and results from the LSTM based NN model. This combined model was able to obtain 81.2% accuracy and outperformed the other implemented models.","PeriodicalId":133021,"journal":{"name":"2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)","volume":"1 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":"131027292","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: 10.1109/3ICT53449.2021.9581373
K. Zor, Kurtuluş Buluş
Recently, electric power systems have been modernised to be integrated with distributed energy systems having intermittent characteristics. Herein, short-term electric load forecasting (STLF), which covers hour, day, or week-ahead predictions of electric loads, is a crucial piece of the modern power system puzzle whose level of complexity has become more and more sophisticated owing to incorporating microgrids and smart grids. Due to the nonlinear feature of electric loads and the uncertainties in the modern power systems, deep learning algorithms are frequently applied to STLF problem which can be described as an arduous challenge because of being affected by several impacts. In this paper, gated recurrent unit (GRU) and long short-term memory (LSTM) networks are implemented in forecasting an hour-ahead electric loads of a large hospital complex located in Adana, Turkey. Overall results belonging to the benchmark of GRU and LSTM networks for STLF revealed that employing GRU networks performed better in terms of mean absolute percentage error (MAPE) by 7.8% and computational time by 15.5% in comparison with utilising LSTM networks.
{"title":"A benchmark of GRU and LSTM networks for short-term electric load forecasting","authors":"K. Zor, Kurtuluş Buluş","doi":"10.1109/3ICT53449.2021.9581373","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9581373","url":null,"abstract":"Recently, electric power systems have been modernised to be integrated with distributed energy systems having intermittent characteristics. Herein, short-term electric load forecasting (STLF), which covers hour, day, or week-ahead predictions of electric loads, is a crucial piece of the modern power system puzzle whose level of complexity has become more and more sophisticated owing to incorporating microgrids and smart grids. Due to the nonlinear feature of electric loads and the uncertainties in the modern power systems, deep learning algorithms are frequently applied to STLF problem which can be described as an arduous challenge because of being affected by several impacts. In this paper, gated recurrent unit (GRU) and long short-term memory (LSTM) networks are implemented in forecasting an hour-ahead electric loads of a large hospital complex located in Adana, Turkey. Overall results belonging to the benchmark of GRU and LSTM networks for STLF revealed that employing GRU networks performed better in terms of mean absolute percentage error (MAPE) by 7.8% and computational time by 15.5% in comparison with utilising LSTM networks.","PeriodicalId":133021,"journal":{"name":"2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)","volume":"6 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":"131454698","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: 10.1109/3ICT53449.2021.9581827
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: 10.1109/3ICT53449.2021.9581987
Emine Cengil, A. Cinar, Muhammet Yıldırım
Object detection is a common research topic for many fields. In particular, objects that are close together are difficult to detect. The breed of cats and dogs includes many species. These species are similar to each other and to some species in the other class. Therefore, it is difficult to distinguish the faces of cats and dogs, especially for some species. The study uses the YOLO algorithms, which has very high sensitivity and speed in numerous object detection challenges. The Oxford pets dataset, consisting of approximately 3600 images, containing images from 37 different types of cat/dog classes, is utilized for training and testing. We propose a method based on YOLOv5 to find cats and dogs. We utilized the YOLOv5 algorithm with different parameters. Four different models are compared and evaluated. Experiments demonstrate that YOLOv5 models achieve successful results for the respective task. The mAP of YOLOv5l is 94.1, demonstrating the efficacy of YOLOv5-based cat/dog detection.
{"title":"A Case Study: Cat-Dog Face Detector Based on YOLOv5","authors":"Emine Cengil, A. Cinar, Muhammet Yıldırım","doi":"10.1109/3ICT53449.2021.9581987","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9581987","url":null,"abstract":"Object detection is a common research topic for many fields. In particular, objects that are close together are difficult to detect. The breed of cats and dogs includes many species. These species are similar to each other and to some species in the other class. Therefore, it is difficult to distinguish the faces of cats and dogs, especially for some species. The study uses the YOLO algorithms, which has very high sensitivity and speed in numerous object detection challenges. The Oxford pets dataset, consisting of approximately 3600 images, containing images from 37 different types of cat/dog classes, is utilized for training and testing. We propose a method based on YOLOv5 to find cats and dogs. We utilized the YOLOv5 algorithm with different parameters. Four different models are compared and evaluated. Experiments demonstrate that YOLOv5 models achieve successful results for the respective task. The mAP of YOLOv5l is 94.1, demonstrating the efficacy of YOLOv5-based cat/dog detection.","PeriodicalId":133021,"journal":{"name":"2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)","volume":"25 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":"129013385","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: 10.1109/3ICT53449.2021.9581629
M. S. Sharif, Bilyaminu Auwal Romo, Harry Maltby, A. Al-Bayatti
In recent years machine learning techniques have been able to perform tasks previously thought impossible or impractical such as image classification and natural language translation, as such this allows for the automation of tasks previously thought only possible by humans. This research work aims to test a naïve post processing grammar correction method using a Long Short Term Memory neural network to rearrange translated sentences from Subject Object Verb to Subject Verb Object. Here machine learning based techniques are used to successfully translate works in an automated fashion rather than manually and post processing translations to increase sentiment and grammar accuracy. The implementation of the proposed methodology uses a bounding box object detection model, optical character recognition model and a natural language processing model to fully translate manga without human intervention. The grammar correction experimentation tries to fix a common problem when machines translate between two natural languages that use different ordering, in this case from Japanese Subject Object Verb to English Subject Verb Object. For this experimentation 2 sequence to sequence Long Short Term Memory neural networks were developed, a character level and a word level model using word embedding to reorder English sentences from Subject Object Verb to Subject Verb Object. The results showed that the methodology works in practice and can automate the translation process successfully.
{"title":"An Effective Hybrid Approach Based on Machine Learning Techniques for Auto-Translation: Japanese to English","authors":"M. S. Sharif, Bilyaminu Auwal Romo, Harry Maltby, A. Al-Bayatti","doi":"10.1109/3ICT53449.2021.9581629","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9581629","url":null,"abstract":"In recent years machine learning techniques have been able to perform tasks previously thought impossible or impractical such as image classification and natural language translation, as such this allows for the automation of tasks previously thought only possible by humans. This research work aims to test a naïve post processing grammar correction method using a Long Short Term Memory neural network to rearrange translated sentences from Subject Object Verb to Subject Verb Object. Here machine learning based techniques are used to successfully translate works in an automated fashion rather than manually and post processing translations to increase sentiment and grammar accuracy. The implementation of the proposed methodology uses a bounding box object detection model, optical character recognition model and a natural language processing model to fully translate manga without human intervention. The grammar correction experimentation tries to fix a common problem when machines translate between two natural languages that use different ordering, in this case from Japanese Subject Object Verb to English Subject Verb Object. For this experimentation 2 sequence to sequence Long Short Term Memory neural networks were developed, a character level and a word level model using word embedding to reorder English sentences from Subject Object Verb to Subject Verb Object. The results showed that the methodology works in practice and can automate the translation process successfully.","PeriodicalId":133021,"journal":{"name":"2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)","volume":"29 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":"129376504","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: 10.1109/3ICT53449.2021.9581569
Majeda Salman, A. Zolait, Mahmood Alaafia, Shaikha Almalood, Zainab Fateel
The importance of studying organizations' continuity of follow-up mechanisms is raised by the absence of research conducted on the follow-up mechanisms, especially during sudden pandemics. Therefore, this study attempts to research the continuity of follow-up mechanisms organizations use to monitor projects progress and accomplishment. Also, explore the predictors, problems, and challenges for managing remote working. Follow-up is the monitoring and evaluation of project progress against standards to enable management to make decisions for interventions towards project completion through team communication. Findings show that continuity of follow-up practice during COVID-19 is influenced by remote monitoring challenges and Organization compliance to pandemic restrictions (R2 = 0.35). Organization compliance to pandemic restrictions is a function of three determinants related to the organization's behavior regarding monitoring structure, internal policies, and communication and resource facilities (R2 = 0.54). Researchers used the mixed method approach consist of quantitative and qualitative methods. A survey was randomly distributed to an achievable sample of 158 respondents, followed by interviews with twelve decision-makers, including managers and executives in selected organizations. The study suggests more technological tools and applications for improving followup performance and overcoming remote monitoring challenges.
{"title":"Continuity of Project's Follow-up Practice During COVID-19: Identifying Predictors and Challenges","authors":"Majeda Salman, A. Zolait, Mahmood Alaafia, Shaikha Almalood, Zainab Fateel","doi":"10.1109/3ICT53449.2021.9581569","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9581569","url":null,"abstract":"The importance of studying organizations' continuity of follow-up mechanisms is raised by the absence of research conducted on the follow-up mechanisms, especially during sudden pandemics. Therefore, this study attempts to research the continuity of follow-up mechanisms organizations use to monitor projects progress and accomplishment. Also, explore the predictors, problems, and challenges for managing remote working. Follow-up is the monitoring and evaluation of project progress against standards to enable management to make decisions for interventions towards project completion through team communication. Findings show that continuity of follow-up practice during COVID-19 is influenced by remote monitoring challenges and Organization compliance to pandemic restrictions (R2 = 0.35). Organization compliance to pandemic restrictions is a function of three determinants related to the organization's behavior regarding monitoring structure, internal policies, and communication and resource facilities (R2 = 0.54). Researchers used the mixed method approach consist of quantitative and qualitative methods. A survey was randomly distributed to an achievable sample of 158 respondents, followed by interviews with twelve decision-makers, including managers and executives in selected organizations. The study suggests more technological tools and applications for improving followup performance and overcoming remote monitoring challenges.","PeriodicalId":133021,"journal":{"name":"2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)","volume":"41 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":"125309658","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: 10.1109/3ICT53449.2021.9581451
M. Bicer, E. Aydın
In addition to being small, light, practical, and cheap to manufacture, microstrip antennas are also exceedingly difficult to obtain the most suitable electrical parameters such as resonance frequency, bandwidth, return loss, gain, efficiency, and standing wave ratio. To achieve this, researchers are trying different physical structures and applying optimization techniques to them in order to obtain the most suitable radiation power and shape in different sizes and materials. Especially at high frequencies, the dielectric property of the material used can change all the parameters of microstrip antennas and affect the antenna performance to a great extent. The purpose of this study is to investigate the impacts of the physical structure and electrical properties of various textile materials and obtaining the most suitable material. For this purpose, textile-based wearable rectangular microstrip antenna designs were carried out on three different resonant frequency bands, which are widely used with different textile products such as felt, photo paper, and fiberglass, and their performances were examined. The proposed antennas on felt, photographic paper, and fiberglass substrates, were designed and manufactured. The feeding line and radiating and ground planes were formed using conductive (copper) tape. The operating frequency range of the antenna was chosen between 2 GHz and 10 GHz, and the simulated gain of the antenna was obtained as 5.31 dB. The measurement S11results demonstrate that the results are in good agreement with the simulated ones. The proposed antenna allows continuous monitoring of patients at high risk of cancer.
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