Pub Date : 2021-09-29DOI: 10.1109/3ICT53449.2021.9581625
Ikram Ben abdel ouahab, Lotfi Elaachak, Yasser A. Alluhaidan, M. Bouhorma
In these days, malware attacks target different kinds of devices as IoT, mobiles, servers even the cloud. It causes several hardware damages and financial losses especially for big companies. Malware attacks represent a serious issue to cybersecurity specialists. In this paper, we propose a new approach to detect unknown malware families based on machine learning classification and visualization technique. A malware binary is converted to grayscale image, then for each image a GIST descriptor is used as input to the machine learning model. For the malware classification part we use 3 machine learning algorithms. These classifiers are so efficient where the highest precision reach 98%. Once we train, test and evaluate models we move to simulate 2 new malware families. We do not expect a good prediction since the model did not know the family; however our goal is to analyze the behavior of our classifiers in the case of new family. Finally, we propose an approach using a filter to know either the classification is normal or it's a zero-day malware.
{"title":"A new approach to detect next generation of malware based on machine learning","authors":"Ikram Ben abdel ouahab, Lotfi Elaachak, Yasser A. Alluhaidan, M. Bouhorma","doi":"10.1109/3ICT53449.2021.9581625","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9581625","url":null,"abstract":"In these days, malware attacks target different kinds of devices as IoT, mobiles, servers even the cloud. It causes several hardware damages and financial losses especially for big companies. Malware attacks represent a serious issue to cybersecurity specialists. In this paper, we propose a new approach to detect unknown malware families based on machine learning classification and visualization technique. A malware binary is converted to grayscale image, then for each image a GIST descriptor is used as input to the machine learning model. For the malware classification part we use 3 machine learning algorithms. These classifiers are so efficient where the highest precision reach 98%. Once we train, test and evaluate models we move to simulate 2 new malware families. We do not expect a good prediction since the model did not know the family; however our goal is to analyze the behavior of our classifiers in the case of new family. Finally, we propose an approach using a filter to know either the classification is normal or it's a zero-day malware.","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":"130642630","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: 10.1109/3ICT53449.2021.9581590
A. Akib, S. Mahmud, M. Mridha
The paper focuses on the Future Micro Hydro Power: generation of hydroelectricity and its monitoring system. The world is moving towards technological advancement day by day. For this reason, the energy need will surge further in the coming days. But we could not yet ensure the proper electricity needs in the poor or developing country. Now it's an essential needy thing to survive this 4.0 industry's time. This ‘Future Micro Hydro Power’ device will generate energy by exploiting the small water sources (i.e., Washroom, Kitchen, Etc.) in the multi steroid buildings. A massive amount of water is used in the house every day. Water taps are used not only in homes but in all modern buildings. We have demonstrated how hydropower will generate from these tiny water sources and how this power can run a house. Here the user will be able to monitor the amount of energy produced and use it if desired. The cost of the devices will be much lower, and their performance will be much higher. After the experimental installation, we got some data that proves its outstanding efficiency.
{"title":"Future Micro Hydro Power: Generation of Hydroelectricity and IoT based Monitoring System","authors":"A. Akib, S. Mahmud, M. Mridha","doi":"10.1109/3ICT53449.2021.9581590","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9581590","url":null,"abstract":"The paper focuses on the Future Micro Hydro Power: generation of hydroelectricity and its monitoring system. The world is moving towards technological advancement day by day. For this reason, the energy need will surge further in the coming days. But we could not yet ensure the proper electricity needs in the poor or developing country. Now it's an essential needy thing to survive this 4.0 industry's time. This ‘Future Micro Hydro Power’ device will generate energy by exploiting the small water sources (i.e., Washroom, Kitchen, Etc.) in the multi steroid buildings. A massive amount of water is used in the house every day. Water taps are used not only in homes but in all modern buildings. We have demonstrated how hydropower will generate from these tiny water sources and how this power can run a house. Here the user will be able to monitor the amount of energy produced and use it if desired. The cost of the devices will be much lower, and their performance will be much higher. After the experimental installation, we got some data that proves its outstanding efficiency.","PeriodicalId":133021,"journal":{"name":"2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)","volume":"3 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":"128631077","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: 10.1109/3ICT53449.2021.9581352
Alhadi A. Klaib, A. Milad, Mustafa Almahdi Algaet
Extensible Markup Language (XML) has become a key technique for transferring data through the internet. Updating and retrieving a large amount of XML data is a very active research field. The XML labelling schemes play an important role in handling XML data efficiently and robustly. Thus, many labelling schemes have been proposed. Nevertheless, these labelling schemes have limitations. Therefore, in this paper, a new method for labelling XML documents is developed. In addition, this approach used the idea of clustering-based XML data and dividing the nodes of an XML document into groups and labelling them accordingly. Two existing labelling schemes were chosen to label the clusters and their nodes as well. The level-based labelling scheme (LLS) and Dewey labelling scheme were used to label the nodes and clusters. The data model of this scheme has been developed. The mechanism of the proposed scheme also has been developed. Finally, this proposed scheme and the other two labelling schemes that used to build the proposed scheme have been implemented.
{"title":"A New Approach for Labelling XML Data","authors":"Alhadi A. Klaib, A. Milad, Mustafa Almahdi Algaet","doi":"10.1109/3ICT53449.2021.9581352","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9581352","url":null,"abstract":"Extensible Markup Language (XML) has become a key technique for transferring data through the internet. Updating and retrieving a large amount of XML data is a very active research field. The XML labelling schemes play an important role in handling XML data efficiently and robustly. Thus, many labelling schemes have been proposed. Nevertheless, these labelling schemes have limitations. Therefore, in this paper, a new method for labelling XML documents is developed. In addition, this approach used the idea of clustering-based XML data and dividing the nodes of an XML document into groups and labelling them accordingly. Two existing labelling schemes were chosen to label the clusters and their nodes as well. The level-based labelling scheme (LLS) and Dewey labelling scheme were used to label the nodes and clusters. The data model of this scheme has been developed. The mechanism of the proposed scheme also has been developed. Finally, this proposed scheme and the other two labelling schemes that used to build the proposed scheme have been implemented.","PeriodicalId":133021,"journal":{"name":"2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)","volume":"28 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":"126166831","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: 10.1109/3ICT53449.2021.9582060
Kameron Bains, Adebamigbe Fasanmade, J. Morden, A. Al-Bayatti, M. S. Sharif, A. S. Alfakeeh
Credit card fraud is a significant problem that is not going to go away. It is a growing problem and surged during the Covid-19 pandemic since more transactions are done without cash in hand now. Credit card frauds are complicated to distinguish as the characteristics of legitimate and fraudulent transactions are very similar. The performance evaluation of various Machine Learning (ML)-based credit card fraud recognition schemes are significantly pretentious due to data processing, including collecting variables and corresponding ML mechanism being used. One possible way to counter this problem is to apply ML algorithms such as Support Vector Machine (SVM), K nearest neighbor (KNN), Naive Bayes, and logistic regression. This research work aims to compare the ML as mentioned earlier models and its impact on credit card scam detection, especially in situations with imbalanced datasets. Moreover, we have proposed state of the art data balancing algorithm to solve data unbalancing problems in such situations. Our experiments show that the logistic regression has an accuracy of 99.91%, and naive bays have an accuracy of 97.65%. K nearest neighbor has an accuracy is 99.92%, support vector machine has an accuracy of 99.95%. The precision and accuracy comparison of our proposed approach shows that our model is state of the art.
{"title":"Defeating the Credit Card Scams Through Machine Learning Algorithms","authors":"Kameron Bains, Adebamigbe Fasanmade, J. Morden, A. Al-Bayatti, M. S. Sharif, A. S. Alfakeeh","doi":"10.1109/3ICT53449.2021.9582060","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9582060","url":null,"abstract":"Credit card fraud is a significant problem that is not going to go away. It is a growing problem and surged during the Covid-19 pandemic since more transactions are done without cash in hand now. Credit card frauds are complicated to distinguish as the characteristics of legitimate and fraudulent transactions are very similar. The performance evaluation of various Machine Learning (ML)-based credit card fraud recognition schemes are significantly pretentious due to data processing, including collecting variables and corresponding ML mechanism being used. One possible way to counter this problem is to apply ML algorithms such as Support Vector Machine (SVM), K nearest neighbor (KNN), Naive Bayes, and logistic regression. This research work aims to compare the ML as mentioned earlier models and its impact on credit card scam detection, especially in situations with imbalanced datasets. Moreover, we have proposed state of the art data balancing algorithm to solve data unbalancing problems in such situations. Our experiments show that the logistic regression has an accuracy of 99.91%, and naive bays have an accuracy of 97.65%. K nearest neighbor has an accuracy is 99.92%, support vector machine has an accuracy of 99.95%. The precision and accuracy comparison of our proposed approach shows that our model is state of the art.","PeriodicalId":133021,"journal":{"name":"2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)","volume":"231 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":"123060860","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: 10.1109/3ICT53449.2021.9581987
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.
{"title":"Design and Fabrication of Rectangular Microstrip Antenna with Various Flexible Substrates","authors":"M. Bicer, E. Aydın","doi":"10.1109/3ICT53449.2021.9581451","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9581451","url":null,"abstract":"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.","PeriodicalId":133021,"journal":{"name":"2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)","volume":"11 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":"126038383","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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