Pub Date : 2020-09-26DOI: 10.1109/IICAIET49801.2020.9257824
Marieh Talebkhah, A. Sali, Mohsen Marjani, M. Gordan, S. Hashim, F. Rokhani
The fast advancements in the fields of mobile internet and the internet of things (IoT) have caused several serious challenges for the traditional centralized cloud computing like large latency, small spectral efficiency (SE), and incompatible machine type of communication. Aimed at resolving the mentioned issues, several innovative technologies have been developed to shift the functions of the centralized cloud computing to the edge device of the network. Various edge computing techniques based on diverse origins have been established to decline the latency while improving SE, and supporting the massive machine-type communications. The present article offers an overview on three edge computing technologies: mobile edge computing, cloudlets, and fog computing. Specifically, standardizing procedures, principle, architecture, and utility of the mentioned technologies will be addressed. In terms of radio access network, the mobile edge computing difference from the fog computing was described. Features of fog computing radio access networks will be addressed as well. In the end, unsolved issues and future research topics will be discussed.
{"title":"Edge computing: Architecture, Applications and Future Perspectives","authors":"Marieh Talebkhah, A. Sali, Mohsen Marjani, M. Gordan, S. Hashim, F. Rokhani","doi":"10.1109/IICAIET49801.2020.9257824","DOIUrl":"https://doi.org/10.1109/IICAIET49801.2020.9257824","url":null,"abstract":"The fast advancements in the fields of mobile internet and the internet of things (IoT) have caused several serious challenges for the traditional centralized cloud computing like large latency, small spectral efficiency (SE), and incompatible machine type of communication. Aimed at resolving the mentioned issues, several innovative technologies have been developed to shift the functions of the centralized cloud computing to the edge device of the network. Various edge computing techniques based on diverse origins have been established to decline the latency while improving SE, and supporting the massive machine-type communications. The present article offers an overview on three edge computing technologies: mobile edge computing, cloudlets, and fog computing. Specifically, standardizing procedures, principle, architecture, and utility of the mentioned technologies will be addressed. In terms of radio access network, the mobile edge computing difference from the fog computing was described. Features of fog computing radio access networks will be addressed as well. In the end, unsolved issues and future research topics will be discussed.","PeriodicalId":300885,"journal":{"name":"2020 IEEE 2nd International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130345330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-26DOI: 10.1109/IICAIET49801.2020.9257826
Yong Kuan Shyang, Jasy Liew Suet Yan
Machine learning models for fine-grained emotion classification can benefit from a larger pool of training data but manually expanding the emotion corpus for training is labor-intensive and time-consuming. While distant supervision provides a viable alternative, the self-labeled emotion corpus is susceptible to a high level of noise. This paper introduces a text augmentation method that can be used to efficiently expand the size of positive examples for the purpose of training by harnessing tweets collected from distant supervision (DS) that are similar to a small set of gold standard seed tweets. Tweets labeled with happiness in EmoTweet-28 (ET) are used as gold standard seeds to augment the training data to include similar DS tweets containing the happiness hashtags. Three pre-trained sentence encoders are used to encode the tweets into multidimensional vectors for similarity scoring between each DS:ET-seed pair. DS tweets with similarity scores exceeding a predefined threshold are added into an augmented set that is subsequently used to train a linear SVM classifier to distinguish between happiness and non-happiness. Our proposed text augmentation method proved to be a more effective approach that can leverage quality training data in larger quantities contributed by both carefully curated and distant supervision emotion corpora.
{"title":"A Text Augmentation Approach using Similarity Measures based on Neural Sentence Embeddings for Emotion Classification on Microblogs","authors":"Yong Kuan Shyang, Jasy Liew Suet Yan","doi":"10.1109/IICAIET49801.2020.9257826","DOIUrl":"https://doi.org/10.1109/IICAIET49801.2020.9257826","url":null,"abstract":"Machine learning models for fine-grained emotion classification can benefit from a larger pool of training data but manually expanding the emotion corpus for training is labor-intensive and time-consuming. While distant supervision provides a viable alternative, the self-labeled emotion corpus is susceptible to a high level of noise. This paper introduces a text augmentation method that can be used to efficiently expand the size of positive examples for the purpose of training by harnessing tweets collected from distant supervision (DS) that are similar to a small set of gold standard seed tweets. Tweets labeled with happiness in EmoTweet-28 (ET) are used as gold standard seeds to augment the training data to include similar DS tweets containing the happiness hashtags. Three pre-trained sentence encoders are used to encode the tweets into multidimensional vectors for similarity scoring between each DS:ET-seed pair. DS tweets with similarity scores exceeding a predefined threshold are added into an augmented set that is subsequently used to train a linear SVM classifier to distinguish between happiness and non-happiness. Our proposed text augmentation method proved to be a more effective approach that can leverage quality training data in larger quantities contributed by both carefully curated and distant supervision emotion corpora.","PeriodicalId":300885,"journal":{"name":"2020 IEEE 2nd International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123960952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-26DOI: 10.1109/IICAIET49801.2020.9257854
M. Bhuiyan, K. Minhad, Md. Jamil Uddin, M. Reaz, M. T. I. Badal, Hadaate Ullah
Radio frequency identification (RFID) technology is currently reader protocol specific. For a common RFID standard to be used as the internet of things (IoT) devices, the reader should either be general or be avoided to facilitate tag communication with a common protocol. A complementery metal oxide semiconductor (CMOS) low noise amplifier (LNA) is recommended for 2.4 GHz IoT RFID. Spiral inductor based LNA cannot overcome the problems of bulky die area, lesser Q factor, limited tuning flexibility etc. Therefore, an LNA with an inductor less approach is designed in 90nm CMOS process cadence software. The post-layout simulation exhibits a 19 dB gain, a 164.2 MHz bandwidth and a 1.55 dB noise figure at 2.4 GHz. The LNA consumes very low power which is only 1.08 mW from a 1.5 V supply. A very compact layout of 127.7 µm2 has been achieved because of the inductor less approach.
{"title":"CMOS LNA for IoT RFID","authors":"M. Bhuiyan, K. Minhad, Md. Jamil Uddin, M. Reaz, M. T. I. Badal, Hadaate Ullah","doi":"10.1109/IICAIET49801.2020.9257854","DOIUrl":"https://doi.org/10.1109/IICAIET49801.2020.9257854","url":null,"abstract":"Radio frequency identification (RFID) technology is currently reader protocol specific. For a common RFID standard to be used as the internet of things (IoT) devices, the reader should either be general or be avoided to facilitate tag communication with a common protocol. A complementery metal oxide semiconductor (CMOS) low noise amplifier (LNA) is recommended for 2.4 GHz IoT RFID. Spiral inductor based LNA cannot overcome the problems of bulky die area, lesser Q factor, limited tuning flexibility etc. Therefore, an LNA with an inductor less approach is designed in 90nm CMOS process cadence software. The post-layout simulation exhibits a 19 dB gain, a 164.2 MHz bandwidth and a 1.55 dB noise figure at 2.4 GHz. The LNA consumes very low power which is only 1.08 mW from a 1.5 V supply. A very compact layout of 127.7 µm2 has been achieved because of the inductor less approach.","PeriodicalId":300885,"journal":{"name":"2020 IEEE 2nd International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"181 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124520318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-26DOI: 10.1109/IICAIET49801.2020.9257812
A. Narzullaev, Z. Muminov, Mavlutdin Narzullaev
There is just a handful of interventions proven to curb the spread of infectious diseases. One of them is contact tracing that involves reaching infected people to investigate where they might have been infected and whom they might have exposed to the virus. Contact tracing has been identified as a core disease control measure by the World Health Organization and has been exercised by state health agencies for decades. In this research, we proposed a new contact tracing method based on machine learning classification algorithms, for infectious diseases, such as COVID-19. The proposed method uses the Wi-Fi signals data from a possible contact and a confirmed patient's smartphones to detect whether the two shared the same physical space. Simulation results show up to 95% tracing accuracy depending on area size.
{"title":"Contact Tracing of Infectious Diseases Using Wi-Fi Signals and Machine Learning Classification","authors":"A. Narzullaev, Z. Muminov, Mavlutdin Narzullaev","doi":"10.1109/IICAIET49801.2020.9257812","DOIUrl":"https://doi.org/10.1109/IICAIET49801.2020.9257812","url":null,"abstract":"There is just a handful of interventions proven to curb the spread of infectious diseases. One of them is contact tracing that involves reaching infected people to investigate where they might have been infected and whom they might have exposed to the virus. Contact tracing has been identified as a core disease control measure by the World Health Organization and has been exercised by state health agencies for decades. In this research, we proposed a new contact tracing method based on machine learning classification algorithms, for infectious diseases, such as COVID-19. The proposed method uses the Wi-Fi signals data from a possible contact and a confirmed patient's smartphones to detect whether the two shared the same physical space. Simulation results show up to 95% tracing accuracy depending on area size.","PeriodicalId":300885,"journal":{"name":"2020 IEEE 2nd International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126538391","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-26DOI: 10.1109/IICAIET49801.2020.9257858
Hayato Takesako, A. Inoue
In light of the social background in which the demand for AI human resources with basic knowledge and skills related to AI has increased in recent years due to the development of AI technology, this study proposes a newly developed learning material for beginners in the AI field. It consists of material for learning basic AI-related knowledge and for developing AI easily through a visual programming tool that uses Scratch. We conducted an evaluation experiment on university students and verified its characteristics as suitable material for learning AI. We perform an understanding level check test on AI before and after using the teaching material and analyze the level of understanding about AI from the difference between the average scores. In addition, we analyze the feeling of use of the teaching materials from two evaluations: an ARCS motivational model and a free description sentence from the subject. The results of the evaluation experiment indicated that this teaching material promoted the subjects interest in AI and their acquisition of basic AI-related knowledge and is therefore suitable for teaching beginners in the field of AI.
{"title":"Development of Learning Material for Newcomers to Field of AI","authors":"Hayato Takesako, A. Inoue","doi":"10.1109/IICAIET49801.2020.9257858","DOIUrl":"https://doi.org/10.1109/IICAIET49801.2020.9257858","url":null,"abstract":"In light of the social background in which the demand for AI human resources with basic knowledge and skills related to AI has increased in recent years due to the development of AI technology, this study proposes a newly developed learning material for beginners in the AI field. It consists of material for learning basic AI-related knowledge and for developing AI easily through a visual programming tool that uses Scratch. We conducted an evaluation experiment on university students and verified its characteristics as suitable material for learning AI. We perform an understanding level check test on AI before and after using the teaching material and analyze the level of understanding about AI from the difference between the average scores. In addition, we analyze the feeling of use of the teaching materials from two evaluations: an ARCS motivational model and a free description sentence from the subject. The results of the evaluation experiment indicated that this teaching material promoted the subjects interest in AI and their acquisition of basic AI-related knowledge and is therefore suitable for teaching beginners in the field of AI.","PeriodicalId":300885,"journal":{"name":"2020 IEEE 2nd International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132908757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-26DOI: 10.1109/IICAIET49801.2020.9257846
N. Yan, Hui Na Chua
The importance of data in the digital economy has encouraged the mass collection, exchange, and processing of data. Despite the benefits of a data centric economy, the increasing volume of data available on consumers exposes them to numerous vulnerabilities. Although various literatures have studied the isolated effects of content sharing (through social media), new dissemination (through the news media), and data breach on data privacy awareness, no study have been done to examine their interrelated effects on data protection regulation awareness. Therefore, this study aims to identify the interrelated effects of content sharing (through social media), news dissemination (through the news media), and data breach on General Data Protection Regulation (GDPR) awareness. The findings of this research can be used to guide authorities in the promotion of data protection regulation awareness in an effective and efficient manner. These effects were subsequently modelled using Path Analysis under the data mining process. This study revealed that news dissemination had the greatest effect on GDPR awareness. This is followed by content sharing and data breach frequency accordingly. Additionally, it was identified that news dissemination had also an indirect effect on GDPR awareness through content sharing.
{"title":"A Path Analysis Model to Identify the Effects of Social Media, News Media and Data Breach on Data Protection Regulation Awareness","authors":"N. Yan, Hui Na Chua","doi":"10.1109/IICAIET49801.2020.9257846","DOIUrl":"https://doi.org/10.1109/IICAIET49801.2020.9257846","url":null,"abstract":"The importance of data in the digital economy has encouraged the mass collection, exchange, and processing of data. Despite the benefits of a data centric economy, the increasing volume of data available on consumers exposes them to numerous vulnerabilities. Although various literatures have studied the isolated effects of content sharing (through social media), new dissemination (through the news media), and data breach on data privacy awareness, no study have been done to examine their interrelated effects on data protection regulation awareness. Therefore, this study aims to identify the interrelated effects of content sharing (through social media), news dissemination (through the news media), and data breach on General Data Protection Regulation (GDPR) awareness. The findings of this research can be used to guide authorities in the promotion of data protection regulation awareness in an effective and efficient manner. These effects were subsequently modelled using Path Analysis under the data mining process. This study revealed that news dissemination had the greatest effect on GDPR awareness. This is followed by content sharing and data breach frequency accordingly. Additionally, it was identified that news dissemination had also an indirect effect on GDPR awareness through content sharing.","PeriodicalId":300885,"journal":{"name":"2020 IEEE 2nd International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128505583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-26DOI: 10.1109/IICAIET49801.2020.9257866
Siti Raihanah Abdani, M. A. Zulkifley, Mazlina Mamat
Palm oil is one of the most important commodities for Malaysia's economy. As the second-largest exporter of palm oil in the world, the government has set up various rules and regulations to promote sustainable plantations. Yet, some parties will take advantage of the rules by expanding their plantation areas beyond the permitted size. Thus, a remote sensing approach to automatically monitor the plantation size is proposed in this paper by using a deep neural network segmentation method. The spatial pyramid pooling (SPP) module is integrated with the well known U-Net architecture to improve the segmentation accuracy. Several variants of U-Net with SPP module are explored through varying the kernel size used in downsampling the input layer. The SPP module is placed right before the bottleneck block between the encoder and decoder sides of the network. The results show that the best accuracy is obtained by using U-Net with SPP of kernel sizes 2, 7 and 14. The proposed method has increased the accuracy from 0.7641 to 0.8152 when tested on Kaggle WiDS Dataset. The increment in performance is attributed to SPP ability in handling various scales input, which is a normal occurrence when the tested images cover a wide range of plantation ages that include young to mature trees.
{"title":"U-Net with Spatial Pyramid Pooling Module for Segmenting Oil Palm Plantations","authors":"Siti Raihanah Abdani, M. A. Zulkifley, Mazlina Mamat","doi":"10.1109/IICAIET49801.2020.9257866","DOIUrl":"https://doi.org/10.1109/IICAIET49801.2020.9257866","url":null,"abstract":"Palm oil is one of the most important commodities for Malaysia's economy. As the second-largest exporter of palm oil in the world, the government has set up various rules and regulations to promote sustainable plantations. Yet, some parties will take advantage of the rules by expanding their plantation areas beyond the permitted size. Thus, a remote sensing approach to automatically monitor the plantation size is proposed in this paper by using a deep neural network segmentation method. The spatial pyramid pooling (SPP) module is integrated with the well known U-Net architecture to improve the segmentation accuracy. Several variants of U-Net with SPP module are explored through varying the kernel size used in downsampling the input layer. The SPP module is placed right before the bottleneck block between the encoder and decoder sides of the network. The results show that the best accuracy is obtained by using U-Net with SPP of kernel sizes 2, 7 and 14. The proposed method has increased the accuracy from 0.7641 to 0.8152 when tested on Kaggle WiDS Dataset. The increment in performance is attributed to SPP ability in handling various scales input, which is a normal occurrence when the tested images cover a wide range of plantation ages that include young to mature trees.","PeriodicalId":300885,"journal":{"name":"2020 IEEE 2nd International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"12 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127295145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-26DOI: 10.1109/IICAIET49801.2020.9257835
Hamzarul Alif Hamzah, N. Tuah, Kit Guan Lim, M. K. Tan, I. Saad, K. Teo
Wireless Sensor Network (WSN) is one of the commonly used technologies in Precision Farming (PF). It provides farmers with accurate real-time information on their farms. In practice, WSN consists of numerous wireless sensor nodes, where each node relies on limited energy sources such as battery to maintain its operation. The energy management issue in WSN has gained attention of scholars, leading to new protocols or schemes developed over the years. Conventionally, PEGASIS protocol selects chain leader without considering the distance and residual energy level of each sensor node. As such, it might increase the energy consumption rate to transmit collected data from sensor node to sink. Inadequate energy management leads to rapid energy drain and eventually shorten the lifespan of WSN. Therefore, this study aims to prolong the lifespan of WSN while minimizing the energy consumption. Genetic Algorithm (GA) is proposed to enhance the chain leader selection scheme of the conventional PEGASIS. The proposed GA will select optimum chain leader by considering the energy consumption rate of each node. As such, the proposed algorithm is able to increase node's transmission as well as improve the lifespan of WSN by 50% as compared to the conventional approach.
{"title":"Genetic Algorithm based Chain Leader Election in Wireless Sensor Network for Precision Farming","authors":"Hamzarul Alif Hamzah, N. Tuah, Kit Guan Lim, M. K. Tan, I. Saad, K. Teo","doi":"10.1109/IICAIET49801.2020.9257835","DOIUrl":"https://doi.org/10.1109/IICAIET49801.2020.9257835","url":null,"abstract":"Wireless Sensor Network (WSN) is one of the commonly used technologies in Precision Farming (PF). It provides farmers with accurate real-time information on their farms. In practice, WSN consists of numerous wireless sensor nodes, where each node relies on limited energy sources such as battery to maintain its operation. The energy management issue in WSN has gained attention of scholars, leading to new protocols or schemes developed over the years. Conventionally, PEGASIS protocol selects chain leader without considering the distance and residual energy level of each sensor node. As such, it might increase the energy consumption rate to transmit collected data from sensor node to sink. Inadequate energy management leads to rapid energy drain and eventually shorten the lifespan of WSN. Therefore, this study aims to prolong the lifespan of WSN while minimizing the energy consumption. Genetic Algorithm (GA) is proposed to enhance the chain leader selection scheme of the conventional PEGASIS. The proposed GA will select optimum chain leader by considering the energy consumption rate of each node. As such, the proposed algorithm is able to increase node's transmission as well as improve the lifespan of WSN by 50% as compared to the conventional approach.","PeriodicalId":300885,"journal":{"name":"2020 IEEE 2nd International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130578274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-26DOI: 10.1109/IICAIET49801.2020.9257829
T. Ganesan, T. Ong, W. Cheah, C. Tee
In mobile payment, trust and confidence are the essential keys in performing a financial transaction. The users feel insecure and hesitant to participate in a transaction due to fear of security and privacy issues raised in mobile payment. The mobile devices are prone to theft of misplacement. When a subject loses his/her phone or when the phone is stolen, it can lead to payment fraud or personal identity theft. To address the complications and restrictions associated with mobile payment services, a responsive Analytical Hierarchy Process (AHP) Pairwise Comparison framework is developed to elicit expert knowledge for security and privacy risk events and consequences dependencies. Risk events and consequences are first acquired from literature analysis. This is followed by an expert's interview to rank the relative important of the risk events and consequences using AHP. In this paper, a secure mobile payment technological model is presented to prioritize the privacy and security risk events and consequences of mobile payment technology.
{"title":"Assessment of Security Risk Impact on Mobile Payment Services","authors":"T. Ganesan, T. Ong, W. Cheah, C. Tee","doi":"10.1109/IICAIET49801.2020.9257829","DOIUrl":"https://doi.org/10.1109/IICAIET49801.2020.9257829","url":null,"abstract":"In mobile payment, trust and confidence are the essential keys in performing a financial transaction. The users feel insecure and hesitant to participate in a transaction due to fear of security and privacy issues raised in mobile payment. The mobile devices are prone to theft of misplacement. When a subject loses his/her phone or when the phone is stolen, it can lead to payment fraud or personal identity theft. To address the complications and restrictions associated with mobile payment services, a responsive Analytical Hierarchy Process (AHP) Pairwise Comparison framework is developed to elicit expert knowledge for security and privacy risk events and consequences dependencies. Risk events and consequences are first acquired from literature analysis. This is followed by an expert's interview to rank the relative important of the risk events and consequences using AHP. In this paper, a secure mobile payment technological model is presented to prioritize the privacy and security risk events and consequences of mobile payment technology.","PeriodicalId":300885,"journal":{"name":"2020 IEEE 2nd International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132951224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-26DOI: 10.1109/IICAIET49801.2020.9257851
Husam Y. Al-Hetari, Y. Alginahi, M. Kabir, Noman Q. Al Naggar, Mahmoud A. Al-Rumaima, M. Hasan
Mechanical ventilators are the instruments that assist breathing of the patients having respiratory diseases e.g., pneumonia and coronavirus disease 2019 (COVID-19). This paper presents a modified lung model under volume-controlled ventilation to describe the lung volume and air flow in terms of air pressure signal from the ventilator. A negative feedback is incorporated in the model to balance the lung volume that is influenced by a lung parameter called positive end expiration pressure. We partially solved the lung model equation which takes the form of a first-order differential equation and then unknown parameters associated with the model were computed using a nonlinear least-squares method. Experimental data required for parameter identification and validation of the lung model were obtained by running a volume-controlled ventilator connected to a reference device and an artificial lung. The proposed model considering negative feedback achieves a better accuracy than that without feedback as demonstrated by test results. The developed model can be used in intensive care units (ICU) to evaluate mechanical ventilation performance and lung functionality in real-time.
{"title":"Modeling Lung Functionality in Volume-Controlled Ventilation for Critical Care Patients","authors":"Husam Y. Al-Hetari, Y. Alginahi, M. Kabir, Noman Q. Al Naggar, Mahmoud A. Al-Rumaima, M. Hasan","doi":"10.1109/IICAIET49801.2020.9257851","DOIUrl":"https://doi.org/10.1109/IICAIET49801.2020.9257851","url":null,"abstract":"Mechanical ventilators are the instruments that assist breathing of the patients having respiratory diseases e.g., pneumonia and coronavirus disease 2019 (COVID-19). This paper presents a modified lung model under volume-controlled ventilation to describe the lung volume and air flow in terms of air pressure signal from the ventilator. A negative feedback is incorporated in the model to balance the lung volume that is influenced by a lung parameter called positive end expiration pressure. We partially solved the lung model equation which takes the form of a first-order differential equation and then unknown parameters associated with the model were computed using a nonlinear least-squares method. Experimental data required for parameter identification and validation of the lung model were obtained by running a volume-controlled ventilator connected to a reference device and an artificial lung. The proposed model considering negative feedback achieves a better accuracy than that without feedback as demonstrated by test results. The developed model can be used in intensive care units (ICU) to evaluate mechanical ventilation performance and lung functionality in real-time.","PeriodicalId":300885,"journal":{"name":"2020 IEEE 2nd International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"439 1-3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132062565","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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