Pub Date : 2022-06-01DOI: 10.1109/iemtronics55184.2022.9795757
Mobasshir Mahbub, R. Shubair
Intelligent reflecting surfaces (IRSs) with the ability to reconfigure inherent electromagnetic reflection and absorption characteristics in real-time provide unparalleled prospects to improve wireless connectivity in adverse circumstances. Unmanned aerial vehicles (UAV)-assisted wireless networks are evolved as a reliable solution to combat non-line of sight (NLoS) scenarios. Thereby, the IRS-empowered UAV-assisted cellular networks will be a significant role-player to improve the coverage and user experiences. The paper aimed to minimize the path loss and maximize the achievable data rate in IRS-UAV-assisted networks. In this context, the work analyzed path loss and achievable rate utilizing millimeter wave (mmWave) carrier considering the conventional UAV model and IRS-empowered UAV communication model. The research obtained that the IRS-empowered UAV communications model can significantly minimize path loss and maximize the achievable data rate compared to the conventional UAV-assisted model.
{"title":"Intelligent Reflecting Surfaces in UAV-Assisted 6G Networks: An Approach for Enhanced Propagation and Spectral Characteristics","authors":"Mobasshir Mahbub, R. Shubair","doi":"10.1109/iemtronics55184.2022.9795757","DOIUrl":"https://doi.org/10.1109/iemtronics55184.2022.9795757","url":null,"abstract":"Intelligent reflecting surfaces (IRSs) with the ability to reconfigure inherent electromagnetic reflection and absorption characteristics in real-time provide unparalleled prospects to improve wireless connectivity in adverse circumstances. Unmanned aerial vehicles (UAV)-assisted wireless networks are evolved as a reliable solution to combat non-line of sight (NLoS) scenarios. Thereby, the IRS-empowered UAV-assisted cellular networks will be a significant role-player to improve the coverage and user experiences. The paper aimed to minimize the path loss and maximize the achievable data rate in IRS-UAV-assisted networks. In this context, the work analyzed path loss and achievable rate utilizing millimeter wave (mmWave) carrier considering the conventional UAV model and IRS-empowered UAV communication model. The research obtained that the IRS-empowered UAV communications model can significantly minimize path loss and maximize the achievable data rate compared to the conventional UAV-assisted model.","PeriodicalId":442879,"journal":{"name":"2022 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114210339","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 : 2022-06-01DOI: 10.1109/iemtronics55184.2022.9795735
Hosam Alamleh, A. A. AlQahtani, Baker Al Smadi
During two decades, there have been a revolution in the field of digital communication and internet access. Today, it became possible for users to access the internet while on the move through an infrastructure of high-speed mobile broadband networks. Technologies such as LTE and 5G became essential. Mobile broadband net-works allow mobility; connection reliability drops during movement. Thus, some failure intolerant processes, such as system updates, necessitates the utilization of a reliable connection. This paper introduces a model that predicts whether the user is mobile or stationery. This is done based on the traffic patterns at the server-side. Distinct network technologies entails distinct nature of traffic patterns. In this paper, machine learning is utilized at the server-side to allow differentiating between data transmitted by a stationary user and data transmitted by a mobile user at the server-side. Supervised training is utilized to train the model. Then, the model was tested and prediction accuracy of this model was 92.6 percent. Finally, the proposed system is a novel work and the first of its kind since it is the first to attempt to predict mobile network user’s mobility at the server-side by utilizing packets’ arrival patterns. The proposed system can be applied at mobile apps and allow them to collect data about the apps users mobility while using this service without needing to access the GPS. Also, it can be used network management and public safety.
{"title":"Server-Side Distinction of User Mobility Using Machine Learning on Incoming Data Traffic","authors":"Hosam Alamleh, A. A. AlQahtani, Baker Al Smadi","doi":"10.1109/iemtronics55184.2022.9795735","DOIUrl":"https://doi.org/10.1109/iemtronics55184.2022.9795735","url":null,"abstract":"During two decades, there have been a revolution in the field of digital communication and internet access. Today, it became possible for users to access the internet while on the move through an infrastructure of high-speed mobile broadband networks. Technologies such as LTE and 5G became essential. Mobile broadband net-works allow mobility; connection reliability drops during movement. Thus, some failure intolerant processes, such as system updates, necessitates the utilization of a reliable connection. This paper introduces a model that predicts whether the user is mobile or stationery. This is done based on the traffic patterns at the server-side. Distinct network technologies entails distinct nature of traffic patterns. In this paper, machine learning is utilized at the server-side to allow differentiating between data transmitted by a stationary user and data transmitted by a mobile user at the server-side. Supervised training is utilized to train the model. Then, the model was tested and prediction accuracy of this model was 92.6 percent. Finally, the proposed system is a novel work and the first of its kind since it is the first to attempt to predict mobile network user’s mobility at the server-side by utilizing packets’ arrival patterns. The proposed system can be applied at mobile apps and allow them to collect data about the apps users mobility while using this service without needing to access the GPS. Also, it can be used network management and public safety.","PeriodicalId":442879,"journal":{"name":"2022 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114692766","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 : 2022-06-01DOI: 10.1109/iemtronics55184.2022.9795815
Javid Akhavan, S. Manoochehri
In-situ control to predict and mitigate defects in Additive Manufacturing (AM) could significantly increase these technologies’ quality and reliability. Thorough knowledge of the AM processes is needed to develop such a controller. Recent studies utilized various methods to acquire data from the process, build insight into the process, and detect anomalies within the process. However, each sensory method has its unique limitations and capabilities. Sensor fusion techniques based on Machine Learning (ML) methods can combine all the data acquisition sources to form a holistic monitoring system for better data aggregation and enhanced detection. This holistic approach could also be used to train a controller on top of the fusion system to master the AM production and increase its reliance. This article summarizes recent studies on sensor utilization, followed by ML-based sensor fusion and control strategies.
{"title":"Sensory Data Fusion Using Machine Learning Methods For In-Situ Defect Registration In Additive Manufacturing: A Review","authors":"Javid Akhavan, S. Manoochehri","doi":"10.1109/iemtronics55184.2022.9795815","DOIUrl":"https://doi.org/10.1109/iemtronics55184.2022.9795815","url":null,"abstract":"In-situ control to predict and mitigate defects in Additive Manufacturing (AM) could significantly increase these technologies’ quality and reliability. Thorough knowledge of the AM processes is needed to develop such a controller. Recent studies utilized various methods to acquire data from the process, build insight into the process, and detect anomalies within the process. However, each sensory method has its unique limitations and capabilities. Sensor fusion techniques based on Machine Learning (ML) methods can combine all the data acquisition sources to form a holistic monitoring system for better data aggregation and enhanced detection. This holistic approach could also be used to train a controller on top of the fusion system to master the AM production and increase its reliance. This article summarizes recent studies on sensor utilization, followed by ML-based sensor fusion and control strategies.","PeriodicalId":442879,"journal":{"name":"2022 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127229395","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}
There is no doubt that big data analysis has a very positive impact on economics, security, and other aspects for countries and enterprises alike. Where we have recently noticed the frantic competition between companies to increase their profits by analyzing the largest amount of data as quickly as possible. Especially analyzing data related to Covid-19 to make the most of information in all areas. Covid-19 has drastically affected many lives in recent years but, even in these hard times, businesses can leverage the current pandemic to make a profit. In this paper, we investigate a variety of tweets using MapReduce, Spark, and Machine Learning methods to determine the sentiment of a given tweet based on the information provided by the dataset. With this information, businesses could learn how to present Covid-19 and pandemic related goods and information in a way that will be well received by its audience. To take this a step further, we will investigate trends in sentiment across demographics tweeting about the virus. This information in sentiment is dynamically useful to understand how specific audiences feel about the pandemic. We explore which Machine Learning methods produce the best results such as Multi-Layer Perceptron neural networks and Logistic Regression.
{"title":"Dynamic Analysis of Demographic Sentiment","authors":"Joshua Weston, Brenden Bickert, Caleb Stasiuk, Fadi Alzhouri, Dariush Ebrahim","doi":"10.1109/iemtronics55184.2022.9795706","DOIUrl":"https://doi.org/10.1109/iemtronics55184.2022.9795706","url":null,"abstract":"There is no doubt that big data analysis has a very positive impact on economics, security, and other aspects for countries and enterprises alike. Where we have recently noticed the frantic competition between companies to increase their profits by analyzing the largest amount of data as quickly as possible. Especially analyzing data related to Covid-19 to make the most of information in all areas. Covid-19 has drastically affected many lives in recent years but, even in these hard times, businesses can leverage the current pandemic to make a profit. In this paper, we investigate a variety of tweets using MapReduce, Spark, and Machine Learning methods to determine the sentiment of a given tweet based on the information provided by the dataset. With this information, businesses could learn how to present Covid-19 and pandemic related goods and information in a way that will be well received by its audience. To take this a step further, we will investigate trends in sentiment across demographics tweeting about the virus. This information in sentiment is dynamically useful to understand how specific audiences feel about the pandemic. We explore which Machine Learning methods produce the best results such as Multi-Layer Perceptron neural networks and Logistic Regression.","PeriodicalId":442879,"journal":{"name":"2022 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123532477","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 : 2022-06-01DOI: 10.1109/iemtronics55184.2022.9795843
Taslina Akter, Yeaser Sadman, Shatabdee Bala
In this article, we generated some data as a result of new technologies, the internet, and linked items. Putting these facts into context and structuring them so that they may be perceived, understood, and reflected is critical. Humans had traditionally studied data. As the availability of data grows larger, humans are progressively turning to computerized technologies that can replicate them. Machine learning refers to technologies that can resolve issues by learning from both data and data modifications. Artificial intelligence (AI) has a significant influence on e-learning studies, and machine learning-based methodologies may be used to improve Technology Enhanced Learning Environments (TELEs). This publication provides an outline of relevant study outcomes in this domain. Initially, we'll go over some basic machine learning ideas. Then, we'll go through the significant latest research in the domain of e-learning that uses machine learning.
{"title":"Use Machine Learning Technologies in E-learning","authors":"Taslina Akter, Yeaser Sadman, Shatabdee Bala","doi":"10.1109/iemtronics55184.2022.9795843","DOIUrl":"https://doi.org/10.1109/iemtronics55184.2022.9795843","url":null,"abstract":"In this article, we generated some data as a result of new technologies, the internet, and linked items. Putting these facts into context and structuring them so that they may be perceived, understood, and reflected is critical. Humans had traditionally studied data. As the availability of data grows larger, humans are progressively turning to computerized technologies that can replicate them. Machine learning refers to technologies that can resolve issues by learning from both data and data modifications. Artificial intelligence (AI) has a significant influence on e-learning studies, and machine learning-based methodologies may be used to improve Technology Enhanced Learning Environments (TELEs). This publication provides an outline of relevant study outcomes in this domain. Initially, we'll go over some basic machine learning ideas. Then, we'll go through the significant latest research in the domain of e-learning that uses machine learning.","PeriodicalId":442879,"journal":{"name":"2022 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS)","volume":"95 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123761240","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 : 2022-06-01DOI: 10.1109/iemtronics55184.2022.9795851
Tasfia Nuzhat, Md.Nazmul Hasan
Commercial electromagnetic (EM) simulator tools solve complicated Maxwell’s equations to design and optimize electromagnetic devices, which is computationally expensive and time consuming. There is a dire need to solve complex electromagnetic problems with least amount of computational resources in a short time. This work proposes the application of machine learning techniques in design process of electromagnetic problem. For the proof of concept, we demonstrated an optimum design process of an artificial magnetic conductor, which is a metasurface unit cell, by applying machine learning algorithms namely, artificial neural network (ANN), k-nearest neighbor (KNN), support vector machine (SVM), extreme gradient boosting (XGBoost), and least absolute shrinkage and selection operator (LASSO). The performances of these machine learning optimization models were evaluated on the test data set based on root mean squared error (RMSE) values. To the best of our knowledge, this is the first work that yields an excellent match with the original EM results from a commercial simulator tool with very small training dataset. Thus, it obviates the need of using computationally expensive and time-consuming electromagnetic simulators and massive training datasets for data-driven design approach of complex electromagnetic problems.
{"title":"Artificial Magnetic Conductor Unit Cell Design Using Machine Learning Algorithms","authors":"Tasfia Nuzhat, Md.Nazmul Hasan","doi":"10.1109/iemtronics55184.2022.9795851","DOIUrl":"https://doi.org/10.1109/iemtronics55184.2022.9795851","url":null,"abstract":"Commercial electromagnetic (EM) simulator tools solve complicated Maxwell’s equations to design and optimize electromagnetic devices, which is computationally expensive and time consuming. There is a dire need to solve complex electromagnetic problems with least amount of computational resources in a short time. This work proposes the application of machine learning techniques in design process of electromagnetic problem. For the proof of concept, we demonstrated an optimum design process of an artificial magnetic conductor, which is a metasurface unit cell, by applying machine learning algorithms namely, artificial neural network (ANN), k-nearest neighbor (KNN), support vector machine (SVM), extreme gradient boosting (XGBoost), and least absolute shrinkage and selection operator (LASSO). The performances of these machine learning optimization models were evaluated on the test data set based on root mean squared error (RMSE) values. To the best of our knowledge, this is the first work that yields an excellent match with the original EM results from a commercial simulator tool with very small training dataset. Thus, it obviates the need of using computationally expensive and time-consuming electromagnetic simulators and massive training datasets for data-driven design approach of complex electromagnetic problems.","PeriodicalId":442879,"journal":{"name":"2022 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS)","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116586483","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 : 2022-06-01DOI: 10.1109/iemtronics55184.2022.9795759
Meryem Abouali, K. Sharma, O. Ajayi, T. Saadawi
With the healthcare system’s ongoing digital transformation and the need for patient data sharing to become an essential step to understanding the patient’s health history, cyber security must stay at the forefront and be made a top priority. As a result, most existing data-sharing systems depend on trusted third parties. As a result, these systems lack interoperability, data fragmentation, integrity, security, and privacy. In our previous work, we designed a framework based on Blockchain to secure patient health records exchange(SPHRS) that is fully controlled by the patient in terms of revoking or granting access and creating access policies for care providers. The framework achieves security by using smart contracts for user identity authentication and verification. The distributed IPFS storage is applied to store the encrypted patient health records and ensure immutability. In addition, NuCypher software takes advantage of a proxy re-encryption protocol to store the encryption and decryption keys securely. In this study, we assess the framework’s performance by testing metrics such as blockchain transactions’ gas consumption, throughput, Average response time, and average. Bytes. Furthermore, the security of the framework is discussed. SPHRS demonstrates how we can establish a novel approach to efficiently secure patient health record sharing. However, it shows a promising result that can potentially transform the digital patient healthcare system.
{"title":"Performance Evaluation of Secured Blockchain-Based Patient Health Records Sharing Framework","authors":"Meryem Abouali, K. Sharma, O. Ajayi, T. Saadawi","doi":"10.1109/iemtronics55184.2022.9795759","DOIUrl":"https://doi.org/10.1109/iemtronics55184.2022.9795759","url":null,"abstract":"With the healthcare system’s ongoing digital transformation and the need for patient data sharing to become an essential step to understanding the patient’s health history, cyber security must stay at the forefront and be made a top priority. As a result, most existing data-sharing systems depend on trusted third parties. As a result, these systems lack interoperability, data fragmentation, integrity, security, and privacy. In our previous work, we designed a framework based on Blockchain to secure patient health records exchange(SPHRS) that is fully controlled by the patient in terms of revoking or granting access and creating access policies for care providers. The framework achieves security by using smart contracts for user identity authentication and verification. The distributed IPFS storage is applied to store the encrypted patient health records and ensure immutability. In addition, NuCypher software takes advantage of a proxy re-encryption protocol to store the encryption and decryption keys securely. In this study, we assess the framework’s performance by testing metrics such as blockchain transactions’ gas consumption, throughput, Average response time, and average. Bytes. Furthermore, the security of the framework is discussed. SPHRS demonstrates how we can establish a novel approach to efficiently secure patient health record sharing. However, it shows a promising result that can potentially transform the digital patient healthcare system.","PeriodicalId":442879,"journal":{"name":"2022 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122220417","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 : 2022-06-01DOI: 10.1109/iemtronics55184.2022.9795704
Adil Rachid, Antonio Miguel Lopez Martinez, Sebastian Moreno Garcia
The reliability of the electrical network and the need to minimize economic losses due to unexpected power outages have led electricity distribution companies to introduce diagnostic and preventive maintenance programs to assess the condition of facilities under normal working and power conditions in order to be able to react quickly in unexpected conditions (fire and floods). The developed system is composed of different types of sensors characterized by their very low power consumption, which detect anomalies in the operation of medium voltage (30Kv) and high voltage (220Kv) line installations located in underground utility tunnels for electricity distribution.This article describes and develops a very low power communication system in the IoT field which improves the energy efficiency of radio communications between sensor nodes (WSN) by integrating systems that facilitate the operation of multiple hops of the wake-up signal. This provides a longer overall lifespan in comparison to other monitoring systems.The developed WSN sensor network is installed and tested in an underground service utility tunnel including medium and high voltage transmission lines that belongs to the Endesa group (ENEL) and is located in the city of Barcelona. A web-type user environment has been designed to view the data sent by the sensor network.
{"title":"Design and implementation of a very-low-power wireless network of sensors in an underground utility tunnel for medium and high voltage transmission lines","authors":"Adil Rachid, Antonio Miguel Lopez Martinez, Sebastian Moreno Garcia","doi":"10.1109/iemtronics55184.2022.9795704","DOIUrl":"https://doi.org/10.1109/iemtronics55184.2022.9795704","url":null,"abstract":"The reliability of the electrical network and the need to minimize economic losses due to unexpected power outages have led electricity distribution companies to introduce diagnostic and preventive maintenance programs to assess the condition of facilities under normal working and power conditions in order to be able to react quickly in unexpected conditions (fire and floods). The developed system is composed of different types of sensors characterized by their very low power consumption, which detect anomalies in the operation of medium voltage (30Kv) and high voltage (220Kv) line installations located in underground utility tunnels for electricity distribution.This article describes and develops a very low power communication system in the IoT field which improves the energy efficiency of radio communications between sensor nodes (WSN) by integrating systems that facilitate the operation of multiple hops of the wake-up signal. This provides a longer overall lifespan in comparison to other monitoring systems.The developed WSN sensor network is installed and tested in an underground service utility tunnel including medium and high voltage transmission lines that belongs to the Endesa group (ENEL) and is located in the city of Barcelona. A web-type user environment has been designed to view the data sent by the sensor network.","PeriodicalId":442879,"journal":{"name":"2022 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133738235","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 : 2022-06-01DOI: 10.1109/iemtronics55184.2022.9795783
A. Noor
While computational methods offer great potential in predicting drug-drug interactions (DDIs), such predictions as of yet have limited utility in supporting clinical decision-making; in particular, there exists especial difficulty in deriving interaction mechanisms from the vast abundance of available information on potential DDIs. Here, we present a backward-chaining inference algorithm that operates on a knowledge graph integrating multiple types of mechanistic information, from metabolizing enzymes to genetic variants. Given two drugs of interest, this algorithm applies complex rules to identify evidence supporting their potential interaction, which in turn suggests their mechanism of interaction. An evaluation of the ruleset using two widely-used drugs with a suspected interaction, the antibiotic levofloxacin and the chemotherapeutic irinotecan, successfully identified pharmacological and biomedical features that support and may explain their interaction. This algorithm represents a first step toward effectively assessing the clinical relevance of identified DDIs, and of identifying pairs of interacting drugs that may be validated in the experimental setting to support clinical decision-making and ultimately improve medication safety.
{"title":"Integrating Mechanistic Information to Predict Drug-Drug Interactions and Associated Relevance for Decision Support","authors":"A. Noor","doi":"10.1109/iemtronics55184.2022.9795783","DOIUrl":"https://doi.org/10.1109/iemtronics55184.2022.9795783","url":null,"abstract":"While computational methods offer great potential in predicting drug-drug interactions (DDIs), such predictions as of yet have limited utility in supporting clinical decision-making; in particular, there exists especial difficulty in deriving interaction mechanisms from the vast abundance of available information on potential DDIs. Here, we present a backward-chaining inference algorithm that operates on a knowledge graph integrating multiple types of mechanistic information, from metabolizing enzymes to genetic variants. Given two drugs of interest, this algorithm applies complex rules to identify evidence supporting their potential interaction, which in turn suggests their mechanism of interaction. An evaluation of the ruleset using two widely-used drugs with a suspected interaction, the antibiotic levofloxacin and the chemotherapeutic irinotecan, successfully identified pharmacological and biomedical features that support and may explain their interaction. This algorithm represents a first step toward effectively assessing the clinical relevance of identified DDIs, and of identifying pairs of interacting drugs that may be validated in the experimental setting to support clinical decision-making and ultimately improve medication safety.","PeriodicalId":442879,"journal":{"name":"2022 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114624954","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 : 2022-06-01DOI: 10.1109/iemtronics55184.2022.9795784
Kate G. Francisco, R. Relano, Mike Louie C. Enriquez, Ronnie S. Concepcion, Jonah Jahara G. Baun, Adrian Genevie G. Janairo, R. R. Vicerra, A. Bandala, E. Dadios, J. Dungca
Non-destructive mapping of underground utilities is one of the fundamental concepts of subsurface imaging technology that has a great contribution to the improvement of many infrastructure concerns. It is incorporated with electrical resistivity measurement of various ground conditions through electrodes with functional geometric configuration. Due to the presence of an electrical field, electromagnetic noise and interference will likely occur and might cause inaccuracy of data. On that note, it is vital to understand the different factors affecting the system and its impact as the initial step in the development of an effective filtering and shielding mechanism. Thus, this paper discusses the possible impacts of parasitic inductance and capacitance affecting the performance of low and very low-frequency antennas, and the collection of various optimization methods as well as the tools and software used in the mitigation of parasitic elements in an electronics system found in different research publications and journals. Furthermore, an AI-based framework was also provided as an initial step in the development of a parasitic antenna filter that performs well for underground imaging single antenna array. Genetic algorithm is the AI technique proposed for the optimization of the antenna filter by providing the best combination of material by considering its conductivity and thickness.
{"title":"Systematic Analysis and Proposed AI-based Technique for Attenuating Inductive and Capacitive Parasitics in Low and Very Low Frequency Antennas","authors":"Kate G. Francisco, R. Relano, Mike Louie C. Enriquez, Ronnie S. Concepcion, Jonah Jahara G. Baun, Adrian Genevie G. Janairo, R. R. Vicerra, A. Bandala, E. Dadios, J. Dungca","doi":"10.1109/iemtronics55184.2022.9795784","DOIUrl":"https://doi.org/10.1109/iemtronics55184.2022.9795784","url":null,"abstract":"Non-destructive mapping of underground utilities is one of the fundamental concepts of subsurface imaging technology that has a great contribution to the improvement of many infrastructure concerns. It is incorporated with electrical resistivity measurement of various ground conditions through electrodes with functional geometric configuration. Due to the presence of an electrical field, electromagnetic noise and interference will likely occur and might cause inaccuracy of data. On that note, it is vital to understand the different factors affecting the system and its impact as the initial step in the development of an effective filtering and shielding mechanism. Thus, this paper discusses the possible impacts of parasitic inductance and capacitance affecting the performance of low and very low-frequency antennas, and the collection of various optimization methods as well as the tools and software used in the mitigation of parasitic elements in an electronics system found in different research publications and journals. Furthermore, an AI-based framework was also provided as an initial step in the development of a parasitic antenna filter that performs well for underground imaging single antenna array. Genetic algorithm is the AI technique proposed for the optimization of the antenna filter by providing the best combination of material by considering its conductivity and thickness.","PeriodicalId":442879,"journal":{"name":"2022 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS)","volume":"231 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114693616","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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