Pub Date : 2021-09-29DOI: 10.1109/3ICT53449.2021.9581463
Elif Işılay Ünlü, A. Cinar
One of the types of cancer that requires early diagnosis is skin cancer. Melanoma is a deadly type of skin cancer. Computer-aided systems can detect the findings in medical examinations that human perception cannot recognize, and these findings can help the clinicans to make an early diagnosis. Therefore, the need for computer aided systems has increased. In this study, a deep learning-based method that segments melanoma with color images taken from dermoscopy devices is proposed. For this method, ISIC 2017 (International Skin Image Collaboration) database is used. It contains 1403 training and 597 test data. The method is based on preprocessing and U-Net architecture. Gaussian and Difference of Gaussian (DoG) filters are used in the preprocessing stage. It is aimed to make skin images more convenient before U-Net. As a result of the segmentation performed with these data, the education success rate reached 96-95%. A high similarity coefficient obtained. On the other hand, as a result of the training of the preprocessed data, accuracy rate has reached 86-85%.
其中一种需要早期诊断的癌症是皮肤癌。黑色素瘤是一种致命的皮肤癌。计算机辅助系统可以检测到医学检查中人类感知无法识别的发现，这些发现可以帮助临床医生做出早期诊断。因此，对计算机辅助系统的需求增加了。在这项研究中，提出了一种基于深度学习的方法，利用皮肤镜设备拍摄的彩色图像对黑色素瘤进行分割。该方法使用ISIC 2017 (International Skin Image Collaboration)数据库。它包含1403个训练数据和597个测试数据。该方法基于预处理和U-Net体系结构。预处理阶段采用高斯滤波和高斯差分滤波(DoG)。它的目的是使皮肤图像在U-Net之前更方便。利用这些数据进行分割，教育成功率达到96-95%。得到了较高的相似系数。另一方面，经过预处理数据的训练，准确率达到86-85%。
{"title":"Segmentation of Benign and Malign lesions on skin images using U-Net","authors":"Elif Işılay Ünlü, A. Cinar","doi":"10.1109/3ICT53449.2021.9581463","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9581463","url":null,"abstract":"One of the types of cancer that requires early diagnosis is skin cancer. Melanoma is a deadly type of skin cancer. Computer-aided systems can detect the findings in medical examinations that human perception cannot recognize, and these findings can help the clinicans to make an early diagnosis. Therefore, the need for computer aided systems has increased. In this study, a deep learning-based method that segments melanoma with color images taken from dermoscopy devices is proposed. For this method, ISIC 2017 (International Skin Image Collaboration) database is used. It contains 1403 training and 597 test data. The method is based on preprocessing and U-Net architecture. Gaussian and Difference of Gaussian (DoG) filters are used in the preprocessing stage. It is aimed to make skin images more convenient before U-Net. As a result of the segmentation performed with these data, the education success rate reached 96-95%. A high similarity coefficient obtained. On the other hand, as a result of the training of the preprocessed data, accuracy rate has reached 86-85%.","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":"131363605","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: 10.1109/3ICT53449.2021.9581580
A. Zolait, Sumaya Alalas, N. Ali, Aya Showaiter
This research aims to measure the impact of cloud computing on people's quality of life in the Kingdom of Bahrain and recognize factors that could impact people's intention to use cloud computing services. An online survey has been used to collect primary data for the research. It was distributed to a random sample of 443 respondents in the Kingdom of Bahrain. The achievable sample comprised 394 represent people of different ages and educational levels. The researchers adapted selected factors from the diffusion of innovation (DOI) theory, including relative advantage, complexity, and compatibility. In addition to the quality of life factors consisting of education, healthcare, wellbeing, and entertainment. These factors are used to establishing the framework of this research. The research limitation was in examining only the variables proposed in the framework. Also, as a consequence of the coronavirus's current situation (COVID-19), collecting data was restricted to the quantitative approach using an online survey. Findings show that administrability of cloud computing usage is the most impacting factor on people's quality of life and, more specifically, on people's education.
{"title":"Quality of Life Integrated Framework: Perspective of Cloud Computing Usage","authors":"A. Zolait, Sumaya Alalas, N. Ali, Aya Showaiter","doi":"10.1109/3ICT53449.2021.9581580","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9581580","url":null,"abstract":"This research aims to measure the impact of cloud computing on people's quality of life in the Kingdom of Bahrain and recognize factors that could impact people's intention to use cloud computing services. An online survey has been used to collect primary data for the research. It was distributed to a random sample of 443 respondents in the Kingdom of Bahrain. The achievable sample comprised 394 represent people of different ages and educational levels. The researchers adapted selected factors from the diffusion of innovation (DOI) theory, including relative advantage, complexity, and compatibility. In addition to the quality of life factors consisting of education, healthcare, wellbeing, and entertainment. These factors are used to establishing the framework of this research. The research limitation was in examining only the variables proposed in the framework. Also, as a consequence of the coronavirus's current situation (COVID-19), collecting data was restricted to the quantitative approach using an online survey. Findings show that administrability of cloud computing usage is the most impacting factor on people's quality of life and, more specifically, on people's education.","PeriodicalId":133021,"journal":{"name":"2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)","volume":"209 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":"114318436","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: 10.1109/3ICT53449.2021.9581683
Dorsaf Swessi, H. Idoudi
With the rapid growth of vehicular technology, Vehicle-to-everything (V2X) communication systems are becoming increasingly challenging, especially regarding security aspects. Using Machine Learning (ML) techniques to build Intrusion Detection Systems (IDS) has shown a high level of accuracy in minimizing V2X communications attacks. However, the effectiveness of ML-based IDSs depends on the availability of a sufficient amount of relevant network traffic logs that cover a wide variety of normal and abnormal samples to train and verify these models. In this paper, we provide the most up-to-date review of existing V2X security datasets. We classify these datasets according to the targeted architecture, the involved attacks, and their severity, etc. Based on these different effectiveness criteria we suggest four distinct yet realistic and reliable datasets including ROAD, VDDD, VeReMi, and VDOS-LRS datasets.
{"title":"A Comparative Review of Security Threats Datasets for Vehicular Networks","authors":"Dorsaf Swessi, H. Idoudi","doi":"10.1109/3ICT53449.2021.9581683","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9581683","url":null,"abstract":"With the rapid growth of vehicular technology, Vehicle-to-everything (V2X) communication systems are becoming increasingly challenging, especially regarding security aspects. Using Machine Learning (ML) techniques to build Intrusion Detection Systems (IDS) has shown a high level of accuracy in minimizing V2X communications attacks. However, the effectiveness of ML-based IDSs depends on the availability of a sufficient amount of relevant network traffic logs that cover a wide variety of normal and abnormal samples to train and verify these models. In this paper, we provide the most up-to-date review of existing V2X security datasets. We classify these datasets according to the targeted architecture, the involved attacks, and their severity, etc. Based on these different effectiveness criteria we suggest four distinct yet realistic and reliable datasets including ROAD, VDDD, VeReMi, and VDOS-LRS datasets.","PeriodicalId":133021,"journal":{"name":"2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)","volume":"47 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":"131777920","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: 10.1109/3ICT53449.2021.9582068
Fadheela Hussain, Riadh Ksantini, M. Hammad
During the second half of 2020, healthcare is and has been the number one target for cybercrime, enormous amount of cyberattacks on hospitals and health systems increased, and specialists trust there are more to come. Attackers who can get the way to reach the electronic health record would exploit it and will use it for their own interest like deal or vend it on the underground economy, hostage the systems and the sensitive data, that has a significant impact on operations. This review tried to analyze how cyber attacker employ Generative Adversarial Networks (GANs) to alter the evidences of patient's medical conditions from image scans and reports. Cyber attacker has different purposes in order to obstruct a political applicant, lockup investigations, obligate insurance scam, execute an act of violence, or even commit homicide. Numerous correlated works constructed on gan in medical images practices had been reviews in the period between 2000 to 2021. Many papers showed how hospital system, physicians and radiology's specialists and the most recent researches showed an extremely exposed to different types of intrusion gan attacks.
{"title":"A Review of Malicious Altering Healthcare Imagery using Artificial Intelligence","authors":"Fadheela Hussain, Riadh Ksantini, M. Hammad","doi":"10.1109/3ICT53449.2021.9582068","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9582068","url":null,"abstract":"During the second half of 2020, healthcare is and has been the number one target for cybercrime, enormous amount of cyberattacks on hospitals and health systems increased, and specialists trust there are more to come. Attackers who can get the way to reach the electronic health record would exploit it and will use it for their own interest like deal or vend it on the underground economy, hostage the systems and the sensitive data, that has a significant impact on operations. This review tried to analyze how cyber attacker employ Generative Adversarial Networks (GANs) to alter the evidences of patient's medical conditions from image scans and reports. Cyber attacker has different purposes in order to obstruct a political applicant, lockup investigations, obligate insurance scam, execute an act of violence, or even commit homicide. Numerous correlated works constructed on gan in medical images practices had been reviews in the period between 2000 to 2021. Many papers showed how hospital system, physicians and radiology's specialists and the most recent researches showed an extremely exposed to different types of intrusion gan attacks.","PeriodicalId":133021,"journal":{"name":"2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)","volume":"148 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":"131793649","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: 10.1109/3ICT53449.2021.9581799
E. Magsino, J. Sim, Rica Rizabel M. Tagabuhin, J. J. Tirados
Tracking individuals, equipment, store locations, and floor level in a multi-story building becomes accessible through the implementation of an indoor positioning system. In this experimental study, we implement a multi-story indoor localization scheme by utilizing multiple WiFi Received Signal Strength Indicator (RSSI) signals installed in various locations of a three-floor residential household. Initially, our work focuses on static target locations spaced one meter apart and captures RSSI readings from four WiFi routers coming from different floors. These RSSI readings are stored in a database of fingerprints. To localize an indoor target, the cross-correlation between the offline and online (captured by a smartphone with a developed RSSI-capturing application) RSSI readings is calculated. Our empirical results have shown a 90% rate of correctly localizing a static indoor location when using only the average of a three-minute time series of RSSI values. We captured the WiFi RSSI values every 200 ms and present the localization utilizing the Time Reversal Resonating Strength (TRRS) concept.
{"title":"Indoor Localization of a Multi-story Residential Household using Multiple WiFi Signals","authors":"E. Magsino, J. Sim, Rica Rizabel M. Tagabuhin, J. J. Tirados","doi":"10.1109/3ICT53449.2021.9581799","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9581799","url":null,"abstract":"Tracking individuals, equipment, store locations, and floor level in a multi-story building becomes accessible through the implementation of an indoor positioning system. In this experimental study, we implement a multi-story indoor localization scheme by utilizing multiple WiFi Received Signal Strength Indicator (RSSI) signals installed in various locations of a three-floor residential household. Initially, our work focuses on static target locations spaced one meter apart and captures RSSI readings from four WiFi routers coming from different floors. These RSSI readings are stored in a database of fingerprints. To localize an indoor target, the cross-correlation between the offline and online (captured by a smartphone with a developed RSSI-capturing application) RSSI readings is calculated. Our empirical results have shown a 90% rate of correctly localizing a static indoor location when using only the average of a three-minute time series of RSSI values. We captured the WiFi RSSI values every 200 ms and present the localization utilizing the Time Reversal Resonating Strength (TRRS) concept.","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":"134109520","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: 10.1109/3ICT53449.2021.9581443
Aysha K. Alharam, H. Otrok, W. Elmedany, Ahsan Baidar Bakht, Nouf Alkaabi
Nowadays, Mobile Crowd Sensing (MCS) became the popular paradigm for sensing data. MCS is vulnerable to many types of threats and faces many challenges. Trustworthiness is one of the main MCS challenges; attackers aim to inject faulty data to corrupt the system or waste its resources. Thus, MCS organizers must ensure that no malicious users are contributing to have trusted sensed data. Faulty sensor readings in MCS can be due to sensor failure or malicious behavior. Attackers targets degrade the system performance and reduce the worker's reputation by injecting false data. This paper evaluates different machine learning algorithms classifying the received sensed data as true, a faulty sensor, or attacker behavior. These algorithms are Decision Tree (DT), Support Vector Machine (SVM), and Random Frost (RF). Evaluating the result for comparison obtained based on accuracy, precision, Recall, f1 score, and the confusion matrix. The result shows that among all classifiers, RF shows the highest accuracy of 97.9%.
{"title":"AI-Based Anomaly and Data Posing Classification in Mobile Crowd Sensing","authors":"Aysha K. Alharam, H. Otrok, W. Elmedany, Ahsan Baidar Bakht, Nouf Alkaabi","doi":"10.1109/3ICT53449.2021.9581443","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9581443","url":null,"abstract":"Nowadays, Mobile Crowd Sensing (MCS) became the popular paradigm for sensing data. MCS is vulnerable to many types of threats and faces many challenges. Trustworthiness is one of the main MCS challenges; attackers aim to inject faulty data to corrupt the system or waste its resources. Thus, MCS organizers must ensure that no malicious users are contributing to have trusted sensed data. Faulty sensor readings in MCS can be due to sensor failure or malicious behavior. Attackers targets degrade the system performance and reduce the worker's reputation by injecting false data. This paper evaluates different machine learning algorithms classifying the received sensed data as true, a faulty sensor, or attacker behavior. These algorithms are Decision Tree (DT), Support Vector Machine (SVM), and Random Frost (RF). Evaluating the result for comparison obtained based on accuracy, precision, Recall, f1 score, and the confusion matrix. The result shows that among all classifiers, RF shows the highest accuracy of 97.9%.","PeriodicalId":133021,"journal":{"name":"2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)","volume":"65 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":"133774767","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: 10.1109/3ICT53449.2021.9581688
R. R. Maaliw, Zoren P. Mabunga, Frederick T. Villa
The extent of the COVID-19 pandemic has devastated world economies and claimed millions of lives. Timely and accurate information such as time-series forecasting is crucial for government, healthcare systems, decision-makers, and policy-implementers in managing the disease's progression. With the potential value of early knowledge to save countless lives, the research investigated and compared the capabilities and robustness of sophisticated deep learning models to traditional time-series forecasting methods. The results show that the Stacked Long Short-Term Memory Networks (SLSTM) outperforms the Exponential Smoothing (ES) and Autoregressive Integrated Moving Average (ARIMA) models for a 15-day forecast horizon. SLSTM attained a collective mean accuracy of 92.17% (confirmed cases) and 82.31% (death cases) using historical data of 419 days from March 6, 2020 to April 28, 2021 of four countries - the Philippines, United States, India, and Brazil.
{"title":"Time-Series Forecasting of COVID-19 Cases Using Stacked Long Short-Term Memory Networks","authors":"R. R. Maaliw, Zoren P. Mabunga, Frederick T. Villa","doi":"10.1109/3ICT53449.2021.9581688","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9581688","url":null,"abstract":"The extent of the COVID-19 pandemic has devastated world economies and claimed millions of lives. Timely and accurate information such as time-series forecasting is crucial for government, healthcare systems, decision-makers, and policy-implementers in managing the disease's progression. With the potential value of early knowledge to save countless lives, the research investigated and compared the capabilities and robustness of sophisticated deep learning models to traditional time-series forecasting methods. The results show that the Stacked Long Short-Term Memory Networks (SLSTM) outperforms the Exponential Smoothing (ES) and Autoregressive Integrated Moving Average (ARIMA) models for a 15-day forecast horizon. SLSTM attained a collective mean accuracy of 92.17% (confirmed cases) and 82.31% (death cases) using historical data of 419 days from March 6, 2020 to April 28, 2021 of four countries - the Philippines, United States, India, and Brazil.","PeriodicalId":133021,"journal":{"name":"2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)","volume":"4 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":"132953796","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: 10.1109/3ICT53449.2021.9581389
Jibin B. Thomas, Muskaan Devvarma, K. Shihabudheen
The COVID-19 pandemic has severely crippled the healthcare industry as a whole. Efficient screening techniques are crucial to suppress the escalation of the disease. Medical image analysis of chest X-rays has recently become increasingly important in radiology examination and screening of infected patients. Studies have shown that Deep CNN models can help in the diagnosis of this infection by automatically classifying chest X-ray images as infected or not. Ensemble modelling these Deep CNN architectures can further improve the performance by reducing the generalisation error when compared to a single model. This paper presents different Ensemble Learning approaches to synergize the features extracted by Deep CNN models to improve the classification. These automatic classification approaches can be used by radiologists to help identify infected chest X-rays and support resistance.
{"title":"Deep Ensemble Approaches for Classification of COVID-19 in Chest X-Ray Images","authors":"Jibin B. Thomas, Muskaan Devvarma, K. Shihabudheen","doi":"10.1109/3ICT53449.2021.9581389","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9581389","url":null,"abstract":"The COVID-19 pandemic has severely crippled the healthcare industry as a whole. Efficient screening techniques are crucial to suppress the escalation of the disease. Medical image analysis of chest X-rays has recently become increasingly important in radiology examination and screening of infected patients. Studies have shown that Deep CNN models can help in the diagnosis of this infection by automatically classifying chest X-ray images as infected or not. Ensemble modelling these Deep CNN architectures can further improve the performance by reducing the generalisation error when compared to a single model. This paper presents different Ensemble Learning approaches to synergize the features extracted by Deep CNN models to improve the classification. These automatic classification approaches can be used by radiologists to help identify infected chest X-rays and support resistance.","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":"116164907","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: 10.1109/3ICT53449.2021.9582119
M. S. Sharif, Madhav Raj Theeng Tamang, Cynthia Fu
Commuting to work is an everyday activity for many which can have a significant effect on our health. Commuting on regular basis can be a cause of chronic stress which is linked to poor mental health, high blood pressure, heart rate, and exhaustion. This research investigates the neurophysiological and psychological impact of commuting in real-time, by analyzing brain waves and applying machine learning. The participants were healthy volunteers with mean age of 30 years. Portable electroencephalogram (EEG) data were acquired as a measure of stress level. EEG data were acquired from each participant using non-invasive NeuroSky MindWave headset for 5 continuous activities during their commute to work. This approach allowed effects to be measured during and following the period of commuting. The results indicate that whether the duration of commute was low or large, when participants were in a calm or relaxed state the bio-signal alpha band exceeded beta band whereas beta band was higher than alpha band when participants were stressed due to their commute. Very promising results have been achieved with an accuracy of 97.5% using Feed-forward neural network. This work focuses on the development of an intelligent model that helps to predict the impact of commuting on participants. In addition, the result obtained from the Positive and Negative Affect Schedule also suggests that participants experience a considerable rise in stress after their commute. For modelling of cognitive and semantic processes underlying social behavior, the most of the recent research projects are still based on individuals, while our research focuses on approaches addressing groups as a complete cohort. This study recorded the experience of commuters with a special focus on the use and limitation of emerging computing technologies in telehealth sensors.
{"title":"Predicting the Health Impacts of Commuting Using EEG Signal Based on Intelligent Approach","authors":"M. S. Sharif, Madhav Raj Theeng Tamang, Cynthia Fu","doi":"10.1109/3ICT53449.2021.9582119","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9582119","url":null,"abstract":"Commuting to work is an everyday activity for many which can have a significant effect on our health. Commuting on regular basis can be a cause of chronic stress which is linked to poor mental health, high blood pressure, heart rate, and exhaustion. This research investigates the neurophysiological and psychological impact of commuting in real-time, by analyzing brain waves and applying machine learning. The participants were healthy volunteers with mean age of 30 years. Portable electroencephalogram (EEG) data were acquired as a measure of stress level. EEG data were acquired from each participant using non-invasive NeuroSky MindWave headset for 5 continuous activities during their commute to work. This approach allowed effects to be measured during and following the period of commuting. The results indicate that whether the duration of commute was low or large, when participants were in a calm or relaxed state the bio-signal alpha band exceeded beta band whereas beta band was higher than alpha band when participants were stressed due to their commute. Very promising results have been achieved with an accuracy of 97.5% using Feed-forward neural network. This work focuses on the development of an intelligent model that helps to predict the impact of commuting on participants. In addition, the result obtained from the Positive and Negative Affect Schedule also suggests that participants experience a considerable rise in stress after their commute. For modelling of cognitive and semantic processes underlying social behavior, the most of the recent research projects are still based on individuals, while our research focuses on approaches addressing groups as a complete cohort. This study recorded the experience of commuters with a special focus on the use and limitation of emerging computing technologies in telehealth sensors.","PeriodicalId":133021,"journal":{"name":"2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)","volume":"8 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":"126384698","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: 10.1109/3ICT53449.2021.9581394
L. A. Aldossary, Mazhar Ali, Abdulla Alasaadi
Monitoring and managing electric power generation, distribution and transmission requires supervisory control and data acquisition (SCADA) systems. As technology has developed, these systems have become huge, complicated, and distributed, which makes them susceptible to new risks. In particular, the lack of security in SCADA systems make them a target for network attacks such as denial of service (DoS) and developing solutions for this issue is the main objective of this thesis. By reviewing various existing system solutions for securing SCADA systems, a new security approach is recommended that employs Artificial Intelligence(AI). AI is an innovative approach that imparts learning ability to software. Here deep learning algorithms and machine learning algorithms are used to develop an intrusion detection system (IDS) to combat cyber-attacks. Various methods and algorithms are evaluated to obtain the best results in intrusion detection. The results reveal the Bi-LSTM IDS technique provides the highest intrusion detection (ID) performance compared with previous techniques to secure SCADA systems
{"title":"Securing SCADA Systems against Cyber-Attacks using Artificial Intelligence","authors":"L. A. Aldossary, Mazhar Ali, Abdulla Alasaadi","doi":"10.1109/3ICT53449.2021.9581394","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9581394","url":null,"abstract":"Monitoring and managing electric power generation, distribution and transmission requires supervisory control and data acquisition (SCADA) systems. As technology has developed, these systems have become huge, complicated, and distributed, which makes them susceptible to new risks. In particular, the lack of security in SCADA systems make them a target for network attacks such as denial of service (DoS) and developing solutions for this issue is the main objective of this thesis. By reviewing various existing system solutions for securing SCADA systems, a new security approach is recommended that employs Artificial Intelligence(AI). AI is an innovative approach that imparts learning ability to software. Here deep learning algorithms and machine learning algorithms are used to develop an intrusion detection system (IDS) to combat cyber-attacks. Various methods and algorithms are evaluated to obtain the best results in intrusion detection. The results reveal the Bi-LSTM IDS technique provides the highest intrusion detection (ID) performance compared with previous techniques to secure SCADA systems","PeriodicalId":133021,"journal":{"name":"2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114187008","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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