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description Publicationkeyboard_double_arrow_right Article , Other literature type 2022Publisher:MDPI AG Authors:Shahira Assem Abdel-Razek;
Shahira Assem Abdel-Razek
Shahira Assem Abdel-Razek in OpenAIREHanaa Salem Marie;
Hanaa Salem Marie
Hanaa Salem Marie in OpenAIREAli Alshehri;
Ali Alshehri
Ali Alshehri in OpenAIREOmar M. Elzeki;
Omar M. Elzeki
Omar M. Elzeki in OpenAIREdoi: 10.3390/su14137734
Room occupancy prediction based on indoor environmental quality may be the breakthrough to ensure energy efficiency and establish an interior ambience tailored to each user. Identifying whether temperature, humidity, lighting, and CO2 levels may be used as efficient predictors of room occupancy accuracy is needed to help designers better utilize the readings and data collected in order to improve interior design, in an effort to better suit users. It also aims to help in energy efficiency and saving in an ever-increasing energy crisis and dangerous levels of climate change. This paper evaluated the accuracy of room occupancy recognition using a dataset with diverse amounts of light, CO2, and humidity. As classification algorithms, K-nearest neighbors (KNN), hybrid Adam optimizer–artificial neural network–back-propagation network (AO–ANN (BP)), and decision trees (DT) were used. Furthermore, this research is based on machine learning interpretability methodologies. Shapley additive explanations (SHAP) improve interpretability by estimating the significance values for each feature for classifiers applied. The results indicate that the KNN performs better than the DT and AO-ANN (BP) classification models have 99.5%. Though the two classifiers are designed to evaluate variations in interpretations, we must ensure that they have accurate detection. The results show that SHAP provides successful implementation following these metrics, with differences detected amongst classifier models that support the assumption that model complexity plays a significant role when predictability is taken into account.
Sustainability arrow_drop_down SustainabilityOther literature type . 2022License: CC BYFull-Text: http://www.mdpi.com/2071-1050/14/13/7734/pdfData sources: Multidisciplinary Digital Publishing Instituteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/su14137734&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routesgold 28 citations 28 popularity Top 10% influence Top 10% impulse Top 10% Powered by BIP!
more_vert Sustainability arrow_drop_down SustainabilityOther literature type . 2022License: CC BYFull-Text: http://www.mdpi.com/2071-1050/14/13/7734/pdfData sources: Multidisciplinary Digital Publishing Instituteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/su14137734&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Other literature type 2022Publisher:MDPI AG Authors:Shahira Assem Abdel-Razek;
Shahira Assem Abdel-Razek
Shahira Assem Abdel-Razek in OpenAIREHanaa Salem Marie;
Hanaa Salem Marie
Hanaa Salem Marie in OpenAIREAli Alshehri;
Ali Alshehri
Ali Alshehri in OpenAIREOmar M. Elzeki;
Omar M. Elzeki
Omar M. Elzeki in OpenAIREdoi: 10.3390/su14137734
Room occupancy prediction based on indoor environmental quality may be the breakthrough to ensure energy efficiency and establish an interior ambience tailored to each user. Identifying whether temperature, humidity, lighting, and CO2 levels may be used as efficient predictors of room occupancy accuracy is needed to help designers better utilize the readings and data collected in order to improve interior design, in an effort to better suit users. It also aims to help in energy efficiency and saving in an ever-increasing energy crisis and dangerous levels of climate change. This paper evaluated the accuracy of room occupancy recognition using a dataset with diverse amounts of light, CO2, and humidity. As classification algorithms, K-nearest neighbors (KNN), hybrid Adam optimizer–artificial neural network–back-propagation network (AO–ANN (BP)), and decision trees (DT) were used. Furthermore, this research is based on machine learning interpretability methodologies. Shapley additive explanations (SHAP) improve interpretability by estimating the significance values for each feature for classifiers applied. The results indicate that the KNN performs better than the DT and AO-ANN (BP) classification models have 99.5%. Though the two classifiers are designed to evaluate variations in interpretations, we must ensure that they have accurate detection. The results show that SHAP provides successful implementation following these metrics, with differences detected amongst classifier models that support the assumption that model complexity plays a significant role when predictability is taken into account.
Sustainability arrow_drop_down SustainabilityOther literature type . 2022License: CC BYFull-Text: http://www.mdpi.com/2071-1050/14/13/7734/pdfData sources: Multidisciplinary Digital Publishing Instituteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/su14137734&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routesgold 28 citations 28 popularity Top 10% influence Top 10% impulse Top 10% Powered by BIP!
more_vert Sustainability arrow_drop_down SustainabilityOther literature type . 2022License: CC BYFull-Text: http://www.mdpi.com/2071-1050/14/13/7734/pdfData sources: Multidisciplinary Digital Publishing Instituteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/su14137734&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Other literature type 2022Publisher:MDPI AG doi: 10.3390/app12136473
The extensive use of Internet of Things (IoT) technology has recently enabled the development of smart cities. Smart cities operate in real-time to improve metropolitan areas’ comfort and efficiency. Sensors in these IoT devices are immediately linked to enormous servers, creating smart city traffic flow. This flow is rapidly increasing and is creating new cybersecurity concerns. Malicious attackers increasingly target essential infrastructure such as electricity transmission and other vital infrastructures. Software-Defined Networking (SDN) is a resilient connectivity technology utilized to address security concerns more efficiently. The controller, which oversees the flows of each appropriate forwarding unit in the SDN architecture, is the most critical component. The controller’s flow statistics are thought to provide relevant information for building an Intrusion Detection System (IDS). As a result, we propose a five-level classification approach based on SDN’s flow statistics to develop a Smart Attacks Learning Machine Advisor (SALMA) system for detecting intrusions and for protecting smart cities from smart threats. We use the Extreme Learning Machine (ELM) technique at all levels. The proposed system was implemented on the NSL-KDD and KDDCUP99 benchmark datasets, and achieved 95% and 99.2%, respectively. As a result, our approach provides an effective method for detecting intrusions in SDNs.
Applied Sciences arrow_drop_down Applied SciencesOther literature type . 2022License: CC BYFull-Text: http://www.mdpi.com/2076-3417/12/13/6473/pdfData sources: Multidisciplinary Digital Publishing Instituteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/app12136473&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routesgold 11 citations 11 popularity Top 10% influence Average impulse Top 10% Powered by BIP!
more_vert Applied Sciences arrow_drop_down Applied SciencesOther literature type . 2022License: CC BYFull-Text: http://www.mdpi.com/2076-3417/12/13/6473/pdfData sources: Multidisciplinary Digital Publishing Instituteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/app12136473&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Other literature type 2022Publisher:MDPI AG doi: 10.3390/app12136473
The extensive use of Internet of Things (IoT) technology has recently enabled the development of smart cities. Smart cities operate in real-time to improve metropolitan areas’ comfort and efficiency. Sensors in these IoT devices are immediately linked to enormous servers, creating smart city traffic flow. This flow is rapidly increasing and is creating new cybersecurity concerns. Malicious attackers increasingly target essential infrastructure such as electricity transmission and other vital infrastructures. Software-Defined Networking (SDN) is a resilient connectivity technology utilized to address security concerns more efficiently. The controller, which oversees the flows of each appropriate forwarding unit in the SDN architecture, is the most critical component. The controller’s flow statistics are thought to provide relevant information for building an Intrusion Detection System (IDS). As a result, we propose a five-level classification approach based on SDN’s flow statistics to develop a Smart Attacks Learning Machine Advisor (SALMA) system for detecting intrusions and for protecting smart cities from smart threats. We use the Extreme Learning Machine (ELM) technique at all levels. The proposed system was implemented on the NSL-KDD and KDDCUP99 benchmark datasets, and achieved 95% and 99.2%, respectively. As a result, our approach provides an effective method for detecting intrusions in SDNs.
Applied Sciences arrow_drop_down Applied SciencesOther literature type . 2022License: CC BYFull-Text: http://www.mdpi.com/2076-3417/12/13/6473/pdfData sources: Multidisciplinary Digital Publishing Instituteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/app12136473&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routesgold 11 citations 11 popularity Top 10% influence Average impulse Top 10% Powered by BIP!
more_vert Applied Sciences arrow_drop_down Applied SciencesOther literature type . 2022License: CC BYFull-Text: http://www.mdpi.com/2076-3417/12/13/6473/pdfData sources: Multidisciplinary Digital Publishing Instituteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/app12136473&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2022Publisher:Elsevier BV Authors:Hanaa Salem;
Ibrahim M. El-Hasnony;Hanaa Salem
Hanaa Salem in OpenAIREA.E. Kabeel;
Emad M.S. El-Said; +1 AuthorsA.E. Kabeel
A.E. Kabeel in OpenAIREHanaa Salem;
Ibrahim M. El-Hasnony;Hanaa Salem
Hanaa Salem in OpenAIREA.E. Kabeel;
Emad M.S. El-Said;A.E. Kabeel
A.E. Kabeel in OpenAIREOmar M. Elzeki;
Omar M. Elzeki
Omar M. Elzeki in OpenAIRERecently, the scientific community has become more interested in solar-driven steam materials and systems for desalination and disinfection. Solar thermal energy for membrane distillation desalination provides a green and sustainable option for building settings where there is a strong connection between water constraint and high solar irradiation. Artificial intelligence (AI) is rapidly being used to optimize water treatments and saltwater desalination because of its high precision and accuracy. Explainable AI (XAI) enables people to better understand and trust a model's predictions and to detect and rectify inaccurate AI predictions. This study analyses recent advances in solar-driven steam materials engineering and the significant technological constraints that impede its wide-scale deployment. Using local interpretable model-agnostic explanations (LIME), our study provides an interpretable solution (in addition to the binary classification result of the developed black-box deep learning (DL) model) so that experts can understand why the machine thinks this way, providing critical insights for the decision-making process. The proposed XAI-DL model is based on a DL network consisting of three cascaded convolutional blocks for processing tabular data. Therefore, the XAI-DL classification model achieves a cooling quality accuracy of 82.64% during the validation stage, supporting the explaining capability. During the testing, the [inlet-cooling-water-temperature] pushes the model lower, whereas the [ambient-temperature], [feed-water-flow-rate], and the [inlet-feed-water-temperature] pushes the model higher. The LIME explanation result is consistent with the statistical analysis of the data. Consequently, the proposed explainer assists non-experts in comparing and improving the untrustworthy model through and clarifies the importance of each feature and its relationship to other features and its relationship to the class. Finally, the XAI-DL fits and supports the different manufacturers of membrane desalination system(s) to inspect cooling quality in their designed system and consistency of interpretability and trust.
Alexandria Engineeri... arrow_drop_down Alexandria Engineering JournalArticle . 2022 . Peer-reviewedLicense: CC BY NC NDData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.aej.2022.03.050&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routesgold 30 citations 30 popularity Top 10% influence Top 10% impulse Top 10% Powered by BIP!
more_vert Alexandria Engineeri... arrow_drop_down Alexandria Engineering JournalArticle . 2022 . Peer-reviewedLicense: CC BY NC NDData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.aej.2022.03.050&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2022Publisher:Elsevier BV Authors:Hanaa Salem;
Ibrahim M. El-Hasnony;Hanaa Salem
Hanaa Salem in OpenAIREA.E. Kabeel;
Emad M.S. El-Said; +1 AuthorsA.E. Kabeel
A.E. Kabeel in OpenAIREHanaa Salem;
Ibrahim M. El-Hasnony;Hanaa Salem
Hanaa Salem in OpenAIREA.E. Kabeel;
Emad M.S. El-Said;A.E. Kabeel
A.E. Kabeel in OpenAIREOmar M. Elzeki;
Omar M. Elzeki
Omar M. Elzeki in OpenAIRERecently, the scientific community has become more interested in solar-driven steam materials and systems for desalination and disinfection. Solar thermal energy for membrane distillation desalination provides a green and sustainable option for building settings where there is a strong connection between water constraint and high solar irradiation. Artificial intelligence (AI) is rapidly being used to optimize water treatments and saltwater desalination because of its high precision and accuracy. Explainable AI (XAI) enables people to better understand and trust a model's predictions and to detect and rectify inaccurate AI predictions. This study analyses recent advances in solar-driven steam materials engineering and the significant technological constraints that impede its wide-scale deployment. Using local interpretable model-agnostic explanations (LIME), our study provides an interpretable solution (in addition to the binary classification result of the developed black-box deep learning (DL) model) so that experts can understand why the machine thinks this way, providing critical insights for the decision-making process. The proposed XAI-DL model is based on a DL network consisting of three cascaded convolutional blocks for processing tabular data. Therefore, the XAI-DL classification model achieves a cooling quality accuracy of 82.64% during the validation stage, supporting the explaining capability. During the testing, the [inlet-cooling-water-temperature] pushes the model lower, whereas the [ambient-temperature], [feed-water-flow-rate], and the [inlet-feed-water-temperature] pushes the model higher. The LIME explanation result is consistent with the statistical analysis of the data. Consequently, the proposed explainer assists non-experts in comparing and improving the untrustworthy model through and clarifies the importance of each feature and its relationship to other features and its relationship to the class. Finally, the XAI-DL fits and supports the different manufacturers of membrane desalination system(s) to inspect cooling quality in their designed system and consistency of interpretability and trust.
Alexandria Engineeri... arrow_drop_down Alexandria Engineering JournalArticle . 2022 . Peer-reviewedLicense: CC BY NC NDData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.aej.2022.03.050&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routesgold 30 citations 30 popularity Top 10% influence Top 10% impulse Top 10% Powered by BIP!
more_vert Alexandria Engineeri... arrow_drop_down Alexandria Engineering JournalArticle . 2022 . Peer-reviewedLicense: CC BY NC NDData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.aej.2022.03.050&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu