{"title":"Rehabilitation of existing university buildings to reduce energy consumption through the application of sustainable design standards (Case study of the building of the Modern Academy of Engineering)","authors":"Mohamed Mahmoud Hassan","doi":"10.58491/2735-4202.3118","DOIUrl":"https://doi.org/10.58491/2735-4202.3118","url":null,"abstract":"","PeriodicalId":510600,"journal":{"name":"Mansoura Engineering Journal","volume":"56 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141112661","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}
Alzahraa H. Nosier, Ahmed M. Elkhawaga, Mohamed E. Nasr, Nessim M. Mahmoud, Amr H. Hussein
{"title":"Side Lobe Level Reduction and Array Thinning of Concentric Circular Antenna Arrays","authors":"Alzahraa H. Nosier, Ahmed M. Elkhawaga, Mohamed E. Nasr, Nessim M. Mahmoud, Amr H. Hussein","doi":"10.58491/2735-4202.3129","DOIUrl":"https://doi.org/10.58491/2735-4202.3129","url":null,"abstract":"","PeriodicalId":510600,"journal":{"name":"Mansoura Engineering Journal","volume":"67 43","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139534618","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}
Ahmed El-Emam, Hossam El-Din Moustafa, Mohamed Moawad, Mohamed Aouf
It has been noticed that three-phase and four-phase computed tomography protocols with contrast serve as standard examinations for diagnosing liver tumors. Additionally, many patients require periodic follow-up, which entails signi fi cant radiation exposure for them. Advancements in image processing facilitate automated liver lesion segmentation. However, the challenge remains in classifying these small lesions by doctors, especially when the liver has different types of lesions with very little intensity difference. Therefore, deep learning can be utilized for the classi fi cation of liver lesions. The present work introduces a CNN-based module for the classi fi cation of liver lesions. The module consists of four stages: data acquisition, preprocessing, modeling, and evaluating. The proposed system has achieved an accuracy of 96 and 97% for three-phase and four-phase protocols, respectively. Moreover it has been shown that the three-phase protocol outperforms the four-phase protocol, according to the dose report, with only a 1% loss of accuracy. However, this loss has not altered the multiclassi fi cation process. Thus, a three-phase protocol is recommended as a diagnostic tool for detecting focal liver lesions.
{"title":"Deep Learning Based Classification of Focal Liver Lesions with 3 and 4 Phase Contrast-Enhanced CT Protocols","authors":"Ahmed El-Emam, Hossam El-Din Moustafa, Mohamed Moawad, Mohamed Aouf","doi":"10.58491/2735-4202.3185","DOIUrl":"https://doi.org/10.58491/2735-4202.3185","url":null,"abstract":"It has been noticed that three-phase and four-phase computed tomography protocols with contrast serve as standard examinations for diagnosing liver tumors. Additionally, many patients require periodic follow-up, which entails signi fi cant radiation exposure for them. Advancements in image processing facilitate automated liver lesion segmentation. However, the challenge remains in classifying these small lesions by doctors, especially when the liver has different types of lesions with very little intensity difference. Therefore, deep learning can be utilized for the classi fi cation of liver lesions. The present work introduces a CNN-based module for the classi fi cation of liver lesions. The module consists of four stages: data acquisition, preprocessing, modeling, and evaluating. The proposed system has achieved an accuracy of 96 and 97% for three-phase and four-phase protocols, respectively. Moreover it has been shown that the three-phase protocol outperforms the four-phase protocol, according to the dose report, with only a 1% loss of accuracy. However, this loss has not altered the multiclassi fi cation process. Thus, a three-phase protocol is recommended as a diagnostic tool for detecting focal liver lesions.","PeriodicalId":510600,"journal":{"name":"Mansoura Engineering Journal","volume":"13 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140519810","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}
Khaled Megahed, Nabil Said Mahmoud, Saad Elden Mostafa Abd-Rabou
{"title":"Application of machine learning models in the capacity prediction of CCFST columns","authors":"Khaled Megahed, Nabil Said Mahmoud, Saad Elden Mostafa Abd-Rabou","doi":"10.58491/2735-4202.3178","DOIUrl":"https://doi.org/10.58491/2735-4202.3178","url":null,"abstract":"","PeriodicalId":510600,"journal":{"name":"Mansoura Engineering Journal","volume":"18 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140527065","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}
{"title":"Effect of the Shape and Volume of Capsulated Phase Change Materialon the Melting Process and Exergy Efficiency","authors":"A. S. Soliman, Ahmed A. Sultan, Mohamed A. Sultan","doi":"10.58491/2735-4202.3182","DOIUrl":"https://doi.org/10.58491/2735-4202.3182","url":null,"abstract":"","PeriodicalId":510600,"journal":{"name":"Mansoura Engineering Journal","volume":"34 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140522144","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}
Ahmed M. Radwan, A. Haikal, Hisham E. Gad, Mohamed M. Abdelsalam
The lane detecting algorithm plays a major role in advanced driver assistance systems and autonomous driving systems. In recent years, deep learning-based lane detection techniques have shown encouraging results; nonetheless, the quality and size of the training data set have a signi fi cant impact on how effective these techniques are. Active learning is a technique that can improve the capacity of deep learning-based lane identi fi cation systems to repeatedly choose and classify valuable samples from a large body of unlabeled data. In this research, a novel 1-dimensional deep learning approach is used to present an augmented Active Learning based Lane Detection Algorithm (ALDA) that picks informative samples based on diversity-and uncertainty-based criteria. Several benchmark datasets, including the CUlane, have been used to assess the suggested technique, In terms of accuracy and robustness, the suggested method ALDA performs better than four cutting-edge lane-detecting algorithms. The fi ndings show that active learning can signi fi cantly reduce the quantity of labeled data required for training while preserving good performance. The suggested method may improve the dependability and security of advanced driver assistance systems and autonomous driving systems. When compared with other distinct Deep Learning approaches, the proposed ALDA obtains an accuracy of 98.01 %, Precision of 98.5173 %, Recall of 95.2296 %, F1 score of 96.845 %, mAP of 92.7 %, and MSE of 0.0097.
{"title":"An Enhanced Lane Detection Technique based on Active Learning","authors":"Ahmed M. Radwan, A. Haikal, Hisham E. Gad, Mohamed M. Abdelsalam","doi":"10.58491/2735-4202.3183","DOIUrl":"https://doi.org/10.58491/2735-4202.3183","url":null,"abstract":"The lane detecting algorithm plays a major role in advanced driver assistance systems and autonomous driving systems. In recent years, deep learning-based lane detection techniques have shown encouraging results; nonetheless, the quality and size of the training data set have a signi fi cant impact on how effective these techniques are. Active learning is a technique that can improve the capacity of deep learning-based lane identi fi cation systems to repeatedly choose and classify valuable samples from a large body of unlabeled data. In this research, a novel 1-dimensional deep learning approach is used to present an augmented Active Learning based Lane Detection Algorithm (ALDA) that picks informative samples based on diversity-and uncertainty-based criteria. Several benchmark datasets, including the CUlane, have been used to assess the suggested technique, In terms of accuracy and robustness, the suggested method ALDA performs better than four cutting-edge lane-detecting algorithms. The fi ndings show that active learning can signi fi cantly reduce the quantity of labeled data required for training while preserving good performance. The suggested method may improve the dependability and security of advanced driver assistance systems and autonomous driving systems. When compared with other distinct Deep Learning approaches, the proposed ALDA obtains an accuracy of 98.01 %, Precision of 98.5173 %, Recall of 95.2296 %, F1 score of 96.845 %, mAP of 92.7 %, and MSE of 0.0097.","PeriodicalId":510600,"journal":{"name":"Mansoura Engineering Journal","volume":"26 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140526748","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}
Ahmed N. Awaad, S. El-Badawy, Elsayed Abd-Elazem Shwaly, Usama Shahdah
{"title":"Estimation of Origin-Destination Matrix Using Open-Source Applications Based on Traffic Counts for Mansoura City in Egypt","authors":"Ahmed N. Awaad, S. El-Badawy, Elsayed Abd-Elazem Shwaly, Usama Shahdah","doi":"10.58491/2735-4202.3193","DOIUrl":"https://doi.org/10.58491/2735-4202.3193","url":null,"abstract":"","PeriodicalId":510600,"journal":{"name":"Mansoura Engineering Journal","volume":"21 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140517584","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}
Ahmed AbdelMonteleb M. Ali, Essam S. Almahmoud, Basem O. Elgendy, Azzam I. Aljutayli, Meshal A. Alhrabi, Khattab I. Alyahya
{"title":"Study the application of different building technology systems Case study: A residential villa in Al-Qassim region, KSA","authors":"Ahmed AbdelMonteleb M. Ali, Essam S. Almahmoud, Basem O. Elgendy, Azzam I. Aljutayli, Meshal A. Alhrabi, Khattab I. Alyahya","doi":"10.58491/2735-4202.3188","DOIUrl":"https://doi.org/10.58491/2735-4202.3188","url":null,"abstract":"","PeriodicalId":510600,"journal":{"name":"Mansoura Engineering Journal","volume":"67 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140520637","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}
{"title":"A Novel Method for the Production of Pig Iron in “Hadisolb” Company (Industrial-Scale Experiments)","authors":"El-Sayed A. Rassoul","doi":"10.58491/2735-4202.3198","DOIUrl":"https://doi.org/10.58491/2735-4202.3198","url":null,"abstract":"","PeriodicalId":510600,"journal":{"name":"Mansoura Engineering Journal","volume":"12 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140518415","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}
Zain Abu Shaeer Abu Shaeer, Ramy I. Shahin, G. I. El–Baghdady, S. Yehia
{"title":"Numerical and Analytical Solution for Nonlinear Free Vibration of Tapered Beams","authors":"Zain Abu Shaeer Abu Shaeer, Ramy I. Shahin, G. I. El–Baghdady, S. Yehia","doi":"10.58491/2735-4202.3179","DOIUrl":"https://doi.org/10.58491/2735-4202.3179","url":null,"abstract":"","PeriodicalId":510600,"journal":{"name":"Mansoura Engineering Journal","volume":"18 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140519731","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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