Pub Date : 2023-01-01DOI: 10.5829/ije.2023.36.05b.10
M. Alaviyan, S. Shakibania, M. Mokmeli, S. Sheibani
{"title":"Full Characterization of Sarcheshmeh and Khatoon-Abad Copper Anode Slimes: Characterization Impact on the Decopperization Operation","authors":"M. Alaviyan, S. Shakibania, M. Mokmeli, S. Sheibani","doi":"10.5829/ije.2023.36.05b.10","DOIUrl":"https://doi.org/10.5829/ije.2023.36.05b.10","url":null,"abstract":"","PeriodicalId":14109,"journal":{"name":"International Journal of Engineering","volume":"1 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70898166","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 : 2023-01-01DOI: 10.5829/ije.2023.36.04a.11
M. Hosseini Nasab, A. Agah
{"title":"Mapping Hydrothermal Alteration Zones Associated with Copper Mineralization using ASTER Data: A Case Study from the Mirjaveh Area, Southeast Iran","authors":"M. Hosseini Nasab, A. Agah","doi":"10.5829/ije.2023.36.04a.11","DOIUrl":"https://doi.org/10.5829/ije.2023.36.04a.11","url":null,"abstract":"","PeriodicalId":14109,"journal":{"name":"International Journal of Engineering","volume":"1 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70898194","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 : 2023-01-01DOI: 10.5829/ije.2023.36.04a.13
F. Behnamfar, M. Almohammad-albakkar
{"title":"Development of Steel Yielding Seismic Dampers Used to Improve Seismic Performance of Structures: A Comprehensive Review","authors":"F. Behnamfar, M. Almohammad-albakkar","doi":"10.5829/ije.2023.36.04a.13","DOIUrl":"https://doi.org/10.5829/ije.2023.36.04a.13","url":null,"abstract":"","PeriodicalId":14109,"journal":{"name":"International Journal of Engineering","volume":"1 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70898260","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 : 2023-01-01DOI: 10.5829/ije.2023.36.04a.08
R. Khodadadi, G. Ardeshir, H. Grailu
{"title":"Compressing Face Images Using Genetic and Gray Wolf Meta-heuristic Algorithms Based on Variable Bit Allocation","authors":"R. Khodadadi, G. Ardeshir, H. Grailu","doi":"10.5829/ije.2023.36.04a.08","DOIUrl":"https://doi.org/10.5829/ije.2023.36.04a.08","url":null,"abstract":"","PeriodicalId":14109,"journal":{"name":"International Journal of Engineering","volume":"16 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70898304","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 : 2023-01-01DOI: 10.5829/ije.2023.36.06c.05
M. Hashempour, M. Kolahdoozan
{"title":"Numerical Modeling of Sediment-flow around Obstacle Inspired by Marine Sponges: Considering Body Configurations","authors":"M. Hashempour, M. Kolahdoozan","doi":"10.5829/ije.2023.36.06c.05","DOIUrl":"https://doi.org/10.5829/ije.2023.36.06c.05","url":null,"abstract":"","PeriodicalId":14109,"journal":{"name":"International Journal of Engineering","volume":"38 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70898393","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 : 2023-01-01DOI: 10.5829/ije.2023.36.06c.07
K. Gaurav, A. Kumar, P. Singh, A. Kumari, M. Kasar, T. Suryawanshi
Disease prediction of a human means predicting the probability of a patient’s disease after examining the combinations of the patient’s symptoms. Monitoring a patient's condition and health information at the initial examination can help doctors to treat a patient's condition effectively. This analysis in the medical industry would lead to a streamlined and expedited treatment of patients. The previous researchers have primarily emphasized machine learning models mainly Support Vector Machine (SVM), K-nearest neighbors (KNN)
{"title":"Human Disease Prediction using Machine Learning Techniques and Real-life Parameters","authors":"K. Gaurav, A. Kumar, P. Singh, A. Kumari, M. Kasar, T. Suryawanshi","doi":"10.5829/ije.2023.36.06c.07","DOIUrl":"https://doi.org/10.5829/ije.2023.36.06c.07","url":null,"abstract":"Disease prediction of a human means predicting the probability of a patient’s disease after examining the combinations of the patient’s symptoms. Monitoring a patient's condition and health information at the initial examination can help doctors to treat a patient's condition effectively. This analysis in the medical industry would lead to a streamlined and expedited treatment of patients. The previous researchers have primarily emphasized machine learning models mainly Support Vector Machine (SVM), K-nearest neighbors (KNN)","PeriodicalId":14109,"journal":{"name":"International Journal of Engineering","volume":"1 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70898415","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 : 2023-01-01DOI: 10.5829/ije.2023.36.07a.02
B. D. Bhavani, S. P. Challagulla, E. Noroozinejad Farsangi, I. Hossain, M. Manne
developed using artificial neural networks (ANNs). Following that, the suggested model is contrasted with the established relationships from the past research. The ANN model's coefficient of correlation ( 𝑅 ) was 0.97. Hence, using an ANN algorithm reduces the necessity of laborious and complex analysis.
{"title":"Enhancing Seismic Design of Non-structural Components Implementing Artificial Intelligence Approach: Predicting Component Dynamic Amplification Factors","authors":"B. D. Bhavani, S. P. Challagulla, E. Noroozinejad Farsangi, I. Hossain, M. Manne","doi":"10.5829/ije.2023.36.07a.02","DOIUrl":"https://doi.org/10.5829/ije.2023.36.07a.02","url":null,"abstract":"developed using artificial neural networks (ANNs). Following that, the suggested model is contrasted with the established relationships from the past research. The ANN model's coefficient of correlation ( 𝑅 ) was 0.97. Hence, using an ANN algorithm reduces the necessity of laborious and complex analysis.","PeriodicalId":14109,"journal":{"name":"International Journal of Engineering","volume":"1 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70898827","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 : 2023-01-01DOI: 10.5829/ije.2023.36.08b.13
A. Shahmandi, M. Ghazavi, K. Barkhordari, M. Hashemi
A series of large-scale laboratory model tests in a unit cell was performed to explore the behaviour of loose sandy soil due to improvement. An unreinforced and geogrid reinforced granular blanket, a single end-bearing stone column, and their combination were used for this purpose. Since the rupture of the geosynthetic reinforcement in the reinforced granular blanket has never been experimentally investigated. A novel method of installing the geogrid was used. Thus, geogrid was allowed to completely mobilize and fail under loads. In this investigation, load-settlement characteristics have been generated by continuing loading even after geogrid rupture until the desired settlement. Parametric studies were carried out to observe the effect of important factors, such as the blanket thickness and the layout of geosynthetic sheets, including the number and place of geogrid layers within the granular blanket. Reinforcing the blanket with geogrid while changing the usual form of the load-settlement characteristics has had a significant effect on enhancing load-carrying capacity and reducing settlement. It can be said using a stone column, granular blanket, or combination of both techniques to boost load-carrying capacity was more effective than reducing settlement. However, the effect of single-layer and double-layer geogrid reinforcement on settlement reduction depends on their placement within the granular blanket. In addition, the efficiency of improvement methods has been superior under looser bed conditions. The best layout was to arrange one layer of geogrid near the top of the blanket or two layers in the middle and near the top.
{"title":"The Effect of Using Reinforced Granular Blanket and Single Stone Column on Improvement of Sandy Soil: Experimental Study","authors":"A. Shahmandi, M. Ghazavi, K. Barkhordari, M. Hashemi","doi":"10.5829/ije.2023.36.08b.13","DOIUrl":"https://doi.org/10.5829/ije.2023.36.08b.13","url":null,"abstract":"A series of large-scale laboratory model tests in a unit cell was performed to explore the behaviour of loose sandy soil due to improvement. An unreinforced and geogrid reinforced granular blanket, a single end-bearing stone column, and their combination were used for this purpose. Since the rupture of the geosynthetic reinforcement in the reinforced granular blanket has never been experimentally investigated. A novel method of installing the geogrid was used. Thus, geogrid was allowed to completely mobilize and fail under loads. In this investigation, load-settlement characteristics have been generated by continuing loading even after geogrid rupture until the desired settlement. Parametric studies were carried out to observe the effect of important factors, such as the blanket thickness and the layout of geosynthetic sheets, including the number and place of geogrid layers within the granular blanket. Reinforcing the blanket with geogrid while changing the usual form of the load-settlement characteristics has had a significant effect on enhancing load-carrying capacity and reducing settlement. It can be said using a stone column, granular blanket, or combination of both techniques to boost load-carrying capacity was more effective than reducing settlement. However, the effect of single-layer and double-layer geogrid reinforcement on settlement reduction depends on their placement within the granular blanket. In addition, the efficiency of improvement methods has been superior under looser bed conditions. The best layout was to arrange one layer of geogrid near the top of the blanket or two layers in the middle and near the top.","PeriodicalId":14109,"journal":{"name":"International Journal of Engineering","volume":"1 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70899200","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 : 2023-01-01DOI: 10.5829/ije.2023.36.07a.16
S. J. Salehi, M. A. Shmasi-Nejad, H. Najafi
{"title":"A New Generalized Step-up Multilevel Inverter Topology Based on Combined T-type and Cross Capacitor Modules","authors":"S. J. Salehi, M. A. Shmasi-Nejad, H. Najafi","doi":"10.5829/ije.2023.36.07a.16","DOIUrl":"https://doi.org/10.5829/ije.2023.36.07a.16","url":null,"abstract":"","PeriodicalId":14109,"journal":{"name":"International Journal of Engineering","volume":"1 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70899293","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 : 2023-01-01DOI: 10.5829/ije.2023.36.08b.14
E. Charoqdouz, H. Hassanpour
Due to its non-interfering nature, face recognition has been the most suitable technology for designing biometric systems in recent years. This technology is used in various industries, such as health care, education, security, and surveillance. Facial recognition technology works best when a person is looking straight into the camera. On the contrary, the performance of facial recognition degrades when encountered with an angled facial image, because they are generally trained using images of a full face. The purpose of this paper is to estimate the feature vector of a full face image when there are several angular facial images of the same person, one example being angular faces in a video. This method extracts the basic features of a facial image using the non-negative matrix factorization (NMF) method. Then, the feature vectors are fused using a generative adversarial network (GAN) to estimate the feature vector associated with the frontal image. The experimental results on the angular images of the FERET dataset show that the proposed method can significantly improve the accuracy of facial recognition technology methods.
{"title":"Feature Extraction from Several Angular Faces Using a Deep Learning Based Fusion Technique for Face Recognition","authors":"E. Charoqdouz, H. Hassanpour","doi":"10.5829/ije.2023.36.08b.14","DOIUrl":"https://doi.org/10.5829/ije.2023.36.08b.14","url":null,"abstract":"Due to its non-interfering nature, face recognition has been the most suitable technology for designing biometric systems in recent years. This technology is used in various industries, such as health care, education, security, and surveillance. Facial recognition technology works best when a person is looking straight into the camera. On the contrary, the performance of facial recognition degrades when encountered with an angled facial image, because they are generally trained using images of a full face. The purpose of this paper is to estimate the feature vector of a full face image when there are several angular facial images of the same person, one example being angular faces in a video. This method extracts the basic features of a facial image using the non-negative matrix factorization (NMF) method. Then, the feature vectors are fused using a generative adversarial network (GAN) to estimate the feature vector associated with the frontal image. The experimental results on the angular images of the FERET dataset show that the proposed method can significantly improve the accuracy of facial recognition technology methods.","PeriodicalId":14109,"journal":{"name":"International Journal of Engineering","volume":"1 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70899344","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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