The proposed method seeks to assist Indian pleasant in selecting the optimum crop to produce based on the characteristics of the soil as well as external factors like temperature and rainfall by using an intelligent system called Crop Recommender. The Indian economy is significantly impacted by the agricultural sector. Whether publicly or covertly, the bulk of Indians are relying on agriculture for their living. As a result, it is undeniable that agriculture is significant to the country. The majority of Indian farmers believe that they should trust their intuition when deciding on a crop to grow in a particular season or they simply employ the methods they have been doing from the beginning of time. They are more at ease just adhering to conventional agricultural practices and standards than truly appreciating how crop yield is influenced by the present weather and soil conditions. The farmer can unintentionally lose money if he makes one bad decision, which would hurt both him and the surrounding agricultural industry. As the agriculture business is the foundation of the entire lateral system. Using the machine learning algorithm, this problem can be resolved. A crucial perspective for identifying a practical and workable solution to the crop production issue is machine learning (ML). Machine learning (ML) may predict a target or outcome from a set of predictors using supervised learning. A recommendation system is implemented using decision trees. The major goals of this system are to provide farmers with recommendations regarding the best crops to sow based on their soil and local rainfall patterns. We have employed the Random Forest Machine Learning technique to forecast the crop. Crop prediction is assessing the crop based on historical data from the past that includes elements like temperature, humidity, ph, and rainfall. It gives us a broad picture of the best crop that can be raised in light of the current field weather conditions. These predictions can be made by Random Forest, a machine learning technique. The highest level of accuracy, up to 90%, will be possible for crop predictions. The random forest algorithm achieved the accuracy about 99.03%.
{"title":"Random forest algorithm use for crop recommendation","authors":"Pradip Mukundrao Paithane","doi":"10.5935/jetia.v9i43.906","DOIUrl":"https://doi.org/10.5935/jetia.v9i43.906","url":null,"abstract":"The proposed method seeks to assist Indian pleasant in selecting the optimum crop to produce based on the characteristics of the soil as well as external factors like temperature and rainfall by using an intelligent system called Crop Recommender. The Indian economy is significantly impacted by the agricultural sector. Whether publicly or covertly, the bulk of Indians are relying on agriculture for their living. As a result, it is undeniable that agriculture is significant to the country. The majority of Indian farmers believe that they should trust their intuition when deciding on a crop to grow in a particular season or they simply employ the methods they have been doing from the beginning of time. They are more at ease just adhering to conventional agricultural practices and standards than truly appreciating how crop yield is influenced by the present weather and soil conditions. The farmer can unintentionally lose money if he makes one bad decision, which would hurt both him and the surrounding agricultural industry. As the agriculture business is the foundation of the entire lateral system. Using the machine learning algorithm, this problem can be resolved. A crucial perspective for identifying a practical and workable solution to the crop production issue is machine learning (ML). Machine learning (ML) may predict a target or outcome from a set of predictors using supervised learning. A recommendation system is implemented using decision trees. The major goals of this system are to provide farmers with recommendations regarding the best crops to sow based on their soil and local rainfall patterns. We have employed the Random Forest Machine Learning technique to forecast the crop. Crop prediction is assessing the crop based on historical data from the past that includes elements like temperature, humidity, ph, and rainfall. It gives us a broad picture of the best crop that can be raised in light of the current field weather conditions. These predictions can be made by Random Forest, a machine learning technique. The highest level of accuracy, up to 90%, will be possible for crop predictions. The random forest algorithm achieved the accuracy about 99.03%.","PeriodicalId":236176,"journal":{"name":"Journal of Engineering and Technology for Industrial Applications","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135262980","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}
A. Jenefa, Naveen V. Edward, Veemaraj Ebenezer, A. Lincy
Ovarian cancer remains a leading cause of cancer-related mortality among women worldwide. Traditional diagnostic methods often lack the precision required for early detection and accurate subtype classification. In this study, we address the challenge of automating ovarian cancer diagnosis by introducing Attention-Based Models (ABMs) in combination with 3D Convolutional Neural Networks (CNNs). Our research seeks to enhance the accuracy and efficiency of ovarian cancer diagnosis, particularly in distinguishing between serous, mucinous, and endometrioid subtypes. Conventional diagnostic approaches are limited by their reliance on manual interpretation of medical images and fail to fully exploit the rich information present in MRI scans. The proposed work leverages ABMs to dynamically focus on critical regions in MRI scans, enabling enhanced feature extraction and improved classification accuracy. We demonstrate our approach on a well-curated dataset, OvaCancerMRI-2023, showcasing the potential for precise and automated diagnosis. Experimental results indicate superior performance in cancer subtype classification compared to traditional methods, with an accuracy of 94% and F1 score of 0.92. Our findings underscore the potential of ABMs and 3D CNNs in revolutionizing ovarian cancer diagnosis, paving the way for early intervention and more effective treatment strategies. In conclusion, this research marks a significant advancement in the realm of ovarian cancer diagnosis, offering a promising avenue for improving patient outcomes and reducing the burden of this devastating disease. The integration of ABMs and 3D CNNs holds substantial potential for enhancing the accuracy and efficiency of ovarian cancer diagnosis, particularly in subtyping, and may contribute to early intervention and improved patient care.
{"title":"ABM-OCD: Advancing ovarian cancer diagnosis with attention-based models and 3D CNNs","authors":"A. Jenefa, Naveen V. Edward, Veemaraj Ebenezer, A. Lincy","doi":"10.5935/jetia.v9i43.904","DOIUrl":"https://doi.org/10.5935/jetia.v9i43.904","url":null,"abstract":"Ovarian cancer remains a leading cause of cancer-related mortality among women worldwide. Traditional diagnostic methods often lack the precision required for early detection and accurate subtype classification. In this study, we address the challenge of automating ovarian cancer diagnosis by introducing Attention-Based Models (ABMs) in combination with 3D Convolutional Neural Networks (CNNs). Our research seeks to enhance the accuracy and efficiency of ovarian cancer diagnosis, particularly in distinguishing between serous, mucinous, and endometrioid subtypes. Conventional diagnostic approaches are limited by their reliance on manual interpretation of medical images and fail to fully exploit the rich information present in MRI scans. The proposed work leverages ABMs to dynamically focus on critical regions in MRI scans, enabling enhanced feature extraction and improved classification accuracy. We demonstrate our approach on a well-curated dataset, OvaCancerMRI-2023, showcasing the potential for precise and automated diagnosis. Experimental results indicate superior performance in cancer subtype classification compared to traditional methods, with an accuracy of 94% and F1 score of 0.92. Our findings underscore the potential of ABMs and 3D CNNs in revolutionizing ovarian cancer diagnosis, paving the way for early intervention and more effective treatment strategies. In conclusion, this research marks a significant advancement in the realm of ovarian cancer diagnosis, offering a promising avenue for improving patient outcomes and reducing the burden of this devastating disease. The integration of ABMs and 3D CNNs holds substantial potential for enhancing the accuracy and efficiency of ovarian cancer diagnosis, particularly in subtyping, and may contribute to early intervention and improved patient care.","PeriodicalId":236176,"journal":{"name":"Journal of Engineering and Technology for Industrial Applications","volume":"177 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135311315","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}
The primary function of a crude distillation unit (CDU) within a petroleum refinery is to effectively segregate crude oil into its constituent fractions or products based on their respective boiling points. The Crude Distillation column often serves as the primary processing unit within most refineries, pivotal in producing a wide range of refinery products. This study examines research articles published between 2013 and 2023 that specifically investigate issues related to crude distillation units. The research endeavours to produce innovative designs and construct mathematical models to enhance production efficiency within this context. The research primarily centres on developing a mathematical model that accurately characterizes the distillation tower. This is achieved using either an Artificial Neural Network or a nonlinear model predictive control approach. The primary objective of simulation and optimization research is identifying optimal operating conditions, typically employing software tools such as Aspen HYSYS or PRO II. The corrosion treatment outcomes conducted at the tower's upper section were satisfactory. The study focused on the issue of corrosion in the overhead lines and pumps around exchangers. This design research aims to investigate potential modifications to the distillation tower's design or preflash process to optimize production outcomes.
{"title":"A comprehensive analysis of the simulation, optimization, corrosion and design aspects of crude distillation units","authors":"Abdulrazzaq Saeed Abdullah, Hassan Wathiq Ayoob","doi":"10.5935/jetia.v9i43.894","DOIUrl":"https://doi.org/10.5935/jetia.v9i43.894","url":null,"abstract":"The primary function of a crude distillation unit (CDU) within a petroleum refinery is to effectively segregate crude oil into its constituent fractions or products based on their respective boiling points. The Crude Distillation column often serves as the primary processing unit within most refineries, pivotal in producing a wide range of refinery products. This study examines research articles published between 2013 and 2023 that specifically investigate issues related to crude distillation units. The research endeavours to produce innovative designs and construct mathematical models to enhance production efficiency within this context. The research primarily centres on developing a mathematical model that accurately characterizes the distillation tower. This is achieved using either an Artificial Neural Network or a nonlinear model predictive control approach. The primary objective of simulation and optimization research is identifying optimal operating conditions, typically employing software tools such as Aspen HYSYS or PRO II. The corrosion treatment outcomes conducted at the tower's upper section were satisfactory. The study focused on the issue of corrosion in the overhead lines and pumps around exchangers. This design research aims to investigate potential modifications to the distillation tower's design or preflash process to optimize production outcomes.","PeriodicalId":236176,"journal":{"name":"Journal of Engineering and Technology for Industrial Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135262982","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}
Dele Roger Simeon, Olatunji Joseph Oladiran, Ayomide O. Abatan, Rabiu A. Aminu
Like other types of buildings, shopping mall buildings in Nigeria receive insufficient maintenance attention. The vast majority of shopping malls exhibit awful structural and aesthetic conditions of deterioration. This study, therefore, aims to investigate the maintenance practices of shopping malls with a view to addressing issues that arise from factors responsible for the deterioration of the building fabrics and components. Data from 97 building maintenance stakeholders from Lagos Island and Mainland malls were gathered using a cross-sectional survey utilizing two sets of structured self-administered questionnaires. The results revealed 31 maintenance practices implemented in shopping malls. The study also uncovered 21 key factors influencing the sourcing decision of maintenance practices in shopping malls. Besides, the results further revealed 22 causative factors that lead to the deterioration of shopping mall building fabrics and components. The study comes to the conclusion that regardless of the sourcing decision, other factors, such as quality and frequency of maintenance, have a significant impact on how quickly a shopping mall deteriorates. It is recommended that maintenance stakeholders should play active roles in ensuring shopping malls are adequately maintained. This may be achieved by developing a defined strategy for routine and preventive maintenance.
{"title":"Appraising the maintenance practices in shopping malls across Lagos metropolis","authors":"Dele Roger Simeon, Olatunji Joseph Oladiran, Ayomide O. Abatan, Rabiu A. Aminu","doi":"10.5935/jetia.v9i43.884","DOIUrl":"https://doi.org/10.5935/jetia.v9i43.884","url":null,"abstract":"Like other types of buildings, shopping mall buildings in Nigeria receive insufficient maintenance attention. The vast majority of shopping malls exhibit awful structural and aesthetic conditions of deterioration. This study, therefore, aims to investigate the maintenance practices of shopping malls with a view to addressing issues that arise from factors responsible for the deterioration of the building fabrics and components. Data from 97 building maintenance stakeholders from Lagos Island and Mainland malls were gathered using a cross-sectional survey utilizing two sets of structured self-administered questionnaires. The results revealed 31 maintenance practices implemented in shopping malls. The study also uncovered 21 key factors influencing the sourcing decision of maintenance practices in shopping malls. Besides, the results further revealed 22 causative factors that lead to the deterioration of shopping mall building fabrics and components. The study comes to the conclusion that regardless of the sourcing decision, other factors, such as quality and frequency of maintenance, have a significant impact on how quickly a shopping mall deteriorates. It is recommended that maintenance stakeholders should play active roles in ensuring shopping malls are adequately maintained. This may be achieved by developing a defined strategy for routine and preventive maintenance.","PeriodicalId":236176,"journal":{"name":"Journal of Engineering and Technology for Industrial Applications","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135263124","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}
The objective of the current work is to apply Taguchi L9 orthogonal array to enhance the welding process factors for friction stir welding (FSW) of AA5083 aluminium alloy plates. Using a randomized procedure, the Taguchi orthogonal array was implemented to identify the FSW process parameters such as the rotating speed of the tool, welding speed
{"title":"Microstructural characterization of friction stir welded AA5083 aluminum alloy joints","authors":"G. Kathiresan, S. Ragunathan, M. P. Prabakaran","doi":"10.5935/jetia.v9i43.910","DOIUrl":"https://doi.org/10.5935/jetia.v9i43.910","url":null,"abstract":"The objective of the current work is to apply Taguchi L9 orthogonal array to enhance the welding process factors for friction stir welding (FSW) of AA5083 aluminium alloy plates. Using a randomized procedure, the Taguchi orthogonal array was implemented to identify the FSW process parameters such as the rotating speed of the tool, welding speed","PeriodicalId":236176,"journal":{"name":"Journal of Engineering and Technology for Industrial Applications","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135312421","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}
This article put forward the determination of the optimal siting and sizing of capacitor banks and PV-DG (Photo-Voltaic Distribution Generation) units in a radial distribution system. A modern population-based optimization algorithm, Hunter-Prey Optimization (HPO), is applied to determine the optimal capacitor bank and PV-DG placement. This algorithm, HPO, got its motivation from the trapping behaviour of the carnivore (predator/hunter) like lions and wolves towards their target animal like deer. The typical IEEE-33 & 69 test bus systems are scrutinized for validating the effectiveness of the suggested algorithm using MATLAB software R2021b version. The acquired results are collated with the existing heuristic algorithms for the active power loss criterion. The nominal or base values for system losses and voltage profile were considered for the comparison, with the results from HPO. The HPO application has an efficient performance in figuring out the most favourable location and capacity of the capacitor banks and PV DGs compared with the other techniques.
{"title":"A multi-objective hunter-prey optimization for optimal integration of capacitor banks and photovoltaic distribution generation units in radial distribution systems","authors":"Soundarya Lahari Pappu, Varaprasad Janamala","doi":"10.5935/jetia.v9i43.907","DOIUrl":"https://doi.org/10.5935/jetia.v9i43.907","url":null,"abstract":"This article put forward the determination of the optimal siting and sizing of capacitor banks and PV-DG (Photo-Voltaic Distribution Generation) units in a radial distribution system. A modern population-based optimization algorithm, Hunter-Prey Optimization (HPO), is applied to determine the optimal capacitor bank and PV-DG placement. This algorithm, HPO, got its motivation from the trapping behaviour of the carnivore (predator/hunter) like lions and wolves towards their target animal like deer. The typical IEEE-33 & 69 test bus systems are scrutinized for validating the effectiveness of the suggested algorithm using MATLAB software R2021b version. The acquired results are collated with the existing heuristic algorithms for the active power loss criterion. The nominal or base values for system losses and voltage profile were considered for the comparison, with the results from HPO. The HPO application has an efficient performance in figuring out the most favourable location and capacity of the capacitor banks and PV DGs compared with the other techniques.","PeriodicalId":236176,"journal":{"name":"Journal of Engineering and Technology for Industrial Applications","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135263128","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":"An innovative Dandelion Optimized Network Control (DONC) based effective energy management system for electric ships","authors":"Soundarya Lahari Pappu, Varaprasad Janamala","doi":"10.5935/jetia.v9i43.908","DOIUrl":"https://doi.org/10.5935/jetia.v9i43.908","url":null,"abstract":"ABSTRACT","PeriodicalId":236176,"journal":{"name":"Journal of Engineering and Technology for Industrial Applications","volume":"142 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135312364","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}
This article reviews the physical conditions that natural, turbulent flows meet to be considered in “Dynamic Equilibrium”, a condition that greatly facilitates the analysis of flows, thanks to the concept of “equiprobability”, in such a way that the tracer dyes can give an essential information of the dynamics of the current. A general State Function is proposed for this dynamic, which allows to study Advection and Dispersion for virtually all types of river beds, achieving a series of compact and precise relationships, both in hydraulics and thermodynamics. This approach allows us to obviate the limiting use of non-linear differential equations, as "mandatory" characterization of fluid dynamics. With this new method, a practical case from the technical literature is analyzed, and it is solved in detail, comparing it with the classic method of Statistical Moments. Conclusions on results, and recommendations are made.
{"title":"Dispersion and turbulence: A close relationship unveiled by means of state function","authors":"A. Constain, Gina Peña Olarte, C. Guzmán","doi":"10.5935/JETIA.V7I30.763","DOIUrl":"https://doi.org/10.5935/JETIA.V7I30.763","url":null,"abstract":"This article reviews the physical conditions that natural, turbulent flows meet to be considered in “Dynamic Equilibrium”, a condition that greatly facilitates the analysis of flows, thanks to the concept of “equiprobability”, in such a way that the tracer dyes can give an essential information of the dynamics of the current. A general State Function is proposed for this dynamic, which allows to study Advection and Dispersion for virtually all types of river beds, achieving a series of compact and precise relationships, both in hydraulics and thermodynamics. This approach allows us to obviate the limiting use of non-linear differential equations, as \"mandatory\" characterization of fluid dynamics. With this new method, a practical case from the technical literature is analyzed, and it is solved in detail, comparing it with the classic method of Statistical Moments. Conclusions on results, and recommendations are made.","PeriodicalId":236176,"journal":{"name":"Journal of Engineering and Technology for Industrial Applications","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125636608","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}
E. Dada, D. Oyewola, Joseph Hurcha Yakubu, A. Fadele
Protein structure prediction is very vital to innovative process of discovering new medications based on the knowledge of a biological target. It is also useful for scientifically exposing the biological basis of convoluted diseases and drug effects. Despite its usefulness, protein structure is very complex, thereby making its prediction to be arduous, timewasting and costly. These drawbacks necessitated the need to develop more effective techniques with high prediction capability. Conventional techniques for predicting protein structure are ineffective, perform poorly, expensive and slow. The reasons for these are due to the vague dissimilar sequences among protein structures, meaningless protein data, high dimensional data, and having to deal with highly imbalanced classification task. We proposed an Ensemble Neural Network learning model that consists of some Neural Network algorithms such as Feed Forward Neural Network (FFNN), Recurrent Neural Network (RNN), Cascade Forward Network (CFN) and Non-linear Autoregressive Network with Exogenous (NARX) models. These models were trained using training algorithms such as Levenberg-Marquardt (LM), Resilient Back Propagation (RBP) and Scaled Conjugate Gradient (SCG) to improve the performance. Experimental results show that our proposed model has superior performance compared to the other models compared.
{"title":"Predicting protein secondary structure based on ensemble Neural Network","authors":"E. Dada, D. Oyewola, Joseph Hurcha Yakubu, A. Fadele","doi":"10.5935/JETIA.V7I27.732","DOIUrl":"https://doi.org/10.5935/JETIA.V7I27.732","url":null,"abstract":"Protein structure prediction is very vital to innovative process of discovering new medications based on the knowledge of a biological target. It is also useful for scientifically exposing the biological basis of convoluted diseases and drug effects. Despite its usefulness, protein structure is very complex, thereby making its prediction to be arduous, timewasting and costly. These drawbacks necessitated the need to develop more effective techniques with high prediction capability. Conventional techniques for predicting protein structure are ineffective, perform poorly, expensive and slow. The reasons for these are due to the vague dissimilar sequences among protein structures, meaningless protein data, high dimensional data, and having to deal with highly imbalanced classification task. We proposed an Ensemble Neural Network learning model that consists of some Neural Network algorithms such as Feed Forward Neural Network (FFNN), Recurrent Neural Network (RNN), Cascade Forward Network (CFN) and Non-linear Autoregressive Network with Exogenous (NARX) models. These models were trained using training algorithms such as Levenberg-Marquardt (LM), Resilient Back Propagation (RBP) and Scaled Conjugate Gradient (SCG) to improve the performance. Experimental results show that our proposed model has superior performance compared to the other models compared.","PeriodicalId":236176,"journal":{"name":"Journal of Engineering and Technology for Industrial Applications","volume":"6 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120992986","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}
J. JencyRubia, R. BabithaLincy, Ahmed Thair Al-Heety
In the current scenario, Intelligent Transportation Systems play a significant role in smart city platform. Automatic moving vehicle detection from video sequences is the core component of the automated traffic management system. Humans can easily detect and recognize objects from complex scenes in a flash. Translating that thought process to a machine, however, requires us to learn the art of object detection using computer vision algorithms. This paper solves the traffic issues of the urban areas with an intelligent automatic transportation system. This paper includes automatic vehicle counting with the help of blob analysis, background subtraction with the use of a dynamic autoregressive moving average model, identify the moving objects with the help of a Boundary block detection algorithm, and tracking the vehicle. This paper analyses the procedure of a video-based traffic congestion system and divides it into greying, binarisation, de-nosing, and moving target detection. The investigational results show that the planned system can provide useful information for traffic surveillance.
{"title":"Moving vehicle detection from video sequences for Traffic Surveillance System","authors":"J. JencyRubia, R. BabithaLincy, Ahmed Thair Al-Heety","doi":"10.5935/JETIA.V7I27.731","DOIUrl":"https://doi.org/10.5935/JETIA.V7I27.731","url":null,"abstract":"In the current scenario, Intelligent Transportation Systems play a significant role in smart city platform. Automatic moving vehicle detection from video sequences is the core component of the automated traffic management system. Humans can easily detect and recognize objects from complex scenes in a flash. Translating that thought process to a machine, however, requires us to learn the art of object detection using computer vision algorithms. This paper solves the traffic issues of the urban areas with an intelligent automatic transportation system. This paper includes automatic vehicle counting with the help of blob analysis, background subtraction with the use of a dynamic autoregressive moving average model, identify the moving objects with the help of a Boundary block detection algorithm, and tracking the vehicle. This paper analyses the procedure of a video-based traffic congestion system and divides it into greying, binarisation, de-nosing, and moving target detection. The investigational results show that the planned system can provide useful information for traffic surveillance.","PeriodicalId":236176,"journal":{"name":"Journal of Engineering and Technology for Industrial Applications","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132313971","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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