Pub Date : 2023-03-17DOI: 10.1109/iCoMET57998.2023.10099152
Muhammad Sarim Amir, Gufran Bhatti, Misbah Anwer, Yumna Iftikhar
Everything is evolving toward IoT (Internet of Things) and online-based in our technological environment. The number of IoT devices and ubiquitous computing systems are growing exponentially. This also increases the risk of network breach. To cater this issue many researchers proposed different techniques and get great results but it can be better since everything in online and it's a matter of security and privacy. This paper presents an efficient and sustainable intrusion detection system by the concatenation of two well-known state of the art “kitsune” datasets (ARP MITM and SSDP Flood). Random Forest, decision tree, and Bi-LSTM (Bi-Directional Long Short Term Memory) were implemented in different training and testing ratios and different numbers of layers. Performance measures show that all the models achieved over 99% accuracy but random forest outperforms both models on the concatenated dataset. Both attacks are determined by the given model hence increasing the performance and the system will notify in case of any malicious activity.
{"title":"Efficient & Sustainable Intrusion Detection System Using Machine Learning & Deep Learning for IoT","authors":"Muhammad Sarim Amir, Gufran Bhatti, Misbah Anwer, Yumna Iftikhar","doi":"10.1109/iCoMET57998.2023.10099152","DOIUrl":"https://doi.org/10.1109/iCoMET57998.2023.10099152","url":null,"abstract":"Everything is evolving toward IoT (Internet of Things) and online-based in our technological environment. The number of IoT devices and ubiquitous computing systems are growing exponentially. This also increases the risk of network breach. To cater this issue many researchers proposed different techniques and get great results but it can be better since everything in online and it's a matter of security and privacy. This paper presents an efficient and sustainable intrusion detection system by the concatenation of two well-known state of the art “kitsune” datasets (ARP MITM and SSDP Flood). Random Forest, decision tree, and Bi-LSTM (Bi-Directional Long Short Term Memory) were implemented in different training and testing ratios and different numbers of layers. Performance measures show that all the models achieved over 99% accuracy but random forest outperforms both models on the concatenated dataset. Both attacks are determined by the given model hence increasing the performance and the system will notify in case of any malicious activity.","PeriodicalId":369792,"journal":{"name":"2023 4th International Conference on Computing, Mathematics and Engineering Technologies (iCoMET)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127993839","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 : 2023-03-17DOI: 10.1109/iCoMET57998.2023.10099192
Muhammad Sohaib, Hamza Shaukat, T. Tauqeer, Arslan Shahid, Usman Younis, Rehan Hafiz
Induction motors comprise more than 90 percent of the industrial load. With time, Induction motors are prone to losses. To save energy consumption, predictive maintenance of motors must be carried out at regular intervals. The industrial monitoring and automation lab has developed state-of-the-art motor test bench facility which is completely automated using LabView - a widely used industrial software. The manual methods of motor testing are not only hectic but also unreliable. A systematic approach has been adopted to measure and analyze various parameters of the induction motor, which will help us to identify key performance factors. This work is towards the development of a high performance motor test bench facility for industrial load. In its current state it can measure up to 15 hp induction motors and perform the tests such as No-Load Test, Full-Load Test, Locked-Rotor Test, Temperature Rise Test, DC Winding Test etc.
{"title":"LabView based Automated Motor Test Bench for Induction Motors","authors":"Muhammad Sohaib, Hamza Shaukat, T. Tauqeer, Arslan Shahid, Usman Younis, Rehan Hafiz","doi":"10.1109/iCoMET57998.2023.10099192","DOIUrl":"https://doi.org/10.1109/iCoMET57998.2023.10099192","url":null,"abstract":"Induction motors comprise more than 90 percent of the industrial load. With time, Induction motors are prone to losses. To save energy consumption, predictive maintenance of motors must be carried out at regular intervals. The industrial monitoring and automation lab has developed state-of-the-art motor test bench facility which is completely automated using LabView - a widely used industrial software. The manual methods of motor testing are not only hectic but also unreliable. A systematic approach has been adopted to measure and analyze various parameters of the induction motor, which will help us to identify key performance factors. This work is towards the development of a high performance motor test bench facility for industrial load. In its current state it can measure up to 15 hp induction motors and perform the tests such as No-Load Test, Full-Load Test, Locked-Rotor Test, Temperature Rise Test, DC Winding Test etc.","PeriodicalId":369792,"journal":{"name":"2023 4th International Conference on Computing, Mathematics and Engineering Technologies (iCoMET)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121411871","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 : 2023-03-17DOI: 10.1109/iCoMET57998.2023.10099357
Gul Zaman Khan, Ibrar Ali Shah, Farhatullah, Muhammad Ikram Ullah, Inam Ullah, Muhammad Ihtesham, Hazrat Junaid, Spogmay Yousafzai, Fouzia Sardar
Lung cancer illness seriously impacts people's health. Medical history-based detection of lung cancers has been utilized but it has unsatisfactory results. Artificial intelligence algorithms are more precise and efficient in classifying lung cancer patients and healthy persons. Additionally, the medical history-based diagnosis of lung cancer disease is costly and time consuming. The life of lung cancer disease is very short after detection. Artificial intelligence-based diagnosis systems can detect the lung cancer disease early and efficiently. However, previous research work as several limitations, for example, some techniques computation time is very high but their accuracy is good while some techniques have less computation time but accuracy is not good. The proposed work suggests a deep convolutional neural network-based diagnosis system for lung cancer disease early and accurate detection. We made use of publically available dataset downloaded from Kaggle online repository and applied deep convolutional neural network for accurate lung cancer detection. Furthermore, we have applied some preprocessing and features selection techniques such as max, min, standard deviation and variance threshold. The proposed CNN model achieved 99.2% validation accuracy, 99.8% training accuracy, 99% precision, and 99% recall in minimum computation time of 6 sec which is acceptable.
{"title":"An Efficient Deep Learning Model based Diagnosis System for Lung Cancer Disease","authors":"Gul Zaman Khan, Ibrar Ali Shah, Farhatullah, Muhammad Ikram Ullah, Inam Ullah, Muhammad Ihtesham, Hazrat Junaid, Spogmay Yousafzai, Fouzia Sardar","doi":"10.1109/iCoMET57998.2023.10099357","DOIUrl":"https://doi.org/10.1109/iCoMET57998.2023.10099357","url":null,"abstract":"Lung cancer illness seriously impacts people's health. Medical history-based detection of lung cancers has been utilized but it has unsatisfactory results. Artificial intelligence algorithms are more precise and efficient in classifying lung cancer patients and healthy persons. Additionally, the medical history-based diagnosis of lung cancer disease is costly and time consuming. The life of lung cancer disease is very short after detection. Artificial intelligence-based diagnosis systems can detect the lung cancer disease early and efficiently. However, previous research work as several limitations, for example, some techniques computation time is very high but their accuracy is good while some techniques have less computation time but accuracy is not good. The proposed work suggests a deep convolutional neural network-based diagnosis system for lung cancer disease early and accurate detection. We made use of publically available dataset downloaded from Kaggle online repository and applied deep convolutional neural network for accurate lung cancer detection. Furthermore, we have applied some preprocessing and features selection techniques such as max, min, standard deviation and variance threshold. The proposed CNN model achieved 99.2% validation accuracy, 99.8% training accuracy, 99% precision, and 99% recall in minimum computation time of 6 sec which is acceptable.","PeriodicalId":369792,"journal":{"name":"2023 4th International Conference on Computing, Mathematics and Engineering Technologies (iCoMET)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126282245","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 : 2023-03-17DOI: 10.1109/iCoMET57998.2023.10099183
Arjeton Uzairi, Arianit Kurti, Zenun Kastrati
Labeling of trademark images with Vienna codes from the Vienna classification is a manual process carried out by domain experts by searching trademark image databases using specific keywords. Manual labeling is both a time-consuming and error-prone process. Therefore, in this paper, we investigate how deep learning techniques can improve and automate labeling of new unlabeled trademark images. Three different deep learning models, namely CNN, LSTM and GRU, are trained and tested on a collected dataset composed of 14,500 unique logos extracted from the European Union Intellectual Property Office Open Data Portal. A set of controlled experiments establishing baseline results on the dataset showed that CNN outperforms the other two models in terms of both accuracy and training time. The experimental results also suggest that deep learning models are an important tool that can be applied by Intellectual Property Offices in real-world applications to automate the trademark image classification task.
{"title":"A Deep Learning-based Solution for Identification of Figurative Elements in Trademark Images","authors":"Arjeton Uzairi, Arianit Kurti, Zenun Kastrati","doi":"10.1109/iCoMET57998.2023.10099183","DOIUrl":"https://doi.org/10.1109/iCoMET57998.2023.10099183","url":null,"abstract":"Labeling of trademark images with Vienna codes from the Vienna classification is a manual process carried out by domain experts by searching trademark image databases using specific keywords. Manual labeling is both a time-consuming and error-prone process. Therefore, in this paper, we investigate how deep learning techniques can improve and automate labeling of new unlabeled trademark images. Three different deep learning models, namely CNN, LSTM and GRU, are trained and tested on a collected dataset composed of 14,500 unique logos extracted from the European Union Intellectual Property Office Open Data Portal. A set of controlled experiments establishing baseline results on the dataset showed that CNN outperforms the other two models in terms of both accuracy and training time. The experimental results also suggest that deep learning models are an important tool that can be applied by Intellectual Property Offices in real-world applications to automate the trademark image classification task.","PeriodicalId":369792,"journal":{"name":"2023 4th International Conference on Computing, Mathematics and Engineering Technologies (iCoMET)","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125670524","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 : 2023-03-17DOI: 10.1109/iCoMET57998.2023.10099312
M. Rafiq, Muhammad Sarwar Ehsan, Asfar Nisar, Samra Abbas
In this manuscript, a mathematical model of a rotavirus infection integrating the vaccinated class is numerically analyzed. Efficient numerical analysis of an epidemic model includes three main features positivity, boundedness, and dynamical consistency. These characteristics have been observed by using various numerical techniques. Standard finite difference scheme, Euler's, RK-4 is widely used to solve non-linear mathematical models. Unfortunately, these schemes have some limitations and do not preserve the essential features of the mathematical model. A competitive non-standard finite difference (NSFD) scheme is proposed to discuss the dynamics of rotavirus in a population. The proposed scheme exhibits the true behavior of the rotavirus disease and shows a good agreement with the theoretical findings. Moreover, the impact of vaccines on the rotavirus dynamics has also been studied.
{"title":"Computationally Efficient Numerical Analysis of Rotavirus Epidemic Model","authors":"M. Rafiq, Muhammad Sarwar Ehsan, Asfar Nisar, Samra Abbas","doi":"10.1109/iCoMET57998.2023.10099312","DOIUrl":"https://doi.org/10.1109/iCoMET57998.2023.10099312","url":null,"abstract":"In this manuscript, a mathematical model of a rotavirus infection integrating the vaccinated class is numerically analyzed. Efficient numerical analysis of an epidemic model includes three main features positivity, boundedness, and dynamical consistency. These characteristics have been observed by using various numerical techniques. Standard finite difference scheme, Euler's, RK-4 is widely used to solve non-linear mathematical models. Unfortunately, these schemes have some limitations and do not preserve the essential features of the mathematical model. A competitive non-standard finite difference (NSFD) scheme is proposed to discuss the dynamics of rotavirus in a population. The proposed scheme exhibits the true behavior of the rotavirus disease and shows a good agreement with the theoretical findings. Moreover, the impact of vaccines on the rotavirus dynamics has also been studied.","PeriodicalId":369792,"journal":{"name":"2023 4th International Conference on Computing, Mathematics and Engineering Technologies (iCoMET)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131654511","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 : 2023-03-17DOI: 10.1109/iCoMET57998.2023.10099300
Faisal Nawab, A. Ibrahim, Shaikh Zeeshan Suheel, Adamu Ahmed Goje
The most important factor to take into account when building solar energy systems is solar irradiation. It is impossible to measure sun irradiation everywhere due to its high cost and difficulties. Additionally, in some places, the GHI was overpredicted by 25% by NASA satellite data. The main goal of this study was to develop an artificial neural network (ANN) model that can reduce the error in satellite data by predicting global horizontal irradiation (GHI) using inputs from satellite data obtained from the NASA Power Data viewer. The MAPE in the satellite was decreased by 35.8% in Peshawar, 10.2% in Islamabad, and 8.9% in Multan using the ANN models. Additionally, the results showed that all ANN models' predictions were more precise than satellite data.
{"title":"Comparison of ANN Global Horizontal Irradiation predictions with Satellite Global Horizontal Irradiation using Statistical evaluation","authors":"Faisal Nawab, A. Ibrahim, Shaikh Zeeshan Suheel, Adamu Ahmed Goje","doi":"10.1109/iCoMET57998.2023.10099300","DOIUrl":"https://doi.org/10.1109/iCoMET57998.2023.10099300","url":null,"abstract":"The most important factor to take into account when building solar energy systems is solar irradiation. It is impossible to measure sun irradiation everywhere due to its high cost and difficulties. Additionally, in some places, the GHI was overpredicted by 25% by NASA satellite data. The main goal of this study was to develop an artificial neural network (ANN) model that can reduce the error in satellite data by predicting global horizontal irradiation (GHI) using inputs from satellite data obtained from the NASA Power Data viewer. The MAPE in the satellite was decreased by 35.8% in Peshawar, 10.2% in Islamabad, and 8.9% in Multan using the ANN models. Additionally, the results showed that all ANN models' predictions were more precise than satellite data.","PeriodicalId":369792,"journal":{"name":"2023 4th International Conference on Computing, Mathematics and Engineering Technologies (iCoMET)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128216388","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 : 2023-03-17DOI: 10.1109/iCoMET57998.2023.10099318
N. Ahmed, M. Nasir, Muhammad Arslan Saleem, Salman Murtaza, Shaheer Abdullah, Ejaz Kamal
Brisk increase in the demand of hydrocarbon-based fuels to generate electricity is contaminating the environment, which is increasing the quest for pure and clean resources for electricity generation. Wind energy is one of the highly used renewable energy resources (RES) in the new millennium. Therefore, a lot of research efforts are conducted in the last few years concerning the forecasting, sizing, and control of wind energy (WE). However, the present body of knowledge is still lacking the robust and intelligent control of small-scale wind energy. Therefore, the current paper is presenting artificial neural networks (ANN) based intelligent control of permanent magnet DC generator (PMDC) followed by the DC/DC converter to provide the stable DC voltage at the output side. The simulation model comprises of a small-scale wind generator system of 10KW rating which is developed in MATLAB-Simulink, and it is observed that the proposed control method is resulting in a balanced and smooth DC output.
{"title":"Artificial neural network based control of wind powered small scale DC generator","authors":"N. Ahmed, M. Nasir, Muhammad Arslan Saleem, Salman Murtaza, Shaheer Abdullah, Ejaz Kamal","doi":"10.1109/iCoMET57998.2023.10099318","DOIUrl":"https://doi.org/10.1109/iCoMET57998.2023.10099318","url":null,"abstract":"Brisk increase in the demand of hydrocarbon-based fuels to generate electricity is contaminating the environment, which is increasing the quest for pure and clean resources for electricity generation. Wind energy is one of the highly used renewable energy resources (RES) in the new millennium. Therefore, a lot of research efforts are conducted in the last few years concerning the forecasting, sizing, and control of wind energy (WE). However, the present body of knowledge is still lacking the robust and intelligent control of small-scale wind energy. Therefore, the current paper is presenting artificial neural networks (ANN) based intelligent control of permanent magnet DC generator (PMDC) followed by the DC/DC converter to provide the stable DC voltage at the output side. The simulation model comprises of a small-scale wind generator system of 10KW rating which is developed in MATLAB-Simulink, and it is observed that the proposed control method is resulting in a balanced and smooth DC output.","PeriodicalId":369792,"journal":{"name":"2023 4th International Conference on Computing, Mathematics and Engineering Technologies (iCoMET)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125737315","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 : 2023-03-17DOI: 10.1109/icomet57998.2023.10099129
{"title":"Sustainable Technologies for Socio-Economic Development","authors":"","doi":"10.1109/icomet57998.2023.10099129","DOIUrl":"https://doi.org/10.1109/icomet57998.2023.10099129","url":null,"abstract":"","PeriodicalId":369792,"journal":{"name":"2023 4th International Conference on Computing, Mathematics and Engineering Technologies (iCoMET)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126200085","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 : 2023-03-17DOI: 10.1109/iCoMET57998.2023.10099377
A. R. Shah, Isma Javed, Usman Shams, Muhammad Asif Naverd, M. Q. Mehmood
Human blood scrutinization is an indispensable step to analyze a particular health condition, comprise of a complete blood cell (CBC) count. CBC accentuates the counting of White blood cells (WBCs), red blood cells (RBCs), and Platelets which are implicitly significant for the analysis of severe maladies such as leukemia, thrombocytopenia, and anemia. Traditional approaches like manual counting and automated analyzer were extensively used, which is monotonous, time intensive, and entail a lot of medical experts. To get rid of aforesaid leisure techniques, here by using a machine learning-based object detection and classification algorithm you only look once (YOLO) to count the blood cells. YOLO with modified configuration has been trained on the customized dataset to detect the WBCs, RBCs, and platelets.
{"title":"Disease estimation using robust AI methods","authors":"A. R. Shah, Isma Javed, Usman Shams, Muhammad Asif Naverd, M. Q. Mehmood","doi":"10.1109/iCoMET57998.2023.10099377","DOIUrl":"https://doi.org/10.1109/iCoMET57998.2023.10099377","url":null,"abstract":"Human blood scrutinization is an indispensable step to analyze a particular health condition, comprise of a complete blood cell (CBC) count. CBC accentuates the counting of White blood cells (WBCs), red blood cells (RBCs), and Platelets which are implicitly significant for the analysis of severe maladies such as leukemia, thrombocytopenia, and anemia. Traditional approaches like manual counting and automated analyzer were extensively used, which is monotonous, time intensive, and entail a lot of medical experts. To get rid of aforesaid leisure techniques, here by using a machine learning-based object detection and classification algorithm you only look once (YOLO) to count the blood cells. YOLO with modified configuration has been trained on the customized dataset to detect the WBCs, RBCs, and platelets.","PeriodicalId":369792,"journal":{"name":"2023 4th International Conference on Computing, Mathematics and Engineering Technologies (iCoMET)","volume":"492 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125442146","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}
Distributed agile development comes with a lot of challenges in particular as it has to do with agile teams working together from different geological locations on the same project. Most probably it happens due to a lack of visibility in the complex development and deployment process, poor communication, and unavailability of the development team and corresponding customer in the same place. These factors affect the performance of the team and increase the overall cost of development. To mitigate all these aspects, we proposed a cloud computing-based Infrastructure which is a combination of both agile as well as cloud computing technology named ‘CBAI’. The proposed Infrastructure assists the team members to work efficiently even if they are different geo-locations without burdening the cost. It provides the basic structure for global agile development and is also efficient in reducing the technical liability, and the need for project backlog.
{"title":"CBAI: Cloud-Based Agile Infrastructure for Enhancing Distributed Agile Development","authors":"Muhammad Ali, Sehrish Munawar Cheema, Zaheer Aslam, Ammerha Naz, Nasir Ayub","doi":"10.1109/iCoMET57998.2023.10099284","DOIUrl":"https://doi.org/10.1109/iCoMET57998.2023.10099284","url":null,"abstract":"Distributed agile development comes with a lot of challenges in particular as it has to do with agile teams working together from different geological locations on the same project. Most probably it happens due to a lack of visibility in the complex development and deployment process, poor communication, and unavailability of the development team and corresponding customer in the same place. These factors affect the performance of the team and increase the overall cost of development. To mitigate all these aspects, we proposed a cloud computing-based Infrastructure which is a combination of both agile as well as cloud computing technology named ‘CBAI’. The proposed Infrastructure assists the team members to work efficiently even if they are different geo-locations without burdening the cost. It provides the basic structure for global agile development and is also efficient in reducing the technical liability, and the need for project backlog.","PeriodicalId":369792,"journal":{"name":"2023 4th International Conference on Computing, Mathematics and Engineering Technologies (iCoMET)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129896708","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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